A Conceptual Rain Effect Model for Ku-band Scatterometers

Zhao, K., A. Stoffelen, J. Verspeek, A. Verhoef and C. Zhao

Satellite scatterometer wind retrieval is affected by rain. Both the precipitating clouds in the atmosphere and the sea surface rain effects can enhance or reduce the backscatter signal. Ku-band scatterometer retrievals suffer more rain effects than the C-band scatterometer due to the shorter wavelength. Because of the lack of understanding of the potential physical mechanism, the current geophysical model functions (GMFs) do not include rain effects, which leads to wind field retrieval biases in rainy areas. The usual method to avoid rain effects is flagging the possible rain-contaminated data in the quality control procedure and removing these flagged data in the processing. However, rain is often associated with extreme weather events, where accurate wind (and rain) retrieval is particularly relevant. Therefore, the authors propose a conceptual model that describes the relationship between Ku-band scatterometer-measured normalized radar cross-section (NRCS) biases and the sea surface wind-induced NRCS and rain rates (RRs). The model assumes that the area-weighted RR in each wind vector cell (WVC) is a function of the rain coverage area fraction. The received NRCS is constituted by a wind and rain contribution. Model parameters are fitted based on Haiyang-2C scatterometer measurements, collocated advanced scatterometer (ASCAT) measurements, and the Level 3 Integrated Multi-satellitE Retrievals (3IMERG) average area-weighted RRs. Scatterometer-measured NRCS biases are much reduced by comparing the original measured NRCS biases and the residual NRCS biases after correction. The model can help to better understand rain effects on scatterometers and paves the way toward a Ku-band scatterometer wind retrieval method considering rain effects.

Bibliographic data

Zhao, K., A. Stoffelen, J. Verspeek, A. Verhoef and C. Zhao. A Conceptual Rain Effect Model for Ku-band Scatterometers
Journal: IEEE Transactions on Geoscience and Remote Sensing, Volume: 61, Year: 2023, doi: 10.1109/TGRS.2023.3264246