From b45321400a3a696fd3a61eea0d013712761029b1 Mon Sep 17 00:00:00 2001 From: Slesa Adhikari Date: Wed, 22 May 2024 14:24:41 -0500 Subject: [PATCH 01/22] Fix US GHG Center shortform typo --- overrides/components/visit-ghg.tsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/overrides/components/visit-ghg.tsx b/overrides/components/visit-ghg.tsx index a7a3c270b..2866828e8 100644 --- a/overrides/components/visit-ghg.tsx +++ b/overrides/components/visit-ghg.tsx @@ -85,7 +85,7 @@ export default function VisitGHG({ radius="square" variation="primary-fill" > - Visit the U.S. GHG Center website + Visit the US GHG Center website @@ -93,4 +93,4 @@ export default function VisitGHG({ ) -}; \ No newline at end of file +}; From 7454a632d51b3932681176041d60a20f3babb77a Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Thu, 27 Jun 2024 14:48:18 -0400 Subject: [PATCH 02/22] modify existing carousel content --- .../carousel_content.json | 6 +++- .../carousel_content.json | 20 ++++++++++-- .../carousel_content.json | 32 +++++++++++++++++++ 3 files changed, 55 insertions(+), 3 deletions(-) diff --git a/stories/theme.AG_.introduction_agriculture/carousel_content.json b/stories/theme.AG_.introduction_agriculture/carousel_content.json index ea9efa216..3216be66b 100644 --- a/stories/theme.AG_.introduction_agriculture/carousel_content.json +++ b/stories/theme.AG_.introduction_agriculture/carousel_content.json @@ -19,5 +19,9 @@ "src":"https://www.youtube.com/embed/SPHtB88ra6c", "title":"Relative Wetness Root Zone Versus Groundwater Comparison", "caption":"These maps combine satellite and ground-based measurements to model the relative amount of water stored at two different depths: plant root level and underground. The brown regions represent dry conditions. The blue regions represent wet areas. The maps do not attempt to represent human consumption of water; but rather, they show changes in water storage related to weather, climate, and seasonal patterns. NASA researchers developed these maps with data incorporated data from the joint NASA-German Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions in partnership with the National Drought Mitigation Center." + },{ + "src":"https://www.youtube.com/embed/6z58cOh_1TA", + "title":"Increasingly Dangerous Climate for Agricultural Workers", + "caption":"A warming climate will create challenges for agricultural workers as well as the crops which they grow. This visualization shows the increased number of days per year that are expected to have a NOAA Heat Index greater than 103 degrees Fahrenheit, a threshold that NOAA labels \u2018dangerous\u2019 given that people struggle to regulate their body temperatures at this level of heat and humidity. These results are from an ensemble of 22 global climate models from the Sixth Coupled Model Intercomparison Project (CMIP6) bias-adjusted by the NASA Earth Exchange (NEX GDDP). Two projections are visualized, one for a moderate emissions climate scenerio (SSP2-4.5) and one for a high emmissions climate scenerio (SSP5-8.5).\nVisualizations by: Mark SubbaRao, Scientific consulting by: Alex C. Ruane\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4972" } -] +] \ No newline at end of file diff --git a/stories/theme.AQ_.introduction_air_quality/carousel_content.json b/stories/theme.AQ_.introduction_air_quality/carousel_content.json index 08d5c6c6e..408feb6f1 100644 --- a/stories/theme.AQ_.introduction_air_quality/carousel_content.json +++ b/stories/theme.AQ_.introduction_air_quality/carousel_content.json @@ -26,10 +26,26 @@ },{ "src":"https://www.youtube.com/embed/aHBDHTXRzxY", "title":"Active Fires As Observed by VIIRS, January-September 2021", - "caption":"This view of fires around the world from Jan. 1 to Sep. 24, 2021, is thanks to data from the Visible Infrared Imaging Radiometer Suite, or (VIIRS), aboard NASA’s Suomi-NPP satellite and the National Oceanic and Atmospheric Administration's NOAA-20 satellite. " + "caption":"This view of fires around the world from Jan. 1 to Sep. 24, 2021, is thanks to data from the Visible Infrared Imaging Radiometer Suite, or (VIIRS), aboard NASA's Suomi-NPP satellite and the National Oceanic and Atmospheric Administration's NOAA-20 satellite. " },{ "src":"https://www.youtube.com/embed/miTwo0oKMB4", "title":"Spread of the Dixie Fire", - "caption":"The largest fire in California’s recorded history was the 2021 Dixie Fire. Combining active fire detections via NASA’s Suomi-NPP satellite with computer models, Earth scientists could update the size of the fire and estimate where it would spread. The data from Suomi-NPP provided updated data every 12 hours. In this visualization, yellow lines show the fire front lines based on the active fire data (red points) every 12 hours. In total, the Dixie fire burned for more than 100 days, including more than a month of fire activity after the perimeter was contained in mid-September." + "caption":"The largest fire in California's recorded history was the 2021 Dixie Fire. Combining active fire detections via NASA's Suomi-NPP satellite with computer models, Earth scientists could update the size of the fire and estimate where it would spread. The data from Suomi-NPP provided updated data every 12 hours. In this visualization, yellow lines show the fire front lines based on the active fire data (red points) every 12 hours. In total, the Dixie fire burned for more than 100 days, including more than a month of fire activity after the perimeter was contained in mid-September." + },{ + "src":"https://www.youtube.com/embed/gQ5HJdKaTKY", + "title":"Predicting Air Pollution with Computer Models: Nitrogen Oxides", + "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" + },{ + "src":"https://www.youtube.com/embed/f6ErmXRAFjw", + "title":"Predicting Air Pollution with Computer Models: PM2.5", + "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" + },{ + "src":"https://www.youtube.com/embed/A9V518Zpzeg", + "title":"Predicting Air Pollution with Computer Models: Near Surface Ozone", + "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" + },{ + "src":"https://www.youtube.com/embed/KnZsVY1mO00", + "title":"Predicting Air Pollution with Computer Models: Carbon Monoxide", + "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" } ] \ No newline at end of file diff --git a/stories/theme.DIS.introduction_disasters/carousel_content.json b/stories/theme.DIS.introduction_disasters/carousel_content.json index c8500c1c8..12419884f 100644 --- a/stories/theme.DIS.introduction_disasters/carousel_content.json +++ b/stories/theme.DIS.introduction_disasters/carousel_content.json @@ -11,5 +11,37 @@ "src":"https://www.youtube.com/embed/TvPjAe4j6qQ", "title":"Ocean Flows", "caption":"This rainbow of colors show the water temperature on the oceans’ surface. The temperature variations correspond to the flow of currents on the surface. Blue is 32 degrees Fahrenheit, green is 50 -70 F, yellow is about 80 F, red is 90 F. This visualization came from computer models by a joint MIT/NASA-JPL project Estimating the Circulation and Climate of the Ocean, Phase II." + },{ + "src":"https://www.youtube.com/embed/B7n62jOJJqI", + "title":"Atlantic Hurricane Wind Speed Plots", + "caption":"These simple visualizations are plots of time vs. wind speed for each tropical storm/hurricane of Atlantic Hurricane seasons from 1950 to the present. Horizontal lines indicate wind speed category thresholds. A line plot for each storm shows the storm's name and a marker at the peak wind speed.

\n\nMost named storms during the Atlantic hurricane season happen between June and November. However, occasionally, storms develop outside of those ranges.\n\nFour versions of the plots are inluded:\n 1. May to December showig only the current year\n 2. May to December showing the current year and strong storms from previous years (ghosted out)\n 3. January to December showig only the current year\n 4. January to December showing the current year and strong storms from previous years (ghosted out)\n \nThe plot for the current year automatically updates every 2 hours during hurricane season.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5072" + },{ + "src":"NRT", + "title":"Sea Surface Temperature (SST) - Near Real Time", + "caption":"This visualization shows Sea Surface Temperature (SST) data from the Jet Propulsion Laboratory (JPL) Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature Analysis. The analysis data is near-real-time (one day latency) and the visualization is updated daily to include the latest available data.\n\nData available here: https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5101" + },{ + "src":"NRT", + "title":"GEOS-FP Near Surface Temperature", + "caption":"Weather models begin their forecasts by integrating observations from ground stations, aircraft, balloons, and a growing constellation of Earth observing satellites to estimate the most realistic atmospheric state. The Goddard Earth Observing System Forward Processing (GEOS-FP) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to demonstrate innovative new ways to use satellite data to improve predictions of weather, air quality, and greenhouse gases and to help NASA collect new observations of our home planet. Near surface temperature is calculated by sampling model fields at 2 meters (about 6 feet) above Earth\u2019s surface, helping scientists communicate to the general public the air temperatures they encounter. Variations reflect differences in daytime and nighttime temperatures and the influence of large scale weather systems. As the planet warms in response to rising greenhouse gas concentrations, scientists are also tracking changes in the frequency of heat waves and their potential impacts on human health.\nVisualizations by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5147" + },{ + "src":"NRT", + "title":"GEOS-FP Near Surface Wind Speed", + "caption":"Weather models begin their forecasts by integrating observations from ground stations, aircraft, balloons, and a growing constellation of Earth observing satellites to estimate the most realistic atmospheric state. The Goddard Earth Observing System Forward Processing (GEOS-FP) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to demonstrate innovative new ways to use satellite data to improve predictions of weather, air quality, and greenhouse gases and to help NASA collect new observations of our home planet. \n\r\nNear surface wind speed is calculated by sampling model fields at 10 meters (about 30 feet) above Earth\u2019s surface. Wind speed is typically largest over ocean because surfaces are smooth and result in less friction than over land. Organized patterns reflect weather fronts and storm systems around the world. Near surface winds help scientists track the movement and severity of storms and air pollution episodes. Increasingly, these kinds of products help wind energy managers plan resources to help meet the world\u2019s demand for energy while reducing the greenhouse gas emissions that cause climate change.\nVisualizations by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5148" + },{ + "src":"NRT", + "title":"GEOS-FP Near Surface Wind Speed", + "caption":"Weather models begin their forecasts by integrating observations from ground stations, aircraft, balloons, and a growing constellation of Earth observing satellites to estimate the most realistic atmospheric state. The Goddard Earth Observing System Forward Processing (GEOS-FP) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to demonstrate innovative new ways to use satellite data to improve predictions of weather, air quality, and greenhouse gases and to help NASA collect new observations of our home planet. \n\r\nNear surface wind speed is calculated by sampling model fields at 10 meters (about 30 feet) above Earth\u2019s surface. Wind speed is typically largest over ocean because surfaces are smooth and result in less friction than over land. Organized patterns reflect weather fronts and storm systems around the world. Near surface winds help scientists track the movement and severity of storms and air pollution episodes. Increasingly, these kinds of products help wind energy managers plan resources to help meet the world\u2019s demand for energy while reducing the greenhouse gas emissions that cause climate change.\nVisualizations by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5148" + },{ + "src":"NRT", + "title":"GEOS-FP Relative Humidity", + "caption":"Weather models begin their forecasts by integrating observations from ground stations, aircraft, balloons, and a growing constellation of Earth observing satellites to estimate the most realistic atmospheric state. The Goddard Earth Observing System Forward Processing (GEOS-FP) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to demonstrate innovative new ways to use satellite data to improve predictions of weather, air quality, and greenhouse gases and to help NASA collect new observations of our home planet. \n\r\nRelative humidity is the ratio of water vapor in the atmosphere relative to the amount of water vapor that would be present if the air were saturated. It is calculated by using a combination of near surface temperature and water vapor fields. Variations reflect differences in daytime and nighttime relative humidity and the influence of large scale weather systems. Near surface humidity is an indicator of the likelihood of rain or fog and also has implications for human health. Humidity influences the evolution of air pollution events and high relative humidity can increase the risk of developing heat-related illnesses on very hot days.\nVisualizations by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5150" + },{ + "src":"NRT", + "title":"Sea Surface Temperature (SST) Anomaly - Near Real Time", + "caption":"This visualization shows Sea Surface Temperature (SST) Anomaly data from the Jet Propulsion Laboratory (JPL) Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature Analysis. The analysis data is near-real-time (one day latency) and the visualization is updated daily to include the latest available data. \n\nData available here: https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5176" + },{ + "src":"https://www.youtube.com/embed/6-71kC1LT-w", + "title":"Predicting Landslides", + "caption":"If a slope's underlying foundation is unstable, heavy rainfall could be all it takes to trigger a landslide. In fact, rainfall is the most common catalyst for landslides. An open sourced computer model developed at NASA's Goddard Space Flight Center uses precipitation data to identify areas all over the globe that are potential landslide hazards. The model first looks at areas that have recently experienced heavy rainfall using data from the Global Precipitation Measurement (GPM) mission. When the rainfall estimates are unusually high, the model checks other known conditions of the area that may encourage landslides, such as recent road construction, steep hills, and other factors. A \"nowcast\" map then combines the rainfall data and these other factors to mark areas that might have landslides. Scientists have used these models to conduct studies on long term landslide patterns and landslide warning signs. Watch the video to learn more.\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Dalia B Kirschbaum, Thomas A. Stanley, Produced by: Joy Ng, Ryan Fitzgibbons, Written by: Kasha Patel\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/13126" } ] \ No newline at end of file From 397271f42c150bf59e23afad64c011cc87758b55 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Thu, 27 Jun 2024 15:00:09 -0400 Subject: [PATCH 03/22] add water resources carousel --- ...theme.WTR.introduction_water_resources.mdx | 21 +------------------ .../carousel_content.json | 19 +++++++++++++++++ stories/theme.WTR.mdx | 9 ++++++++ 3 files changed, 29 insertions(+), 20 deletions(-) create mode 100644 stories/theme.WTR.introduction_water_resources/carousel_content.json diff --git a/stories/theme.WTR.introduction_water_resources.mdx b/stories/theme.WTR.introduction_water_resources.mdx index 036215277..d24b08f05 100644 --- a/stories/theme.WTR.introduction_water_resources.mdx +++ b/stories/theme.WTR.introduction_water_resources.mdx @@ -21,23 +21,4 @@ taxonomy: - name: Topics values: - water resources ---- - -import CardGallery from "./components/card_gallery"; -import { waterResourcesStoryIds } from "../overrides/common/story-data"; - - - - ## Info - - Water is a vital resource to life on Earth. - While the majority of Earth's surface is covered in water, only 1% of Earth's water is readily available for use. - This includes water used in essential activities such as consumption or agricultural practices required to sustain life. - As populations expand and demand for water grows, understanding how changing temperatures and precipitation patterns impact global water supply is important. - The water cycle describes how water moves throughout the planet's atmosphere, oceans and land; highlighting connections among each phase of the cycle. - Using a combination of Earth observing satellites and sensors, ground-based monitoring and scientific modeling, we can observe Earth's water resources at all stages of the water cycle. - This information can be used to make informed management decision surrounding the most vital resource on planet Earth. - - - - \ No newline at end of file +--- \ No newline at end of file diff --git a/stories/theme.WTR.introduction_water_resources/carousel_content.json b/stories/theme.WTR.introduction_water_resources/carousel_content.json new file mode 100644 index 000000000..c55d7d574 --- /dev/null +++ b/stories/theme.WTR.introduction_water_resources/carousel_content.json @@ -0,0 +1,19 @@ +[ + { + "src":"https://www.youtube.com/embed/rQhSM2OvnlE", + "title":"Variability of Water Storage in Global Hydrological Basins", + "caption":"Knowing the extent of human influence on the global hydrological cycle is essential for the sustainability of freshwater resources on Earth. However, a lack of water level observations for the world\u2019s ponds, lakes, and reservoirs has limited quantification of reservoir (human-managed) versus natural changes to surface water storage. In this study, scientists used data from NASA's ICESat-2 satellite laser altimeter to quantify global variability in water level over 227,386 water bodies from October 2018 to July 2020. \n\nBy combining this dataset with a global database of human-managed reservoirs, the study found that 57% of seasonal water storage variability occurs in human-managed reservoirs. Global maps of the results organized by hydrologic basin reveal that natural variability in surface water level is greatest in tropical basins like the Amazon and the Congo and lowest in northern and Arctic areas such as Northern Canada and Alaska. In contrast, human-management of surface water storage in arid and semi-arid regions like the Western US, Middle East, Southern Africa and Australia, where human influence drives nearly 100% of seasonal storage variability. Overall, the finding that humans are responsible for the majority of seasonal surface water storage variability shows that we are now a key regulator of the water cycle. \n\nAs economic development, population growth, and climate change continue to pressure global water resources in the future, measurements from satellites like ICESat-2 will continue to provide vital information about how humans are managing freshwater resources worldwide.\n\nThis animation uses data from the study to visualize two quantities: the variability of water level, and the variability of the percent of water storage from man-made reservoirs.\nVisualizations by: Trent L. Schindler, Scientific consulting by: Sarah Cooley, Produced by: Ryan Fitzgibbons\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4889" + },{ + "src":" https://www.youtube.com/embed/fDiL_kzokF0", + "title":"A 3D View of an Atmospheric River from an Earth System Model", + "caption":"Features in Earth\u2019s atmosphere, spawned by the heat of the Sun and the rotation of the Earth, transport water and energy around the globe. Clouds and precipitation shown here are from NASA\u2019s MERRA-2 reanalysis, a retrospective blend of a weather model and conventional and satellite observations. \n\nWithin the mid-latitudes, winds move clouds from west to east. Within the tropics easterly trade winds converge along the equator to create a moisture rich cluster of clouds, convection, and precipitation called the intertropical convergence zone, or ITCZ. Disturbances in its flow transport immense amounts of moisture and energy from the tropics to the poles. Studies have shown that atmospheric rivers account for the vast majority of the poleward transport of water vapor.\n\nThe American Meteorological Society defines an atmospheric river as \u201ca long, narrow, and transient corridor of strong horizontal water vapor transport that is typically associated with a low-level jet stream ahead of the cold front of an extratropical cyclone.\u201d A common measure for the strength of an atmospheric river is the integrated water vapor transport, or the amount of moisture that is moved from one place to another by the flow of the atmosphere. The blue shading shown here gives a three-dimensional view of the water vapor transport. Tropical moisture is pulled in from the ITCZ and in this example, converges with other moisture sources to form an atmospheric river. The feature then travels towards the west coast of the United States as a sub-class of atmospheric rivers commonly referred to as the \u201cpineapple express\u201d due to its origin near Hawai\u2019i.\n\nThe atmospheric river is guided by the semi-permanent sub-tropical high pressure off the coast of California and the Baja Peninsula as well as the Aleutian low in the Gulf of Alaska. The pressure gradient between the clockwise flow of the Californian high and the counterclockwise flow of the Aleutian low funnel the atmospheric moisture into a narrow corridor. The more intense the pressure gradient is, the stronger the winds are that transport the water vapor. Extreme rainfall has also been associated with the more intense gradients.\n\nMuch of the moisture stays close to the surface but the rising motion of the low pressure to the north results in the air cooling, condensing the water vapor into a liquid. Precipitation over the ocean falls along the feature\u2019s cold front on its northern side.\n\nAnother way that air can rise and condense into precipitation is through orographic lift. When air encounters the mountains along the west coast of the United States, it is forced upwards. The rising air becomes saturated, causing rain and snow to fall, particularly on the windward side of the mountain. The flow of air continues eastward, depleted of its moisture.\n\nThe precipitation that falls because of atmospheric rivers is important for the hydrologic cycle in the western United States. The winter buildup of the snowpack provides valuable freshwater resources. Despite being beneficial at times, atmospheric river induced precipitation can also be destructive. The occurrence of extreme atmospheric river precipitation events, such as the one that occurred in this example, can result in widespread flooding and mudslides.\n\nAtmospheric rivers are not unique to the west coast of North America and occur around the globe, including Europe, New Zealand, the Middle East, Greenland, and Antarctica. The study of global phenomenon such as atmospheric rivers over the past four decades is made possible through NASA\u2019s MERRA-2 reanalysis, a spatially and temporally consistent blend of satellite and conventional observations with a numerical model. With a dataset that provides hourly information around the globe since 1980, there is still much that can be learned about Earth\u2019s atmosphere and the transport of water and energy around the globe.\n\nNarrated atmospheric rivers movie\n\nVisualizers: Greg Shirah (lead), Horace Mitchell, Cindy Starr, Kel Elkins\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4960" + },{ + "src":"https://www.youtube.com/embed/J9vSshArkx0", + "title":"50 Years of Harmful Algal Blooms: Rotating Globe Unwraps to Robinson Projection", + "caption":"Mass fish deaths have ridden our planet's beaches and coastlines. New health risks have taken over coastal communities and millions of fishery-based employees are out of work. To bring light to the issue and show how new technology may revolutionize data we collect the current best data available to show the scope of the problem. As our global climate continues to change, more nutrients enter our water sources, coastal waters get warmer, algal blooms will continue to flourish in this ideal environment.\n\nIn January 2024, NASA is scheduled to launch the PACE (https://pace.gsfc.nasa.gov/ satellite which is designed to improve and increase the data we have collected surrounding harmful algal blooms.\n\nThis data visualization depicts the scope of the data we currently have collected over the past 50 years. Much of it is taken from ships at sea and inland water ways. From this we can see that this is a global issue and learning more about this problem will greatly further our understanding of the issue at hand.\nVisualizations by: Alex Kekesi, Scientific consulting by: Ivona Cetinic, Bridget Seegers, Produced by: Emme Watkins, Technical support: Laurence Schuler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5184" + },{ + "src":"https://www.youtube.com/embed/wp7WpoRtL8E", + "title":"Rivers of the World: A Unifying Journey through Landsat's Lens, Set to Vltava by Bed\u0159ich Smetana", + "caption":"This visualization is a captivating collaboration between NASA, USGS, and Bed\u0159ich Smetana's celebrated composition, Vltava (The Moldau River). The video aims to capture the essence of Smetana's masterpiece, embodying the power and beauty of rivers while showcasing stunning images of Earth's waterways from the Landsat Satellite Program. The visuals unfold like a poetic tribute to our planet, seamlessly transitioning between awe-inspiring satellite images of rivers and watersheds across the globe. The viewer is transported on a journey through these life-sustaining arteries, witnessing their intricate patterns and breathtaking colors as seen from space. This visualization was created to emphasize the importance of preserving our planet's vital ecosystems and to inspire a collective commitment towards protecting Earth's most precious resources. The collaboration highlights how space-based observations can transcend borders, fostering unity and a shared responsibility for our environment. The imagery in the video is derived from NASA and USGS's Landsat Satellite Program, which has been capturing detailed images of Earth's surface since 1972. The visuals are synchronized with Smetana's Vltava to create an immersive and memorable experience that celebrates the beauty and interconnectedness of our planet's rivers.\n\nMusic Credit - \"Smetana Ma Vlast Classical Music Public Domain\" Musophen Symphony [Orange Free Sounds]\nProduced by: Chris Burns\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14329" + } +] \ No newline at end of file diff --git a/stories/theme.WTR.mdx b/stories/theme.WTR.mdx index c5f35f682..487d159f9 100644 --- a/stories/theme.WTR.mdx +++ b/stories/theme.WTR.mdx @@ -24,6 +24,9 @@ taxonomy: import CardGallery from "./components/card_gallery"; import { waterResourcesStoryIds } from "../overrides/common/story-data"; +import Carousel from "../overrides/common/embedded-video-carousel"; +import contentArray from './theme.WTR.introduction_water_resources/carousel_content.json'; + ## Info @@ -38,4 +41,10 @@ import { waterResourcesStoryIds } from "../overrides/common/story-data"; + +

