diff --git a/datasets/nldas-cover.png b/datasets/nldas-cover.png new file mode 100644 index 000000000..8e3e63467 Binary files /dev/null and b/datasets/nldas-cover.png differ diff --git a/datasets/nldas2.data.mdx b/datasets/nldas2.data.mdx new file mode 100644 index 000000000..e8d253422 --- /dev/null +++ b/datasets/nldas2.data.mdx @@ -0,0 +1,88 @@ +--- +id: nldas2 +name: "NLDAS-2 Precipitation Forcing Dataset" +description: "NLDAS-2 is a surface meteorological analysis and land-surface model dataset running in operations to produce outputs of soil moisture, snow, surface fluxes, streamflow, etc. for drought monitoring and other applications." +media: + src: ::file ./nldas-cover.png + alt: Landsat 8 — OLI image of the Mississippi River below Memphis, Tennessee on September 16, 2023 at near record low water levels, limiting barge shipments, threatening drinking water supplies, agriculture, and ecosystems.y. + author: + name: LDAS-NASA + url: https://eoimages.gsfc.nasa.gov/images/imagerecords/151000/151897/mississippi_oli_2023259_lrg.jpg +taxonomy: + - name: Topics + values: + - Water Resources + - Hydrology + - Precipitation + - Surface Meteorology + - Drought + - Agriculture + - Disasters +layers: + - id: nldas2 + stacCol: nldas2 + name: Precipitation + type: raster + description: "Precipitation Dataset" + initialDatetime: newest + zoomExtent: + - 0 + - 20 + sourceParams: + colormap_name: magma + rescale: + - 0 + - 200 + nodata: 0 + compare: + datasetId: nldas3 + layerId: nldas3 + mapLabel: | + ::js ({dateFns, datetime, compareDatetime}) => { + return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + } + legend: + unit: + label: mm/month + type: gradient + min: "0" + max: "200" + stops: + - "#000004" + - "#3B0F70" + - "#8C2981" + - "#F37C21" + - "#FCFFA4" + - "#fc8d59" +--- + + + **Temporal Extent:** Jan 2003 to Dec 2021 (January 1979 to present available from the NASA GES DISC) + **Temporal Resolution:** Monthly-averaged (Hourly data available from the NASA GES DISC) + **Spatial Extent:** CONUS (25 to 53 North and -125 to -67 West) + **Spatial Resolution:** 0.125° x 0.125° + **Data type:** Research
+ + The precipitation from NLDAS-2 over CONUS is derived from a daily gridded precipitation analysis at 0.125-degrees from NOAA CPC. The data is temporally disaggregated to hourly using a variety of data sources, primarily radar-estimated precipitation amounts from Doppler Stage II data. Outside of CONUS, but still in the 25 to 53 North domain, different datasets are used to generate NLDAS-2 precipitation. See https://ldas.gsfc.nasa.gov/nldas/v2/forcing for details. + + ## Source Data Product Citation + - https://doi.org/10.5067/THUF4J1RLSYG NLDAS Primary Forcing Data L4 Monthly 0.125 x 0.125 degree V2.0, Edited by David M. Mocko, NASA/GSFC/HSL, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed:[Data Access Date], 10.5067/2DPKB5B5N14O + + ## Version History + V2.0 + + + + + + ## Key Publications + Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., Mocko, D., 2012. Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products: WATER AND ENERGY FLUX ANALYSIS. Journal of Geophysical Research: Atmospheres. Vol. 117, No. D3. https://doi.org/10.1029/2011JD016048 + + ## Learn More + - https://ldas.gsfc.nasa.gov/nldas/v2/forcing + - Data Story: Mapping Water Availability over North America + ## License + [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0) + + + diff --git a/datasets/nldas3.data.mdx b/datasets/nldas3.data.mdx new file mode 100644 index 000000000..9972451a5 --- /dev/null +++ b/datasets/nldas3.data.mdx @@ -0,0 +1,105 @@ +--- +id: nldas3 +name: "NLDAS-3 Precipitation Forcing Dataset" +description: "NASA is co-developing high-resolution retrospective and real-time data for water resources and agricultural applications" +media: + src: ::file ./nldas-cover.png + alt: Landsat 8 — OLI image of the Mississippi River below