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공공누리This item is licensed Korea Open Government License

Title
Passive microwave precipitation retrieval algorithm using a priori databases with various cloud microphysics schemes: Tropical cyclone applications
Author(s)
최예지신동빈김지섭조민수
Publisher
IEEE
Publication Year
2019-11-08
Abstract
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.
Keyword
전지구강수관측; 미세물리모수화방법; 수동마이크로파강수산출; 태풍강수관측
Journal Title
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING;
Citation Volume
58
ISSN
0196-2892
DOI
10.1109/TGRS.2019.2948262
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Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
URI
https://repository.kisti.re.kr/handle/10580/16761
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