+ +
+ + \ No newline at end of file From 09808fdeec6e0de3c405a4e461a6c4b05006f550 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Thu, 27 Jun 2024 15:23:39 -0400 Subject: [PATCH 04/22] add ENR carousel --- ...theme.ENR.introduction_energy_generation.mdx | 17 +---------------- .../carousel_content.json | 11 +++++++++++ stories/theme.ENR.mdx | 9 +++++++++ 3 files changed, 21 insertions(+), 16 deletions(-) create mode 100644 stories/theme.ENR.introduction_energy_generation/carousel_content.json diff --git a/stories/theme.ENR.introduction_energy_generation.mdx b/stories/theme.ENR.introduction_energy_generation.mdx index a9fefe30e..189ab01f9 100644 --- a/stories/theme.ENR.introduction_energy_generation.mdx +++ b/stories/theme.ENR.introduction_energy_generation.mdx @@ -25,19 +25,4 @@ taxonomy: - name: Topics values: - energy generation ---- - -import CardGallery from "./components/card_gallery"; -import { energyStoryIds } from "../overrides/common/story-data"; - - - - ## Info - - Whether deciding the optimal location for solar panels or designing sustainable buildings, federal agencies are using modern techniques to inform daily decisions made by individuals and industry alike. - Researchers use information from satellites and sensors in combination with ground-based gauges to learn how changes in climate are linked with changes in energy supply and demand across the globe. - The results from this research can be used by households, towns, cities and states to make more informed decisions surrounding community planning and energy use. - - - - \ No newline at end of file +--- \ No newline at end of file diff --git a/stories/theme.ENR.introduction_energy_generation/carousel_content.json b/stories/theme.ENR.introduction_energy_generation/carousel_content.json new file mode 100644 index 000000000..5c3c0f88f --- /dev/null +++ b/stories/theme.ENR.introduction_energy_generation/carousel_content.json @@ -0,0 +1,11 @@ +[ + { + "src":"null", + "title":"GEOS-FP Near Surface Wind Speed", + "caption":"Weather models begin their forecasts by integrating observations from ground stations, aircraft, balloons, and a growing constellation of Earth observing satellites to estimate the most realistic atmospheric state. The Goddard Earth Observing System Forward Processing (GEOS-FP) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to demonstrate innovative new ways to use satellite data to improve predictions of weather, air quality, and greenhouse gases and to help NASA collect new observations of our home planet. \n\r\nNear surface wind speed is calculated by sampling model fields at 10 meters (about 30 feet) above Earth\u2019s surface. Wind speed is typically largest over ocean because surfaces are smooth and result in less friction than over land. Organized patterns reflect weather fronts and storm systems around the world. Near surface winds help scientists track the movement and severity of storms and air pollution episodes. Increasingly, these kinds of products help wind energy managers plan resources to help meet the world\u2019s demand for energy while reducing the greenhouse gas emissions that cause climate change.\nVisualizations by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5148" + },{ + "src":"https://www.youtube.com/embed/SB-IYcHb2so", + "title":"Change in Night Lights between 2012 and 2023 - EIC Version", + "caption":"Discover the pivotal role of NASA's Black Marble in bridging scientific discovery and actionable environmental stewardship. By capturing Earth's night lights, Black Marble reveals both the planet's beauty and the profound impact of human activities. This webpage showcases newly developed nighttime light maps comparing changes observed from 2012 to 2023, offering a decade-long perspective on urban growth, infrastructure development, and the socioeconomic dynamics of communities worldwide. This data empowers users to foster informed, sustainable practices that enhance community resilience and mitigate the adverse effects of climate change. By illuminating the intricate interplay between human activity and the environment, Black Marble underpins a human-centered approach to environmental change, promoting a future where scientific innovation leads to tangible, positive impacts on global sustainability.\n\nThis version is specifically formatted for the NASA Earth Information Center (EIC), which has a wider-than-standard video aspect ratio. The original version of this visualization, and many other night lights visualizations, are available here: https://svs.gsfc.nasa.gov/5276/\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5313" + } +] \ No newline at end of file diff --git a/stories/theme.ENR.mdx b/stories/theme.ENR.mdx index 976378b70..2fb05a2c1 100644 --- a/stories/theme.ENR.mdx +++ b/stories/theme.ENR.mdx @@ -28,6 +28,9 @@ taxonomy: import CardGallery from "./components/card_gallery"; import { energyStoryIds } from "../overrides/common/story-data"; +import Carousel from "../overrides/common/embedded-video-carousel"; +import contentArray from './theme.ENR.introduction_energy_generation/carousel_content.json'; + ## Info @@ -38,4 +41,10 @@ import { energyStoryIds } from "../overrides/common/story-data"; + +
+ +
+ + \ No newline at end of file From 3eaff4079f470d05ca861fb5b322b6c803ffbe6d Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Thu, 27 Jun 2024 15:27:25 -0400 Subject: [PATCH 05/22] add BIO carousel --- .../theme.BIO.introduction_biodiversity.mdx | 21 +------------------ .../carousel_content.json | 19 +++++++++++++++++ stories/theme.BIO.mdx | 9 ++++++++ 3 files changed, 29 insertions(+), 20 deletions(-) create mode 100644 stories/theme.BIO.introduction_biodiversity/carousel_content.json diff --git a/stories/theme.BIO.introduction_biodiversity.mdx b/stories/theme.BIO.introduction_biodiversity.mdx index 2dccdcb61..598283ffb 100644 --- a/stories/theme.BIO.introduction_biodiversity.mdx +++ b/stories/theme.BIO.introduction_biodiversity.mdx @@ -24,23 +24,4 @@ taxonomy: - name: Theme Page values: - biodiversity ---- - -import CardGallery from "./components/card_gallery"; -import { biodiversityStoryIds } from "../overrides/common/story-data"; - - - - ## Info - - Planetary change includes more than understanding the physical components of our planet, it also includes understanding how diversity of life on Earth is changing too. - Biodiversity refers to the variety, or diversity, of all life on Earth. - However, changes in temperature, precipitation and land cover directly impact the ability of species to survive and the habitats where they are found. - Researchers in local habitats work on the ground, directly monitoring vegetation and wildlife, while researchers using remote sensing techniques study biodiversity from space-based and airborne missions. - Both approaches provide critical information on species richness and distribution across the globe. - Additionally, modeling can be used to forecast how species and their habitat may change in the future. - This information is also used across multiple scales of research and government to inform management practices. - - - - \ No newline at end of file +--- \ No newline at end of file diff --git a/stories/theme.BIO.introduction_biodiversity/carousel_content.json b/stories/theme.BIO.introduction_biodiversity/carousel_content.json new file mode 100644 index 000000000..ea03d5933 --- /dev/null +++ b/stories/theme.BIO.introduction_biodiversity/carousel_content.json @@ -0,0 +1,19 @@ +[ + { + "src":"https://www.youtube.com/embed/WPOTTd27yyg", + "title":"Ecological insights from three decades of animal movement tracking across a changing Arctic", + "caption":"The Arctic Animal Movement Archive (AAMA) is a new and growing collection of studies describing movements of animals in and near the Arctic. The AAMA includes millions of locations of thousands of animals over more than three decades, recorded by hundreds of scientists and institutions. By compiling these data, the AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. We have used the AAMA to document climatic influences on the migration phenology of golden eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, species-specific changes in terrestrial mammal movement rates in response to increasing temperature, and the utility of animal-borne sensors as proxies for ambient air temperature. The AAMA is a living archive that can be used to uncover other such changes, investigate their causes and consequences, and recognize larger ecosystem changes taking place in the Arctic.\n\nThis visualization shows multiple years of AAMA data as if all of the data were from the same year. Several different groupings of animals are shown: marine mammals, raptors, seabirds, shorebirds, terrestrial mammals, and waterbirds. Snow and sea ice are also shown for context as they correlate to animal movements.\n\nVisualizers: Greg Shirah (lead), Lori Perkins\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4877" + },{ + "src":"https://www.youtube.com/embed/Ok2iQlTw5DQ", + "title":"GEDI Forest Height - Global View", + "caption":"The Global Ecosystem Dynamics Investigation (GEDI) produces high resolution laser ranging observations of the 3D structure of the Earth. GEDI's precise measurements of forest canopy height, canopy vertical structure, and surface elevation greatly advance our ability to characterize important carbon and water cycling processes, biodiversity, and habitat.\n\nGEDI's data on surface structure are also of immense value for weather forecasting, forest management, glacier and snowpack monitoring, and the generation of more accurate digital elevation models. GEDI provides the missing piece - 3D structure - in NASA's observational assets which enables us to better understand how the Earth behaves as a system, and guides the actions we can take to sustain critical resources.\n\nThe GEDI instrument is a geodetic-class, light detection and ranging (lidar) laser system comprised of 3 lasers that produce 8 parallel tracks of observations. Each laser fires 242 times per second and illuminates a 25 m spot (a footprint) on the surface over which 3D structure is measured. Each footprint is separated by 60 m along track, with an across-track distance of about 600 m between each of the 8 tracks. GEDI expected to produce about 10 billion cloud-free observations during its nominal 24-month mission length.\n\nTo learn more, visit the GEDI webpage: https://gedi.umd.edu/\n\nThis visualization depicts a global view of forest height data collected by the GEDI instrument aboard the International Space Station. Brown and dark green represent shorter vegetation. Bright green and white represent taller vegetation. This visualization uses data collected between April 2018 and April 2019. Height is exaggerated to depict variation at this scale.\n\nVisualizers: Kel Elkins (lead), Horace Mitchell\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4950" + },{ + "src":"https://www.youtube.com/embed/ImEdEQtuDkI", + "title":"USFS/GEDI Old Growth Forest Visualizations", + "caption":"The U.S. Forest Service has studied mature and old-growth forests \u2013 broadly characterized as forests at an advanced stage of development \u2013 in hundreds of thousands of plots across the country. To define, identify, and create the first formal accounting of these national resources, the team assessed decades of field-gathered Forest Inventory and Analysis (FIA) data covering a wide variety of forest types and ecological zones across the country.\n \nComplementing the Forest Service\u2019s analysis, NASA-funded scientists are drawing on a space-based instrument called GEDI (Global Ecosystem Dynamics Investigation) to provide a broad and detailed picture of these forests. From its perch on the International Space Station, GEDI\u2019s laser imager (lidar) is able to peer through dense canopies to observe nearly all of Earth\u2019s temperate and tropical forests. By recording the way the laser pulses are reflected by the ground and by plant material (stems, branches, and leaves) at different heights, GEDI makes detailed measurements of the three-dimensional structure of the planet\u2019s surface. It can even estimate the weight and stature of individual trees.\n \nThe Forest Service plans to work alongside NASA to gather aerial and satellite imagery and map mature and old-growth forests at finer scales. Such data can also help the Forest Service create a long-term monitoring system. Meanwhile, a team of interagency experts will analyze and assess threats and risks to these areas.\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5095" + },{ + "src":"https://www.youtube.com/embed/HA0LQQi_E28", + "title":"Atmospheric Carbon Dioxide Tagged by Source", + "caption":"Carbon dioxide (CO2) is the most prevalent greenhouse gas driving global climate change. However, its increase in the atmosphere would be even more rapid without land and ocean carbon sinks, which collectively absorb about half of human emissions every year. Advanced computer modeling techniques in NASA's Global Modeling and Assimilation Office allow us to disentangle the influences of sources and sinks and to better understand where carbon is coming from and going to.\n\nVisualizations by: Andrew J Christensen, Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5110" + } +] \ No newline at end of file diff --git a/stories/theme.BIO.mdx b/stories/theme.BIO.mdx index fe92ca49b..c8557f23a 100644 --- a/stories/theme.BIO.mdx +++ b/stories/theme.BIO.mdx @@ -27,6 +27,9 @@ taxonomy: import CardGallery from "./components/card_gallery"; import { biodiversityStoryIds } from "../overrides/common/story-data"; +import Carousel from "../overrides/common/embedded-video-carousel"; +import contentArray from './theme.BIO.introduction_biodiversity/carousel_content.json'; + ## Info @@ -41,4 +44,10 @@ import { biodiversityStoryIds } from "../overrides/common/story-data"; + +
+ +
+ + \ No newline at end of file From c6053a6c0c30d8d94666c0df66263f7f0025276b Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Thu, 27 Jun 2024 15:29:57 -0400 Subject: [PATCH 06/22] add GHG carousel --- ...heme.GHG.introduction_greenhouse_gases.mdx | 23 +-------- .../carousel_content.json | 51 +++++++++++++++++++ stories/theme.GHG.mdx | 14 +++-- 3 files changed, 62 insertions(+), 26 deletions(-) create mode 100644 stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json diff --git a/stories/theme.GHG.introduction_greenhouse_gases.mdx b/stories/theme.GHG.introduction_greenhouse_gases.mdx index 56a73b4e4..9da127e0c 100644 --- a/stories/theme.GHG.introduction_greenhouse_gases.mdx +++ b/stories/theme.GHG.introduction_greenhouse_gases.mdx @@ -24,25 +24,4 @@ taxonomy: - name: Topics values: - greenhouse gases ---- - -import CardGallery from "./components/card_gallery"; -import { greenhouseGasesStoryIds } from "../overrides/common/story-data"; - - - - ## Info - - Greenhouse gases (GHGs) refers to a suite of gases, including carbon dioxide and methane, found in Earth's atmosphere that naturally trap heat and maintain Earth's global temperature. - However, human activities over the last century led to unprecedented amounts of GHGs being released into the atmosphere resulting in warming the planet at an alarming rate. - - Earth's climate is changing at a pace that threatens human health, society and the natural environment. - These changes include warmer air and ocean temperatures, changes in precipitation patterns, retreating snow and ice, increasingly severe weather events, such as hurricanes of greater intensity and sea level rise, among other impacts. - Federal agencies are working together to develop a Greenhouse Gas Monitoring and Information System (GHGMIS) for the U.S. to improve measurement of GHG emissions and sinks and track progress towards meet climate mitigation goals. - This system uses these advanced capabilities, including the expanded use of GHG observational data and models, to provide enhanced GHG emissions and uptake data estimates that can be used by decision-makers. - - - - - - \ No newline at end of file +--- \ No newline at end of file diff --git a/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json new file mode 100644 index 000000000..b1e73daf5 --- /dev/null +++ b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json @@ -0,0 +1,51 @@ +[ + { + "src":"https://www.youtube.com/embed/c3btzmkiZAA", + "title":"Methane Emissions in the United States", + "caption":"\nMethane is the second most important greenhouse gas contributing to climate change. While emissions are substantially lower than for carbon dioxide, the biggest driver of climate change, methane is more efficient at trapping heat on a molecule by molecule basis. As a result, understanding the sources of methane and how they can be reduced, quickly, is a major effort of policymakers and environmental managers around the world.\r\n\r\nThis visualization presents gridded methane emissions across the United States for the year 2012. The gridded methane inventory is designed to be consistent with EPA\u2019s 2016 Inventory of U.S. Greenhouse Gas Emissions and Sinks (https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) for the year 2012, which presents national totals for different source types. Gridded estimates with 0.1 degree spatial resolution are produced using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. Gridded inventories, developed with support from NASA\u2019s Carbon Monitoring System, help researchers use satellite, airborne, and in situ observations to independently evaluate EPA inventories and provide recommendations on refinements that may be needed. Additional detail and dataset access are available at the EPA website (https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions).\r\n\r\nThe gridded inventory presents totals for different major methane source types. Agriculture emissions in this visualization include manure management, enteric fermentation, rice cultivation, and field burning. Waste emissions include landfills, wastewater treatment, and composting. Natural Gas emissions include emissions from production, processing, and transmission. Coal emissions include both active and abandoned coal mines.\nVisualizations by: Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5041" + },{ + "src":"https://www.youtube.com/embed/d-bFeE4YZ6s", + "title":"Net Ecosystem Exchange of Carbon Dioxide", + "caption":"\nEach year, ecosystems absorb about 25% of human carbon dioxide emissions, providing an invaluable mitigation that has substantially slowed the rate of climate change. Carbon dioxide is absorbed by plants during photosynthesis and released during respiration, a process with occurs in both plants and soils. The balance of this absorption and release, called net ecosystem exchange, serves to reduce atmospheric carbon dioxide concentrations. However, understanding how much carbon is absorbed by ecosystems and how this may change in a future faced with increases in extreme weather associated with climate change is a major challenge for scientists. Despite the critical importance of ecosystem-atmosphere carbon dioxide exchange, which scientists call flux, directly measuring the global exchange of carbon dioxide molecules is not possible. Satellite observations of vegetation, from satellite instruments like the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), provide valuable information that can be combined with computer models to provide realistic, observationally based estimates of carbon flux and track how underlying processes change over time.\r\n\r\nThis visualization shows net ecosystem exchange calculated by the Carnegie-Ames-Stanford-Approach \u2013 Global Fire Emissions Database version 3 (CASA-GFED3), a simple model that combines AVHRR and MODIS data with estimates of temperature and precipitation from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Global patterns of net ecosystem exchange reflect seasonal changes in vegetation with plants absorbing more carbon during spring and summer growing seasons and releasing much of this carbon back to the atmosphere during fall and winter months. The nearly 20-year record of CASA-GFED3 data also reflects interannual variability in carbon flux that can result from unusual climate patterns like droughts. Scientists across the world use the CASA-GFED3 data as input to atmospheric models, which then use satellite observations to evaluate and further refine the flux estimates. This understanding, supported by NASA\u2019s Carbon Monitoring System, helps scientists improve the representation of vegetation in climate model predictions and provides actionable information about the processes controlling greenhouse gas concentrations to policymakers.\nVisualizations by: Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5047" + },{ + "src":"https://www.youtube.com/embed/RCVUIcVKkXs", + "title":"Methane Emissions from Wetlands", + "caption":"\nMethane is an important greenhouse gas that contributes substantially to global warming. On a molecule by molecule basis, methane is much more efficient at trapping heat than carbon dioxide, the main driver of warming. Though human activities, including agriculture, oil and natural gas production and use, and waste disposal, collectively contribute the majority of methane to the atmosphere, about a third of total methane emissions comes from wetlands. Wetland habitats are filled with things like waterlogged soils and permafrost, which makes them sizable carbon sinks. However, as the climate changes, these carbon-rich soils are vulnerable to flooding and to rising temperatures, which can release more carbon to the atmosphere in the form or methane. Understanding methane emissions from natural sources like wetlands is critically important to scientists and policymakers who are working to ensure that changes in natural systems don\u2019t counteract progress in combatting climate change made by reducing emissions from human activities.\r\n\r\nThis animation shows estimates of wetland methane emissions produced by the Lund\u2013Potsdam\u2013Jena Dynamic Global Vegetation Model (LPJ-DGVM) Wald Schnee und Landscaft version (LPJ-wsl). LPJ-wsl is a prognostic model, meaning that it can be used to simulate future changes in wetland emissions and independently verified with remote sensing data products. The model includes a complex, topography dependent model of near surface hydrology, and a permafrost and dynamic snow model, allowing it to produce realistic distributions of inundated area. Highlighted areas show concentrated methane sources from tropical and high latitude ecosystems. The LPJ-wsl model is regularly used in conjunction with NASA\u2019s GEOS model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations, compare against satellite and airborne data, and to improve understanding and prediction of wetland emissions.\nVisualizations by: Mark SubbaRao, Produced by: Kathleen Gaeta, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5054" + },{ + "src":"https://www.youtube.com/embed/HA0LQQi_E28", + "title":"Atmospheric Carbon Dioxide Tagged by Source", + "caption":"Carbon dioxide (CO2) is the most prevalent greenhouse gas driving global climate change. However, its increase in the atmosphere would be even more rapid without land and ocean carbon sinks, which collectively absorb about half of human emissions every year. Advanced computer modeling techniques in NASA's Global Modeling and Assimilation Office allow us to disentangle the influences of sources and sinks and to better understand where carbon is coming from and going to.\n\nVisualizations by: Andrew J Christensen, Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5110" + },{ + "src":"https://www.youtube.com/embed/-aSBfn6_pUY", + "title":"Global Atmospheric Carbon Dioxide (CO\u2082)", + "caption":"NASA\u2019s Orbiting Carbon Observatory, 2 (OCO-2) provides the most complete dataset tracking the concentration of atmospheric carbon dioxide (CO\u2082), the main driver of climate change. Every day, OCO-2 measures sunlight reflected from Earth\u2019s surface to infer the dry-air column-averaged CO\u2082 mixing ratio and provides around 100,000 cloud-free observations. Despite these advances, OCO-2 data contain many gaps where sunlight is not present or where clouds or aerosols are too thick to retrieve CO\u2082 data. In order to fill gaps and provide science and applications users a spatially complete product, OCO-2 data are assimilated into NASA\u2019s Goddard Earth Observing System (GEOS), a complex modeling and data assimilation system used for studying the Earth\u2019s weather and climate. GEOS is also informed by satellite observations of nighttime lights and vegetation greenness along with about 1 million weather observations collected every hour. These data help scientists infer CO2 mixing ratios even when a direct OCO-2 observation is not present and provide additional information on the altitude of CO\u2082 plumes that the satellite is not able to see. Together, OCO-2 and GEOS create one of the most complete pictures of CO\u2082.\n\nThe visualization featured on this page shows the atmosphere in three dimensions and highlights the accumulation of CO\u2082 during a single calendar year (January 1-December 31, 2021). Every year, the world\u2019s vegetation and oceans absorb about half of human CO\u2082 emissions, providing an incredibly valuable service that has mitigated the rate of accumulation of greenhouse gases in the atmosphere. However, around 2.5 parts per million remain in the atmosphere every year causing a steady upward march in concentrations that scientists have tracked since the 1950s at surface stations.\n\nThe volumetric visualization starts in January 1, 2021, showing the higher CO\u2082 concentrations, which are closer to the ground, revealing the seasonal movement of high CO\u2082 at a global scale. During the months of June-September (summer months for northern hemisphere), global CO\u2082 concentrations tend to be lowest because northern hemisphere plants actively absorb CO\u2082 from the atmosphere via photosynthesis. During northern hemisphere fall and winter months, much of this CO\u2082 is re-released to the atmosphere due to respiration and can be seen building up. By June and July 2021, plants again draw CO\u2082 out of the atmosphere, but notably higher concentrations remain in contrast to the nearly transparent colors of the previous year. The diurnal rhythm of CO\u2082 is apparent over our planet's largest forests, such as the Amazon rainforest in South America and the Congo rainforest in Central Africa. The fast-paced pulse in those rainforests is due to the day-night cycle; plants absorb CO\u2082 during the day via photosynthesis when the sun is out, then stop absorbing CO\u2082 at night. In addition to highlighting the buildup of atmospheric CO\u2082, this visualization shows how interconnected the world\u2019s greenhouse gas problem is. NASA\u2019s unique combination of observations and models plays a critical role in helping scientists track increases in CO\u2082 as they happen to better understand their climate impact.\n\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Lesley Ott, Brad Weir\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5115" + },{ + "src":"https://www.youtube.com/embed/2rdEjFn_7jg", + "title":"Global Atmospheric Methane (CH\u2084)", + "caption":"Methane (CH\u2084) is a powerful greenhouse gas that traps heat 28 times more effectively than carbon dioxide over a 100-year timescale. Concentrations of methane have increased by more than 150% since industrial activities and intensive agriculture began. After carbon dioxide, methane is responsible for about 20% of climate change in the twentieth century. Methane is produced under conditions where little to no oxygen is available. About 30% of methane emissions are produced by wetlands, including ponds, lakes and rivers. Another 20% is produced by agriculture, due to a combination of livestock, waste management and rice cultivation. Activities related to oil, gas, and coal extraction release an additional 30%. The remainder of methane emissions come from minor sources such as wildfires, biomass burning, permafrost, termites, dams, and the ocean. Scientists around the world are working to better understand the budget of methane with the ultimate goals of reducing greenhouse gas emissions and improving prediction of environmental change. \n\nThe NASA SVS visualization presented here shows the complex patterns of methane emissions produced around the globe and throughout the year from the different sources described above. The visualization was created using output from the Global Modeling and Assimilation Office (GMAO), GEOS modeling system, developed and maintained by scientists at NASA. Wetland emissions were estimated by the LPJ-wsl model, which simulates the temperature and moisture dependent methane emission processes using a variety of satellite data to determine what parts of the globe are covered by wetlands. Other methane emission sources come from inventories of human activity.\n\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Lesley Ott, Brad Weir\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5116" + },{ + "src":"https://www.youtube.com/embed/P_OmxbB-iHg", + "title":"Trends in atmospheric Nitrous Oxide (N\u2082O)", + "caption":"\nVisualizations by: Helen-Nicole Kostis\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5117" + },{ + "src":"https://www.youtube.com/embed/9V3Hhax5C1M", + "title":"Trends in atmospheric Methane (CH\u2084)", + "caption":"\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Lesley Ott, Brad Weir\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5118" + },{ + "src":"https://www.youtube.com/embed/bvK-SzcyuKs", + "title":"OCO-2 and Keeling Curve: Trends in global atmospheric Carbon Dioxide (CO\u2082)", + "caption":"\nVisualizations by: Helen-Nicole Kostis\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5119" + },{ + "src":"https://youtube.com/shorts/ZYIkApOYwIA", + "title":"ODIAC: a map of human made carbon dioxide emissions", + "caption":"The Open-source Data Inventory for Anthropogenic CO2 (A.K.A. ODIAC) is a global high-resolution (1x1km) emission data product for fossil fuel carbon dioxide (CO2) emission. ODIAC was originally designed and developed under the Greenhouse gas Observing SATellite (GOSAT) project at Japan\u2019s National Institute for Environmental Studies (NIES). Since then, ODIAC has been maintained at the Universities Space Research Association (USRA) in collaboration with NASA, NIES and the Appalachian State University. ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO2 emissions. ODIAC has been a key input data for NASA\u2019s carbon modeling. ODIAC has been also widely used in the international science research community for a variety of applications across key policy relevant scales (global to local).\nVisualizations by: Mark SubbaRao, Scientific consulting by: Tomohiro Oda\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5121" + },{ + "src":"NRT", + "title":"NASA Climate Spiral 1880-Present", + "caption":"The visualization presents monthly global temperature anomalies. This visualization is updated roughly two weeks after the end of each month.\n\nTemperature anomalies are deviations from a long term global average. In this case the period 1951-1980 is used to define the baseline for the anomaly. These temperatures are based on the GISS Surface Temperature Analysis (GISTEMP v4), an estimate of global surface temperature change. The data file used to create this visualization is publicaly accessible (https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv).\n\nThe term 'climate spiral' (https://www.climate-lab-book.ac.uk/spirals/) describes an animated radial plot of global temperatures. Climate scientist Ed Hawkins from the National Centre for Atmospheric Science, University of Reading popularized this style of visualization in 2016.\n\nThe Goddard Institute for Space Studies (GISS) in New York is a NASA laboratory managed by the Earth Sciences Division of the agency\u2019s Goddard Space Flight Center in Greenbelt, Maryland. The laboratory is affiliated with Columbia University\u2019s Earth Institute and School of Engineering and Applied Science in New York.\nVisualizations by: Mark SubbaRao\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5190" + },{ + "src":"NRT", + "title":"Global Temperature Graph 1880-Present", + "caption":"The visualization presents the seasonal cycle of temperature variation on the earth's surface. This visualization is updated roughly two weeks after the end of each month.\n\nTemperature anomalies are deviations from a long term global average. In this case the period 1951-1980 is used to define the baseline for the anomaly. These temperatures are based on the GISS Surface Temperature Analysis (GISTEMP v4), an estimate of global surface temperature change. The data file used to create this visualization is publicaly accessible (https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv). The seasonal temperature offsets are based on the MERRA-2 reanalysis data here.\n\nThe Goddard Institute of Space Studies (GISS) is a NASA laboratory managed by the Earth Sciences Division of the agency\u2019s Goddard Space Flight Center in Greenbelt, Maryland. The laboratory is affiliated with Columbia University\u2019s Earth Institute and School of Engineering and Applied Science in New York.\nVisualizations by: Mark SubbaRao\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5191" + } +] \ No newline at end of file diff --git a/stories/theme.GHG.mdx b/stories/theme.GHG.mdx index a9ced2bba..7a7cf1ba1 100644 --- a/stories/theme.GHG.mdx +++ b/stories/theme.GHG.mdx @@ -28,6 +28,9 @@ import CardGallery from "./components/card_gallery"; import { greenhouseGasesStoryIds } from "../overrides/common/story-data"; import VisitGHG from "../overrides/components/visit-ghg"; +import Carousel from "../overrides/common/embedded-video-carousel"; +import contentArray from './theme.GHG.introduction_greenhouse_gases/carousel_content.json'; + @@ -45,7 +48,10 @@ import VisitGHG from "../overrides/components/visit-ghg"; - + +
+ +
+ + + \ No newline at end of file From d1f61b93db4e8bee40a8907a184029bc341a6c43 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Thu, 27 Jun 2024 15:35:04 -0400 Subject: [PATCH 07/22] add SLR carousel --- .../theme.SLR.introduction_sea_level_rise.mdx | 22 +-------- .../carousel_content.json | 47 +++++++++++++++++++ stories/theme.SLR.mdx | 9 ++++ 3 files changed, 57 insertions(+), 21 deletions(-) create mode 100644 stories/theme.SLR.introduction_sea_level_rise/carousel_content.json diff --git a/stories/theme.SLR.introduction_sea_level_rise.mdx b/stories/theme.SLR.introduction_sea_level_rise.mdx index 50b1a47d2..abf26a6b2 100644 --- a/stories/theme.SLR.introduction_sea_level_rise.mdx +++ b/stories/theme.SLR.introduction_sea_level_rise.mdx @@ -24,24 +24,4 @@ taxonomy: - name: Topics values: - sea level rise ---- - -import CardGallery from "./components/card_gallery"; -import { seaLevelRiseStoryIds } from "../overrides/common/story-data"; - - - - ## Info - - The effects of sea level rise have been observed across the world. - Hazards such as coastal flooding can pose health and safety risks for seaside residents and displace vulnerable communities. - - At the local scale, scientists study a variety of factors that contribute to regional sea level rise including periodic change in sea level due to storm surges, ice melt, the amount of water stored on land (including rivers, lakes and aquifers), land subsistence and changes in water temperature and salinity. - At the global scale, loss of ice through melting glaciers and icesheets, thermal expansion from heat trapped in oceans and changes in the amount of water stored on land all influence the amount of change in sea level observed. - Understanding how these factors change on a global scale also allows us to better understand changes in Earth's atmosphere and oceans. - - Using a combination space-based observations, ground-based monitoring and modeling, federal agencies work with local organizations across the country and internationally to prepare for and mitigate the impacts of sea level rise. - - - - \ No newline at end of file +--- \ No newline at end of file diff --git a/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json b/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json new file mode 100644 index 000000000..cb1ac734a --- /dev/null +++ b/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json @@ -0,0 +1,47 @@ +[ + { + "src":" https://www.youtube.com/embed/TvPjAe4j6qQ", + "title":"Global Sea Surface Currents and Temperature", + "caption":"This visualization shows sea surface current flows. The flows are colored by corresponding sea surface temperature data. This visualization is rendered for display on very high resolution devices like hyperwalls or for print media.

