Improvement of Liquid Water Content Retrieval Accuracy by Multilevel Detection in Cloud Tomography

Abstract

new multilevel detection scheme for cloud tomography is developed. This scheme solves problems intrinsic to conventional single-level detection, such as the lateral boundary problem and the low accuracy of liquid water content (LWC) retrieval for clouds without distinct liquid water cores. Sensitivity studies show that the new multilevel detection scheme can significantly enhance the well posedness of the inverse problem and increases the accuracy of the retrieval. These improvements are achieved not only for clouds with distinct liquid water cores but also for clouds with weak or no liquid water cores, which are difficult to accurately reconstruct using a single-level detection scheme. The settlement of the lateral boundary problem also leads to a natural and easy way of solving the detection time limit problem in cloud tomography. By using a multi-aircraft flight (MAF) scheme, segmental retrieval can be applied to make the applicable scope of cloud tomography much broader. Considering the detection time limit and the cost in practice, the feasible flight scheme at present is MAF with two detection levels. Although only one detection level is added to the conventional single-level scheme, the accuracy of LWC retrieval can be improved by 1.4%?13.1%.