Paper Info
Title
Passive Microwave Precipitation Retrieval Algorithm With $A~Priori$ Databases of Various Cloud Microphysics Schemes: Tropical Cyclone Applications
Abstract
The accuracy of a physically based passive microwave precipitation retrieval algorithm is affected by the quality of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</italic> 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</italic> knowledge (i.e., databases). In the retrieval process, a specific <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</italic> 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</italic> 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.
Year
DOI
Venue
2020
10.1109/TGRS.2019.2948262
IEEE Transactions on Geoscience and Remote Sensing
Keywords
DocType
Volume
Databases,Clouds,Rain,Customer relationship management,Microwave theory and techniques,Tropical cyclones,Sea measurements
Journal
58
Issue
ISSN
Citations 
4
0196-2892
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Yeji Choi103.72
Dong-Bin Shin201.35
Jiseob Kim300.34
Minsu Joh400.34