This visualization was produced using model output from the joint MIT/JPL project entitled Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) (http://ecco2.org/). ECCO2 uses the MIT general circulation model (MITgcm) to synthesize satellite and in-situ data of the global ocean and sea-ice at resolutions that begin to resolve ocean eddies and other narrow current systems, which transport heat and carbon in the oceans. The ECCO2 model simulates ocean flows at all depths, but only surface flows are used in this visualization.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/3912" + },{ + "src":"https://www.youtube.com/embed/FEOlYWUbsHo", + "title":"A Decade of Sea Surface Salinity", + "caption":"The heat of the sun forces evaporation at the ocean's surface, which puts water vapor into the atmosphere but leaves minerals and salts behind, keeping the ocean salty. The salinity of the ocean also varies from place to place, because evaporation varies based on the sea surface temperature and wind, rivers and rain storms inject fresh water into the ocean, and melting or freezing sea ice affects the salinity of polar waters.\nVisualizations by: Alex Kekesi, Scientific consulting by: Nadya Vinogradova-Shiffer\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5017" + },{ + "src":"https://www.youtube.com/embed/9QMLSKmL4FU", + "title":"Arctic Sea Ice Spiral", + "caption":"\nThis data visualization shows the Arctic sea ice extent from October 1978 to September 2022. The amount of Arctic sea ice varies seasonally, typically reaching a maximum in March and a minimum in September. Recently, the Arctic sea ice minimum has been decreasing at a rate of 13% per decade. Please see Global Climate Change Vital Signs: Arctic Sea Ice Minimum Extent (https://climate.nasa.gov/vital-signs/arctic-sea-ice/) for more information.\nVisualizations by: Mark SubbaRao\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5028" + },{ + "src":"https://www.youtube.com/embed/35QjTwIG-eg", + "title":"Arctic Sea Ice Minimum 2022", + "caption":"Satellite-based passive microwave images of the sea ice have provided a reliable tool for continuously monitoring changes in the Arctic ice since 1979. Every summer the Arctic ice cap melts down to what scientists call its \"minimum\" before colder weather begins to cause ice cover to increase. An analysis of satellite data by NASA and the National Snow and Ice Data Center (NSIDC) at the University of Colorado Boulder shows that the 2022 minimum extent, which was likely reached on Sept. 18, measured 1.80 million square miles (4.67 million square kilometers).\n\nThe Japan Aerospace Exploration Agency (JAXA) provides many water-related products derived from data acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument aboard the Global Change Observation Mission 1st-Water \"SHIZUKU\" (GCOM-W1) satellite. Two JAXA datasets used in this animation are the 10-km daily sea ice concentration and the 10 km daily 89 GHz Brightness Temperature.\n\nIn this animation, the daily Arctic sea ice and seasonal land cover change progress through time, from the yearly maximum ice extent on February 25 2022, through its minimum on September 18 2022. Over the water, Arctic sea ice changes from day to day showing a running 3-day minimum sea ice concentration in the region where the concentration is greater than 15%. The blueish white color of the sea ice is derived from a 3-day running minimum of the AMSR2 89 GHz brightness temperature. The yellow boundary shows the minimum extent averaged over the 30-year period from 1981 to 2010. Over the terrain, monthly data from the seasonal Blue Marble Next Generation fades slowly from month to month.\nVisualizations by: Trent L. Schindler, Produced by: Roberto Molar-Candanosa, Scientific consulting by: Walt Meier\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5030" + },{ + "src":"https://www.youtube.com/embed/njoTDqwFuuU", + "title":"Annual Arctic Sea Ice Minimum Area 1979-2022, With Graph", + "caption":"Satellite-based passive microwave images of the sea ice have provided a reliable tool for continuously monitoring changes in the Arctic ice since 1979. Every summer the Arctic ice cap melts down to what scientists call its \"minimum\" before colder weather begins to cause ice cover to increase. This graph displays the area of the minimum sea ice coverage each year from 1979 through 2022. In 2022, the Arctic minimum sea ice covered an area of 4.16 million square kilometers (1.6 million square miles). \n\nThis visualization shows the expanse of the annual minimum Arctic sea ice for each year from 1979 through 2022 as derived from passive microwave data.\nVisualizations by: Trent L. Schindler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5036" + },{ + "src":"https://www.youtube.com/embed/ljfk9hqrBsc", + "title":"Daily Polar Sea Ice, Two Year History", + "caption":"This visualization shows the daily Arctic and Antarctic sea ice and seasonal land cover change over a two-year history from the present, with a single frame rendered for each day (available from the drop-down of each image window), and an animation created from these frames, \n\nThe Japan Aerospace Exploration Agency (JAXA) provides many water-related products derived from data acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument aboard the Global Change Observation Mission 1st-Water \"SHIZUKU\" (GCOM-W1) satellite. Two JAXA datasets are used in this animation: the 10-km daily sea ice concentration and the 10 km daily 89 GHz Brightness Temperature.\n\nIn this visualization sea ice changes from day to day, with the amount of ice shown being determined by the AMSR2 sea ice concentration data. A running 3-day minimum is used, with a minimum threshhold concentration of 15%. The blueish white color of the sea ice is derived from a 3-day running minimum of the AMSR2 89 GHz brightness temperature. Over the terrain, monthly data from the seasonal Blue Marble Next Generation fades slowly from month to month.\n\nThe numerical portion of the frame filename begins with the four-digit year, followed by the three-digit day of the year for that frame.\nVisualizations by: Trent L. Schindler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5099" + },{ + "src":"https://www.youtube.com/embed/0907sP0XAZE", + "title":"Sea Level Through a Porthole", + "caption":"As the planet warms and polar ice melts, our global average sea level is rising. Although exact ocean heights vary due to local geography, climate over time, and dynamic fluid interactions with gravity and planetary rotation, scientists observe sea level trends by comparing measurements against a 20 year spatial and temporal mean reference. These visualizations use the visual metaphor of a submerged porthole window to observe how far our oceans rose between 1993 and 2022.\nVisualizations by: Andrew J Christensen\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5114" + },{ + "src":"NRT", + "title":"Sea Surface Temperature (SST) Anomaly - Near Real Time", + "caption":"This visualization shows Sea Surface Temperature (SST) Anomaly data from the Jet Propulsion Laboratory (JPL) Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature Analysis. The analysis data is near-real-time (one day latency) and the visualization is updated daily to include the latest available data. \n\nData available here: https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5176" + },{ + "src":"https://www.youtube.com/embed/pRR5E95F7N0", + "title":"2023 Temperature Measurements", + "caption":"The locations of the temperature measurements that were used in the 2023 GISS Surface Temperature Analysis (v4). The data on land comes from the weather stations that make up the Global Historical Climatology Network (GHCN). Over water temperature measurements come from International Comprehensive Ocean-Atmosphere Data Set (ICOADS). This dataset provides surface marine observational records from ships, buoys, and other platform types.\nVisualizations by: Mark SubbaRao\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5208" + },{ + "src":"https://www.youtube.com/embed/QQHVHf5QD-s", + "title":"Slow Reveal Graphs: Global Mean Sea Level 1993-2023", + "caption":"Slow reveal graphs are an instructional routine using scaffolded visuals and discourse to help students (in K-12 and beyond) make sense of data. This is a slow reveal graph of the SVS visualization of rising Global Mean Sea Level (https://svs.gsfc.nasa.gov/5221/).\n\nVisualizations by: Mark SubbaRao, Produced by: Stacie Marvin\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5312" + },{ + "src":"https://www.youtube.com/embed/z04TBQw9KkU", + "title":"GRACE and GRACE-FO polar ice mass loss", + "caption":"The mass of the Polar ice sheets have changed over the last decades. Research based on observations from the Gravity Recovery and Climate Experiment (GRACE) satellites (2002-2017) and GRACE Follow-On (since 2018 - ) indicates that between 2002 and 2023, Antarctica shed approximately 150 gigatons of ice per year, causing global sea level to rise by 0.4 millimeters per year; and Greenland shed approximately 270 gigatons of ice per year, causing global sea level to rise by 0.03 inches (0.8 millimeters) per year.\n\nThese images, created from GRACE and GRACE-FO data, show changes in polar land ice mass since 2002. Orange and red shades indicate areas that lost ice mass, while light blue shades indicate areas that gained ice mass. White indicates areas where there has been very little or no change in ice mass since 2002.\n\nThe average flow lines (grey; created from satellite radar interferometry) of the icesheets converge into the locations of prominent outlet glaciers, and coincide with areas of highest mass loss. This supports other observations that warming ocean waters near polar icesheets play a key role in contemporary ice mass loss.\nScientific consulting by: Felix W. Landerer\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/31166" + } +] \ No newline at end of file diff --git a/stories/theme.SLR.mdx b/stories/theme.SLR.mdx index 74e5ba7f2..908ed10cc 100644 --- a/stories/theme.SLR.mdx +++ b/stories/theme.SLR.mdx @@ -27,6 +27,9 @@ taxonomy: import CardGallery from "./components/card_gallery"; import { seaLevelRiseStoryIds } from "../overrides/common/story-data"; +import Carousel from "../overrides/common/embedded-video-carousel"; +import contentArray from './theme.SLR.introduction_sea_level_rise/carousel_content.json'; + ## Info @@ -42,4 +45,10 @@ import { seaLevelRiseStoryIds } from "../overrides/common/story-data"; + +

+ +
+ + \ No newline at end of file From da4392779e27ba3ca84890925d4d3c5992502db0 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Thu, 27 Jun 2024 15:37:52 -0400 Subject: [PATCH 08/22] add WLF carousel --- stories/theme.WLF.introduction_wildfires.mdx | 20 +------------- .../carousel_content.json | 27 +++++++++++++++++++ stories/theme.WLF.mdx | 9 +++++++ 3 files changed, 37 insertions(+), 19 deletions(-) create mode 100644 stories/theme.WLF.introduction_wildfires/carousel_content.json diff --git a/stories/theme.WLF.introduction_wildfires.mdx b/stories/theme.WLF.introduction_wildfires.mdx index 5b70b64f5..696535c2f 100644 --- a/stories/theme.WLF.introduction_wildfires.mdx +++ b/stories/theme.WLF.introduction_wildfires.mdx @@ -24,22 +24,4 @@ taxonomy: - name: Topics values: - wildfires ---- - -import CardGallery from "./components/card_gallery"; -import { wildfiresStoryIds } from "../overrides/common/story-data"; - - - - ## Info - - Changes in climate, weather, vegetation and the landscape all play a role in whether a spark becomes a flame. - Wildfires, also referred to as wildland fires, pose threats to human safety across large geographic regions and can cause widespread health and ecological impacts. - For instance, smoke from fires can impact air quality in areas across the country or even the world. - At the same time, wildfires are a natural process that maintains ecosystem stability reinforcing the need to understand the multifaceted nature of fires across the landscape. - The unique vantage point offered by Earth observing satellites provides researchers and land managers with the ability to assess broadscale extents of fire related hazards that is not possible with traditional ground-based monitoring. - This information is used by agencies at multiple levels of government and management teams on the ground during all stages of wildfires, including monitoring fire prone regions, tracking and responding to active wildfires and assessing post wildfire zones. - - - - \ No newline at end of file +--- \ No newline at end of file diff --git a/stories/theme.WLF.introduction_wildfires/carousel_content.json b/stories/theme.WLF.introduction_wildfires/carousel_content.json new file mode 100644 index 000000000..8c3cf11aa --- /dev/null +++ b/stories/theme.WLF.introduction_wildfires/carousel_content.json @@ -0,0 +1,27 @@ +[ + { + "src":"https://www.youtube.com/embed/plMk_6Rg6uw", + "title":"Lightning Events Detected from the International Space Station (ISS) 2017-2023", + "caption":"The Lightning Imaging Sensor (LIS) on the International Space Station (ISS) detects lightning occurring in the Earth\u2019s tropical and mid-latitude regions. The LIS provides datasets consisting of near-real time and non-quality controlled data as well as final quality controlled datasets that are manually reviewed. This data uncovers the variability and distribution of lightning and can be used for storm detection and lightning-atmosphere interaction studies. \r\n\r\nThis visualization shows the global distribution of lightning strikes between January 2017 and July 2023 using the final quality controlled science dataset. Each data point contains the latitude and longitude of the strike as well as the time it was detected by the LIS. A roving window of 10 days was used to visualize the seasonal patterns of lightning.\n\nVisualizations by: Michala Garrison\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5011" + },{ + "src":"https://www.youtube.com/embed/dUOrk3zz2Fc", + "title":"Carbon Emissions from Fires: Jan 2003 - Jan 2022", + "caption":"\nThis visualization uses the Global Fire Emissions Database (https://www.globalfiredata.org) version 4 to show the weekly carbon emissions from fires from January 2003 through January 2022. The data has a spatial resolution of 0.25 degrees in both latitude and longitude. The monthly fire carbon emissions with small fires from the GFED4s dataset was multiplied by the daily fractional contribution to get the daily carbon emission. This was summed over each 7-day period beginning on January 1st each year. Day of year 365 (and day 366 in leap years) was not included. \n\nThe perceptually uniform color scales used in this visualization were developed by Peter Koversi and are available (https://colorcet.com/gallery.html). See Peter Kovesi. Good Colour Maps: How to Design Them. arXiv:1509.03700 [cs.GR] 2015 for additional information.\n\nVisualizations by: Cindy Starr\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5012" + },{ + "src":"https://www.youtube.com/embed/SA96Jha1un8", + "title":"Tracking the Spread of the Caldor and Dixie Fires", + "caption":"This visualization highlights data from a new fire detection and tracking approach (Chen et al., 2022) based on near-real time active fire detections from the VIIRS sensor on the Suomi-NPP satellite. Every 12 hours, the fire tracking algorithm uses new active fire detections to update the total fire perimeter and estimate the position of active fire lines where the fire may continue to spread. This approach provides a detailed perspective on the behavior of the Caldor and Dixie fires, identifying periods of rapid fire expansion and active fire detections within the perimeter from continued flaming and smoldering behind the active fire fronts.\n\nThe visualization shows the progression over time as the fire spreads. The yellow outlines track the position of the active fire lines every 12 hours for the last 60 hours, with the latest location of the fire front shown in the brightest shade of yellow. The red points show the location of active fire detections within the perimeter, while the grey region shows the estimated total area burned. The graph shows the cumulative burned area in square kilometers.\nVisualizations by: Cindy Starr, Scientific consulting by: Doug C. Morton\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5088" + },{ + "src":"NRT", + "title":"Active Fires As Observed by VIIRS, 2022-Present", + "caption":"This visualization shows active fires as observed by the Visible Infrared Imaging Radiometer Suite, or VIIRS, since the beginning of 2022. The VIIRS instrument flies on the Joint Polar Satellite System\u2019s Suomi-NPP and NOAA-20 polar-orbiting satellites. Instruments on polar orbiting satellites typically observe a wildfire at a given location a few times a day as they orbit the Earth from pole to pole. VIIRS detects hot spots at a resolution of 375 meters per pixel, which means it can detect smaller, lower temperature fires than other fire-observing satellites. Its observations are about three times more detailed than those from the MODIS instrument, for example. VIIRS also provides nighttime fire detection capabilities through its Day-Night Band, which can measure low-intensity visible light emitted by small and fledgling fires. This visualization uses data from the Suomi-NPP VIIRS instrument, and will be updated periodically.\nVisualizations by: Trent L. Schindler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5113" + },{ + "src":"https://www.youtube.com/embed/GHn_ZxNiF4M", + "title":"Wildfires101: Animations", + "caption":"\nVisualizations by: Alex Bodnar, Jonathan North, Adriana Manrique Gutierrez, Produced by: Katie Jepson\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14286" + },{ + "src":"https://www.youtube.com/embed/wvo5f8Xu_xY", + "title":"Column Carbon Monoxide (CO) from Canada Wildfires", + "caption":"Aerosols and trace gases emitted from wildfires are an important component of the Earth-atmosphere system as they can impact radiation, clouds, the carbon cycle, and human health. Produced by the GMAO using observations from MODIS, the Quick Fire Emissions Dataset (QFED) serves as a top-down estimate of aerosol and trace gas emissions to be used as the input for constituent modeling within the Goddard Earth Observing System (GEOS). Upon emission into GEOS through the GOCART aerosol module, aerosols and trace gases are transported via the dynamics in the model, while aerosols are also coupled to radiative and moist model processes. A feature unique to carbon monoxide (CO) emitted in response to wildfires within GEOS is that it can be tagged based on its region of origin. \n\nIn the spring of 2023, the Canadian biomass burning season had an early and aggressive start, saturating the troposphere with smoke. The accumulated emission of CO from biomass burning across Canada was quadruple the previous maximum from the past two decades since the launch of MODIS. This animation demonstrates the transport of total column CO that originated due to biomass burning over North America during the first week of June in 2023 using the GEOS Forward Processing (FP) system. Though emissions are localized, as shown by the red triangles indicating fire hotspots based on QFED, CO from biomass burning is transported thousands of miles and can have a widespread impact.\nTechnical support: Mark Malanoski, Scientific consulting by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/31234" + } +] \ No newline at end of file diff --git a/stories/theme.WLF.mdx b/stories/theme.WLF.mdx index 1066a729d..f95b24a91 100644 --- a/stories/theme.WLF.mdx +++ b/stories/theme.WLF.mdx @@ -27,6 +27,9 @@ taxonomy: import CardGallery from "./components/card_gallery"; import { wildfiresStoryIds } from "../overrides/common/story-data"; +import Carousel from "../overrides/common/embedded-video-carousel"; +import contentArray from './theme.WLF.introduction_wildfires/carousel_content.json'; + ## Info @@ -40,4 +43,10 @@ import { wildfiresStoryIds } from "../overrides/common/story-data"; + +
+ +
+ + \ No newline at end of file From 86fb524450ade7db69327349fd3f45f6ba37c217 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Thu, 27 Jun 2024 15:40:28 -0400 Subject: [PATCH 09/22] remove near real time video content --- .../carousel_content.json | 24 ------------------- .../carousel_content.json | 8 ------- .../carousel_content.json | 4 ---- .../carousel_content.json | 4 ---- 4 files changed, 40 deletions(-) diff --git a/stories/theme.DIS.introduction_disasters/carousel_content.json b/stories/theme.DIS.introduction_disasters/carousel_content.json index 12419884f..e8512cb61 100644 --- a/stories/theme.DIS.introduction_disasters/carousel_content.json +++ b/stories/theme.DIS.introduction_disasters/carousel_content.json @@ -15,30 +15,6 @@ "src":"https://www.youtube.com/embed/B7n62jOJJqI", "title":"Atlantic Hurricane Wind Speed Plots", "caption":"These simple visualizations are plots of time vs. wind speed for each tropical storm/hurricane of Atlantic Hurricane seasons from 1950 to the present. Horizontal lines indicate wind speed category thresholds. A line plot for each storm shows the storm's name and a marker at the peak wind speed.

\n\nMost named storms during the Atlantic hurricane season happen between June and November. However, occasionally, storms develop outside of those ranges.\n\nFour versions of the plots are inluded:\n 1. May to December showig only the current year\n 2. May to December showing the current year and strong storms from previous years (ghosted out)\n 3. January to December showig only the current year\n 4. January to December showing the current year and strong storms from previous years (ghosted out)\n \nThe plot for the current year automatically updates every 2 hours during hurricane season.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5072" - },{ - "src":"NRT", - "title":"Sea Surface Temperature (SST) - Near Real Time", - "caption":"This visualization shows Sea Surface Temperature (SST) data from the Jet Propulsion Laboratory (JPL) Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature Analysis. The analysis data is near-real-time (one day latency) and the visualization is updated daily to include the latest available data.\n\nData available here: https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5101" - },{ - "src":"NRT", - "title":"GEOS-FP Near Surface Temperature", - "caption":"Weather models begin their forecasts by integrating observations from ground stations, aircraft, balloons, and a growing constellation of Earth observing satellites to estimate the most realistic atmospheric state. The Goddard Earth Observing System Forward Processing (GEOS-FP) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to demonstrate innovative new ways to use satellite data to improve predictions of weather, air quality, and greenhouse gases and to help NASA collect new observations of our home planet. Near surface temperature is calculated by sampling model fields at 2 meters (about 6 feet) above Earth\u2019s surface, helping scientists communicate to the general public the air temperatures they encounter. Variations reflect differences in daytime and nighttime temperatures and the influence of large scale weather systems. As the planet warms in response to rising greenhouse gas concentrations, scientists are also tracking changes in the frequency of heat waves and their potential impacts on human health.\nVisualizations by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5147" - },{ - "src":"NRT", - "title":"GEOS-FP Near Surface Wind Speed", - "caption":"Weather models begin their forecasts by integrating observations from ground stations, aircraft, balloons, and a growing constellation of Earth observing satellites to estimate the most realistic atmospheric state. The Goddard Earth Observing System Forward Processing (GEOS-FP) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to demonstrate innovative new ways to use satellite data to improve predictions of weather, air quality, and greenhouse gases and to help NASA collect new observations of our home planet. \n\r\nNear surface wind speed is calculated by sampling model fields at 10 meters (about 30 feet) above Earth\u2019s surface. Wind speed is typically largest over ocean because surfaces are smooth and result in less friction than over land. Organized patterns reflect weather fronts and storm systems around the world. Near surface winds help scientists track the movement and severity of storms and air pollution episodes. Increasingly, these kinds of products help wind energy managers plan resources to help meet the world\u2019s demand for energy while reducing the greenhouse gas emissions that cause climate change.\nVisualizations by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5148" - },{ - "src":"NRT", - "title":"GEOS-FP Near Surface Wind Speed", - "caption":"Weather models begin their forecasts by integrating observations from ground stations, aircraft, balloons, and a growing constellation of Earth observing satellites to estimate the most realistic atmospheric state. The Goddard Earth Observing System Forward Processing (GEOS-FP) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to demonstrate innovative new ways to use satellite data to improve predictions of weather, air quality, and greenhouse gases and to help NASA collect new observations of our home planet. \n\r\nNear surface wind speed is calculated by sampling model fields at 10 meters (about 30 feet) above Earth\u2019s surface. Wind speed is typically largest over ocean because surfaces are smooth and result in less friction than over land. Organized patterns reflect weather fronts and storm systems around the world. Near surface winds help scientists track the movement and severity of storms and air pollution episodes. Increasingly, these kinds of products help wind energy managers plan resources to help meet the world\u2019s demand for energy while reducing the greenhouse gas emissions that cause climate change.\nVisualizations by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5148" - },{ - "src":"NRT", - "title":"GEOS-FP Relative Humidity", - "caption":"Weather models begin their forecasts by integrating observations from ground stations, aircraft, balloons, and a growing constellation of Earth observing satellites to estimate the most realistic atmospheric state. The Goddard Earth Observing System Forward Processing (GEOS-FP) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to demonstrate innovative new ways to use satellite data to improve predictions of weather, air quality, and greenhouse gases and to help NASA collect new observations of our home planet. \n\r\nRelative humidity is the ratio of water vapor in the atmosphere relative to the amount of water vapor that would be present if the air were saturated. It is calculated by using a combination of near surface temperature and water vapor fields. Variations reflect differences in daytime and nighttime relative humidity and the influence of large scale weather systems. Near surface humidity is an indicator of the likelihood of rain or fog and also has implications for human health. Humidity influences the evolution of air pollution events and high relative humidity can increase the risk of developing heat-related illnesses on very hot days.\nVisualizations by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5150" - },{ - "src":"NRT", - "title":"Sea Surface Temperature (SST) Anomaly - Near Real Time", - "caption":"This visualization shows Sea Surface Temperature (SST) Anomaly data from the Jet Propulsion Laboratory (JPL) Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature Analysis. The analysis data is near-real-time (one day latency) and the visualization is updated daily to include the latest available data. \n\nData available here: https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5176" },{ "src":"https://www.youtube.com/embed/6-71kC1LT-w", "title":"Predicting Landslides", diff --git a/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json index b1e73daf5..503a48b06 100644 --- a/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json +++ b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json @@ -39,13 +39,5 @@ "src":"https://youtube.com/shorts/ZYIkApOYwIA", "title":"ODIAC: a map of human made carbon dioxide emissions", "caption":"The Open-source Data Inventory for Anthropogenic CO2 (A.K.A. ODIAC) is a global high-resolution (1x1km) emission data product for fossil fuel carbon dioxide (CO2) emission. ODIAC was originally designed and developed under the Greenhouse gas Observing SATellite (GOSAT) project at Japan\u2019s National Institute for Environmental Studies (NIES). Since then, ODIAC has been maintained at the Universities Space Research Association (USRA) in collaboration with NASA, NIES and the Appalachian State University. ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO2 emissions. ODIAC has been a key input data for NASA\u2019s carbon modeling. ODIAC has been also widely used in the international science research community for a variety of applications across key policy relevant scales (global to local).\nVisualizations by: Mark SubbaRao, Scientific consulting by: Tomohiro Oda\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5121" - },{ - "src":"NRT", - "title":"NASA Climate Spiral 1880-Present", - "caption":"The visualization presents monthly global temperature anomalies. This visualization is updated roughly two weeks after the end of each month.\n\nTemperature anomalies are deviations from a long term global average. In this case the period 1951-1980 is used to define the baseline for the anomaly. These temperatures are based on the GISS Surface Temperature Analysis (GISTEMP v4), an estimate of global surface temperature change. The data file used to create this visualization is publicaly accessible (https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv).\n\nThe term 'climate spiral' (https://www.climate-lab-book.ac.uk/spirals/) describes an animated radial plot of global temperatures. Climate scientist Ed Hawkins from the National Centre for Atmospheric Science, University of Reading popularized this style of visualization in 2016.\n\nThe Goddard Institute for Space Studies (GISS) in New York is a NASA laboratory managed by the Earth Sciences Division of the agency\u2019s Goddard Space Flight Center in Greenbelt, Maryland. The laboratory is affiliated with Columbia University\u2019s Earth Institute and School of Engineering and Applied Science in New York.\nVisualizations by: Mark SubbaRao\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5190" - },{ - "src":"NRT", - "title":"Global Temperature Graph 1880-Present", - "caption":"The visualization presents the seasonal cycle of temperature variation on the earth's surface. This visualization is updated roughly two weeks after the end of each month.\n\nTemperature anomalies are deviations from a long term global average. In this case the period 1951-1980 is used to define the baseline for the anomaly. These temperatures are based on the GISS Surface Temperature Analysis (GISTEMP v4), an estimate of global surface temperature change. The data file used to create this visualization is publicaly accessible (https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv). The seasonal temperature offsets are based on the MERRA-2 reanalysis data here.\n\nThe Goddard Institute of Space Studies (GISS) is a NASA laboratory managed by the Earth Sciences Division of the agency\u2019s Goddard Space Flight Center in Greenbelt, Maryland. The laboratory is affiliated with Columbia University\u2019s Earth Institute and School of Engineering and Applied Science in New York.\nVisualizations by: Mark SubbaRao\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5191" } ] \ No newline at end of file diff --git a/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json b/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json index cb1ac734a..a8bca752c 100644 --- a/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json +++ b/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json @@ -27,10 +27,6 @@ "src":"https://www.youtube.com/embed/0907sP0XAZE", "title":"Sea Level Through a Porthole", "caption":"As the planet warms and polar ice melts, our global average sea level is rising. Although exact ocean heights vary due to local geography, climate over time, and dynamic fluid interactions with gravity and planetary rotation, scientists observe sea level trends by comparing measurements against a 20 year spatial and temporal mean reference. These visualizations use the visual metaphor of a submerged porthole window to observe how far our oceans rose between 1993 and 2022.\nVisualizations by: Andrew J Christensen\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5114" - },{ - "src":"NRT", - "title":"Sea Surface Temperature (SST) Anomaly - Near Real Time", - "caption":"This visualization shows Sea Surface Temperature (SST) Anomaly data from the Jet Propulsion Laboratory (JPL) Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature Analysis. The analysis data is near-real-time (one day latency) and the visualization is updated daily to include the latest available data. \n\nData available here: https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5176" },{ "src":"https://www.youtube.com/embed/pRR5E95F7N0", "title":"2023 Temperature Measurements", diff --git a/stories/theme.WLF.introduction_wildfires/carousel_content.json b/stories/theme.WLF.introduction_wildfires/carousel_content.json index 8c3cf11aa..ff2fe2ad7 100644 --- a/stories/theme.WLF.introduction_wildfires/carousel_content.json +++ b/stories/theme.WLF.introduction_wildfires/carousel_content.json @@ -11,10 +11,6 @@ "src":"https://www.youtube.com/embed/SA96Jha1un8", "title":"Tracking the Spread of the Caldor and Dixie Fires", "caption":"This visualization highlights data from a new fire detection and tracking approach (Chen et al., 2022) based on near-real time active fire detections from the VIIRS sensor on the Suomi-NPP satellite. Every 12 hours, the fire tracking algorithm uses new active fire detections to update the total fire perimeter and estimate the position of active fire lines where the fire may continue to spread. This approach provides a detailed perspective on the behavior of the Caldor and Dixie fires, identifying periods of rapid fire expansion and active fire detections within the perimeter from continued flaming and smoldering behind the active fire fronts.\n\nThe visualization shows the progression over time as the fire spreads. The yellow outlines track the position of the active fire lines every 12 hours for the last 60 hours, with the latest location of the fire front shown in the brightest shade of yellow. The red points show the location of active fire detections within the perimeter, while the grey region shows the estimated total area burned. The graph shows the cumulative burned area in square kilometers.\nVisualizations by: Cindy Starr, Scientific consulting by: Doug C. Morton\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5088" - },{ - "src":"NRT", - "title":"Active Fires As Observed by VIIRS, 2022-Present", - "caption":"This visualization shows active fires as observed by the Visible Infrared Imaging Radiometer Suite, or VIIRS, since the beginning of 2022. The VIIRS instrument flies on the Joint Polar Satellite System\u2019s Suomi-NPP and NOAA-20 polar-orbiting satellites. Instruments on polar orbiting satellites typically observe a wildfire at a given location a few times a day as they orbit the Earth from pole to pole. VIIRS detects hot spots at a resolution of 375 meters per pixel, which means it can detect smaller, lower temperature fires than other fire-observing satellites. Its observations are about three times more detailed than those from the MODIS instrument, for example. VIIRS also provides nighttime fire detection capabilities through its Day-Night Band, which can measure low-intensity visible light emitted by small and fledgling fires. This visualization uses data from the Suomi-NPP VIIRS instrument, and will be updated periodically.\nVisualizations by: Trent L. Schindler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5113" },{ "src":"https://www.youtube.com/embed/GHn_ZxNiF4M", "title":"Wildfires101: Animations", From bbc2152da796d615a9b137a9393e44e5e4ebaebb Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Thu, 27 Jun 2024 15:43:58 -0400 Subject: [PATCH 10/22] replace youtube shorts video with actual video --- .../carousel_content.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json index 503a48b06..3742be9ba 100644 --- a/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json +++ b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json @@ -36,7 +36,7 @@ "title":"OCO-2 and Keeling Curve: Trends in global atmospheric Carbon Dioxide (CO\u2082)", "caption":"\nVisualizations by: Helen-Nicole Kostis\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5119" },{ - "src":"https://youtube.com/shorts/ZYIkApOYwIA", + "src":"https://www.youtube.com/embed/2xxjiu0C_-Q", "title":"ODIAC: a map of human made carbon dioxide emissions", "caption":"The Open-source Data Inventory for Anthropogenic CO2 (A.K.A. ODIAC) is a global high-resolution (1x1km) emission data product for fossil fuel carbon dioxide (CO2) emission. ODIAC was originally designed and developed under the Greenhouse gas Observing SATellite (GOSAT) project at Japan\u2019s National Institute for Environmental Studies (NIES). Since then, ODIAC has been maintained at the Universities Space Research Association (USRA) in collaboration with NASA, NIES and the Appalachian State University. ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO2 emissions. ODIAC has been a key input data for NASA\u2019s carbon modeling. ODIAC has been also widely used in the international science research community for a variety of applications across key policy relevant scales (global to local).\nVisualizations by: Mark SubbaRao, Scientific consulting by: Tomohiro Oda\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5121" } From 4cfe6fb2116cc2d5ebee6870d374681dea2874f9 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Thu, 27 Jun 2024 15:48:17 -0400 Subject: [PATCH 11/22] add methane and JGI chimpanzee videos --- stories/theme.BIO.mdx | 13 +++++++++++++ stories/theme.GHG.mdx | 13 +++++++++++++ 2 files changed, 26 insertions(+) diff --git a/stories/theme.BIO.mdx b/stories/theme.BIO.mdx index c8557f23a..95c1a81ca 100644 --- a/stories/theme.BIO.mdx +++ b/stories/theme.BIO.mdx @@ -44,6 +44,19 @@ import contentArray from './theme.BIO.introduction_biodiversity/carousel_content + +

