Satellite-based microwave sensors that respond to the vertical distribution of hydrometeors have been continuously employed in the investigation of precipitation systems characteristics. Rain/no-rain classification (RNC) methods often are either applied before retrieving precipitation information from a number of algorithms based on passive microwave measurements or adopted to build the precipitation event-based databases. As a simple rain indicator, the polarized corrected temperature (PCT) at 89-GHz (PCT89) method using the global precipitation measurement (GPM) microwave imager (GMI) has been employed by many researchers, because it can estimate the scattering intensity while minimizing the effects of the surface emissivity at high resolution. This article presents a new consideration using the PCT89-based RNC method through statistical verification. Precipitating clouds were subdivided into 11 types (three stratiform types and four convective types) by the GPM dual frequency precipitation radar (DPR) precipitation classification algorithm. Quantitative comparison of verification results was performed in the tropics from January to December 2015 and major sources of uncertainty were analyzed from the perspective of the precipitation mechanism. Results showed a tendency of false identification for stratiform types except for those located near the convective core, and thus the method was susceptible to failure in the identification of convective types. Consequently, this method leads to an increase of 70% and 54% in the number of two significant stratiform types compared to DPR, while the convective types decreased by up to 53%. This article suggests that the precipitations identified by the PCT89 have features that enhance the bias toward the stratiform type.
|Number of pages||14|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Publication status||Published - 2020 Dec|
Bibliographical noteFunding Information:
Manuscript received August 21, 2019; revised February 29, 2020; accepted April 14, 2020. Date of publication April 30, 2020; date of current version November 24, 2020. This work was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education through the Basic Science Research Program under Grant NRF-2019R1A2C2007999. (Corresponding author: Dong-Bin Shin.) The authors are with the Department of Atmospheric Sciences, Yonsei University, Seoul 03722, South Korea (e-mail: email@example.com).
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)