Memphis, Tennessee on September 16, 2023 at near record low water levels, limiting barge shipments, threatening drinking water supplies, agriculture, and ecosystems.y. + author: + name: LDAS-NASA + url: https://eoimages.gsfc.nasa.gov/images/imagerecords/151000/151897/mississippi_oli_2023259_lrg.jpg +taxonomy: + - name: Topics + values: + - Water Resources + - Hydrology + - Precipitation + - Surface Meteorology + - Drought + - Agriculture + - Disasters +layers: + - id: nldas3 + stacCol: nldas3 + name: Precipitation + type: raster + description: "Precipitation Dataset" + initialDatetime: newest + zoomExtent: + - 0 + - 20 + sourceParams: + colormap_name: magma + rescale: + - 0 + - 200 + nodata: 0 + compare: + datasetId: nldas3 + layerId: nldas3 + mapLabel: | + ::js ({dateFns, datetime, compareDatetime}) => { + return `${dateFns.format(datetime, 'LLL yyyy')} VS ${dateFns.format(compareDatetime, 'LLL yyyy')}`; + } + legend: + unit: + label: mm/month + type: gradient + min: "0" + max: "200" + stops: + - "#000004" + - "#3B0F70" + - "#8C2981" + - "#F37C21" + - "#FCFFA4" + - "#fc8d59" +--- + + + **Temporal Extent of sample NLDAS-3 data:** Jan 2001 to Dec 2021 + **Temporal Resolution of sample NLDAS-3 data:** Monthly + **Spatial Extent:** North and Central America (7-72 North, 169-52 West) + **Spatial Resolution:** 0.01° x 0.01° + **Data type:** Research
+ + This sample dataset presents a fine scale precipitation analysis from the development of NLDAS-3. NLDAS-3 is currently under development and the final product will include precipitation fields (included here) as well as other surface meteorology such as temperature, radiation, surface pressure, and wind speed. NLDAS-3 represents the next generation (i.e., phase 3) of the North American Land Data Assimilation System phase 2 (NLDAS-2). NLDAS-3 precipitation has been derived by assimilating daily precipitation amounts at 4 km of the NASA Integrated Multi-SatellitE Retrievals for Global Precipitation Measurements GPM (IMERG) and the Environment and Climate Change Canada (ECCC) Canadian Precipitation Analysis (CaPA) into the NASA’s Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). NLDAS-3 precipitation at 4 km and daily time steps was then downscaled to 1 km using a cloud-cover based algorithm based on datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS) then converted to hourly using a temporal disaggregation which employs MERRA-2 and IMERG datasets. NLDAS-3 covers North and Central America (from latitude 7 to 72 North and longitude 169 to 52 West) at a resolution of 0.01. The sample data available from this page are monthly-averages and an initial version of the still-in-development of the NLDAS-3 precipitation. + + ## Source Data Product Citation + Maina et al., (2024), NLDAS-3 surface meteorology 0.01 degree x 0.01 degree V1, Greenbelt, MD, USA, NASA Center for Climate Simulation (NCCS) DataPortal, Accessed: [Data Access Date] + + ## Version History + V2.0 + + + + + + ## Key Publications + Maina et al., (2024), NLDAS-3 surface meteorology 0.01 degree x 0.01 degree V1, Greenbelt, MD, USA, NASA Center for Climate Simulation (NCCS) DataPortal, Accessed: July 24, 2024. + + Bratseth. (1986). Statistical interpolation by means of successive corrections - BRATSETH - 1986 - Tellus A - Wiley Online Library. Retrieved October 12, 2022, from https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1600-0870.1986.tb00476.x + + Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F., Schaake, J. C., et al. (2003). Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. Journal of Geophysical Research: Atmospheres, 108(D22). https://doi.org/10.1029/2002JD003118 + + Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., et al. (2017). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1 + + Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803 + + Huffman, G. J., Bolvin, D. T., & Nelkin, E. J. (2015). Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation. NASA/GSFC Code, 612(2015), 47. + + Kemp, E. M., Wegiel, J. W., Kumar, S. V., Geiger, J. V., Mocko, D. M., Jacob, J. P., & Peters-Lidard, C. D. (2022). A NASA–Air Force Precipitation Analysis for Near-Real-Time Operations. Journal of Hydrometeorology, 23(6), 965–989. https://doi.org/10.1175/JHM-D-21-0228.1 + + Lespinas, F., Fortin, V., Roy, G., Rasmussen, P., & Stadnyk, T. (2015). Performance Evaluation of the Canadian Precipitation Analysis (CaPA). Journal of Hydrometeorology, 16(5), 2045–2064. https://doi.org/10.1175/JHM-D-14-0191.1 + + Maina et al., (2024), NLDAS-3 surface meteorology 0.01 degree x 0.01 degree V1, Greenbelt, MD, USA, NASA Center for Climate Simulation (NCCS) DataPortal, Accessed: July 24, 2024. + + ## Learn More + - https://ldas.gsfc.nasa.gov/nldas/v3 + - [Data Story: Mapping Water Availability over North America](https://deploy-preview-403--visex.netlify.app/stories/nldas) + + ## License + [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) (CC BY 4.0) + + + diff --git a/stories/lis-logo.png b/stories/lis-logo.png new file mode 100644 index 000000000..c305d6774 Binary files /dev/null and b/stories/lis-logo.png differ diff --git a/stories/nldas-cover.png b/stories/nldas-cover.png new file mode 100644 index 000000000..8e3e63467 Binary files /dev/null and b/stories/nldas-cover.png differ diff --git a/stories/nldas-fig1.png b/stories/nldas-fig1.png new file mode 100644 index 000000000..3e8c1fa4d Binary files /dev/null and b/stories/nldas-fig1.png differ diff --git a/stories/nldas-fig7.png b/stories/nldas-fig7.png new file mode 100644 index 000000000..58d332af1 Binary files /dev/null and b/stories/nldas-fig7.png differ diff --git a/stories/nldas3-fig1b.png b/stories/nldas3-fig1b.png new file mode 100644 index 000000000..efe2db692 Binary files /dev/null and b/stories/nldas3-fig1b.png differ diff --git a/stories/nldas3.stories.mdx b/stories/nldas3.stories.mdx new file mode 100644 index 000000000..ccc765ed0 --- /dev/null +++ b/stories/nldas3.stories.mdx @@ -0,0 +1,351 @@ +--- +id: 'nldas' +name: Mapping Water Availability over North America +description: 'NASA is co-developing high-resolution retrospective and real-time data for water resources and agricultural applications' +featured: true +media: + src: ::file ./nldas-cover.png + alt: Landsat 8 — OLI image of the Mississippi River below Memphis, Tennessee on September 16, 2023 at near record low water levels, limiting barge shipments, threatening drinking water supplies, agriculture, and ecosystems. + author: + name: LDAS-NASA + url: https://eoimages.gsfc.nasa.gov/images/imagerecords/151000/151897/mississippi_oli_2023259_lrg.jpg +pubDate: 2024-07-25 +taxonomy: + - name: Topics + values: + - Water Resources + - Hydrology + - Precipitation + - Surface Meteorology + - Drought + - Agriculture + - Disasters +--- + + + **Authors**: David Mocko, Fadji Maina, Kim Locke, Sujay Kumar, Kristen Whitney, Rishi Anand, Siddharth Chaudhary, Chris Hain + + ## The North American Land Data Assimilation System (NLDAS) + NLDAS is a widely used land modeling environment that generates estimates of land surface fluxes and states such as soil moisture, snow, and streamflow. These estimates are critical for drought and flood monitoring, water availability and water resource management, climate assessments, and other uses. For instance, the University of Nebraska-Lincoln’s [National Drought Mitigation Center](https://drought.unl.edu/) (NDMC) relies on NLDAS data for their drought assessments published weekly in the [U.S. Drought Monitor](https://www.drought.gov/data-maps-tools/us-drought-monitor). Applications like [OpenET](https://etdata.org/) and [QuickDRI](https://quickdri.unl.edu/) also rely on the quality of NLDAS meteorological forcing and model outputs. The phase 2 of NLDAS (NLDAS-2) is currently operational at NOAA, with long-term archives of data back to Jan 1979 available from NASA. +