+ + + Learn how earth data are being used to help the Jane Goodall Institute monitor the health of chimpanzee habitats. + +
+ +
diff --git a/stories/theme.GHG.mdx b/stories/theme.GHG.mdx index 7a7cf1ba1..020c69c51 100644 --- a/stories/theme.GHG.mdx +++ b/stories/theme.GHG.mdx @@ -48,6 +48,19 @@ import contentArray from './theme.GHG.introduction_greenhouse_gases/carousel_con + +
+ + + Learn how earth data are being used to help the EPA monitor methane emissions. + +
+ +
From 02e7008a1e06aa1bbd563e0d659348b8525d4f08 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Fri, 28 Jun 2024 11:20:24 -0400 Subject: [PATCH 12/22] Um. How long has this content been duplicated?? --- stories/theme.AG_.mdx | 10 ---------- stories/theme.AQ_.mdx | 16 ---------------- stories/theme.DIS.mdx | 8 -------- 3 files changed, 34 deletions(-) diff --git a/stories/theme.AG_.mdx b/stories/theme.AG_.mdx index 1814881af..73bc27fc6 100644 --- a/stories/theme.AG_.mdx +++ b/stories/theme.AG_.mdx @@ -51,16 +51,6 @@ import contentArray from './theme.AG_.introduction_agriculture/carousel_content.
- - - ## Agriculture - Producing food has always been challenging, and in the 21st century, human-caused climate change is already affecting food security through increasing temperatures, increased frequency of extreme events and changing precipitation patterns. - - Earth data have increasingly become part of the food farming process. - Observations from satellites, aircraft, ground sensors and surveys, combined with high-end computer modeling are used by scientists working with Federal agencies who collaborate with farmers, ranchers, fishermen and decision-makers to share their understanding of the relationship between the Earth system and the environments that provide food across the globe. - - -
diff --git a/stories/theme.AQ_.mdx b/stories/theme.AQ_.mdx index 9e99e1814..07451a158 100644 --- a/stories/theme.AQ_.mdx +++ b/stories/theme.AQ_.mdx @@ -61,22 +61,6 @@ import contentArray from './theme.AQ_.introduction_air_quality/carousel_content.
- - - ## Air Quality - Air pollution is a global hazard, so it takes a combination of airborne, ground and satellite-based tools to better understand the origins and movement of pollutants, as well as the impacts on air quality. - The causes of air pollution vary from human activities, such as coal-fired power plants, to natural events, like wildfires and dust storms. - - Ground-based measurements are also used to assess air quality and the concentrations of different types of atmospheric pollution. Satellite data help fill the gaps between ground-based monitors, so there is global coverage over all neighborhoods. - - Satellite-acquired data have many health and air-quality applications, including: - * Monitoring the movement of wildfire smoke and dust plumes. - * Tracking the path of ash from volcanic eruptions. - * Identifying concentrations of nitrogen dioxide, sulfur dioxide and other pollutants near cities, suburbs and major transportation systems. - * Understanding how concentrations of these pollutants are changing over time. - - - ### DID YOU KNOW? diff --git a/stories/theme.DIS.mdx b/stories/theme.DIS.mdx index ae369f1e0..6fedeef1b 100644 --- a/stories/theme.DIS.mdx +++ b/stories/theme.DIS.mdx @@ -52,14 +52,6 @@ import contentArray from './theme.DIS.introduction_disasters/carousel_content.js
- - - ## Disasters - Hurricanes, tropical cyclones, blizzards, landslides, floods and droughts -- when they arrive in communities they can turn into a disaster. As climate change is spurring more extreme weather events, disasters are becoming more costly and damaging. Earth data and rapid information sharing between agencies are more important than ever. - Before, during and after disasters strike, Federal agencies coordinate with decision-makers and local governments, providing actionable data to recover from disaster impacts and build resilient communities. - - -
From 8b6d3bf11361aca58248d7aee75e01a09a4453bd Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Fri, 28 Jun 2024 11:20:38 -0400 Subject: [PATCH 13/22] remove null reference to NRT content --- .../carousel_content.json | 4 ---- 1 file changed, 4 deletions(-) diff --git a/stories/theme.ENR.introduction_energy_generation/carousel_content.json b/stories/theme.ENR.introduction_energy_generation/carousel_content.json index 5c3c0f88f..6902b8524 100644 --- a/stories/theme.ENR.introduction_energy_generation/carousel_content.json +++ b/stories/theme.ENR.introduction_energy_generation/carousel_content.json @@ -1,9 +1,5 @@ [ { - "src":"null", - "title":"GEOS-FP Near Surface Wind Speed", - "caption":"Weather models begin their forecasts by integrating observations from ground stations, aircraft, balloons, and a growing constellation of Earth observing satellites to estimate the most realistic atmospheric state. The Goddard Earth Observing System Forward Processing (GEOS-FP) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to demonstrate innovative new ways to use satellite data to improve predictions of weather, air quality, and greenhouse gases and to help NASA collect new observations of our home planet. \n\r\nNear surface wind speed is calculated by sampling model fields at 10 meters (about 30 feet) above Earth\u2019s surface. Wind speed is typically largest over ocean because surfaces are smooth and result in less friction than over land. Organized patterns reflect weather fronts and storm systems around the world. Near surface winds help scientists track the movement and severity of storms and air pollution episodes. Increasingly, these kinds of products help wind energy managers plan resources to help meet the world\u2019s demand for energy while reducing the greenhouse gas emissions that cause climate change.\nVisualizations by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5148" - },{ "src":"https://www.youtube.com/embed/SB-IYcHb2so", "title":"Change in Night Lights between 2012 and 2023 - EIC Version", "caption":"Discover the pivotal role of NASA's Black Marble in bridging scientific discovery and actionable environmental stewardship. By capturing Earth's night lights, Black Marble reveals both the planet's beauty and the profound impact of human activities. This webpage showcases newly developed nighttime light maps comparing changes observed from 2012 to 2023, offering a decade-long perspective on urban growth, infrastructure development, and the socioeconomic dynamics of communities worldwide. This data empowers users to foster informed, sustainable practices that enhance community resilience and mitigate the adverse effects of climate change. By illuminating the intricate interplay between human activity and the environment, Black Marble underpins a human-centered approach to environmental change, promoting a future where scientific innovation leads to tangible, positive impacts on global sustainability.\n\nThis version is specifically formatted for the NASA Earth Information Center (EIC), which has a wider-than-standard video aspect ratio. The original version of this visualization, and many other night lights visualizations, are available here: https://svs.gsfc.nasa.gov/5276/\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5313" From 6e0ea54732763b4f46d5b9546ef175e6d0eec804 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Fri, 28 Jun 2024 11:21:02 -0400 Subject: [PATCH 14/22] add prose figure image content --- .../elnino_crops.jpg | Bin 0 -> 70318 bytes stories/theme.AG_.mdx | 20 ++++++++++++++---- .../AGGI.png | Bin 0 -> 350476 bytes stories/theme.GHG.mdx | 18 ++++++++++++---- .../oceans_spilhaus.jpg | Bin 0 -> 154961 bytes stories/theme.SLR.mdx | 14 +++++++++++- 6 files changed, 43 insertions(+), 9 deletions(-) create mode 100644 stories/theme.AG_.introduction_agriculture/elnino_crops.jpg create mode 100644 stories/theme.GHG.introduction_greenhouse_gases/AGGI.png create mode 100644 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Info - Producing food has always been challenging, and in the 21st century, human-caused climate change is already affecting food security through increasing temperatures, increased frequency of extreme events, and changing precipitation patterns. + ## Agriculture + Producing food has always been challenging, and in the 21st century, human-caused climate change is already affecting food security through increasing temperatures, increased frequency of extreme events and changing precipitation patterns. - Earth data has increasingly become part of the food farming process. - Observations from satellites, aircraft, ground sensors, and surveys, combined with high-end computer modeling are used by scientists working with federal agencies who collaborate with farmers, ranchers, fishermen, and decision-makers to share their understanding of the relationship between the Earth system and the environments that provide food across the globe. + Earth data have increasingly become part of the food farming process. + Observations from satellites, aircraft, ground sensors and surveys, combined with high-end computer modeling are used by scientists working with Federal agencies who collaborate with farmers, ranchers, fishermen and decision-makers to share their understanding of the relationship between the Earth system and the environments that provide food across the globe. +

+ a chloropleth map demonstrating the forecasted impact of elnino on crop yields. The colorbar spans from negative (orange) to positive (purple). Positive impacts are located in the northern and southern tips of Africa, India, China, and the southern parts of Australia. Negative impacts are concentrated in the United States, southern parts of South America, and Central Asia. + + a global map forecasting the impact of El Niño on crop yields. Areas colored in shades of orange indicate regions expected to experience negative impacts on crop yields, while areas in shades of purple are expected to see positive impacts. The map highlights significant regional variations, with negative impacts forecasted for parts of South America, Southern Africa, Southeast Asia, and Australia. Conversely, positive impacts are anticipated in certain regions of North and South America, Europe, and Southwest Asia. This visualization underscores the varied effects of El Niño on agricultural productivity across different global regions. + +
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precipitation patterns, retreating snow and ice, increasingly severe weather events, such as hurricanes of greater intensity and sea level rise, among other impacts. Federal agencies are working together to develop a Greenhouse Gas Monitoring and Information System (GHGMIS) for the U.S. to improve measurement of GHG emissions and sinks and track progress towards meet climate mitigation goals. This system uses these advanced capabilities, including the expanded use of GHG observational data and models, to provide enhanced GHG emissions and uptake data estimates that can be used by decision-makers. - - - + +

+ The image shows a NOAA Annual Greenhouse Gas Index from 1980 to 2020. A stacked bar chart on the left displays the annual increase in greenhouse gas concentrations, with red representing CO₂, purple for CH₄, blue for N₂O, and yellow for other gases. The chart indicates a continuous rise in greenhouse gas levels over the four decades. On the right, a donut chart illustrates the relative contributions to global warming, showing CO₂ as the largest contributor, followed by CH₄, N₂O, and other gases. + + The NOAA Annual Greenhouse Gas Index from 1979 to 2021, illustrating the cumulative contributions of different greenhouse gases to global warming. The stacked bar chart shows the increase in greenhouse gas concentrations over time, with each bar representing a year and the different colors indicating the contributions of specific gases: carbon dioxide (CO₂) in red, methane (CH₄) in purple, nitrous oxide (N₂O) in blue, and other gases in yellow. The accompanying donut chart on the right highlights the relative contributions of these gases to global warming, emphasizing the dominant role of CO₂, followed by CH₄, N₂O, and other gases. + +
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zR^m#PQuFRCv%orp(joXSj|Y>(IftJ_s#r11Aj)D+Vd%=h+7pt4LXPw?or}7O7eIR7U0py_Eco1gWMxkWq)`LRMgTSkpD;*tZVhUzbAT*y<%*SUA>V&{v~)J#lfU zXzjYZ3-JXltNRs@1f*!-jX95Waf#ldTO$G`KmP!bGTGT?rWLN}5%F$8*ggKz0aI@6 ziE-r^vhk=&h!UWIEm(nR013YpA2sESUl%?<23gk$^|UYmPyh!3EQVbbDa4YC_bJ_= z{{V5|glSdUm~F6n7y>3mR!-%A2eJYHOfrMS4MXtC1$5Tfqh~#f5XeA&%h$HQis?AD_ZZZXq5dFUER-_yxLDJ}dL1U zT^Y}*a-2Of{{RFMp;RCOZQP^tgRngO`5`RAzhWj91cg6vx+_C2xr#&s7eLB`lkp-!IaKkS8w>STp|wHQB>K{0#rU3m<3z`EO4c-KLNG?N&giO6dJW(Xc`@qscL=B zD10yk!*e*T4mp<;Rz%bD849!{FoFA&UGI_nkocclet*hJkY!t@I~eV}wQxo%806aq zQ`g)HEEbF(wjee(h(qU6kO^+@k_*REbURs`!dxZn@Atbe$sHK9$||{258BacJXHeC z^p^w*j1wG(BT#I1t*f>O1V4NbcM_B)tz+~(JWL0S!eVl;WB1+rT9m4#k5Oz}KYpR9 Pfa+JM5?B8K&4>Tl+voPN literal 0 HcmV?d00001 diff --git a/stories/theme.SLR.mdx b/stories/theme.SLR.mdx index 908ed10cc..1d951a07c 100644 --- a/stories/theme.SLR.mdx +++ b/stories/theme.SLR.mdx @@ -30,7 +30,7 @@ import { seaLevelRiseStoryIds } from "../overrides/common/story-data"; import Carousel from "../overrides/common/embedded-video-carousel"; import contentArray from './theme.SLR.introduction_sea_level_rise/carousel_content.json'; - + ## Info @@ -43,6 +43,18 @@ import contentArray from './theme.SLR.introduction_sea_level_rise/carousel_conte Using a combination space-based observations, ground-based monitoring and modeling, federal agencies work with local organizations across the country and internationally to prepare for and mitigate the impacts of sea level rise. +
+ This image shows a stylized map projection of the Earth's oceans with a time-series plot overlaid on it. The y-axis of the plot is labeled 'Ocean Heat Increase since 1957 (zettajoules),' and the x-axis represents years from 1960 to 2020. The orange line graph indicates the trend of increasing ocean heat content over this period, with a notable rise from the 1980s onward. The map highlights major oceans, including the Arctic Ocean, North Atlantic Ocean, Indian Ocean, Southern Ocean, and the North and South Pacific Oceans, set against a dark background for visual contrast. + + The cumulative increase in ocean heat content from 1957 to 2020, measured in zettajoules. The orange line represents the trend in ocean heat content, showing a significant and consistent rise, particularly from the 1980s onward. The background map highlights the global distribution of the oceans, emphasizing the widespread impact of increasing heat content across different ocean basins. + +
From 53b1a84d026d0b3113aa2a844afe7a7df08cfc68 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Mon, 1 Jul 2024 11:46:34 -0400 Subject: [PATCH 15/22] remove the double space, thanks: find */carousel_content.json -type f -exec sed -i '' "s/go to /go to /" {} + --- .../carousel_content.json | 2 +- .../carousel_content.json | 8 ++++---- .../carousel_content.json | 8 ++++---- .../carousel_content.json | 4 ++-- .../carousel_content.json | 2 +- .../carousel_content.json | 20 +++++++++---------- .../carousel_content.json | 20 +++++++++---------- .../carousel_content.json | 10 +++++----- .../carousel_content.json | 8 ++++---- 9 files changed, 41 insertions(+), 41 deletions(-) diff --git a/stories/theme.AG_.introduction_agriculture/carousel_content.json b/stories/theme.AG_.introduction_agriculture/carousel_content.json index 3216be66b..904950454 100644 --- a/stories/theme.AG_.introduction_agriculture/carousel_content.json +++ b/stories/theme.AG_.introduction_agriculture/carousel_content.json @@ -22,6 +22,6 @@ },{ "src":"https://www.youtube.com/embed/6z58cOh_1TA", "title":"Increasingly Dangerous Climate for Agricultural Workers", - "caption":"A warming climate will create challenges for agricultural workers as well as the crops which they grow. This visualization shows the increased number of days per year that are expected to have a NOAA Heat Index greater than 103 degrees Fahrenheit, a threshold that NOAA labels \u2018dangerous\u2019 given that people struggle to regulate their body temperatures at this level of heat and humidity. These results are from an ensemble of 22 global climate models from the Sixth Coupled Model Intercomparison Project (CMIP6) bias-adjusted by the NASA Earth Exchange (NEX GDDP). Two projections are visualized, one for a moderate emissions climate scenerio (SSP2-4.5) and one for a high emmissions climate scenerio (SSP5-8.5).\nVisualizations by: Mark SubbaRao, Scientific consulting by: Alex C. Ruane\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4972" + "caption":"A warming climate will create challenges for agricultural workers as well as the crops which they grow. This visualization shows the increased number of days per year that are expected to have a NOAA Heat Index greater than 103 degrees Fahrenheit, a threshold that NOAA labels \u2018dangerous\u2019 given that people struggle to regulate their body temperatures at this level of heat and humidity. These results are from an ensemble of 22 global climate models from the Sixth Coupled Model Intercomparison Project (CMIP6) bias-adjusted by the NASA Earth Exchange (NEX GDDP). Two projections are visualized, one for a moderate emissions climate scenerio (SSP2-4.5) and one for a high emmissions climate scenerio (SSP5-8.5).\nVisualizations by: Mark SubbaRao, Scientific consulting by: Alex C. Ruane\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4972" } ] \ No newline at end of file diff --git a/stories/theme.AQ_.introduction_air_quality/carousel_content.json b/stories/theme.AQ_.introduction_air_quality/carousel_content.json index 408feb6f1..4e76a0a60 100644 --- a/stories/theme.AQ_.introduction_air_quality/carousel_content.json +++ b/stories/theme.AQ_.introduction_air_quality/carousel_content.json @@ -34,18 +34,18 @@ },{ "src":"https://www.youtube.com/embed/gQ5HJdKaTKY", "title":"Predicting Air Pollution with Computer Models: Nitrogen Oxides", - "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" + "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" },{ "src":"https://www.youtube.com/embed/f6ErmXRAFjw", "title":"Predicting Air Pollution with Computer Models: PM2.5", - "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" + "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" },{ "src":"https://www.youtube.com/embed/A9V518Zpzeg", "title":"Predicting Air Pollution with Computer Models: Near Surface Ozone", - "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" + "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" },{ "src":"https://www.youtube.com/embed/KnZsVY1mO00", "title":"Predicting Air Pollution with Computer Models: Carbon Monoxide", - "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" + "caption":"Soot. Exhaust. Ghosting smog. Air pollutants can travel in wind and wildfire smoke, brew by day, and change by the hour.\n\nPredictions of air pollution are created using complex models that combine information about weather and the emissions, transformation, and transport of chemical species and particles. The Goddard Earth Observing System Composition Forecasting (GEOS-CF) system is a research model maintained by NASA\u2019s Global Modeling and Assimilation Office to help scientists understand the causes and impact of air pollution. It is one of the highest resolution and most detailed models of its kind in the world, made possible through ongoing collaborations between NASA and university scientists. GEOS-CF tracks the concentrations of hundreds of gas phase chemical species and dozens of types of particles characterized by their composition and size.\n\n It is used by a wide variety of stakeholders around the world to develop new methods for improving local predictions, understanding the impact of pollution on human health, and improving the quality of NASA satellite datasets.\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14439" } ] \ No newline at end of file diff --git a/stories/theme.BIO.introduction_biodiversity/carousel_content.json b/stories/theme.BIO.introduction_biodiversity/carousel_content.json index ea03d5933..728acddbb 100644 --- a/stories/theme.BIO.introduction_biodiversity/carousel_content.json +++ b/stories/theme.BIO.introduction_biodiversity/carousel_content.json @@ -2,18 +2,18 @@ { "src":"https://www.youtube.com/embed/WPOTTd27yyg", "title":"Ecological insights from three decades of animal movement tracking across a changing Arctic", - "caption":"The Arctic Animal Movement Archive (AAMA) is a new and growing collection of studies describing movements of animals in and near the Arctic. The AAMA includes millions of locations of thousands of animals over more than three decades, recorded by hundreds of scientists and institutions. By compiling these data, the AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. We have used the AAMA to document climatic influences on the migration phenology of golden eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, species-specific changes in terrestrial mammal movement rates in response to increasing temperature, and the utility of animal-borne sensors as proxies for ambient air temperature. The AAMA is a living archive that can be used to uncover other such changes, investigate their causes and consequences, and recognize larger ecosystem changes taking place in the Arctic.\n\nThis visualization shows multiple years of AAMA data as if all of the data were from the same year. Several different groupings of animals are shown: marine mammals, raptors, seabirds, shorebirds, terrestrial mammals, and waterbirds. Snow and sea ice are also shown for context as they correlate to animal movements.\n\nVisualizers: Greg Shirah (lead), Lori Perkins\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4877" + "caption":"The Arctic Animal Movement Archive (AAMA) is a new and growing collection of studies describing movements of animals in and near the Arctic. The AAMA includes millions of locations of thousands of animals over more than three decades, recorded by hundreds of scientists and institutions. By compiling these data, the AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. We have used the AAMA to document climatic influences on the migration phenology of golden eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, species-specific changes in terrestrial mammal movement rates in response to increasing temperature, and the utility of animal-borne sensors as proxies for ambient air temperature. The AAMA is a living archive that can be used to uncover other such changes, investigate their causes and consequences, and recognize larger ecosystem changes taking place in the Arctic.\n\nThis visualization shows multiple years of AAMA data as if all of the data were from the same year. Several different groupings of animals are shown: marine mammals, raptors, seabirds, shorebirds, terrestrial mammals, and waterbirds. Snow and sea ice are also shown for context as they correlate to animal movements.\n\nVisualizers: Greg Shirah (lead), Lori Perkins\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4877" },{ "src":"https://www.youtube.com/embed/Ok2iQlTw5DQ", "title":"GEDI Forest Height - Global View", - "caption":"The Global Ecosystem Dynamics Investigation (GEDI) produces high resolution laser ranging observations of the 3D structure of the Earth. GEDI's precise measurements of forest canopy height, canopy vertical structure, and surface elevation greatly advance our ability to characterize important carbon and water cycling processes, biodiversity, and habitat.\n\nGEDI's data on surface structure are also of immense value for weather forecasting, forest management, glacier and snowpack monitoring, and the generation of more accurate digital elevation models. GEDI provides the missing piece - 3D structure - in NASA's observational assets which enables us to better understand how the Earth behaves as a system, and guides the actions we can take to sustain critical resources.\n\nThe GEDI instrument is a geodetic-class, light detection and ranging (lidar) laser system comprised of 3 lasers that produce 8 parallel tracks of observations. Each laser fires 242 times per second and illuminates a 25 m spot (a footprint) on the surface over which 3D structure is measured. Each footprint is separated by 60 m along track, with an across-track distance of about 600 m between each of the 8 tracks. GEDI expected to produce about 10 billion cloud-free observations during its nominal 24-month mission length.\n\nTo learn more, visit the GEDI webpage: https://gedi.umd.edu/\n\nThis visualization depicts a global view of forest height data collected by the GEDI instrument aboard the International Space Station. Brown and dark green represent shorter vegetation. Bright green and white represent taller vegetation. This visualization uses data collected between April 2018 and April 2019. Height is exaggerated to depict variation at this scale.\n\nVisualizers: Kel Elkins (lead), Horace Mitchell\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4950" + "caption":"The Global Ecosystem Dynamics Investigation (GEDI) produces high resolution laser ranging observations of the 3D structure of the Earth. GEDI's precise measurements of forest canopy height, canopy vertical structure, and surface elevation greatly advance our ability to characterize important carbon and water cycling processes, biodiversity, and habitat.\n\nGEDI's data on surface structure are also of immense value for weather forecasting, forest management, glacier and snowpack monitoring, and the generation of more accurate digital elevation models. GEDI provides the missing piece - 3D structure - in NASA's observational assets which enables us to better understand how the Earth behaves as a system, and guides the actions we can take to sustain critical resources.\n\nThe GEDI instrument is a geodetic-class, light detection and ranging (lidar) laser system comprised of 3 lasers that produce 8 parallel tracks of observations. Each laser fires 242 times per second and illuminates a 25 m spot (a footprint) on the surface over which 3D structure is measured. Each footprint is separated by 60 m along track, with an across-track distance of about 600 m between each of the 8 tracks. GEDI expected to produce about 10 billion cloud-free observations during its nominal 24-month mission length.\n\nTo learn more, visit the GEDI webpage: https://gedi.umd.edu/\n\nThis visualization depicts a global view of forest height data collected by the GEDI instrument aboard the International Space Station. Brown and dark green represent shorter vegetation. Bright green and white represent taller vegetation. This visualization uses data collected between April 2018 and April 2019. Height is exaggerated to depict variation at this scale.\n\nVisualizers: Kel Elkins (lead), Horace Mitchell\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4950" },{ "src":"https://www.youtube.com/embed/ImEdEQtuDkI", "title":"USFS/GEDI Old Growth Forest Visualizations", - "caption":"The U.S. Forest Service has studied mature and old-growth forests \u2013 broadly characterized as forests at an advanced stage of development \u2013 in hundreds of thousands of plots across the country. To define, identify, and create the first formal accounting of these national resources, the team assessed decades of field-gathered Forest Inventory and Analysis (FIA) data covering a wide variety of forest types and ecological zones across the country.\n \nComplementing the Forest Service\u2019s analysis, NASA-funded scientists are drawing on a space-based instrument called GEDI (Global Ecosystem Dynamics Investigation) to provide a broad and detailed picture of these forests. From its perch on the International Space Station, GEDI\u2019s laser imager (lidar) is able to peer through dense canopies to observe nearly all of Earth\u2019s temperate and tropical forests. By recording the way the laser pulses are reflected by the ground and by plant material (stems, branches, and leaves) at different heights, GEDI makes detailed measurements of the three-dimensional structure of the planet\u2019s surface. It can even estimate the weight and stature of individual trees.\n \nThe Forest Service plans to work alongside NASA to gather aerial and satellite imagery and map mature and old-growth forests at finer scales. Such data can also help the Forest Service create a long-term monitoring system. Meanwhile, a team of interagency experts will analyze and assess threats and risks to these areas.\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5095" + "caption":"The U.S. Forest Service has studied mature and old-growth forests \u2013 broadly characterized as forests at an advanced stage of development \u2013 in hundreds of thousands of plots across the country. To define, identify, and create the first formal accounting of these national resources, the team assessed decades of field-gathered Forest Inventory and Analysis (FIA) data covering a wide variety of forest types and ecological zones across the country.\n \nComplementing the Forest Service\u2019s analysis, NASA-funded scientists are drawing on a space-based instrument called GEDI (Global Ecosystem Dynamics Investigation) to provide a broad and detailed picture of these forests. From its perch on the International Space Station, GEDI\u2019s laser imager (lidar) is able to peer through dense canopies to observe nearly all of Earth\u2019s temperate and tropical forests. By recording the way the laser pulses are reflected by the ground and by plant material (stems, branches, and leaves) at different heights, GEDI makes detailed measurements of the three-dimensional structure of the planet\u2019s surface. It can even estimate the weight and stature of individual trees.\n \nThe Forest Service plans to work alongside NASA to gather aerial and satellite imagery and map mature and old-growth forests at finer scales. Such data can also help the Forest Service create a long-term monitoring system. Meanwhile, a team of interagency experts will analyze and assess threats and risks to these areas.\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5095" },{ "src":"https://www.youtube.com/embed/HA0LQQi_E28", "title":"Atmospheric Carbon Dioxide Tagged by Source", - "caption":"Carbon dioxide (CO2) is the most prevalent greenhouse gas driving global climate change. However, its increase in the atmosphere would be even more rapid without land and ocean carbon sinks, which collectively absorb about half of human emissions every year. Advanced computer modeling techniques in NASA's Global Modeling and Assimilation Office allow us to disentangle the influences of sources and sinks and to better understand where carbon is coming from and going to.\n\nVisualizations by: Andrew J Christensen, Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5110" + "caption":"Carbon dioxide (CO2) is the most prevalent greenhouse gas driving global climate change. However, its increase in the atmosphere would be even more rapid without land and ocean carbon sinks, which collectively absorb about half of human emissions every year. Advanced computer modeling techniques in NASA's Global Modeling and Assimilation Office allow us to disentangle the influences of sources and sinks and to better understand where carbon is coming from and going to.\n\nVisualizations by: Andrew J Christensen, Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5110" } ] \ No newline at end of file diff --git a/stories/theme.DIS.introduction_disasters/carousel_content.json b/stories/theme.DIS.introduction_disasters/carousel_content.json index e8512cb61..07fcc3c18 100644 --- a/stories/theme.DIS.introduction_disasters/carousel_content.json +++ b/stories/theme.DIS.introduction_disasters/carousel_content.json @@ -14,10 +14,10 @@ },{ "src":"https://www.youtube.com/embed/B7n62jOJJqI", "title":"Atlantic Hurricane Wind Speed Plots", - "caption":"These simple visualizations are plots of time vs. wind speed for each tropical storm/hurricane of Atlantic Hurricane seasons from 1950 to the present. Horizontal lines indicate wind speed category thresholds. A line plot for each storm shows the storm's name and a marker at the peak wind speed.

