Scatterometers provide consistent observations of ocean surface wind with reliable quality. In tropical regions, Quality Control (QC) rejects observations effected by rain clouds to guarantee the quality of wind products obtained with references to Geophysical Model Functions (GMFs). GMFs map scatterometer observed normalized radar cross-sections (NRCS) to wind fields without considering rain effects. The rejected groups have information of both rains and winds, and can be utilized for quantitative modelling of rain effects. Wind scatterometers usually operate at C-or Ku-band, they are affected differently by rain clouds due to differences in sensing wavelengths. Collocated observations with both frequencies would enable quantitative evaluation of rain effects. This study exploits these collocated C- and Ku-band measurements and seeks to ultimately develop a method for improving surface wind retrieval using a rain-cloud correction factor, with the preliminary model outline proposed. A vector radiative transfer based layer scattering model for rain clouds above ocean surface that accounts for scattering and attenuation effects of rain column and sea surface roughness will be used in quantitative analysis of rain cloud effects on surface wind retrieval at C- and Ku-band. Rain drop size distribution (DSD) and surface modifications from rains under different wind speed are considered in the model. For rain-free condition, a modified IEM surface scattering model is utilized in the quantitative analysis. Model validation and evaluation of the proposed correction factor are conducted using collocated data obtained from existing C- and Ku-band scatterometer observations, with time lag less than 5 minutes and spatial difference less than 25 km, and references to rain rates from the Meteosat Second Generation (MSG) Satellite.
Xingou Xu, Saibun Tjuatja, Ad Stoffelen, Xiaolong Dong
. A Study on Combined C- and Ku-Band Rain Effects for Wind Scatterometry Quality Control
Journal: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Year: 2020, doi: https://doi.org/10.1109/IGARSS39084.2020.9323343