+ + + NASA 2023 stakeholder workshop participant responses to the question, “How do you use NLDAS?” + +
+ + + + + + ## Community-Centered Model Development + Building on stakeholder feedback and advancements in Earth observations and data assimilation, teams across NASA are working on major enhancements to the NLDAS environment called “NLDAS Phase 3” (NLDAS-3). The vision for NLDAS-3 is to provide near field scale (~1 km), high quality, observation-informed estimates of land surface and hydrology conditions. Specifically, NLDAS-3 aims to: + - Improve the spatial resolution by providing meteorological forcing and land-surface model output at 1-km resolution; + - Expand the spatial coverage to encompass all of North and Central America, including Alaska, Hawaii, and Puerto Rico (see figures below); + - Utilize high quality surface meteorological data; + - Reduce the current NLDAS-2 latency of 3.5 days to near real-time; + - Upgrade the land surface model to include the latest scientific advancements, such as improved representation of groundwater processes (He et al., 2023); and + - Incorporate observational constraints by assimilating remote sensing datasets, which can help capture human management of water and vegetation, such as irrigation, groundwater withdrawal, droughts, floods, and hydrological impacts of wildland fire and other disturbances + +
+ + + Spatial domain of NLDAS-2 (CONUS) vs. NLDAS-3 (North and Central America). + +
+ + **Summary of NLDAS-3 improvements** +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
AttributeNLDAS-2NLDAS-3
Spatial CoverageCONUS (25-53 North / 125-67 West)North America including Alaska, Hawaii, Puerto Rico and Central America (7-72 North / 169-52 West)
Spatial resolution12.5-km1.0-km
Latency~4 days~7 hours
PrecipitationCDC daily 12.5-km analysis over CONUSAssimilation using gauges, CaPA, IMERG, with MERRA-2/GEOS-IT as background
Surface meteorologyNARR with constant lapse rate adjustmentsMERRA-2/GEOS-IT with advanced downscaling
Land Surface Modeling4 (Noah, VIC, Mosaic, SAC)1 (Noah-MP)
Data assimilationNoneAssimilation of remotely sensed datasets of soil moisture (e.g., SMAP), leaf area index (e.g., MODIS), snow (e.g., AMSR), terrestrial water storage (GRACE-FO)
+
+
+ + To make these improvements, science and modeling teams across NASA centers are using the open source NASA Land Information System (LIS) framework (Kumar et al., 2006; Peters-Lidard et al., 2007), a state-of-the-art software suite for high performance terrestrial hydrology modeling and data assimilation. LIS software is open-source and available at [GitHub](https://github.com/NASA-LIS/LISF). +
+ + +
+ + + + + + ## Precipitation Data Updates + NASA is updating NLDAS precipitation by leveraging NASA’s reanalysis and remote sensing data products. Specifically, we are working on a new data assimilation approach used successfully in operational environments as well as advanced optimal interpolation techniques (Kemp et al. 2022) to blend well-known and widely used precipitation datasets. Daily precipitation amounts at 4 km of the NASA Integrated Multi-SatellitE Retrievals for GPM (IMERG; Huffman et al. 2020) and the ECCC Canadian Precipitation Analysis (CaPA; Lespinas et al. 2015) are assimilated into NASA Modern Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2; Gelaro et al. 2017) precipitation data. Then the precipitation analysis at 4 km is downscaled to a 1 km fine-scale resolution using a cloud cover frequency-based algorithm and disaggregated into hourly using IMERG and MERRA-2 datasets. Below are examples of NLDAS-2 and NLDAS-3 precipitation data over North America to illustrate improved spatial resolution of NLDAS-3 data. + + + +
+ + + + NLDAS-2 and NLDAS-3 precipitation outputs for August 2017. +