\n\nMost named storms during the Atlantic hurricane season happen between June and November. However, occasionally, storms develop outside of those ranges.\n\nFour versions of the plots are inluded:\n 1. May to December showig only the current year\n 2. May to December showing the current year and strong storms from previous years (ghosted out)\n 3. January to December showig only the current year\n 4. January to December showing the current year and strong storms from previous years (ghosted out)\n \nThe plot for the current year automatically updates every 2 hours during hurricane season.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5072" + "caption":"These simple visualizations are plots of time vs. wind speed for each tropical storm/hurricane of Atlantic Hurricane seasons from 1950 to the present. Horizontal lines indicate wind speed category thresholds. A line plot for each storm shows the storm's name and a marker at the peak wind speed.

\n\nMost named storms during the Atlantic hurricane season happen between June and November. However, occasionally, storms develop outside of those ranges.\n\nFour versions of the plots are inluded:\n 1. May to December showig only the current year\n 2. May to December showing the current year and strong storms from previous years (ghosted out)\n 3. January to December showig only the current year\n 4. January to December showing the current year and strong storms from previous years (ghosted out)\n \nThe plot for the current year automatically updates every 2 hours during hurricane season.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5072" },{ "src":"https://www.youtube.com/embed/6-71kC1LT-w", "title":"Predicting Landslides", - "caption":"If a slope's underlying foundation is unstable, heavy rainfall could be all it takes to trigger a landslide. In fact, rainfall is the most common catalyst for landslides. An open sourced computer model developed at NASA's Goddard Space Flight Center uses precipitation data to identify areas all over the globe that are potential landslide hazards. The model first looks at areas that have recently experienced heavy rainfall using data from the Global Precipitation Measurement (GPM) mission. When the rainfall estimates are unusually high, the model checks other known conditions of the area that may encourage landslides, such as recent road construction, steep hills, and other factors. A \"nowcast\" map then combines the rainfall data and these other factors to mark areas that might have landslides. Scientists have used these models to conduct studies on long term landslide patterns and landslide warning signs. Watch the video to learn more.\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Dalia B Kirschbaum, Thomas A. Stanley, Produced by: Joy Ng, Ryan Fitzgibbons, Written by: Kasha Patel\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/13126" + "caption":"If a slope's underlying foundation is unstable, heavy rainfall could be all it takes to trigger a landslide. In fact, rainfall is the most common catalyst for landslides. An open sourced computer model developed at NASA's Goddard Space Flight Center uses precipitation data to identify areas all over the globe that are potential landslide hazards. The model first looks at areas that have recently experienced heavy rainfall using data from the Global Precipitation Measurement (GPM) mission. When the rainfall estimates are unusually high, the model checks other known conditions of the area that may encourage landslides, such as recent road construction, steep hills, and other factors. A \"nowcast\" map then combines the rainfall data and these other factors to mark areas that might have landslides. Scientists have used these models to conduct studies on long term landslide patterns and landslide warning signs. Watch the video to learn more.\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Dalia B Kirschbaum, Thomas A. Stanley, Produced by: Joy Ng, Ryan Fitzgibbons, Written by: Kasha Patel\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/13126" } ] \ No newline at end of file diff --git a/stories/theme.ENR.introduction_energy_generation/carousel_content.json b/stories/theme.ENR.introduction_energy_generation/carousel_content.json index 6902b8524..f91b86d0e 100644 --- a/stories/theme.ENR.introduction_energy_generation/carousel_content.json +++ b/stories/theme.ENR.introduction_energy_generation/carousel_content.json @@ -2,6 +2,6 @@ { "src":"https://www.youtube.com/embed/SB-IYcHb2so", "title":"Change in Night Lights between 2012 and 2023 - EIC Version", - "caption":"Discover the pivotal role of NASA's Black Marble in bridging scientific discovery and actionable environmental stewardship. By capturing Earth's night lights, Black Marble reveals both the planet's beauty and the profound impact of human activities. This webpage showcases newly developed nighttime light maps comparing changes observed from 2012 to 2023, offering a decade-long perspective on urban growth, infrastructure development, and the socioeconomic dynamics of communities worldwide. This data empowers users to foster informed, sustainable practices that enhance community resilience and mitigate the adverse effects of climate change. By illuminating the intricate interplay between human activity and the environment, Black Marble underpins a human-centered approach to environmental change, promoting a future where scientific innovation leads to tangible, positive impacts on global sustainability.\n\nThis version is specifically formatted for the NASA Earth Information Center (EIC), which has a wider-than-standard video aspect ratio. The original version of this visualization, and many other night lights visualizations, are available here: https://svs.gsfc.nasa.gov/5276/\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5313" + "caption":"Discover the pivotal role of NASA's Black Marble in bridging scientific discovery and actionable environmental stewardship. By capturing Earth's night lights, Black Marble reveals both the planet's beauty and the profound impact of human activities. This webpage showcases newly developed nighttime light maps comparing changes observed from 2012 to 2023, offering a decade-long perspective on urban growth, infrastructure development, and the socioeconomic dynamics of communities worldwide. This data empowers users to foster informed, sustainable practices that enhance community resilience and mitigate the adverse effects of climate change. By illuminating the intricate interplay between human activity and the environment, Black Marble underpins a human-centered approach to environmental change, promoting a future where scientific innovation leads to tangible, positive impacts on global sustainability.\n\nThis version is specifically formatted for the NASA Earth Information Center (EIC), which has a wider-than-standard video aspect ratio. The original version of this visualization, and many other night lights visualizations, are available here: https://svs.gsfc.nasa.gov/5276/\nVisualizations by: Kel Elkins\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5313" } ] \ No newline at end of file diff --git a/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json index 3742be9ba..f649562d2 100644 --- a/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json +++ b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json @@ -2,42 +2,42 @@ { "src":"https://www.youtube.com/embed/c3btzmkiZAA", "title":"Methane Emissions in the United States", - "caption":"\nMethane is the second most important greenhouse gas contributing to climate change. While emissions are substantially lower than for carbon dioxide, the biggest driver of climate change, methane is more efficient at trapping heat on a molecule by molecule basis. As a result, understanding the sources of methane and how they can be reduced, quickly, is a major effort of policymakers and environmental managers around the world.\r\n\r\nThis visualization presents gridded methane emissions across the United States for the year 2012. The gridded methane inventory is designed to be consistent with EPA\u2019s 2016 Inventory of U.S. Greenhouse Gas Emissions and Sinks (https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) for the year 2012, which presents national totals for different source types. Gridded estimates with 0.1 degree spatial resolution are produced using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. Gridded inventories, developed with support from NASA\u2019s Carbon Monitoring System, help researchers use satellite, airborne, and in situ observations to independently evaluate EPA inventories and provide recommendations on refinements that may be needed. Additional detail and dataset access are available at the EPA website (https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions).\r\n\r\nThe gridded inventory presents totals for different major methane source types. Agriculture emissions in this visualization include manure management, enteric fermentation, rice cultivation, and field burning. Waste emissions include landfills, wastewater treatment, and composting. Natural Gas emissions include emissions from production, processing, and transmission. Coal emissions include both active and abandoned coal mines.\nVisualizations by: Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5041" + "caption":"\nMethane is the second most important greenhouse gas contributing to climate change. While emissions are substantially lower than for carbon dioxide, the biggest driver of climate change, methane is more efficient at trapping heat on a molecule by molecule basis. As a result, understanding the sources of methane and how they can be reduced, quickly, is a major effort of policymakers and environmental managers around the world.\r\n\r\nThis visualization presents gridded methane emissions across the United States for the year 2012. The gridded methane inventory is designed to be consistent with EPA\u2019s 2016 Inventory of U.S. Greenhouse Gas Emissions and Sinks (https://www.epa.gov/ghgemissions/us-greenhouse-gas-inventory-report-1990-2014) for the year 2012, which presents national totals for different source types. Gridded estimates with 0.1 degree spatial resolution are produced using a wide range of databases at the state, county, local, and point source level to allocate the spatial and temporal distribution of emissions for individual source types. Gridded inventories, developed with support from NASA\u2019s Carbon Monitoring System, help researchers use satellite, airborne, and in situ observations to independently evaluate EPA inventories and provide recommendations on refinements that may be needed. Additional detail and dataset access are available at the EPA website (https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions).\r\n\r\nThe gridded inventory presents totals for different major methane source types. Agriculture emissions in this visualization include manure management, enteric fermentation, rice cultivation, and field burning. Waste emissions include landfills, wastewater treatment, and composting. Natural Gas emissions include emissions from production, processing, and transmission. Coal emissions include both active and abandoned coal mines.\nVisualizations by: Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5041" },{ "src":"https://www.youtube.com/embed/d-bFeE4YZ6s", "title":"Net Ecosystem Exchange of Carbon Dioxide", - "caption":"\nEach year, ecosystems absorb about 25% of human carbon dioxide emissions, providing an invaluable mitigation that has substantially slowed the rate of climate change. Carbon dioxide is absorbed by plants during photosynthesis and released during respiration, a process with occurs in both plants and soils. The balance of this absorption and release, called net ecosystem exchange, serves to reduce atmospheric carbon dioxide concentrations. However, understanding how much carbon is absorbed by ecosystems and how this may change in a future faced with increases in extreme weather associated with climate change is a major challenge for scientists. Despite the critical importance of ecosystem-atmosphere carbon dioxide exchange, which scientists call flux, directly measuring the global exchange of carbon dioxide molecules is not possible. Satellite observations of vegetation, from satellite instruments like the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), provide valuable information that can be combined with computer models to provide realistic, observationally based estimates of carbon flux and track how underlying processes change over time.\r\n\r\nThis visualization shows net ecosystem exchange calculated by the Carnegie-Ames-Stanford-Approach \u2013 Global Fire Emissions Database version 3 (CASA-GFED3), a simple model that combines AVHRR and MODIS data with estimates of temperature and precipitation from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Global patterns of net ecosystem exchange reflect seasonal changes in vegetation with plants absorbing more carbon during spring and summer growing seasons and releasing much of this carbon back to the atmosphere during fall and winter months. The nearly 20-year record of CASA-GFED3 data also reflects interannual variability in carbon flux that can result from unusual climate patterns like droughts. Scientists across the world use the CASA-GFED3 data as input to atmospheric models, which then use satellite observations to evaluate and further refine the flux estimates. This understanding, supported by NASA\u2019s Carbon Monitoring System, helps scientists improve the representation of vegetation in climate model predictions and provides actionable information about the processes controlling greenhouse gas concentrations to policymakers.\nVisualizations by: Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5047" + "caption":"\nEach year, ecosystems absorb about 25% of human carbon dioxide emissions, providing an invaluable mitigation that has substantially slowed the rate of climate change. Carbon dioxide is absorbed by plants during photosynthesis and released during respiration, a process with occurs in both plants and soils. The balance of this absorption and release, called net ecosystem exchange, serves to reduce atmospheric carbon dioxide concentrations. However, understanding how much carbon is absorbed by ecosystems and how this may change in a future faced with increases in extreme weather associated with climate change is a major challenge for scientists. Despite the critical importance of ecosystem-atmosphere carbon dioxide exchange, which scientists call flux, directly measuring the global exchange of carbon dioxide molecules is not possible. Satellite observations of vegetation, from satellite instruments like the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), provide valuable information that can be combined with computer models to provide realistic, observationally based estimates of carbon flux and track how underlying processes change over time.\r\n\r\nThis visualization shows net ecosystem exchange calculated by the Carnegie-Ames-Stanford-Approach \u2013 Global Fire Emissions Database version 3 (CASA-GFED3), a simple model that combines AVHRR and MODIS data with estimates of temperature and precipitation from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Global patterns of net ecosystem exchange reflect seasonal changes in vegetation with plants absorbing more carbon during spring and summer growing seasons and releasing much of this carbon back to the atmosphere during fall and winter months. The nearly 20-year record of CASA-GFED3 data also reflects interannual variability in carbon flux that can result from unusual climate patterns like droughts. Scientists across the world use the CASA-GFED3 data as input to atmospheric models, which then use satellite observations to evaluate and further refine the flux estimates. This understanding, supported by NASA\u2019s Carbon Monitoring System, helps scientists improve the representation of vegetation in climate model predictions and provides actionable information about the processes controlling greenhouse gas concentrations to policymakers.\nVisualizations by: Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5047" },{ "src":"https://www.youtube.com/embed/RCVUIcVKkXs", "title":"Methane Emissions from Wetlands", - "caption":"\nMethane is an important greenhouse gas that contributes substantially to global warming. On a molecule by molecule basis, methane is much more efficient at trapping heat than carbon dioxide, the main driver of warming. Though human activities, including agriculture, oil and natural gas production and use, and waste disposal, collectively contribute the majority of methane to the atmosphere, about a third of total methane emissions comes from wetlands. Wetland habitats are filled with things like waterlogged soils and permafrost, which makes them sizable carbon sinks. However, as the climate changes, these carbon-rich soils are vulnerable to flooding and to rising temperatures, which can release more carbon to the atmosphere in the form or methane. Understanding methane emissions from natural sources like wetlands is critically important to scientists and policymakers who are working to ensure that changes in natural systems don\u2019t counteract progress in combatting climate change made by reducing emissions from human activities.\r\n\r\nThis animation shows estimates of wetland methane emissions produced by the Lund\u2013Potsdam\u2013Jena Dynamic Global Vegetation Model (LPJ-DGVM) Wald Schnee und Landscaft version (LPJ-wsl). LPJ-wsl is a prognostic model, meaning that it can be used to simulate future changes in wetland emissions and independently verified with remote sensing data products. The model includes a complex, topography dependent model of near surface hydrology, and a permafrost and dynamic snow model, allowing it to produce realistic distributions of inundated area. Highlighted areas show concentrated methane sources from tropical and high latitude ecosystems. The LPJ-wsl model is regularly used in conjunction with NASA\u2019s GEOS model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations, compare against satellite and airborne data, and to improve understanding and prediction of wetland emissions.\nVisualizations by: Mark SubbaRao, Produced by: Kathleen Gaeta, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5054" + "caption":"\nMethane is an important greenhouse gas that contributes substantially to global warming. On a molecule by molecule basis, methane is much more efficient at trapping heat than carbon dioxide, the main driver of warming. Though human activities, including agriculture, oil and natural gas production and use, and waste disposal, collectively contribute the majority of methane to the atmosphere, about a third of total methane emissions comes from wetlands. Wetland habitats are filled with things like waterlogged soils and permafrost, which makes them sizable carbon sinks. However, as the climate changes, these carbon-rich soils are vulnerable to flooding and to rising temperatures, which can release more carbon to the atmosphere in the form or methane. Understanding methane emissions from natural sources like wetlands is critically important to scientists and policymakers who are working to ensure that changes in natural systems don\u2019t counteract progress in combatting climate change made by reducing emissions from human activities.\r\n\r\nThis animation shows estimates of wetland methane emissions produced by the Lund\u2013Potsdam\u2013Jena Dynamic Global Vegetation Model (LPJ-DGVM) Wald Schnee und Landscaft version (LPJ-wsl). LPJ-wsl is a prognostic model, meaning that it can be used to simulate future changes in wetland emissions and independently verified with remote sensing data products. The model includes a complex, topography dependent model of near surface hydrology, and a permafrost and dynamic snow model, allowing it to produce realistic distributions of inundated area. Highlighted areas show concentrated methane sources from tropical and high latitude ecosystems. The LPJ-wsl model is regularly used in conjunction with NASA\u2019s GEOS model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations, compare against satellite and airborne data, and to improve understanding and prediction of wetland emissions.\nVisualizations by: Mark SubbaRao, Produced by: Kathleen Gaeta, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5054" },{ "src":"https://www.youtube.com/embed/HA0LQQi_E28", "title":"Atmospheric Carbon Dioxide Tagged by Source", - "caption":"Carbon dioxide (CO2) is the most prevalent greenhouse gas driving global climate change. However, its increase in the atmosphere would be even more rapid without land and ocean carbon sinks, which collectively absorb about half of human emissions every year. Advanced computer modeling techniques in NASA's Global Modeling and Assimilation Office allow us to disentangle the influences of sources and sinks and to better understand where carbon is coming from and going to.\n\nVisualizations by: Andrew J Christensen, Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5110" + "caption":"Carbon dioxide (CO2) is the most prevalent greenhouse gas driving global climate change. However, its increase in the atmosphere would be even more rapid without land and ocean carbon sinks, which collectively absorb about half of human emissions every year. Advanced computer modeling techniques in NASA's Global Modeling and Assimilation Office allow us to disentangle the influences of sources and sinks and to better understand where carbon is coming from and going to.\n\nVisualizations by: Andrew J Christensen, Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5110" },{ "src":"https://www.youtube.com/embed/-aSBfn6_pUY", "title":"Global Atmospheric Carbon Dioxide (CO\u2082)", - "caption":"NASA\u2019s Orbiting Carbon Observatory, 2 (OCO-2) provides the most complete dataset tracking the concentration of atmospheric carbon dioxide (CO\u2082), the main driver of climate change. Every day, OCO-2 measures sunlight reflected from Earth\u2019s surface to infer the dry-air column-averaged CO\u2082 mixing ratio and provides around 100,000 cloud-free observations. Despite these advances, OCO-2 data contain many gaps where sunlight is not present or where clouds or aerosols are too thick to retrieve CO\u2082 data. In order to fill gaps and provide science and applications users a spatially complete product, OCO-2 data are assimilated into NASA\u2019s Goddard Earth Observing System (GEOS), a complex modeling and data assimilation system used for studying the Earth\u2019s weather and climate. GEOS is also informed by satellite observations of nighttime lights and vegetation greenness along with about 1 million weather observations collected every hour. These data help scientists infer CO2 mixing ratios even when a direct OCO-2 observation is not present and provide additional information on the altitude of CO\u2082 plumes that the satellite is not able to see. Together, OCO-2 and GEOS create one of the most complete pictures of CO\u2082.\n\nThe visualization featured on this page shows the atmosphere in three dimensions and highlights the accumulation of CO\u2082 during a single calendar year (January 1-December 31, 2021). Every year, the world\u2019s vegetation and oceans absorb about half of human CO\u2082 emissions, providing an incredibly valuable service that has mitigated the rate of accumulation of greenhouse gases in the atmosphere. However, around 2.5 parts per million remain in the atmosphere every year causing a steady upward march in concentrations that scientists have tracked since the 1950s at surface stations.\n\nThe volumetric visualization starts in January 1, 2021, showing the higher CO\u2082 concentrations, which are closer to the ground, revealing the seasonal movement of high CO\u2082 at a global scale. During the months of June-September (summer months for northern hemisphere), global CO\u2082 concentrations tend to be lowest because northern hemisphere plants actively absorb CO\u2082 from the atmosphere via photosynthesis. During northern hemisphere fall and winter months, much of this CO\u2082 is re-released to the atmosphere due to respiration and can be seen building up. By June and July 2021, plants again draw CO\u2082 out of the atmosphere, but notably higher concentrations remain in contrast to the nearly transparent colors of the previous year. The diurnal rhythm of CO\u2082 is apparent over our planet's largest forests, such as the Amazon rainforest in South America and the Congo rainforest in Central Africa. The fast-paced pulse in those rainforests is due to the day-night cycle; plants absorb CO\u2082 during the day via photosynthesis when the sun is out, then stop absorbing CO\u2082 at night. In addition to highlighting the buildup of atmospheric CO\u2082, this visualization shows how interconnected the world\u2019s greenhouse gas problem is. NASA\u2019s unique combination of observations and models plays a critical role in helping scientists track increases in CO\u2082 as they happen to better understand their climate impact.\n\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Lesley Ott, Brad Weir\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5115" + "caption":"NASA\u2019s Orbiting Carbon Observatory, 2 (OCO-2) provides the most complete dataset tracking the concentration of atmospheric carbon dioxide (CO\u2082), the main driver of climate change. Every day, OCO-2 measures sunlight reflected from Earth\u2019s surface to infer the dry-air column-averaged CO\u2082 mixing ratio and provides around 100,000 cloud-free observations. Despite these advances, OCO-2 data contain many gaps where sunlight is not present or where clouds or aerosols are too thick to retrieve CO\u2082 data. In order to fill gaps and provide science and applications users a spatially complete product, OCO-2 data are assimilated into NASA\u2019s Goddard Earth Observing System (GEOS), a complex modeling and data assimilation system used for studying the Earth\u2019s weather and climate. GEOS is also informed by satellite observations of nighttime lights and vegetation greenness along with about 1 million weather observations collected every hour. These data help scientists infer CO2 mixing ratios even when a direct OCO-2 observation is not present and provide additional information on the altitude of CO\u2082 plumes that the satellite is not able to see. Together, OCO-2 and GEOS create one of the most complete pictures of CO\u2082.\n\nThe visualization featured on this page shows the atmosphere in three dimensions and highlights the accumulation of CO\u2082 during a single calendar year (January 1-December 31, 2021). Every year, the world\u2019s vegetation and oceans absorb about half of human CO\u2082 emissions, providing an incredibly valuable service that has mitigated the rate of accumulation of greenhouse gases in the atmosphere. However, around 2.5 parts per million remain in the atmosphere every year causing a steady upward march in concentrations that scientists have tracked since the 1950s at surface stations.\n\nThe volumetric visualization starts in January 1, 2021, showing the higher CO\u2082 concentrations, which are closer to the ground, revealing the seasonal movement of high CO\u2082 at a global scale. During the months of June-September (summer months for northern hemisphere), global CO\u2082 concentrations tend to be lowest because northern hemisphere plants actively absorb CO\u2082 from the atmosphere via photosynthesis. During northern hemisphere fall and winter months, much of this CO\u2082 is re-released to the atmosphere due to respiration and can be seen building up. By June and July 2021, plants again draw CO\u2082 out of the atmosphere, but notably higher concentrations remain in contrast to the nearly transparent colors of the previous year. The diurnal rhythm of CO\u2082 is apparent over our planet's largest forests, such as the Amazon rainforest in South America and the Congo rainforest in Central Africa. The fast-paced pulse in those rainforests is due to the day-night cycle; plants absorb CO\u2082 during the day via photosynthesis when the sun is out, then stop absorbing CO\u2082 at night. In addition to highlighting the buildup of atmospheric CO\u2082, this visualization shows how interconnected the world\u2019s greenhouse gas problem is. NASA\u2019s unique combination of observations and models plays a critical role in helping scientists track increases in CO\u2082 as they happen to better understand their climate impact.\n\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Lesley Ott, Brad Weir\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5115" },{ "src":"https://www.youtube.com/embed/2rdEjFn_7jg", "title":"Global Atmospheric Methane (CH\u2084)", - "caption":"Methane (CH\u2084) is a powerful greenhouse gas that traps heat 28 times more effectively than carbon dioxide over a 100-year timescale. Concentrations of methane have increased by more than 150% since industrial activities and intensive agriculture began. After carbon dioxide, methane is responsible for about 20% of climate change in the twentieth century. Methane is produced under conditions where little to no oxygen is available. About 30% of methane emissions are produced by wetlands, including ponds, lakes and rivers. Another 20% is produced by agriculture, due to a combination of livestock, waste management and rice cultivation. Activities related to oil, gas, and coal extraction release an additional 30%. The remainder of methane emissions come from minor sources such as wildfires, biomass burning, permafrost, termites, dams, and the ocean. Scientists around the world are working to better understand the budget of methane with the ultimate goals of reducing greenhouse gas emissions and improving prediction of environmental change. \n\nThe NASA SVS visualization presented here shows the complex patterns of methane emissions produced around the globe and throughout the year from the different sources described above. The visualization was created using output from the Global Modeling and Assimilation Office (GMAO), GEOS modeling system, developed and maintained by scientists at NASA. Wetland emissions were estimated by the LPJ-wsl model, which simulates the temperature and moisture dependent methane emission processes using a variety of satellite data to determine what parts of the globe are covered by wetlands. Other methane emission sources come from inventories of human activity.\n\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Lesley Ott, Brad Weir\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5116" + "caption":"Methane (CH\u2084) is a powerful greenhouse gas that traps heat 28 times more effectively than carbon dioxide over a 100-year timescale. Concentrations of methane have increased by more than 150% since industrial activities and intensive agriculture began. After carbon dioxide, methane is responsible for about 20% of climate change in the twentieth century. Methane is produced under conditions where little to no oxygen is available. About 30% of methane emissions are produced by wetlands, including ponds, lakes and rivers. Another 20% is produced by agriculture, due to a combination of livestock, waste management and rice cultivation. Activities related to oil, gas, and coal extraction release an additional 30%. The remainder of methane emissions come from minor sources such as wildfires, biomass burning, permafrost, termites, dams, and the ocean. Scientists around the world are working to better understand the budget of methane with the ultimate goals of reducing greenhouse gas emissions and improving prediction of environmental change. \n\nThe NASA SVS visualization presented here shows the complex patterns of methane emissions produced around the globe and throughout the year from the different sources described above. The visualization was created using output from the Global Modeling and Assimilation Office (GMAO), GEOS modeling system, developed and maintained by scientists at NASA. Wetland emissions were estimated by the LPJ-wsl model, which simulates the temperature and moisture dependent methane emission processes using a variety of satellite data to determine what parts of the globe are covered by wetlands. Other methane emission sources come from inventories of human activity.\n\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Lesley Ott, Brad Weir\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5116" },{ "src":"https://www.youtube.com/embed/P_OmxbB-iHg", "title":"Trends in atmospheric Nitrous Oxide (N\u2082O)", - "caption":"\nVisualizations by: Helen-Nicole Kostis\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5117" + "caption":"\nVisualizations by: Helen-Nicole Kostis\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5117" },{ "src":"https://www.youtube.com/embed/9V3Hhax5C1M", "title":"Trends in atmospheric Methane (CH\u2084)", - "caption":"\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Lesley Ott, Brad Weir\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5118" + "caption":"\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Lesley Ott, Brad Weir\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5118" },{ "src":"https://www.youtube.com/embed/bvK-SzcyuKs", "title":"OCO-2 and Keeling Curve: Trends in global atmospheric Carbon Dioxide (CO\u2082)", - "caption":"\nVisualizations by: Helen-Nicole Kostis\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5119" + "caption":"\nVisualizations by: Helen-Nicole Kostis\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5119" },{ "src":"https://www.youtube.com/embed/2xxjiu0C_-Q", "title":"ODIAC: a map of human made carbon dioxide emissions", - "caption":"The Open-source Data Inventory for Anthropogenic CO2 (A.K.A. ODIAC) is a global high-resolution (1x1km) emission data product for fossil fuel carbon dioxide (CO2) emission. ODIAC was originally designed and developed under the Greenhouse gas Observing SATellite (GOSAT) project at Japan\u2019s National Institute for Environmental Studies (NIES). Since then, ODIAC has been maintained at the Universities Space Research Association (USRA) in collaboration with NASA, NIES and the Appalachian State University. ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO2 emissions. ODIAC has been a key input data for NASA\u2019s carbon modeling. ODIAC has been also widely used in the international science research community for a variety of applications across key policy relevant scales (global to local).\nVisualizations by: Mark SubbaRao, Scientific consulting by: Tomohiro Oda\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5121" + "caption":"The Open-source Data Inventory for Anthropogenic CO2 (A.K.A. ODIAC) is a global high-resolution (1x1km) emission data product for fossil fuel carbon dioxide (CO2) emission. ODIAC was originally designed and developed under the Greenhouse gas Observing SATellite (GOSAT) project at Japan\u2019s National Institute for Environmental Studies (NIES). Since then, ODIAC has been maintained at the Universities Space Research Association (USRA) in collaboration with NASA, NIES and the Appalachian State University. ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO2 emissions. ODIAC has been a key input data for NASA\u2019s carbon modeling. ODIAC has been also widely used in the international science research community for a variety of applications across key policy relevant scales (global to local).\nVisualizations by: Mark SubbaRao, Scientific consulting by: Tomohiro Oda\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5121" } ] \ No newline at end of file diff --git a/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json b/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json index a8bca752c..fb74da93f 100644 --- a/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json +++ b/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json @@ -2,42 +2,42 @@ { "src":" https://www.youtube.com/embed/TvPjAe4j6qQ", "title":"Global Sea Surface Currents and Temperature", - "caption":"This visualization shows sea surface current flows. The flows are colored by corresponding sea surface temperature data. This visualization is rendered for display on very high resolution devices like hyperwalls or for print media.

