A Forward Model for Data Assimilation of GNSS Ocean Reflectometry Delay-Doppler Maps
| Autor: | James L. Garrison, Ross N. Hoffman, S. Mark Leidner, Ad Stoffelen, Feixiong Huang, G. Grieco, Bachir Annane |
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| Rok vydání: | 2021 |
| Předmět: |
Mean squared error
Meteorology 0211 other engineering and technologies 02 engineering and technology Scatterometer Numerical weather prediction Wind speed Swell Data assimilation GNSS applications Hurricane Weather Research and Forecasting model General Earth and Planetary Sciences Environmental science Electrical and Electronic Engineering 021101 geological & geomatics engineering |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing. 59:2643-2656 |
| ISSN: | 1558-0644 0196-2892 |
| DOI: | 10.1109/tgrs.2020.3002801 |
| Popis: | Delay-Doppler maps (DDMs) are generally the lowest level of calibrated observables produced from global navigation satellite system reflectometry (GNSS-R). A forward model is presented to relate the DDM, in units of absolute power at the receiver, to the ocean surface wind field. This model and the related Jacobian are designed for use in assimilating DDM observables into weather forecast models. Given that the forward model represents a full set of DDM measurements, direct assimilation of this lower level data product is expected to be more effective than using individual specular-point wind speed retrievals. The forward model is assessed by comparing DDMs computed from hurricane weather research and forecasting (HWRF) model winds against measured DDMs from the Cyclone Global Navigation Satellite System (CYGNSS) Level 1a data. Quality controls are proposed as a result of observed discrepancies due to the effect of swell, power calibration bias, inaccurate specular point position, and model representativeness error. DDM assimilation is demonstrated using a variational analysis method (VAM) applied to three cases from June 2017, specifically selected due to the large deviation between scatterometer winds and European Centre for Medium-Range Weather Forecasts (ECMWF) predictions. DDM assimilation reduced the root-mean-square error (RMSE) by 15%, 28%, and 48%, respectively, in each of the three examples. |
| Databáze: | OpenAIRE |
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