【摘要】 采用Jason-2校正辐射计的亮温数据与GPM Ku波段测雨雷达的降雨率数据,开展基于Jason-2校正辐射计的降雨率估计算法研究。为避免波束填充效应,对Ku波段测雨雷达降雨率数据进行网格化处理,分别构建3×3,5×5及7×7网格的建模数据集和验证数据集,通过建模数据集建立了3种基于校正辐射计的降雨率估计算法研究,并通过验证数据集进行检验。结果表明:对GPM Ku波段测雨雷达的降雨数据进行3×3,5×5及7×7的网格化处理,可以在降雨率估计算法研究时有效避免波束填充效应,7×7网格化处理方法效果最优。对3种不同的算法比对,发现利用校正辐射计3个通道亮温信息的线性组合形式估计降雨率效果最优,相对偏差可达42.33%。更多还原

【Abstract】 Rainfalls have negative impacts on the measurement accuracy of the satellite radar altimeter, and thus obtaining the rain rate in the altimeter observation area is very important. In this paper, we explored the rain rate estimation algorithms by using the brightness data from the Jason-2 Advanced Microwave Radiometer(AMR) and the rain rate data from the GPM Ku band precipitation radar. In order to avoid the beam-filling effect, the rainfall data of Ku-band rain radar were gridded. The datasets with grids of 3×3, 5×5 and 7×7 were constructed for model development and validation, respectively. Three rain rate estimation algorithms were established and assessed. The results showed that the beam filling effect could be effectively avoided by dividing the Ku PR rain rate data into 3×3, 5×5 and 7×7 grids, with the best results found for 7×7 grids. The comparison between the different algorithms showed that the linear algorithm was the best, with the percentage bias of 42.33%.更多还原