This visualization was produced using model output from the joint MIT/JPL project entitled Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) (http://ecco2.org/). ECCO2 uses the MIT general circulation model (MITgcm) to synthesize satellite and in-situ data of the global ocean and sea-ice at resolutions that begin to resolve ocean eddies and other narrow current systems, which transport heat and carbon in the oceans. The ECCO2 model simulates ocean flows at all depths, but only surface flows are used in this visualization.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/3912" + "caption":"This visualization shows sea surface current flows. The flows are colored by corresponding sea surface temperature data. This visualization is rendered for display on very high resolution devices like hyperwalls or for print media.

This visualization was produced using model output from the joint MIT/JPL project entitled Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) (http://ecco2.org/). ECCO2 uses the MIT general circulation model (MITgcm) to synthesize satellite and in-situ data of the global ocean and sea-ice at resolutions that begin to resolve ocean eddies and other narrow current systems, which transport heat and carbon in the oceans. The ECCO2 model simulates ocean flows at all depths, but only surface flows are used in this visualization.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/3912" },{ "src":"https://www.youtube.com/embed/FEOlYWUbsHo", "title":"A Decade of Sea Surface Salinity", - "caption":"The heat of the sun forces evaporation at the ocean's surface, which puts water vapor into the atmosphere but leaves minerals and salts behind, keeping the ocean salty. The salinity of the ocean also varies from place to place, because evaporation varies based on the sea surface temperature and wind, rivers and rain storms inject fresh water into the ocean, and melting or freezing sea ice affects the salinity of polar waters.\nVisualizations by: Alex Kekesi, Scientific consulting by: Nadya Vinogradova-Shiffer\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5017" + "caption":"The heat of the sun forces evaporation at the ocean's surface, which puts water vapor into the atmosphere but leaves minerals and salts behind, keeping the ocean salty. The salinity of the ocean also varies from place to place, because evaporation varies based on the sea surface temperature and wind, rivers and rain storms inject fresh water into the ocean, and melting or freezing sea ice affects the salinity of polar waters.\nVisualizations by: Alex Kekesi, Scientific consulting by: Nadya Vinogradova-Shiffer\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5017" },{ "src":"https://www.youtube.com/embed/9QMLSKmL4FU", "title":"Arctic Sea Ice Spiral", - "caption":"\nThis data visualization shows the Arctic sea ice extent from October 1978 to September 2022. The amount of Arctic sea ice varies seasonally, typically reaching a maximum in March and a minimum in September. Recently, the Arctic sea ice minimum has been decreasing at a rate of 13% per decade. Please see Global Climate Change Vital Signs: Arctic Sea Ice Minimum Extent (https://climate.nasa.gov/vital-signs/arctic-sea-ice/) for more information.\nVisualizations by: Mark SubbaRao\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5028" + "caption":"\nThis data visualization shows the Arctic sea ice extent from October 1978 to September 2022. The amount of Arctic sea ice varies seasonally, typically reaching a maximum in March and a minimum in September. Recently, the Arctic sea ice minimum has been decreasing at a rate of 13% per decade. Please see Global Climate Change Vital Signs: Arctic Sea Ice Minimum Extent (https://climate.nasa.gov/vital-signs/arctic-sea-ice/) for more information.\nVisualizations by: Mark SubbaRao\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5028" },{ "src":"https://www.youtube.com/embed/35QjTwIG-eg", "title":"Arctic Sea Ice Minimum 2022", - "caption":"Satellite-based passive microwave images of the sea ice have provided a reliable tool for continuously monitoring changes in the Arctic ice since 1979. Every summer the Arctic ice cap melts down to what scientists call its \"minimum\" before colder weather begins to cause ice cover to increase. An analysis of satellite data by NASA and the National Snow and Ice Data Center (NSIDC) at the University of Colorado Boulder shows that the 2022 minimum extent, which was likely reached on Sept. 18, measured 1.80 million square miles (4.67 million square kilometers).\n\nThe Japan Aerospace Exploration Agency (JAXA) provides many water-related products derived from data acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument aboard the Global Change Observation Mission 1st-Water \"SHIZUKU\" (GCOM-W1) satellite. Two JAXA datasets used in this animation are the 10-km daily sea ice concentration and the 10 km daily 89 GHz Brightness Temperature.\n\nIn this animation, the daily Arctic sea ice and seasonal land cover change progress through time, from the yearly maximum ice extent on February 25 2022, through its minimum on September 18 2022. Over the water, Arctic sea ice changes from day to day showing a running 3-day minimum sea ice concentration in the region where the concentration is greater than 15%. The blueish white color of the sea ice is derived from a 3-day running minimum of the AMSR2 89 GHz brightness temperature. The yellow boundary shows the minimum extent averaged over the 30-year period from 1981 to 2010. Over the terrain, monthly data from the seasonal Blue Marble Next Generation fades slowly from month to month.\nVisualizations by: Trent L. Schindler, Produced by: Roberto Molar-Candanosa, Scientific consulting by: Walt Meier\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5030" + "caption":"Satellite-based passive microwave images of the sea ice have provided a reliable tool for continuously monitoring changes in the Arctic ice since 1979. Every summer the Arctic ice cap melts down to what scientists call its \"minimum\" before colder weather begins to cause ice cover to increase. An analysis of satellite data by NASA and the National Snow and Ice Data Center (NSIDC) at the University of Colorado Boulder shows that the 2022 minimum extent, which was likely reached on Sept. 18, measured 1.80 million square miles (4.67 million square kilometers).\n\nThe Japan Aerospace Exploration Agency (JAXA) provides many water-related products derived from data acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument aboard the Global Change Observation Mission 1st-Water \"SHIZUKU\" (GCOM-W1) satellite. Two JAXA datasets used in this animation are the 10-km daily sea ice concentration and the 10 km daily 89 GHz Brightness Temperature.\n\nIn this animation, the daily Arctic sea ice and seasonal land cover change progress through time, from the yearly maximum ice extent on February 25 2022, through its minimum on September 18 2022. Over the water, Arctic sea ice changes from day to day showing a running 3-day minimum sea ice concentration in the region where the concentration is greater than 15%. The blueish white color of the sea ice is derived from a 3-day running minimum of the AMSR2 89 GHz brightness temperature. The yellow boundary shows the minimum extent averaged over the 30-year period from 1981 to 2010. Over the terrain, monthly data from the seasonal Blue Marble Next Generation fades slowly from month to month.\nVisualizations by: Trent L. Schindler, Produced by: Roberto Molar-Candanosa, Scientific consulting by: Walt Meier\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5030" },{ "src":"https://www.youtube.com/embed/njoTDqwFuuU", "title":"Annual Arctic Sea Ice Minimum Area 1979-2022, With Graph", - "caption":"Satellite-based passive microwave images of the sea ice have provided a reliable tool for continuously monitoring changes in the Arctic ice since 1979. Every summer the Arctic ice cap melts down to what scientists call its \"minimum\" before colder weather begins to cause ice cover to increase. This graph displays the area of the minimum sea ice coverage each year from 1979 through 2022. In 2022, the Arctic minimum sea ice covered an area of 4.16 million square kilometers (1.6 million square miles). \n\nThis visualization shows the expanse of the annual minimum Arctic sea ice for each year from 1979 through 2022 as derived from passive microwave data.\nVisualizations by: Trent L. Schindler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5036" + "caption":"Satellite-based passive microwave images of the sea ice have provided a reliable tool for continuously monitoring changes in the Arctic ice since 1979. Every summer the Arctic ice cap melts down to what scientists call its \"minimum\" before colder weather begins to cause ice cover to increase. This graph displays the area of the minimum sea ice coverage each year from 1979 through 2022. In 2022, the Arctic minimum sea ice covered an area of 4.16 million square kilometers (1.6 million square miles). \n\nThis visualization shows the expanse of the annual minimum Arctic sea ice for each year from 1979 through 2022 as derived from passive microwave data.\nVisualizations by: Trent L. Schindler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5036" },{ "src":"https://www.youtube.com/embed/ljfk9hqrBsc", "title":"Daily Polar Sea Ice, Two Year History", - "caption":"This visualization shows the daily Arctic and Antarctic sea ice and seasonal land cover change over a two-year history from the present, with a single frame rendered for each day (available from the drop-down of each image window), and an animation created from these frames, \n\nThe Japan Aerospace Exploration Agency (JAXA) provides many water-related products derived from data acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument aboard the Global Change Observation Mission 1st-Water \"SHIZUKU\" (GCOM-W1) satellite. Two JAXA datasets are used in this animation: the 10-km daily sea ice concentration and the 10 km daily 89 GHz Brightness Temperature.\n\nIn this visualization sea ice changes from day to day, with the amount of ice shown being determined by the AMSR2 sea ice concentration data. A running 3-day minimum is used, with a minimum threshhold concentration of 15%. The blueish white color of the sea ice is derived from a 3-day running minimum of the AMSR2 89 GHz brightness temperature. Over the terrain, monthly data from the seasonal Blue Marble Next Generation fades slowly from month to month.\n\nThe numerical portion of the frame filename begins with the four-digit year, followed by the three-digit day of the year for that frame.\nVisualizations by: Trent L. Schindler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5099" + "caption":"This visualization shows the daily Arctic and Antarctic sea ice and seasonal land cover change over a two-year history from the present, with a single frame rendered for each day (available from the drop-down of each image window), and an animation created from these frames, \n\nThe Japan Aerospace Exploration Agency (JAXA) provides many water-related products derived from data acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument aboard the Global Change Observation Mission 1st-Water \"SHIZUKU\" (GCOM-W1) satellite. Two JAXA datasets are used in this animation: the 10-km daily sea ice concentration and the 10 km daily 89 GHz Brightness Temperature.\n\nIn this visualization sea ice changes from day to day, with the amount of ice shown being determined by the AMSR2 sea ice concentration data. A running 3-day minimum is used, with a minimum threshhold concentration of 15%. The blueish white color of the sea ice is derived from a 3-day running minimum of the AMSR2 89 GHz brightness temperature. Over the terrain, monthly data from the seasonal Blue Marble Next Generation fades slowly from month to month.\n\nThe numerical portion of the frame filename begins with the four-digit year, followed by the three-digit day of the year for that frame.\nVisualizations by: Trent L. Schindler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5099" },{ "src":"https://www.youtube.com/embed/0907sP0XAZE", "title":"Sea Level Through a Porthole", - "caption":"As the planet warms and polar ice melts, our global average sea level is rising. Although exact ocean heights vary due to local geography, climate over time, and dynamic fluid interactions with gravity and planetary rotation, scientists observe sea level trends by comparing measurements against a 20 year spatial and temporal mean reference. These visualizations use the visual metaphor of a submerged porthole window to observe how far our oceans rose between 1993 and 2022.\nVisualizations by: Andrew J Christensen\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5114" + "caption":"As the planet warms and polar ice melts, our global average sea level is rising. Although exact ocean heights vary due to local geography, climate over time, and dynamic fluid interactions with gravity and planetary rotation, scientists observe sea level trends by comparing measurements against a 20 year spatial and temporal mean reference. These visualizations use the visual metaphor of a submerged porthole window to observe how far our oceans rose between 1993 and 2022.\nVisualizations by: Andrew J Christensen\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5114" },{ "src":"https://www.youtube.com/embed/pRR5E95F7N0", "title":"2023 Temperature Measurements", - "caption":"The locations of the temperature measurements that were used in the 2023 GISS Surface Temperature Analysis (v4). The data on land comes from the weather stations that make up the Global Historical Climatology Network (GHCN). Over water temperature measurements come from International Comprehensive Ocean-Atmosphere Data Set (ICOADS). This dataset provides surface marine observational records from ships, buoys, and other platform types.\nVisualizations by: Mark SubbaRao\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5208" + "caption":"The locations of the temperature measurements that were used in the 2023 GISS Surface Temperature Analysis (v4). The data on land comes from the weather stations that make up the Global Historical Climatology Network (GHCN). Over water temperature measurements come from International Comprehensive Ocean-Atmosphere Data Set (ICOADS). This dataset provides surface marine observational records from ships, buoys, and other platform types.\nVisualizations by: Mark SubbaRao\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5208" },{ "src":"https://www.youtube.com/embed/QQHVHf5QD-s", "title":"Slow Reveal Graphs: Global Mean Sea Level 1993-2023", - "caption":"Slow reveal graphs are an instructional routine using scaffolded visuals and discourse to help students (in K-12 and beyond) make sense of data. This is a slow reveal graph of the SVS visualization of rising Global Mean Sea Level (https://svs.gsfc.nasa.gov/5221/).\n\nVisualizations by: Mark SubbaRao, Produced by: Stacie Marvin\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5312" + "caption":"Slow reveal graphs are an instructional routine using scaffolded visuals and discourse to help students (in K-12 and beyond) make sense of data. This is a slow reveal graph of the SVS visualization of rising Global Mean Sea Level (https://svs.gsfc.nasa.gov/5221/).\n\nVisualizations by: Mark SubbaRao, Produced by: Stacie Marvin\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5312" },{ "src":"https://www.youtube.com/embed/z04TBQw9KkU", "title":"GRACE and GRACE-FO polar ice mass loss", - "caption":"The mass of the Polar ice sheets have changed over the last decades. Research based on observations from the Gravity Recovery and Climate Experiment (GRACE) satellites (2002-2017) and GRACE Follow-On (since 2018 - ) indicates that between 2002 and 2023, Antarctica shed approximately 150 gigatons of ice per year, causing global sea level to rise by 0.4 millimeters per year; and Greenland shed approximately 270 gigatons of ice per year, causing global sea level to rise by 0.03 inches (0.8 millimeters) per year.\n\nThese images, created from GRACE and GRACE-FO data, show changes in polar land ice mass since 2002. Orange and red shades indicate areas that lost ice mass, while light blue shades indicate areas that gained ice mass. White indicates areas where there has been very little or no change in ice mass since 2002.\n\nThe average flow lines (grey; created from satellite radar interferometry) of the icesheets converge into the locations of prominent outlet glaciers, and coincide with areas of highest mass loss. This supports other observations that warming ocean waters near polar icesheets play a key role in contemporary ice mass loss.\nScientific consulting by: Felix W. Landerer\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/31166" + "caption":"The mass of the Polar ice sheets have changed over the last decades. Research based on observations from the Gravity Recovery and Climate Experiment (GRACE) satellites (2002-2017) and GRACE Follow-On (since 2018 - ) indicates that between 2002 and 2023, Antarctica shed approximately 150 gigatons of ice per year, causing global sea level to rise by 0.4 millimeters per year; and Greenland shed approximately 270 gigatons of ice per year, causing global sea level to rise by 0.03 inches (0.8 millimeters) per year.\n\nThese images, created from GRACE and GRACE-FO data, show changes in polar land ice mass since 2002. Orange and red shades indicate areas that lost ice mass, while light blue shades indicate areas that gained ice mass. White indicates areas where there has been very little or no change in ice mass since 2002.\n\nThe average flow lines (grey; created from satellite radar interferometry) of the icesheets converge into the locations of prominent outlet glaciers, and coincide with areas of highest mass loss. This supports other observations that warming ocean waters near polar icesheets play a key role in contemporary ice mass loss.\nScientific consulting by: Felix W. Landerer\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/31166" } ] \ No newline at end of file diff --git a/stories/theme.WLF.introduction_wildfires/carousel_content.json b/stories/theme.WLF.introduction_wildfires/carousel_content.json index ff2fe2ad7..9d6ea3194 100644 --- a/stories/theme.WLF.introduction_wildfires/carousel_content.json +++ b/stories/theme.WLF.introduction_wildfires/carousel_content.json @@ -2,22 +2,22 @@ { "src":"https://www.youtube.com/embed/plMk_6Rg6uw", "title":"Lightning Events Detected from the International Space Station (ISS) 2017-2023", - "caption":"The Lightning Imaging Sensor (LIS) on the International Space Station (ISS) detects lightning occurring in the Earth\u2019s tropical and mid-latitude regions. The LIS provides datasets consisting of near-real time and non-quality controlled data as well as final quality controlled datasets that are manually reviewed. This data uncovers the variability and distribution of lightning and can be used for storm detection and lightning-atmosphere interaction studies. \r\n\r\nThis visualization shows the global distribution of lightning strikes between January 2017 and July 2023 using the final quality controlled science dataset. Each data point contains the latitude and longitude of the strike as well as the time it was detected by the LIS. A roving window of 10 days was used to visualize the seasonal patterns of lightning.\n\nVisualizations by: Michala Garrison\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5011" + "caption":"The Lightning Imaging Sensor (LIS) on the International Space Station (ISS) detects lightning occurring in the Earth\u2019s tropical and mid-latitude regions. The LIS provides datasets consisting of near-real time and non-quality controlled data as well as final quality controlled datasets that are manually reviewed. This data uncovers the variability and distribution of lightning and can be used for storm detection and lightning-atmosphere interaction studies. \r\n\r\nThis visualization shows the global distribution of lightning strikes between January 2017 and July 2023 using the final quality controlled science dataset. Each data point contains the latitude and longitude of the strike as well as the time it was detected by the LIS. A roving window of 10 days was used to visualize the seasonal patterns of lightning.\n\nVisualizations by: Michala Garrison\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5011" },{ "src":"https://www.youtube.com/embed/dUOrk3zz2Fc", "title":"Carbon Emissions from Fires: Jan 2003 - Jan 2022", - "caption":"\nThis visualization uses the Global Fire Emissions Database (https://www.globalfiredata.org) version 4 to show the weekly carbon emissions from fires from January 2003 through January 2022. The data has a spatial resolution of 0.25 degrees in both latitude and longitude. The monthly fire carbon emissions with small fires from the GFED4s dataset was multiplied by the daily fractional contribution to get the daily carbon emission. This was summed over each 7-day period beginning on January 1st each year. Day of year 365 (and day 366 in leap years) was not included. \n\nThe perceptually uniform color scales used in this visualization were developed by Peter Koversi and are available (https://colorcet.com/gallery.html). See Peter Kovesi. Good Colour Maps: How to Design Them. arXiv:1509.03700 [cs.GR] 2015 for additional information.\n\nVisualizations by: Cindy Starr\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5012" + "caption":"\nThis visualization uses the Global Fire Emissions Database (https://www.globalfiredata.org) version 4 to show the weekly carbon emissions from fires from January 2003 through January 2022. The data has a spatial resolution of 0.25 degrees in both latitude and longitude. The monthly fire carbon emissions with small fires from the GFED4s dataset was multiplied by the daily fractional contribution to get the daily carbon emission. This was summed over each 7-day period beginning on January 1st each year. Day of year 365 (and day 366 in leap years) was not included. \n\nThe perceptually uniform color scales used in this visualization were developed by Peter Koversi and are available (https://colorcet.com/gallery.html). See Peter Kovesi. Good Colour Maps: How to Design Them. arXiv:1509.03700 [cs.GR] 2015 for additional information.\n\nVisualizations by: Cindy Starr\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5012" },{ "src":"https://www.youtube.com/embed/SA96Jha1un8", "title":"Tracking the Spread of the Caldor and Dixie Fires", - "caption":"This visualization highlights data from a new fire detection and tracking approach (Chen et al., 2022) based on near-real time active fire detections from the VIIRS sensor on the Suomi-NPP satellite. Every 12 hours, the fire tracking algorithm uses new active fire detections to update the total fire perimeter and estimate the position of active fire lines where the fire may continue to spread. This approach provides a detailed perspective on the behavior of the Caldor and Dixie fires, identifying periods of rapid fire expansion and active fire detections within the perimeter from continued flaming and smoldering behind the active fire fronts.\n\nThe visualization shows the progression over time as the fire spreads. The yellow outlines track the position of the active fire lines every 12 hours for the last 60 hours, with the latest location of the fire front shown in the brightest shade of yellow. The red points show the location of active fire detections within the perimeter, while the grey region shows the estimated total area burned. The graph shows the cumulative burned area in square kilometers.\nVisualizations by: Cindy Starr, Scientific consulting by: Doug C. Morton\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5088" + "caption":"This visualization highlights data from a new fire detection and tracking approach (Chen et al., 2022) based on near-real time active fire detections from the VIIRS sensor on the Suomi-NPP satellite. Every 12 hours, the fire tracking algorithm uses new active fire detections to update the total fire perimeter and estimate the position of active fire lines where the fire may continue to spread. This approach provides a detailed perspective on the behavior of the Caldor and Dixie fires, identifying periods of rapid fire expansion and active fire detections within the perimeter from continued flaming and smoldering behind the active fire fronts.\n\nThe visualization shows the progression over time as the fire spreads. The yellow outlines track the position of the active fire lines every 12 hours for the last 60 hours, with the latest location of the fire front shown in the brightest shade of yellow. The red points show the location of active fire detections within the perimeter, while the grey region shows the estimated total area burned. The graph shows the cumulative burned area in square kilometers.\nVisualizations by: Cindy Starr, Scientific consulting by: Doug C. Morton\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5088" },{ "src":"https://www.youtube.com/embed/GHn_ZxNiF4M", "title":"Wildfires101: Animations", - "caption":"\nVisualizations by: Alex Bodnar, Jonathan North, Adriana Manrique Gutierrez, Produced by: Katie Jepson\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14286" + "caption":"\nVisualizations by: Alex Bodnar, Jonathan North, Adriana Manrique Gutierrez, Produced by: Katie Jepson\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14286" },{ "src":"https://www.youtube.com/embed/wvo5f8Xu_xY", "title":"Column Carbon Monoxide (CO) from Canada Wildfires", - "caption":"Aerosols and trace gases emitted from wildfires are an important component of the Earth-atmosphere system as they can impact radiation, clouds, the carbon cycle, and human health. Produced by the GMAO using observations from MODIS, the Quick Fire Emissions Dataset (QFED) serves as a top-down estimate of aerosol and trace gas emissions to be used as the input for constituent modeling within the Goddard Earth Observing System (GEOS). Upon emission into GEOS through the GOCART aerosol module, aerosols and trace gases are transported via the dynamics in the model, while aerosols are also coupled to radiative and moist model processes. A feature unique to carbon monoxide (CO) emitted in response to wildfires within GEOS is that it can be tagged based on its region of origin. \n\nIn the spring of 2023, the Canadian biomass burning season had an early and aggressive start, saturating the troposphere with smoke. The accumulated emission of CO from biomass burning across Canada was quadruple the previous maximum from the past two decades since the launch of MODIS. This animation demonstrates the transport of total column CO that originated due to biomass burning over North America during the first week of June in 2023 using the GEOS Forward Processing (FP) system. Though emissions are localized, as shown by the red triangles indicating fire hotspots based on QFED, CO from biomass burning is transported thousands of miles and can have a widespread impact.\nTechnical support: Mark Malanoski, Scientific consulting by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/31234" + "caption":"Aerosols and trace gases emitted from wildfires are an important component of the Earth-atmosphere system as they can impact radiation, clouds, the carbon cycle, and human health. Produced by the GMAO using observations from MODIS, the Quick Fire Emissions Dataset (QFED) serves as a top-down estimate of aerosol and trace gas emissions to be used as the input for constituent modeling within the Goddard Earth Observing System (GEOS). Upon emission into GEOS through the GOCART aerosol module, aerosols and trace gases are transported via the dynamics in the model, while aerosols are also coupled to radiative and moist model processes. A feature unique to carbon monoxide (CO) emitted in response to wildfires within GEOS is that it can be tagged based on its region of origin. \n\nIn the spring of 2023, the Canadian biomass burning season had an early and aggressive start, saturating the troposphere with smoke. The accumulated emission of CO from biomass burning across Canada was quadruple the previous maximum from the past two decades since the launch of MODIS. This animation demonstrates the transport of total column CO that originated due to biomass burning over North America during the first week of June in 2023 using the GEOS Forward Processing (FP) system. Though emissions are localized, as shown by the red triangles indicating fire hotspots based on QFED, CO from biomass burning is transported thousands of miles and can have a widespread impact.\nTechnical support: Mark Malanoski, Scientific consulting by: Joseph V. Ardizzone\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/31234" } ] \ No newline at end of file diff --git a/stories/theme.WTR.introduction_water_resources/carousel_content.json b/stories/theme.WTR.introduction_water_resources/carousel_content.json index c55d7d574..b4d2f4d57 100644 --- a/stories/theme.WTR.introduction_water_resources/carousel_content.json +++ b/stories/theme.WTR.introduction_water_resources/carousel_content.json @@ -2,18 +2,18 @@ { "src":"https://www.youtube.com/embed/rQhSM2OvnlE", "title":"Variability of Water Storage in Global Hydrological Basins", - "caption":"Knowing the extent of human influence on the global hydrological cycle is essential for the sustainability of freshwater resources on Earth. However, a lack of water level observations for the world\u2019s ponds, lakes, and reservoirs has limited quantification of reservoir (human-managed) versus natural changes to surface water storage. In this study, scientists used data from NASA's ICESat-2 satellite laser altimeter to quantify global variability in water level over 227,386 water bodies from October 2018 to July 2020. \n\nBy combining this dataset with a global database of human-managed reservoirs, the study found that 57% of seasonal water storage variability occurs in human-managed reservoirs. Global maps of the results organized by hydrologic basin reveal that natural variability in surface water level is greatest in tropical basins like the Amazon and the Congo and lowest in northern and Arctic areas such as Northern Canada and Alaska. In contrast, human-management of surface water storage in arid and semi-arid regions like the Western US, Middle East, Southern Africa and Australia, where human influence drives nearly 100% of seasonal storage variability. Overall, the finding that humans are responsible for the majority of seasonal surface water storage variability shows that we are now a key regulator of the water cycle. \n\nAs economic development, population growth, and climate change continue to pressure global water resources in the future, measurements from satellites like ICESat-2 will continue to provide vital information about how humans are managing freshwater resources worldwide.\n\nThis animation uses data from the study to visualize two quantities: the variability of water level, and the variability of the percent of water storage from man-made reservoirs.\nVisualizations by: Trent L. Schindler, Scientific consulting by: Sarah Cooley, Produced by: Ryan Fitzgibbons\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4889" + "caption":"Knowing the extent of human influence on the global hydrological cycle is essential for the sustainability of freshwater resources on Earth. However, a lack of water level observations for the world\u2019s ponds, lakes, and reservoirs has limited quantification of reservoir (human-managed) versus natural changes to surface water storage. In this study, scientists used data from NASA's ICESat-2 satellite laser altimeter to quantify global variability in water level over 227,386 water bodies from October 2018 to July 2020. \n\nBy combining this dataset with a global database of human-managed reservoirs, the study found that 57% of seasonal water storage variability occurs in human-managed reservoirs. Global maps of the results organized by hydrologic basin reveal that natural variability in surface water level is greatest in tropical basins like the Amazon and the Congo and lowest in northern and Arctic areas such as Northern Canada and Alaska. In contrast, human-management of surface water storage in arid and semi-arid regions like the Western US, Middle East, Southern Africa and Australia, where human influence drives nearly 100% of seasonal storage variability. Overall, the finding that humans are responsible for the majority of seasonal surface water storage variability shows that we are now a key regulator of the water cycle. \n\nAs economic development, population growth, and climate change continue to pressure global water resources in the future, measurements from satellites like ICESat-2 will continue to provide vital information about how humans are managing freshwater resources worldwide.\n\nThis animation uses data from the study to visualize two quantities: the variability of water level, and the variability of the percent of water storage from man-made reservoirs.\nVisualizations by: Trent L. Schindler, Scientific consulting by: Sarah Cooley, Produced by: Ryan Fitzgibbons\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4889" },{ "src":" https://www.youtube.com/embed/fDiL_kzokF0", "title":"A 3D View of an Atmospheric River from an Earth System Model", - "caption":"Features in Earth\u2019s atmosphere, spawned by the heat of the Sun and the rotation of the Earth, transport water and energy around the globe. Clouds and precipitation shown here are from NASA\u2019s MERRA-2 reanalysis, a retrospective blend of a weather model and conventional and satellite observations. \n\nWithin the mid-latitudes, winds move clouds from west to east. Within the tropics easterly trade winds converge along the equator to create a moisture rich cluster of clouds, convection, and precipitation called the intertropical convergence zone, or ITCZ. Disturbances in its flow transport immense amounts of moisture and energy from the tropics to the poles. Studies have shown that atmospheric rivers account for the vast majority of the poleward transport of water vapor.\n\nThe American Meteorological Society defines an atmospheric river as \u201ca long, narrow, and transient corridor of strong horizontal water vapor transport that is typically associated with a low-level jet stream ahead of the cold front of an extratropical cyclone.\u201d A common measure for the strength of an atmospheric river is the integrated water vapor transport, or the amount of moisture that is moved from one place to another by the flow of the atmosphere. The blue shading shown here gives a three-dimensional view of the water vapor transport. Tropical moisture is pulled in from the ITCZ and in this example, converges with other moisture sources to form an atmospheric river. The feature then travels towards the west coast of the United States as a sub-class of atmospheric rivers commonly referred to as the \u201cpineapple express\u201d due to its origin near Hawai\u2019i.\n\nThe atmospheric river is guided by the semi-permanent sub-tropical high pressure off the coast of California and the Baja Peninsula as well as the Aleutian low in the Gulf of Alaska. The pressure gradient between the clockwise flow of the Californian high and the counterclockwise flow of the Aleutian low funnel the atmospheric moisture into a narrow corridor. The more intense the pressure gradient is, the stronger the winds are that transport the water vapor. Extreme rainfall has also been associated with the more intense gradients.\n\nMuch of the moisture stays close to the surface but the rising motion of the low pressure to the north results in the air cooling, condensing the water vapor into a liquid. Precipitation over the ocean falls along the feature\u2019s cold front on its northern side.\n\nAnother way that air can rise and condense into precipitation is through orographic lift. When air encounters the mountains along the west coast of the United States, it is forced upwards. The rising air becomes saturated, causing rain and snow to fall, particularly on the windward side of the mountain. The flow of air continues eastward, depleted of its moisture.\n\nThe precipitation that falls because of atmospheric rivers is important for the hydrologic cycle in the western United States. The winter buildup of the snowpack provides valuable freshwater resources. Despite being beneficial at times, atmospheric river induced precipitation can also be destructive. The occurrence of extreme atmospheric river precipitation events, such as the one that occurred in this example, can result in widespread flooding and mudslides.\n\nAtmospheric rivers are not unique to the west coast of North America and occur around the globe, including Europe, New Zealand, the Middle East, Greenland, and Antarctica. The study of global phenomenon such as atmospheric rivers over the past four decades is made possible through NASA\u2019s MERRA-2 reanalysis, a spatially and temporally consistent blend of satellite and conventional observations with a numerical model. With a dataset that provides hourly information around the globe since 1980, there is still much that can be learned about Earth\u2019s atmosphere and the transport of water and energy around the globe.\n\nNarrated atmospheric rivers movie\n\nVisualizers: Greg Shirah (lead), Horace Mitchell, Cindy Starr, Kel Elkins\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4960" + "caption":"Features in Earth\u2019s atmosphere, spawned by the heat of the Sun and the rotation of the Earth, transport water and energy around the globe. Clouds and precipitation shown here are from NASA\u2019s MERRA-2 reanalysis, a retrospective blend of a weather model and conventional and satellite observations. \n\nWithin the mid-latitudes, winds move clouds from west to east. Within the tropics easterly trade winds converge along the equator to create a moisture rich cluster of clouds, convection, and precipitation called the intertropical convergence zone, or ITCZ. Disturbances in its flow transport immense amounts of moisture and energy from the tropics to the poles. Studies have shown that atmospheric rivers account for the vast majority of the poleward transport of water vapor.\n\nThe American Meteorological Society defines an atmospheric river as \u201ca long, narrow, and transient corridor of strong horizontal water vapor transport that is typically associated with a low-level jet stream ahead of the cold front of an extratropical cyclone.\u201d A common measure for the strength of an atmospheric river is the integrated water vapor transport, or the amount of moisture that is moved from one place to another by the flow of the atmosphere. The blue shading shown here gives a three-dimensional view of the water vapor transport. Tropical moisture is pulled in from the ITCZ and in this example, converges with other moisture sources to form an atmospheric river. The feature then travels towards the west coast of the United States as a sub-class of atmospheric rivers commonly referred to as the \u201cpineapple express\u201d due to its origin near Hawai\u2019i.\n\nThe atmospheric river is guided by the semi-permanent sub-tropical high pressure off the coast of California and the Baja Peninsula as well as the Aleutian low in the Gulf of Alaska. The pressure gradient between the clockwise flow of the Californian high and the counterclockwise flow of the Aleutian low funnel the atmospheric moisture into a narrow corridor. The more intense the pressure gradient is, the stronger the winds are that transport the water vapor. Extreme rainfall has also been associated with the more intense gradients.\n\nMuch of the moisture stays close to the surface but the rising motion of the low pressure to the north results in the air cooling, condensing the water vapor into a liquid. Precipitation over the ocean falls along the feature\u2019s cold front on its northern side.\n\nAnother way that air can rise and condense into precipitation is through orographic lift. When air encounters the mountains along the west coast of the United States, it is forced upwards. The rising air becomes saturated, causing rain and snow to fall, particularly on the windward side of the mountain. The flow of air continues eastward, depleted of its moisture.\n\nThe precipitation that falls because of atmospheric rivers is important for the hydrologic cycle in the western United States. The winter buildup of the snowpack provides valuable freshwater resources. Despite being beneficial at times, atmospheric river induced precipitation can also be destructive. The occurrence of extreme atmospheric river precipitation events, such as the one that occurred in this example, can result in widespread flooding and mudslides.\n\nAtmospheric rivers are not unique to the west coast of North America and occur around the globe, including Europe, New Zealand, the Middle East, Greenland, and Antarctica. The study of global phenomenon such as atmospheric rivers over the past four decades is made possible through NASA\u2019s MERRA-2 reanalysis, a spatially and temporally consistent blend of satellite and conventional observations with a numerical model. With a dataset that provides hourly information around the globe since 1980, there is still much that can be learned about Earth\u2019s atmosphere and the transport of water and energy around the globe.\n\nNarrated atmospheric rivers movie\n\nVisualizers: Greg Shirah (lead), Horace Mitchell, Cindy Starr, Kel Elkins\n\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/4960" },{ "src":"https://www.youtube.com/embed/J9vSshArkx0", "title":"50 Years of Harmful Algal Blooms: Rotating Globe Unwraps to Robinson Projection", - "caption":"Mass fish deaths have ridden our planet's beaches and coastlines. New health risks have taken over coastal communities and millions of fishery-based employees are out of work. To bring light to the issue and show how new technology may revolutionize data we collect the current best data available to show the scope of the problem. As our global climate continues to change, more nutrients enter our water sources, coastal waters get warmer, algal blooms will continue to flourish in this ideal environment.\n\nIn January 2024, NASA is scheduled to launch the PACE (https://pace.gsfc.nasa.gov/ satellite which is designed to improve and increase the data we have collected surrounding harmful algal blooms.\n\nThis data visualization depicts the scope of the data we currently have collected over the past 50 years. Much of it is taken from ships at sea and inland water ways. From this we can see that this is a global issue and learning more about this problem will greatly further our understanding of the issue at hand.\nVisualizations by: Alex Kekesi, Scientific consulting by: Ivona Cetinic, Bridget Seegers, Produced by: Emme Watkins, Technical support: Laurence Schuler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5184" + "caption":"Mass fish deaths have ridden our planet's beaches and coastlines. New health risks have taken over coastal communities and millions of fishery-based employees are out of work. To bring light to the issue and show how new technology may revolutionize data we collect the current best data available to show the scope of the problem. As our global climate continues to change, more nutrients enter our water sources, coastal waters get warmer, algal blooms will continue to flourish in this ideal environment.\n\nIn January 2024, NASA is scheduled to launch the PACE (https://pace.gsfc.nasa.gov/ satellite which is designed to improve and increase the data we have collected surrounding harmful algal blooms.\n\nThis data visualization depicts the scope of the data we currently have collected over the past 50 years. Much of it is taken from ships at sea and inland water ways. From this we can see that this is a global issue and learning more about this problem will greatly further our understanding of the issue at hand.\nVisualizations by: Alex Kekesi, Scientific consulting by: Ivona Cetinic, Bridget Seegers, Produced by: Emme Watkins, Technical support: Laurence Schuler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5184" },{ "src":"https://www.youtube.com/embed/wp7WpoRtL8E", "title":"Rivers of the World: A Unifying Journey through Landsat's Lens, Set to Vltava by Bed\u0159ich Smetana", - "caption":"This visualization is a captivating collaboration between NASA, USGS, and Bed\u0159ich Smetana's celebrated composition, Vltava (The Moldau River). The video aims to capture the essence of Smetana's masterpiece, embodying the power and beauty of rivers while showcasing stunning images of Earth's waterways from the Landsat Satellite Program. The visuals unfold like a poetic tribute to our planet, seamlessly transitioning between awe-inspiring satellite images of rivers and watersheds across the globe. The viewer is transported on a journey through these life-sustaining arteries, witnessing their intricate patterns and breathtaking colors as seen from space. This visualization was created to emphasize the importance of preserving our planet's vital ecosystems and to inspire a collective commitment towards protecting Earth's most precious resources. The collaboration highlights how space-based observations can transcend borders, fostering unity and a shared responsibility for our environment. The imagery in the video is derived from NASA and USGS's Landsat Satellite Program, which has been capturing detailed images of Earth's surface since 1972. The visuals are synchronized with Smetana's Vltava to create an immersive and memorable experience that celebrates the beauty and interconnectedness of our planet's rivers.\n\nMusic Credit - \"Smetana Ma Vlast Classical Music Public Domain\" Musophen Symphony [Orange Free Sounds]\nProduced by: Chris Burns\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14329" + "caption":"This visualization is a captivating collaboration between NASA, USGS, and Bed\u0159ich Smetana's celebrated composition, Vltava (The Moldau River). The video aims to capture the essence of Smetana's masterpiece, embodying the power and beauty of rivers while showcasing stunning images of Earth's waterways from the Landsat Satellite Program. The visuals unfold like a poetic tribute to our planet, seamlessly transitioning between awe-inspiring satellite images of rivers and watersheds across the globe. The viewer is transported on a journey through these life-sustaining arteries, witnessing their intricate patterns and breathtaking colors as seen from space. This visualization was created to emphasize the importance of preserving our planet's vital ecosystems and to inspire a collective commitment towards protecting Earth's most precious resources. The collaboration highlights how space-based observations can transcend borders, fostering unity and a shared responsibility for our environment. The imagery in the video is derived from NASA and USGS's Landsat Satellite Program, which has been capturing detailed images of Earth's surface since 1972. The visuals are synchronized with Smetana's Vltava to create an immersive and memorable experience that celebrates the beauty and interconnectedness of our planet's rivers.\n\nMusic Credit - \"Smetana Ma Vlast Classical Music Public Domain\" Musophen Symphony [Orange Free Sounds]\nProduced by: Chris Burns\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/14329" } ] \ No newline at end of file From 1559afad96f1a79bee0541788be11b5e77ab76e9 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Mon, 1 Jul 2024 13:41:52 -0400 Subject: [PATCH 16/22] Update stories/theme.BIO.introduction_biodiversity/carousel_content.json Co-authored-by: Slesa Adhikari --- .../theme.BIO.introduction_biodiversity/carousel_content.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stories/theme.BIO.introduction_biodiversity/carousel_content.json b/stories/theme.BIO.introduction_biodiversity/carousel_content.json index 728acddbb..a7d6dcaf5 100644 --- a/stories/theme.BIO.introduction_biodiversity/carousel_content.json +++ b/stories/theme.BIO.introduction_biodiversity/carousel_content.json @@ -14,6 +14,6 @@ },{ "src":"https://www.youtube.com/embed/HA0LQQi_E28", "title":"Atmospheric Carbon Dioxide Tagged by Source", - "caption":"Carbon dioxide (CO2) is the most prevalent greenhouse gas driving global climate change. However, its increase in the atmosphere would be even more rapid without land and ocean carbon sinks, which collectively absorb about half of human emissions every year. Advanced computer modeling techniques in NASA's Global Modeling and Assimilation Office allow us to disentangle the influences of sources and sinks and to better understand where carbon is coming from and going to.\n\nVisualizations by: Andrew J Christensen, Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5110" + "caption":"Carbon dioxide (CO₂) is the most prevalent greenhouse gas driving global climate change. However, its increase in the atmosphere would be even more rapid without land and ocean carbon sinks, which collectively absorb about half of human emissions every year. Advanced computer modeling techniques in NASA's Global Modeling and Assimilation Office allow us to disentangle the influences of sources and sinks and to better understand where carbon is coming from and going to.\n\nVisualizations by: Andrew J Christensen, Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5110" } ] \ No newline at end of file From c937bca9353d887cafd3e643d9bb208202c13df9 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Mon, 1 Jul 2024 13:42:08 -0400 Subject: [PATCH 17/22] Update stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json Co-authored-by: Slesa Adhikari --- .../carousel_content.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json index f649562d2..dc4f77045 100644 --- a/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json +++ b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json @@ -14,7 +14,7 @@ },{ "src":"https://www.youtube.com/embed/HA0LQQi_E28", "title":"Atmospheric Carbon Dioxide Tagged by Source", - "caption":"Carbon dioxide (CO2) is the most prevalent greenhouse gas driving global climate change. However, its increase in the atmosphere would be even more rapid without land and ocean carbon sinks, which collectively absorb about half of human emissions every year. Advanced computer modeling techniques in NASA's Global Modeling and Assimilation Office allow us to disentangle the influences of sources and sinks and to better understand where carbon is coming from and going to.\n\nVisualizations by: Andrew J Christensen, Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5110" + "caption":"Carbon dioxide (CO₂) is the most prevalent greenhouse gas driving global climate change. However, its increase in the atmosphere would be even more rapid without land and ocean carbon sinks, which collectively absorb about half of human emissions every year. Advanced computer modeling techniques in NASA's Global Modeling and Assimilation Office allow us to disentangle the influences of sources and sinks and to better understand where carbon is coming from and going to.\n\nVisualizations by: Andrew J Christensen, Mark SubbaRao, Scientific consulting by: Lesley Ott\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5110" },{ "src":"https://www.youtube.com/embed/-aSBfn6_pUY", "title":"Global Atmospheric Carbon Dioxide (CO\u2082)", From 5a085e7076df3f11064af393e3ca09a006bf051f Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Mon, 1 Jul 2024 13:45:20 -0400 Subject: [PATCH 18/22] Update carousel_content.json remove bold tag --- .../theme.DIS.introduction_disasters/carousel_content.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/stories/theme.DIS.introduction_disasters/carousel_content.json b/stories/theme.DIS.introduction_disasters/carousel_content.json index 07fcc3c18..13e23afed 100644 --- a/stories/theme.DIS.introduction_disasters/carousel_content.json +++ b/stories/theme.DIS.introduction_disasters/carousel_content.json @@ -14,10 +14,10 @@ },{ "src":"https://www.youtube.com/embed/B7n62jOJJqI", "title":"Atlantic Hurricane Wind Speed Plots", - "caption":"These simple visualizations are plots of time vs. wind speed for each tropical storm/hurricane of Atlantic Hurricane seasons from 1950 to the present. Horizontal lines indicate wind speed category thresholds. A line plot for each storm shows the storm's name and a marker at the peak wind speed.

