The accuracy of a physically based passive microwave precipitation retrieval algorithm is affected by the quality of the a priori knowledge it employs, which indicates the relationship between the precipitation information obtained from cloud-resolving models (CRMs) and the simulated brightness temperatures (TBs) from radiative transfer models. As various microphysical assumptions reflecting a wide variety of sophisticated microphysical properties are applied to the CRMs, the TBs simulated based on the model-driven 3-D precipitation fields are determined by the selected microphysical assumption. In this article, we developed a prototype precipitation retrieval algorithm that incorporates various cloud microphysics schemes in its a priori knowledge (i.e., databases). In the retrieval process, a specific a priori database is selected for every target precipitation scene by comparing the similarities of the simulated and observed microwave emission and scattering signatures. The prototype algorithm was tested through application to precipitation retrieval for tropical cyclones at various intensity stages, which occurred over the northwestern Pacific region in 2015. The a priori databases constructed using the weather research and forecasting double-moment (WDM6) and Thompson Aerosol Aware schemes are superior when used for weak-to-moderate rainfall systems, whereas the databases constructed with the other schemes are superior within strong rain rate regions. The retrieval results obtained using the best-performing database are generally superior for all rain rate regions. Furthermore, we confirm that the database quality is more important than the number of databases. In comparison with the data from the dual-precipitation radar, the retrieval’s correlations, bias, and root mean square are 0.75, 0.14, and 5.62, respectively.

中文翻译：

---

具有各种云微物理方案的先验数据库的被动微波降水检索算法：热带气旋应用

基于物理的被动微波降水反演算法的准确性受其使用的先验知识质量的影响，先验知识表明从云解析模型 (CRM) 获得的降水信息与来自云的模拟亮温 (TB) 之间的关系。辐射传输模型。由于将反映各种复杂微物理特性的各种微物理假设应用于 CRM，因此基于模型驱动的 3-D 降水场模拟的 TB 由选定的微物理假设确定。在本文中，我们开发了原型降水检索算法，该算法将各种云微物理方案纳入其先验知识（即数据库）。在检索过程中，通过比较模拟和观察到的微波发射和散射特征的相似性，为每个目标降水场景选择一个特定的先验数据库。原型算法通过应用于 2015 年发生在西北太平洋地区的不同强度阶段热带气旋的降水反演进行了测试。 使用天气研究和预报双矩（WDM6）和 Thompson Aerosol Aware 方案构建的先验数据库当用于弱到中雨系统时更优，而用其他方案构建的数据库在强降雨率区域内更优。使用性能最佳的数据库获得的检索结果通常对所有降雨率区域都具有优势。此外，我们确认数据库质量比数据库数量更重要。与双降水雷达的数据相比，反演的相关性、偏差和均方根分别为0.75、0.14和5.62。