+ + + + ## Data Browser + Users are encouraged to further examine the NLDAS-2 and sample NLDAS-3 precipitation using the following two data catalogs links. They contain monthly-averaged precipitation from both products for a ~20-year period. Click on “Explore Data” in the upper right of these two data catalogs. + - NLDAS-2 + - NLDAS-3 + + + + + + ## Explore the Data in the Cloud + Below are links to sample monthly precipitation forcing data in Jupyter Notebooks for you to further explore and examine NLDAS-2 and the sample NLDAS-3 data. + - [Notebook 1](https://nasa-impact.github.io/veda-docs/notebooks/datasets/nldas_time_series.html) shows you how to create time series for NLDAS-3 data. + - [Notebook 2](https://nasa-impact.github.io/veda-docs/notebooks/datasets/nldas_compare2_3.html) shows you how to compare NLDAS-2 and NLDAS-3 data. + + + + + + ## Surface Meteorology Data Updates + The other NLDAS-3 meteorological forcings are obtained by downscaling MERRA-2 datasets (except for Shortwave Down radiation, which will use CERES/POWER data) using the methods as follows: + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Meteorological VariableDownscaling Method
Surface PressureElevation-based following Cosgrove et al., 2003
Longwave Down RadiationElevation-based following Cosgrove et al., 2003
Air TemperatureDynamic lapse rate bias correction, following Rouf et al., 2020
Air HumidityAdjusted to be consistent after the Air Temperature adjustment
Wind SpeedMicroMet methodology following Liston & Elder, 2006
Shortwave Down RadiationSlope-aspect corrections of CERES/POWER data from Rutan et al., 2015
+
+
+ + + + + + ## Land surface model and Data Assimilation Updates + NLDAS-3 will employ the Noah-MP land surface model shown below (He et al., 2023) and implemented within LIS. +
+ + + +
+ Schematic diagrams of energy and water budgets and processes represented in Noah-MP version 5.0. + + **Integrating remote sensing data is crucial for accurately depicting the influence of human activities on the land surface, such as agricultural irrigation, groundwater extraction, reservoir management, and wildfires.** To better detect these impacts, we are working to assimilate the following NASA satellite data products in NLDAS-3: + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Land Surface VariableRemote Sensing Platform
Soil MoistureSMAP, AMSR-E, AMSR-2
SnowAMSR-E, AMSR-2, MODIS
Leaf Area IndexMODIS/VIIRS
Terrestrial Water StorageGRACE/GRACE-FO
Radar Altimetry Water LevelSWOT
+
+
+ + + The multivariate assimilation (also known as reanalysis) will produce land (e.g., vegetation states, evapotranspiration, snow dynamics) and hydrologic (soil moisture, groundwater and surface water storage) variables listed below at a spatial resolution of 1 km. + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + +
CategoryVariables
Energy fluxes and statesLatent heat flux, sensible heat flux, ground heat flux
Surface state variablesSnow water equivalent, snow depth, snow cover, skin temperature, albedo, evapotranspiration, leaf area index
Carbon variablesGross primary production, net primary production
Soil and subsurface state hydrologic variablesSoil moisture, terrestrial water storage, groundwater storage, water table depth, surface runoff, subsurface runoff, streamflow
+
+
+ + + Data will be provided in netCDF-4 format through NASA’s open source cloud environments. Based on the needs of the stakeholder community, a collection of selected variables will also be made available in cloud-optimized GEOTIFF and GIS-based formats, among others. + + + + + + + ## Provide Your Feedback + What information on water availability and land surface meteorology do you need from NLDAS-3? Here are recent requests for downstream products and applications from the community: + - Soil moisture percentiles and outlooks + - Drought percentiles, indices and forecasts + - ET and PET products + - Soil hydrologic conductivity + - Modeled ET and runoff + - GIS-enabled data + - Weekly change maps + - Model uncertainty information + + NASA model developers are collecting feedback via our NLDAS GitHub discussion page [here](https://github.com/Earth-Information-System/NLDAS-3/discussions) and with periodic stakeholder workshops. Please join the NASA NLDAS community and help us create the next state-of-the-art land surface and hydrology model! + + + + + + ## Learn More + - [NASA NLDAS-3 Website](https://ldas.gsfc.nasa.gov/nldas/v3) + - [NASA NLDAS-3 GitHub Discussions Page](https://github.com/Earth-Information-System/NLDAS-3/discussions) + ## Acknowledgements + Thanks to the NLDAS-3 developers and science teams at the NASA Goddard Hydrological Sciences Laboratory and Marshall Space Flight Center SPoRT and all the NLDAS stakeholders who have participated in workshops, provided valuable feedback, and developed downstream applications for assessing water availability. + + + + + + ## References + - ​https://lis.gsfc.nasa.gov/ + - Cosgrove, B.A., Lohmann, D., Mitchell, K.E., Houser, P.R., Wood, E.F., Schaake, J.C., Robock, A., Marshall, C., Sheffield, J., Duan, Q., Luo, L., Higgins, R.W., Pinker, R.T., Tarpley, J.D., Meng, J., 2003. Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. Journal of Geophysical Research: Atmospheres 108. https://doi.org/10.1029/2002JD003118 + - Gelaro, R., McCarty, W., Suárez, M.J., Todling, R., Molod, A., Takacs, L., Randles, C.A., Darmenov, A., Bosilovich, M.G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., Silva, A.M. da, Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J.E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S.D., Sienkiewicz, M., Zhao, B., 2017. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate 30, 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1 + - He, C., Valayamkunnath, P., Barlage, M., Chen, F., Gochis, D., Cabell, R., Schneider, T., Rasmussen, R., Niu, G.-Y., Yang, Z.-L., Niyogi, D., Ek, M., 2023. Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability. Geoscientific Model Development 16, 5131–5151. https://doi.org/10.5194/gmd-16-5131-2023 + - Kemp, E.M., Wegiel, J.W., Kumar, S.V., Geiger, J.V., Mocko, D.M., Jacob, J.P., Peters-Lidard, C.D., 2022. A NASA–Air Force Precipitation Analysis for Near-Real-Time Operations. Journal of Hydrometeorology 23, 965–989. https://doi.org/10.1175/JHM-D-21-0228.1 + - Kumar, S.V., Peters-Lidard, C.D., Tian, Y., Houser, P.R., Geiger, J., Olden, S., Lighty, L., Eastman, J.L., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K., Wood, E.F., Sheffield, J., 2006. Land information system: An interoperable framework for high resolution land surface modeling. Environmental Modelling & Software 21, 1402–1415. https://doi.org/10.1016/j.envsoft.2005.07.004 + - Liston, G.E., Elder, K., 2006. A Meteorological Distribution System for High-Resolution Terrestrial Modeling (MicroMet). https://doi.org/10.1175/JHM486.1 + - Peters-Lidard, C.D., Houser, P.R., Tian, Y., Kumar, S.V., Geiger, J., Olden, S., Lighty, L., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K., Wood, E.F., Sheffield, J., 2007. High-performance Earth system modeling with NASA/GSFC’s Land Information System. Innovations Syst Softw Eng 3, 157–165. https://doi.org/10.1007/s11334-007-0028-x + - Rouf, T., Mei, Y., Maggioni, V., Houser, P., Noonan, M., 2020. A Physically Based Atmospheric Variables Downscaling Technique. Journal of Hydrometeorology 21, 93–108. https://doi.org/10.1175/JHM-D-19-0109.1 + - Rutan, D.A., Kato, S., Doelling, D.R., Rose, F.G., Nguyen, L.T., Caldwell, T.E., Loeb, N.G., 2015. CERES Synoptic Product: Methodology and Validation of Surface Radiant Flux. https://doi.org/10.1175/JTECH-D-14-00165.1 + + + + + + ## Additional Resources + - SPoRT Land Information System + - FLDAS Surface Soil Moisture Anomalies + - A Global Reanalysis for Water, Energy, and Carbon Cycle Variables + + \ No newline at end of file