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Evaluation of RapidScat Ocean Vector Winds for Data Assimilation and Reanalysis
Will McCarty1, Mohar Chattopadhyay2, Austin Conaty2
1Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD.
Monthly Weather Review
|August 5, 2020
Summary
RapidScat, a unique space-based scatterometer, provided valuable wind vector data from the International Space Station (ISS). Despite increasing data differences and biases over its mission, RapidScat wind data reduced forecast errors.
Area of Science:
- * Earth Science
- * Atmospheric Science
- * Remote Sensing
Background:
- * RapidScat was a low-cost scatterometer mission following QuikSCAT.
- * It operated on the International Space Station (ISS) from October 2014 to August 2016.
- * Its unique low-inclination orbit provided novel scatterometry data.
Purpose of the Study:
- * To describe the assimilation and evaluation methods for RapidScat data.
- * To analyze the performance of RapidScat wind vector retrievals.
- * To assess the impact of RapidScat data on numerical weather prediction models.
Main Methods:
- * Assimilation of near-real-time RapidScat retrievals into the Global Modeling and Assimilation Office (GMAO) forward processing system.
- * Passive comparison of RapidScat full data products with MERRA-2 reanalysis data.
- * Evaluation using the Forecast Sensitivity Observation Impact (FSOI) metric for forecast error reduction.
Main Results:
- * Time series statistics revealed an increasing root mean squared (RMS) difference between RapidScat observations and GMAO model background fields.
- * Both observations and model backgrounds exhibited biases in zonal and meridional wind components.
- * RapidScat data demonstrated a net reduction in forecast error, though this impact diminished over the mission's lifespan.
Conclusions:
- * RapidScat provided unique wind vector data from a non-sun-synchronous orbit.
- * Data quality, indicated by RMS differences and biases, showed degradation over time.
- * Despite limitations, RapidScat contributed to improving weather forecast accuracy through data assimilation.
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