Comparison of Ocean Surface Rain Rates From the Global Precipitation Mission and the Meteosat Second-Generation Satellite for Wind Scatterometer Quality Control

Xu, X., Stoffelen, A., and Meirink, J. F

Ocean surface rain rate information is crucial for quality control (QC) research in wind scatterometry. High-quality precipitation retrievals from microwave instruments are available from the Global Precipitation Mission (GPM). In addition, rain rates can be estimated with high spatiotemporal resolution from geostationary passive visible-infrared imagers such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellites. The two products are complementary in observing time and regions. We compare them at different spatial scales, and show that the best consistency is obtained for grids with a size of 25 km or larger; where 25 km corresponds to a common scatterometer wind vector cell size. The results show that correlation coefficient of rain rates from GPM and MSG products for rain rates less than 5 mm/h is about 0.2 and ranges from 0.2 to about 0.5 for rain rates higher than 5 mm/h, while bias and root mean square deviation fluctuate about 2 and 3 mm/h, respectively. Also, QC indicator performances are analyzed with references to MSG and GPM rain rates respectively, for tropical regions. References to these QC indicators further indicate better consistency with both products at rain rates higher than 5 mm/h. The effectiveness of a newly proposed QC indicator has been confirmed as well. The three-way comparison of rain products and scatterometer QC indicators provides a reliable reference for research and for the application of corrections for rain effects on scatterometers.

Bibliographic data

Xu, X., Stoffelen, A., and Meirink, J. F. Comparison of Ocean Surface Rain Rates From the Global Precipitation Mission and the Meteosat Second-Generation Satellite for Wind Scatterometer Quality Control
Journal: IEEE Journal of Selected Topics in Applied Earth Observations, Remote Sensing, Volume: 13, Year: 2020, doi: https://doi.org/10.1109/JSTARS.2020.2995178