\n\nMost named storms during the Atlantic hurricane season happen between June and November. However, occasionally, storms develop outside of those ranges.\n\nFour versions of the plots are inluded:\n 1. May to December showig only the current year\n 2. May to December showing the current year and strong storms from previous years (ghosted out)\n 3. January to December showig only the current year\n 4. January to December showing the current year and strong storms from previous years (ghosted out)\n \nThe plot for the current year automatically updates every 2 hours during hurricane season.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5072" + "caption":"These simple visualizations are plots of time vs. wind speed for each tropical storm/hurricane of Atlantic Hurricane seasons from 1950 to the present. Horizontal lines indicate wind speed category thresholds. A line plot for each storm shows the storm's name and a marker at the peak wind speed.

\n\nMost named storms during the Atlantic hurricane season happen between June and November. However, occasionally, storms develop outside of those ranges.\n\nFour versions of the plots are inluded:\n 1. May to December showig only the current year\n 2. May to December showing the current year and strong storms from previous years (ghosted out)\n 3. January to December showig only the current year\n 4. January to December showing the current year and strong storms from previous years (ghosted out)\n \nThe plot for the current year automatically updates every 2 hours during hurricane season.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5072" },{ "src":"https://www.youtube.com/embed/6-71kC1LT-w", "title":"Predicting Landslides", "caption":"If a slope's underlying foundation is unstable, heavy rainfall could be all it takes to trigger a landslide. In fact, rainfall is the most common catalyst for landslides. An open sourced computer model developed at NASA's Goddard Space Flight Center uses precipitation data to identify areas all over the globe that are potential landslide hazards. The model first looks at areas that have recently experienced heavy rainfall using data from the Global Precipitation Measurement (GPM) mission. When the rainfall estimates are unusually high, the model checks other known conditions of the area that may encourage landslides, such as recent road construction, steep hills, and other factors. A \"nowcast\" map then combines the rainfall data and these other factors to mark areas that might have landslides. Scientists have used these models to conduct studies on long term landslide patterns and landslide warning signs. Watch the video to learn more.\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Dalia B Kirschbaum, Thomas A. Stanley, Produced by: Joy Ng, Ryan Fitzgibbons, Written by: Kasha Patel\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/13126" } -] \ No newline at end of file +] From 3463721e9b3d88deed50ed1255efb8e06d0e0cca Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Mon, 1 Jul 2024 13:46:29 -0400 Subject: [PATCH 19/22] Update theme.AG_.mdx --- stories/theme.AG_.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stories/theme.AG_.mdx b/stories/theme.AG_.mdx index 7099fd28a..280755d18 100644 --- a/stories/theme.AG_.mdx +++ b/stories/theme.AG_.mdx @@ -30,7 +30,7 @@ import contentArray from './theme.AG_.introduction_agriculture/carousel_content. - ## Agriculture + ## Info Producing food has always been challenging, and in the 21st century, human-caused climate change is already affecting food security through increasing temperatures, increased frequency of extreme events and changing precipitation patterns. Earth data have increasingly become part of the food farming process. From 0de1c8a9e481562b8014be7548fea622e8e58c18 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Mon, 1 Jul 2024 13:48:11 -0400 Subject: [PATCH 20/22] Apply suggestions from code review Co-authored-by: Slesa Adhikari --- stories/theme.AG_.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/stories/theme.AG_.mdx b/stories/theme.AG_.mdx index 280755d18..9269cc29b 100644 --- a/stories/theme.AG_.mdx +++ b/stories/theme.AG_.mdx @@ -45,7 +45,7 @@ import contentArray from './theme.AG_.introduction_agriculture/carousel_content. attrAuthor='The NASA Scientific Visualization Studio' attrUrl='https://svs.gsfc.nasa.gov/31278' > - a global map forecasting the impact of El Niño on crop yields. Areas colored in shades of orange indicate regions expected to experience negative impacts on crop yields, while areas in shades of purple are expected to see positive impacts. The map highlights significant regional variations, with negative impacts forecasted for parts of South America, Southern Africa, Southeast Asia, and Australia. Conversely, positive impacts are anticipated in certain regions of North and South America, Europe, and Southwest Asia. This visualization underscores the varied effects of El Niño on agricultural productivity across different global regions. + A global map forecasting the impact of El Niño on crop yields. Areas colored in shades of orange indicate regions expected to experience negative impacts on crop yields, while areas in shades of purple are expected to see positive impacts. The map highlights significant regional variations, with negative impacts forecasted for parts of South America, Southern Africa, Southeast Asia, and Australia. Conversely, positive impacts are anticipated in certain regions of North and South America, Europe, and Southwest Asia. This visualization underscores the varied effects of El Niño on agricultural productivity across different global regions.

From 50b3c46f6f7c735a7fc25f93fd6e4a8747659cfa Mon Sep 17 00:00:00 2001 From: Slesa Adhikari Date: Mon, 1 Jul 2024 16:58:17 -0500 Subject: [PATCH 21/22] Fix typos - subscript/superscript - remove html tags - fix other typos --- datasets/co2.mdx | 44 +++++++++---------- .../locfeature.HYPER/carousel_content.json | 2 +- stories/theme.AQ_.mdx | 2 +- .../carousel_content.json | 6 +-- .../carousel_content.json | 4 +- .../carousel_content.json | 2 +- 6 files changed, 30 insertions(+), 30 deletions(-) diff --git a/datasets/co2.mdx b/datasets/co2.mdx index e479eaecb..43ea2a71b 100644 --- a/datasets/co2.mdx +++ b/datasets/co2.mdx @@ -1,7 +1,7 @@ --- id: co2 name: "Carbon Dioxide" -description: "The Impact of the COVID-19 Pandemic on Atmospheric CO2" +description: "The Impact of the COVID-19 Pandemic on Atmospheric CO₂" media: src: ::file ./co2--dataset-cover.jpg alt: Power plant shooting steam at the sky. @@ -16,7 +16,7 @@ taxonomy: layers: - id: co2-mean stacCol: co2-mean - name: Mean CO2 + name: Mean CO₂ type: raster description: "The average background concentration of carbon dioxide (CO₂) in our atmosphere." initialDatetime: newest @@ -53,7 +53,7 @@ layers: - mean - id: co2-diff stacCol: co2-diff - name: Difference CO2 + name: Difference CO₂ type: raster description: "The changes in carbon dioxide (CO₂) levels in our atmosphere versus previous years." initialDatetime: newest @@ -89,30 +89,30 @@ layers: - ## Tracking CO2 + ## Tracking CO₂ - Lockdowns and other social distancing measures implemented in response to the COVID-19 pandemic have led to temporary reductions in carbon dioxide (CO2) emissions from fossil fuel combustion and other human activities. + Lockdowns and other social distancing measures implemented in response to the COVID-19 pandemic have led to temporary reductions in carbon dioxide (CO₂) emissions from fossil fuel combustion and other human activities. -Scientists largely agree that the build-up of excess CO2 and other greenhouse gases within Earth's atmosphere has contributed to the rapid increase of global climate change. Determining whether these temporary reductions in CO2 emission are significant enough to contribute to the overall lowering of the world's carbon footprint will require more time and rigorous scientific study. +Scientists largely agree that the build-up of excess CO₂ and other greenhouse gases within Earth's atmosphere has contributed to the rapid increase of global climate change. Determining whether these temporary reductions in CO₂ emission are significant enough to contribute to the overall lowering of the world's carbon footprint will require more time and rigorous scientific study. -However, initial studies suggest that although COVID-19-related CO2 emission reductions are expected to slow the speed at which CO2 accumulates in the atmosphere, they will not reduce the overall atmospheric concentration of CO2. +However, initial studies suggest that although COVID-19-related CO₂ emission reductions are expected to slow the speed at which CO₂ accumulates in the atmosphere, they will not reduce the overall atmospheric concentration of CO₂. -CO2 emission reductions have been accompanied by comparable, or even greater, reductions in emissions of short-lived air pollutants, such as nitrogen dioxide (NO2). While fossil fuel combustion emits far more CO2 than NO2, much smaller relative changes are expected for atmospheric CO2 because it has a much longer atmospheric lifetime and there is much more CO2 in the atmosphere than NO2. Therefore, time-dependent, regional-scale changes in CO2 concentrations are expected to be no larger than 1 part per million (ppm), out of the normal 415 ppm CO2 background - a change of only 0.25%. +CO₂ emission reductions have been accompanied by comparable, or even greater, reductions in emissions of short-lived air pollutants, such as nitrogen dioxide (NO2). While fossil fuel combustion emits far more CO₂ than NO2, much smaller relative changes are expected for atmospheric CO₂ because it has a much longer atmospheric lifetime and there is much more CO₂ in the atmosphere than NO2. Therefore, time-dependent, regional-scale changes in CO₂ concentrations are expected to be no larger than 1 part per million (ppm), out of the normal 415 ppm CO₂ background - a change of only 0.25%. -To track atmospheric CO2 changes resulting from the lockdowns, observations collected by the NASA Orbiting Carbon Observatory-2 (OCO-2) satellite and Japan's Greenhouse gases Observing SATellite (GOSAT) during the first few months of 2020 were compared to results collected in previous years. The OCO-2 results were used to search for changes on regional scales over the globe. Targeted observations from GOSAT were used to track changes in large urban areas, such as Beijing, Tokyo, Mumbai, and New York. Both types of observations yielded key insights into the CO2 changes accompanying the economic disruptions caused by the COVID-19 pandemic. +To track atmospheric CO₂ changes resulting from the lockdowns, observations collected by the NASA Orbiting Carbon Observatory-2 (OCO-2) satellite and Japan's Greenhouse gases Observing SATellite (GOSAT) during the first few months of 2020 were compared to results collected in previous years. The OCO-2 results were used to search for changes on regional scales over the globe. Targeted observations from GOSAT were used to track changes in large urban areas, such as Beijing, Tokyo, Mumbai, and New York. Both types of observations yielded key insights into the CO₂ changes accompanying the economic disruptions caused by the COVID-19 pandemic. - ### Regional Scale Changes in CO2 across the Globe + ### Regional Scale Changes in CO₂ across the Globe - To determine whether short-term reductions in CO2 emissions from coronavirus shutdowns are even detectable on a regional scale, scientists must create new methods of data analysis with enough sensitivity and precision to distinguish between normal seasonal changes in background CO2 levels and small perturbations caused by coronavirus shutdowns. + To determine whether short-term reductions in CO₂ emissions from coronavirus shutdowns are even detectable on a regional scale, scientists must create new methods of data analysis with enough sensitivity and precision to distinguish between normal seasonal changes in background CO₂ levels and small perturbations caused by coronavirus shutdowns. - To do this, scientists compare the timing of model-derived global atmospheric CO2 concentration variations constrained by OCO-2 measurements with CO2 emission changes estimated from fossil fuel use statistics from the Global Carbon Project. These comparisons focus on months coinciding with peak COVID-19 isolation periods to see if the emission reductions were accompanied by detectable, regional-scale CO2 changes. + To do this, scientists compare the timing of model-derived global atmospheric CO₂ concentration variations constrained by OCO-2 measurements with CO₂ emission changes estimated from fossil fuel use statistics from the Global Carbon Project. These comparisons focus on months coinciding with peak COVID-19 isolation periods to see if the emission reductions were accompanied by detectable, regional-scale CO₂ changes. - The maps below show these comparisons for the peak periods of the lockdowns in China (early February), southern Europe (early April) and the eastern U.S. (late April). The results show small (about 0.5 parts per million, or 0.125%) reductions in CO2 over each region at times that are well aligned with the largest CO2 emissions reductions in those regions reported by the Global Carbon Project. The CO2 map for late April (panel c) also appears to show a rebound in CO2 levels over East Asia and northern Pacific Ocean in late April, as China began to emerge from its coronavirus lockdowns. Many features are not likely to be associated with the lockdowns. The enhanced CO2 values in the southern hemisphere are probably due in part to the large wildfires over Australia in late December 2019, while the enhanced values in central Asia in April include contributions from wildfires in Siberia. + The maps below show these comparisons for the peak periods of the lockdowns in China (early February), southern Europe (early April) and the eastern U.S. (late April). The results show small (about 0.5 parts per million, or 0.125%) reductions in CO₂ over each region at times that are well aligned with the largest CO₂ emissions reductions in those regions reported by the Global Carbon Project. The CO₂ map for late April (panel c) also appears to show a rebound in CO₂ levels over East Asia and northern Pacific Ocean in late April, as China began to emerge from its coronavirus lockdowns. Many features are not likely to be associated with the lockdowns. The enhanced CO₂ values in the southern hemisphere are probably due in part to the large wildfires over Australia in late December 2019, while the enhanced values in central Asia in April include contributions from wildfires in Siberia. @@ -121,13 +121,13 @@ To track atmospheric CO2 changes resulting from the lockdowns, observations coll
Atmospheric CO2 differences in ppm **Top row**: Reported country-by-country reductions in fossil fuel use during the most intense periods of the COVID-19 lockdowns in a.) China (early February), b.) Europe (early April) and c.) Northeast U.S. (late April). Brighter blue colors indicate greater reductions. - **Bottom row**: observed changes in atmospheric CO2 concentration differences derived from OCO-2 measurements. Blue shades indicate reductions in CO2, while red shades indicate increases relative to the baseline CO2 climatology. + **Bottom row**: observed changes in atmospheric CO₂ concentration differences derived from OCO-2 measurements. Blue shades indicate reductions in CO₂, while red shades indicate increases relative to the baseline CO₂ climatology.
@@ -136,27 +136,27 @@ To track atmospheric CO2 changes resulting from the lockdowns, observations coll
Bar chart of CO2 concentration in Beijing for years 2017 through 2020 - Monthly time series of lower atmospheric CO2 enhancements over Beijing, China for January 2017 through April 2020 derived from GOSAT data. The results for January through April of prior years are shown in blue, while those for 2020 are shown in green. + Monthly time series of lower atmospheric CO₂ enhancements over Beijing, China for January 2017 through April 2020 derived from GOSAT data. The results for January through April of prior years are shown in blue, while those for 2020 are shown in green.
- ### CO2 Changes over Large Urban Areas + ### CO₂ Changes over Large Urban Areas - Scientists use GOSAT data to determine changes in atmospheric CO2 over large urban areas, which experienced the largest changes in economic activity associated with the onset of the COVID-19 pandemic. While OCO-2 is optimized for detecting the subtle, regional-scale changes in CO2, GOSAT has advantages for tracking changes in CO2 emissions over large cities. + Scientists use GOSAT data to determine changes in atmospheric CO₂ over large urban areas, which experienced the largest changes in economic activity associated with the onset of the COVID-19 pandemic. While OCO-2 is optimized for detecting the subtle, regional-scale changes in CO₂, GOSAT has advantages for tracking changes in CO₂ emissions over large cities. - GOSAT observations were analyzed to reveal CO2 concentration enhancements, such as fossil fuel emissions that contribute to higher levels of CO2 lower down in atmosphere over cities, relative to the CO2 concentrations at higher altitudes, which are less affected by city emissions. The figure below shows the CO2 concentration enhancements over Beijing, China, derived from GOSAT observations collected in January through April of each year from 2017 to 2020. The results from earlier years illustrate the amount of month-to-month variability in the observed CO2 enhancements that is typical during this season. However, while the CO2 concentration enhancements vary substantially from month-to-month, they are generally much lower in 2020 than in earlier years. + GOSAT observations were analyzed to reveal CO₂ concentration enhancements, such as fossil fuel emissions that contribute to higher levels of CO₂ lower down in atmosphere over cities, relative to the CO₂ concentrations at higher altitudes, which are less affected by city emissions. The figure below shows the CO₂ concentration enhancements over Beijing, China, derived from GOSAT observations collected in January through April of each year from 2017 to 2020. The results from earlier years illustrate the amount of month-to-month variability in the observed CO₂ enhancements that is typical during this season. However, while the CO₂ concentration enhancements vary substantially from month-to-month, they are generally much lower in 2020 than in earlier years. - Further inspection of the Beijing results reveals that all months in 2020 have smaller CO2 enhancements relative to prior years. While this behavior is consistent with reported COVID-19-related reductions in fossil fuel emissions from Beijing, it is important to remember that these results include variations in CO2 concentrations not only from COVID-19 shutdowns, but also from other processes such as photosynthesis and respiration by plants and transport by passing weather systems. + Further inspection of the Beijing results reveals that all months in 2020 have smaller CO₂ enhancements relative to prior years. While this behavior is consistent with reported COVID-19-related reductions in fossil fuel emissions from Beijing, it is important to remember that these results include variations in CO₂ concentrations not only from COVID-19 shutdowns, but also from other processes such as photosynthesis and respiration by plants and transport by passing weather systems. - Similar results were derived for the other cities. Shanghai shows reduced CO2 enhancements from February through April 2020. For New York, CO2 values were higher in January 2020, close to normal for February, and lower in March, as lockdowns were imposed. There is no data for New York in April due to cloud cover. In New Delhi, Mumbai and Dhaka, the story is somewhat more mixed. The CO2 enhancements are smaller or almost the same in February, reflecting the large role of natural processes, such as year-to-year differences in CO2 uptake and release by forests and crops. In March 2020, CO2 enhancements are higher than in earlier years in New Delhi, and lower in Mumbai and Dhaka. The CO2 enhancements decrease across all three cities in April, as lockdowns are implemented. However, these changes are very difficult to attribute to the pandemic because of the large-scale natural CO2 changes seen across India during this season. + Similar results were derived for the other cities. Shanghai shows reduced CO₂ enhancements from February through April 2020. For New York, CO₂ values were higher in January 2020, close to normal for February, and lower in March, as lockdowns were imposed. There is no data for New York in April due to cloud cover. In New Delhi, Mumbai and Dhaka, the story is somewhat more mixed. The CO₂ enhancements are smaller or almost the same in February, reflecting the large role of natural processes, such as year-to-year differences in CO₂ uptake and release by forests and crops. In March 2020, CO₂ enhancements are higher than in earlier years in New Delhi, and lower in Mumbai and Dhaka. The CO₂ enhancements decrease across all three cities in April, as lockdowns are implemented. However, these changes are very difficult to attribute to the pandemic because of the large-scale natural CO₂ changes seen across India during this season. diff --git a/stories/locfeature.HYPER/carousel_content.json b/stories/locfeature.HYPER/carousel_content.json index 15b553091..0819cfd1c 100644 --- a/stories/locfeature.HYPER/carousel_content.json +++ b/stories/locfeature.HYPER/carousel_content.json @@ -37,7 +37,7 @@ "caption":"This 3-D view of our atmosphere shows the rise of carbon dioxide levels from 2020 to 2021. The world’s vegetation and oceans absorb about half of human carbon dioxide emissions. However data stretching as far back as the 1950s, taken from sensors on the ground, show a steady upward march in carbon dioxide concentrations." },{ "src":"https://www.youtube.com/embed/d-bFeE4YZ6s", - "title":"Net Ecosystem CO2 Exchange", + "title":"Net Ecosystem CO₂ Exchange", "caption":"In colors of green and purple, this map shows ecosystems emitting and absorbing carbon dioxide from 2003 to 2017. Green shows plants absorbing the carbon dioxide, with more absorption during the spring and summer growing seasons. Purples shows plants releasing much of this carbon dioxide back to the atmosphere during the fall and winter. " },{ "src":"https://www.youtube.com/embed/35QjTwIG-eg", diff --git a/stories/theme.AQ_.mdx b/stories/theme.AQ_.mdx index 07451a158..98e966ac0 100644 --- a/stories/theme.AQ_.mdx +++ b/stories/theme.AQ_.mdx @@ -64,7 +64,7 @@ import contentArray from './theme.AQ_.introduction_air_quality/carousel_content. ### DID YOU KNOW? - The ozone hole is primarily caused by human-produced chemicals like chlorofluorocarbons (CFCs), which were banned by an international treaty in 1989 to protect our natural sunscreen. Modern global warming is driven by greenhouse gases like carbon dioxide (CO2) and is primarily linked to the burning of fossil fuels. + The ozone hole is primarily caused by human-produced chemicals like chlorofluorocarbons (CFCs), which were banned by an international treaty in 1989 to protect our natural sunscreen. Modern global warming is driven by greenhouse gases like carbon dioxide (CO₂) and is primarily linked to the burning of fossil fuels.
\n\nMost named storms during the Atlantic hurricane season happen between June and November. However, occasionally, storms develop outside of those ranges.\n\nFour versions of the plots are inluded:\n 1. May to December showig only the current year\n 2. May to December showing the current year and strong storms from previous years (ghosted out)\n 3. January to December showig only the current year\n 4. January to December showing the current year and strong storms from previous years (ghosted out)\n \nThe plot for the current year automatically updates every 2 hours during hurricane season.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5072" + "caption":"These simple visualizations are plots of time vs. wind speed for each tropical storm/hurricane of Atlantic Hurricane seasons from 1950 to the present. Horizontal lines indicate wind speed category thresholds. A line plot for each storm shows the storm's name and a marker at the peak wind speed. \n\nMost named storms during the Atlantic hurricane season happen between June and November. However, occasionally, storms develop outside of those ranges.\n\nFour versions of the plots are inluded:\n 1. May to December showig only the current year\n 2. May to December showing the current year and strong storms from previous years (ghosted out)\n 3. January to December showig only the current year\n 4. January to December showing the current year and strong storms from previous years (ghosted out)\n \nThe plot for the current year automatically updates every 2 hours during hurricane season.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5072" },{ "src":"https://www.youtube.com/embed/6-71kC1LT-w", "title":"Predicting Landslides", diff --git a/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json index dc4f77045..4e9a4328c 100644 --- a/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json +++ b/stories/theme.GHG.introduction_greenhouse_gases/carousel_content.json @@ -18,7 +18,7 @@ },{ "src":"https://www.youtube.com/embed/-aSBfn6_pUY", "title":"Global Atmospheric Carbon Dioxide (CO\u2082)", - "caption":"NASA\u2019s Orbiting Carbon Observatory, 2 (OCO-2) provides the most complete dataset tracking the concentration of atmospheric carbon dioxide (CO\u2082), the main driver of climate change. Every day, OCO-2 measures sunlight reflected from Earth\u2019s surface to infer the dry-air column-averaged CO\u2082 mixing ratio and provides around 100,000 cloud-free observations. Despite these advances, OCO-2 data contain many gaps where sunlight is not present or where clouds or aerosols are too thick to retrieve CO\u2082 data. In order to fill gaps and provide science and applications users a spatially complete product, OCO-2 data are assimilated into NASA\u2019s Goddard Earth Observing System (GEOS), a complex modeling and data assimilation system used for studying the Earth\u2019s weather and climate. GEOS is also informed by satellite observations of nighttime lights and vegetation greenness along with about 1 million weather observations collected every hour. These data help scientists infer CO2 mixing ratios even when a direct OCO-2 observation is not present and provide additional information on the altitude of CO\u2082 plumes that the satellite is not able to see. Together, OCO-2 and GEOS create one of the most complete pictures of CO\u2082.\n\nThe visualization featured on this page shows the atmosphere in three dimensions and highlights the accumulation of CO\u2082 during a single calendar year (January 1-December 31, 2021). Every year, the world\u2019s vegetation and oceans absorb about half of human CO\u2082 emissions, providing an incredibly valuable service that has mitigated the rate of accumulation of greenhouse gases in the atmosphere. However, around 2.5 parts per million remain in the atmosphere every year causing a steady upward march in concentrations that scientists have tracked since the 1950s at surface stations.\n\nThe volumetric visualization starts in January 1, 2021, showing the higher CO\u2082 concentrations, which are closer to the ground, revealing the seasonal movement of high CO\u2082 at a global scale. During the months of June-September (summer months for northern hemisphere), global CO\u2082 concentrations tend to be lowest because northern hemisphere plants actively absorb CO\u2082 from the atmosphere via photosynthesis. During northern hemisphere fall and winter months, much of this CO\u2082 is re-released to the atmosphere due to respiration and can be seen building up. By June and July 2021, plants again draw CO\u2082 out of the atmosphere, but notably higher concentrations remain in contrast to the nearly transparent colors of the previous year. The diurnal rhythm of CO\u2082 is apparent over our planet's largest forests, such as the Amazon rainforest in South America and the Congo rainforest in Central Africa. The fast-paced pulse in those rainforests is due to the day-night cycle; plants absorb CO\u2082 during the day via photosynthesis when the sun is out, then stop absorbing CO\u2082 at night. In addition to highlighting the buildup of atmospheric CO\u2082, this visualization shows how interconnected the world\u2019s greenhouse gas problem is. NASA\u2019s unique combination of observations and models plays a critical role in helping scientists track increases in CO\u2082 as they happen to better understand their climate impact.\n\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Lesley Ott, Brad Weir\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5115" + "caption":"NASA\u2019s Orbiting Carbon Observatory, 2 (OCO-2) provides the most complete dataset tracking the concentration of atmospheric carbon dioxide (CO\u2082), the main driver of climate change. Every day, OCO-2 measures sunlight reflected from Earth\u2019s surface to infer the dry-air column-averaged CO\u2082 mixing ratio and provides around 100,000 cloud-free observations. Despite these advances, OCO-2 data contain many gaps where sunlight is not present or where clouds or aerosols are too thick to retrieve CO\u2082 data. In order to fill gaps and provide science and applications users a spatially complete product, OCO-2 data are assimilated into NASA\u2019s Goddard Earth Observing System (GEOS), a complex modeling and data assimilation system used for studying the Earth\u2019s weather and climate. GEOS is also informed by satellite observations of nighttime lights and vegetation greenness along with about 1 million weather observations collected every hour. These data help scientists infer CO₂ mixing ratios even when a direct OCO-2 observation is not present and provide additional information on the altitude of CO\u2082 plumes that the satellite is not able to see. Together, OCO-2 and GEOS create one of the most complete pictures of CO\u2082.\n\nThe visualization featured on this page shows the atmosphere in three dimensions and highlights the accumulation of CO\u2082 during a single calendar year (January 1-December 31, 2021). Every year, the world\u2019s vegetation and oceans absorb about half of human CO\u2082 emissions, providing an incredibly valuable service that has mitigated the rate of accumulation of greenhouse gases in the atmosphere. However, around 2.5 parts per million remain in the atmosphere every year causing a steady upward march in concentrations that scientists have tracked since the 1950s at surface stations.\n\nThe volumetric visualization starts in January 1, 2021, showing the higher CO\u2082 concentrations, which are closer to the ground, revealing the seasonal movement of high CO\u2082 at a global scale. During the months of June-September (summer months for northern hemisphere), global CO\u2082 concentrations tend to be lowest because northern hemisphere plants actively absorb CO\u2082 from the atmosphere via photosynthesis. During northern hemisphere fall and winter months, much of this CO\u2082 is re-released to the atmosphere due to respiration and can be seen building up. By June and July 2021, plants again draw CO\u2082 out of the atmosphere, but notably higher concentrations remain in contrast to the nearly transparent colors of the previous year. The diurnal rhythm of CO\u2082 is apparent over our planet's largest forests, such as the Amazon rainforest in South America and the Congo rainforest in Central Africa. The fast-paced pulse in those rainforests is due to the day-night cycle; plants absorb CO\u2082 during the day via photosynthesis when the sun is out, then stop absorbing CO\u2082 at night. In addition to highlighting the buildup of atmospheric CO\u2082, this visualization shows how interconnected the world\u2019s greenhouse gas problem is. NASA\u2019s unique combination of observations and models plays a critical role in helping scientists track increases in CO\u2082 as they happen to better understand their climate impact.\n\nVisualizations by: Helen-Nicole Kostis, Scientific consulting by: Lesley Ott, Brad Weir\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5115" },{ "src":"https://www.youtube.com/embed/2rdEjFn_7jg", "title":"Global Atmospheric Methane (CH\u2084)", @@ -38,6 +38,6 @@ },{ "src":"https://www.youtube.com/embed/2xxjiu0C_-Q", "title":"ODIAC: a map of human made carbon dioxide emissions", - "caption":"The Open-source Data Inventory for Anthropogenic CO2 (A.K.A. ODIAC) is a global high-resolution (1x1km) emission data product for fossil fuel carbon dioxide (CO2) emission. ODIAC was originally designed and developed under the Greenhouse gas Observing SATellite (GOSAT) project at Japan\u2019s National Institute for Environmental Studies (NIES). Since then, ODIAC has been maintained at the Universities Space Research Association (USRA) in collaboration with NASA, NIES and the Appalachian State University. ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO2 emissions. ODIAC has been a key input data for NASA\u2019s carbon modeling. ODIAC has been also widely used in the international science research community for a variety of applications across key policy relevant scales (global to local).\nVisualizations by: Mark SubbaRao, Scientific consulting by: Tomohiro Oda\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5121" + "caption":"The Open-source Data Inventory for Anthropogenic CO₂ (A.K.A. ODIAC) is a global high-resolution (1x1km) emission data product for fossil fuel carbon dioxide (CO₂) emission. ODIAC was originally designed and developed under the Greenhouse gas Observing SATellite (GOSAT) project at Japan\u2019s National Institute for Environmental Studies (NIES). Since then, ODIAC has been maintained at the Universities Space Research Association (USRA) in collaboration with NASA, NIES and the Appalachian State University. ODIAC pioneered the combined use of space-based nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO₂ emissions. ODIAC has been a key input data for NASA\u2019s carbon modeling. ODIAC has been also widely used in the international science research community for a variety of applications across key policy relevant scales (global to local).\nVisualizations by: Mark SubbaRao, Scientific consulting by: Tomohiro Oda\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5121" } ] \ No newline at end of file diff --git a/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json b/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json index fb74da93f..e7dba5a78 100644 --- a/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json +++ b/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json @@ -2,7 +2,7 @@ { "src":" https://www.youtube.com/embed/TvPjAe4j6qQ", "title":"Global Sea Surface Currents and Temperature", - "caption":"This visualization shows sea surface current flows. The flows are colored by corresponding sea surface temperature data. This visualization is rendered for display on very high resolution devices like hyperwalls or for print media.

This visualization was produced using model output from the joint MIT/JPL project entitled Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) (http://ecco2.org/). ECCO2 uses the MIT general circulation model (MITgcm) to synthesize satellite and in-situ data of the global ocean and sea-ice at resolutions that begin to resolve ocean eddies and other narrow current systems, which transport heat and carbon in the oceans. The ECCO2 model simulates ocean flows at all depths, but only surface flows are used in this visualization.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/3912" + "caption":"This visualization shows sea surface current flows. The flows are colored by corresponding sea surface temperature data. This visualization is rendered for display on very high resolution devices like hyperwalls or for print media. This visualization was produced using model output from the joint MIT/JPL project entitled Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) (http://ecco2.org/). ECCO2 uses the MIT general circulation model (MITgcm) to synthesize satellite and in-situ data of the global ocean and sea-ice at resolutions that begin to resolve ocean eddies and other narrow current systems, which transport heat and carbon in the oceans. The ECCO2 model simulates ocean flows at all depths, but only surface flows are used in this visualization.\nVisualizations by: Greg Shirah\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/3912" },{ "src":"https://www.youtube.com/embed/FEOlYWUbsHo", "title":"A Decade of Sea Surface Salinity", From 99dc7b5b86631e22199c3a0066e6597d4cab5f09 Mon Sep 17 00:00:00 2001 From: Alex Gurvich Date: Wed, 3 Jul 2024 16:00:02 -0400 Subject: [PATCH 22/22] reupload porthole video --- .../carousel_content.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json b/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json index e7dba5a78..f2bd449da 100644 --- a/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json +++ b/stories/theme.SLR.introduction_sea_level_rise/carousel_content.json @@ -24,7 +24,7 @@ "title":"Daily Polar Sea Ice, Two Year History", "caption":"This visualization shows the daily Arctic and Antarctic sea ice and seasonal land cover change over a two-year history from the present, with a single frame rendered for each day (available from the drop-down of each image window), and an animation created from these frames, \n\nThe Japan Aerospace Exploration Agency (JAXA) provides many water-related products derived from data acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument aboard the Global Change Observation Mission 1st-Water \"SHIZUKU\" (GCOM-W1) satellite. Two JAXA datasets are used in this animation: the 10-km daily sea ice concentration and the 10 km daily 89 GHz Brightness Temperature.\n\nIn this visualization sea ice changes from day to day, with the amount of ice shown being determined by the AMSR2 sea ice concentration data. A running 3-day minimum is used, with a minimum threshhold concentration of 15%. The blueish white color of the sea ice is derived from a 3-day running minimum of the AMSR2 89 GHz brightness temperature. Over the terrain, monthly data from the seasonal Blue Marble Next Generation fades slowly from month to month.\n\nThe numerical portion of the frame filename begins with the four-digit year, followed by the three-digit day of the year for that frame.\nVisualizations by: Trent L. Schindler\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5099" },{ - "src":"https://www.youtube.com/embed/0907sP0XAZE", + "src":"https://www.youtube.com/embed/f-TS-JuZQkQ", "title":"Sea Level Through a Porthole", "caption":"As the planet warms and polar ice melts, our global average sea level is rising. Although exact ocean heights vary due to local geography, climate over time, and dynamic fluid interactions with gravity and planetary rotation, scientists observe sea level trends by comparing measurements against a 20 year spatial and temporal mean reference. These visualizations use the visual metaphor of a submerged porthole window to observe how far our oceans rose between 1993 and 2022.\nVisualizations by: Andrew J Christensen\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/5114" },{ @@ -40,4 +40,4 @@ "title":"GRACE and GRACE-FO polar ice mass loss", "caption":"The mass of the Polar ice sheets have changed over the last decades. Research based on observations from the Gravity Recovery and Climate Experiment (GRACE) satellites (2002-2017) and GRACE Follow-On (since 2018 - ) indicates that between 2002 and 2023, Antarctica shed approximately 150 gigatons of ice per year, causing global sea level to rise by 0.4 millimeters per year; and Greenland shed approximately 270 gigatons of ice per year, causing global sea level to rise by 0.03 inches (0.8 millimeters) per year.\n\nThese images, created from GRACE and GRACE-FO data, show changes in polar land ice mass since 2002. Orange and red shades indicate areas that lost ice mass, while light blue shades indicate areas that gained ice mass. White indicates areas where there has been very little or no change in ice mass since 2002.\n\nThe average flow lines (grey; created from satellite radar interferometry) of the icesheets converge into the locations of prominent outlet glaciers, and coincide with areas of highest mass loss. This supports other observations that warming ocean waters near polar icesheets play a key role in contemporary ice mass loss.\nScientific consulting by: Felix W. Landerer\nFor more information or to download this public domain video, go to https://svs.gsfc.nasa.gov/31166" } -] \ No newline at end of file +]