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This repository is the outcome of a NASA-funded project headed by the SWOT Science Team. It aims to develop tools for planning upcoming oceanographic campaigns that will take place after the launch of SWOT (Surface Water and Ocean Topography) satellite.

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oceanliner: observing system simulation experiments (OSSEs) to subsample high-resolution model output as if by gliders, ships, or other in situ platforms

This notebook is being developed as part of a NASA-funded SWOT Science Team project to develop tools for planning in situ oceanographic campaigns that will take place after SWOT's launch, notably as part of the Adopt-a-Crossover program.

This package takes an input trajectory (e.g., the path of an ocean glider), subsamples output from a high-resolution ocean simulation along that trajectory, and returns a set of subsampled variables (e.g., standard physical variables temperature, salinity, velocity; derived physical quantities such as steric height; biogeochemical quantities if available). We envision this package having two potential uses: 1) designing in situ sampling strategies, and 2) interpreting in situ data in the context of a high resolution model.

See run_oceanliner.ipynb

Led by Kyla Drushka ([email protected]) with contributions from Manjaree Binjolkar. Code was developed in part during OceanHackWeek21 with team Dhruv Balwada, Cassia Cai, Diana LaScala-Gruenewald and Iury Simoes-Sousa.

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This repository is the outcome of a NASA-funded project headed by the SWOT Science Team. It aims to develop tools for planning upcoming oceanographic campaigns that will take place after the launch of SWOT (Surface Water and Ocean Topography) satellite.

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  • Jupyter Notebook 95.9%
  • Python 4.1%