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Generating Proxy SWOT Water Surface Elevations Using WRF‐Hydro and the CNES SWOT Hydrology Simulator
Author(s) -
Elmer Nicholas J.,
Hain Christopher,
Hossain Faisal,
Desroches Damien,
Pottier Claire
Publication year - 2020
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2020wr027464
Subject(s) - swot analysis , weather research and forecasting model , ocean surface topography , data assimilation , proxy (statistics) , environmental science , computer science , meteorology , geography , geology , climatology , business , machine learning , marketing
The Surface Water Ocean Topography (SWOT) mission will launch in early 2022 to provide the first global inventory of terrestrial surface water. Although SWOT is primarily a research mission with key science objectives in both the oceanography and hydrology domains, SWOT data are expected to have application potential to address many societal needs. To identify SWOT applications, prepare for the use of SWOT data, and quantify SWOT impacts prior to launch, realistic proxy SWOT observations with representative measurement errors are required. This paper provides a step‐by‐step description of two methods for deriving proxy SWOT water surface elevations (WSEs) from an Observing System Simulation Experiment (OSSE) using the Weather Research and Forecasting hydrological extension package (WRF‐Hydro). The first, a basic method, provides a simple and efficient way to sample WRF‐Hydro output according to the SWOT orbit and add random white noise to simulate measurement error, similar to many previous approaches. An alternate method using the Centre National d'Etudes Spatiales (CNES) Large‐Scale SWOT Hydrology Simulator accounts for additional sources of measurement error and produces output in formats comparable to that expected from official SWOT products. The basic method is ideal for river hydrology applications in which a full representation of SWOT measurement errors and spatial resolution is unnecessary, whereas the CNES simulator approach is better‐suited for more rigorous scientific studies that require a comprehensive error budget.