物理学报Issue(14)：526-542,17.DOI:10.7498/aps.62.149201
基于最优估计理论、联合星载主被动传感器资料的液态云微物理特性反演研究*
Retrieval of liquid cloud microphysical properties from spaceborne active and passive sensor data based on optimal estimation theory∗
摘要
Abstract
In order to meet the urgent requirement for accurate retrieval of liquid cloud microphysical properties, integrating the detecting advantages of active and passive sensors and combining radar reflectivity and optical depth information from CloudSat and Aqua, a new retrieval algorithm of liquid cloud microphysical parameters is proposed according to the optimal estimation theory. By assuming the lognormal size distribution of cloud droplets and establishing functional relationships between measurement and retrieval variables based on forward physical model, with the prior information about spectral distribution parameters, the optimal solutions of spectral parameters are obtained after iteratively calculating, then the microphysical parameters of liquid cloud could be retrieved based on forward physical model, and the uncertainty can be calculated according to error propagation theory. By designing retrieval scheme and using measured case data, the retrieval results are compared with the data published by CloudSat official institutions and those retrieved using empirical algorithms, showing that retrievals of liqiud cloud microphysical parameters based on optimal estimation theory by using combined active and passive sensor data are well consistent with official released data, which makes up for the disadvantages of empirical algorithms that have large error and poor expansibility and gives some important references for retrieval research of liquid cloud microphysical parameters based on domestic spaceborne and airborne W-band millimeter-wave radar data.关键词
CloudSat/Aqua/液态云/最优估计理论Key words
CloudSat/Aqua/liquid cloud/optimal estimation theory引用本文复制引用
..基于最优估计理论、联合星载主被动传感器资料的液态云微物理特性反演研究*[J].物理学报,2013,(14):526-542,17.基金项目
国家自然科学基金(批准号:41076118)和国家自然科学基金青年科学基金(批准号:41005018)资助的课题.*Project supported by the National Natural Science Foundation of China (Grant No.41076118) and the Young Scientists Fund of the National Natural Science Foundation of China (Grant No.41005018) （批准号:41076118）
