Assimilation of GNSS Reflectometry Delay-Doppler Maps into Global Ocean Surface Wind Analyses

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Feixiong Huang, James L Garrison , S Mark Leidner, Giuseppe Grieco , Ad Stoffelen , Bachir Annane, Ross N Hoffman


Direct remote sensing observations (e.g. radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g. ocean surface winds, soil moisture, or atmospheric water vapor). In the particular case of Global Navigation Satellite System Reflectometry (GNSS-R), the lower-level delay-Doppler map (DDM) observable shows a complicated relationship to the ocean surface wind field. Prior studies have demonstrated DA using GNSS-R wind retrievals produced from DDMs. The complexity of the DDM dependence on winds, however, suggests that the alternative approach of directly ingesting DDM observables into DA systems, without performing a wind retrieval, may be beneficial. We demonstrate assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two-dimensional variational analysis method. Bias correction and quality control methods are described.
Several models for the required observation error covariance matrix are developed and evaluated, concluding that a diagonal matrix scaled with DDM magnitude performs as well as a fully populated matrix empirically tuned to a large ensemble of CYGNSS observation data. 10-meter surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) operational forecast are used as the background. Collocated scatterometer (ASCAT, OSCAT) winds are considered the truth for comparison. Results using one month (June 2017) of data show a reduction in the root-mean-square error (RMSE) from 1.17 to 1.07 m/s and bias from -0.14 to -0.08 m/s for the wind speed at the specular point. Within a 150-km-wide swath along the specular point track, the RMSE was reduced from 1.20 to 1.13 m/s. Wind speed results from DA show smaller RMSE and bias than other CYGNSS wind products available at this time.



Earth Sciences


Global, GNSS-R, Winds, Data-assimilation


Published: 2020-10-26 17:40

Last Updated: 2021-05-15 20:25

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CC BY Attribution 4.0 International

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Comment #26 Feixiong Huang @ 2021-04-21 03:51

The published article is now at: