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.
|Number of pages||17|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Publication status||Published - 2020 Apr|
Bibliographical noteFunding Information:
Manuscript received May 21, 2019; revised September 7, 2019 and October 14, 2019; accepted October 16, 2019. Date of publication November 8, 2019; date of current version March 25, 2020. This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF-2019R1A2C2007999. The work of Y. Choi and M. Joh was supported by the subproject of the KISTI’s Project, The National Supercomputing Infrastructure Construction and Service, under Grant K-19-L02-C01-S01. (Corresponding author: Dong-Bin Shin.) Y. Choi and M. Joh are with the Supercomputing Infrastructure Center, Korea Institute of Science and Technology Information, Daejeon 34141, South Korea.
This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF-2019R1A2C2007999. The work of Y. Choi and M. Joh was supported by the subproject of the KISTI's Project, The National Supercomputing Infrastructure Construction and Service, under Grant K-19-L02-C01-S01.
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)