Editorial Type:
Article
Article Type:
Research Article

Rainfall Retrieval and Nowcasting Based on Multispectral Satellite Images. Part I: Retrieval Study on Daytime 10-Minute Rain Rate

Xiao-Yong Zhuge School of Atmospheric Sciences, and Key Laboratory of Mesoscale Severe Weather of Ministry of Education, Nanjing University, Nanjing, China

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Fan Yu School of Atmospheric Sciences, and Key Laboratory of Mesoscale Severe Weather of Ministry of Education, Nanjing University, Nanjing, China

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Cheng-Wei Zhang Meteorological Observatory of Shenzhen Air Traffic Management Station of CAAC, Shenzhen, China

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Print Publication:
01 Dec 2011
DOI:
https://doi.org/10.1175/2011JHM1373.1
Page(s):
1255–1270
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Abstract

This study develops a method for both precipitation area and intensity retrievals based on multispectral geostationary satellite images. This method can be applied to continuous observation of large-scale precipitation so as to solve the problem from the measurements of rainfall radar and rain gauge. Satellite observation is instantaneous, whereas the rain gauge records accumulative data during a time interval. For this reason, collocated 10-min rain gauge measurements and infrared (IR) and visible (VIS) data from the FengYun-2C (FY-2C) geostationary satellite are employed to improve the accuracy of satellite rainfall retrieval. First of all, the rainfall probability identification matrix (RPIM) is used to distinguish rainfall clouds from nonrainfall clouds. This RPIM is more efficient in improving the retrieval accuracy of rainfall area than previous threshold combination screening methods. Second, the multispectral segmented curve-fitting rainfall algorithm (MSCFRA) is proposed and tested to estimate the 10-min rain rates. Rainfall samples taken from June to August 2008 are used to assess the performance of the rainfall algorithm. Assessment results show that the MSCFRA improves the accuracy of rainfall estimation for both stratiform cloud rainfall and convective cloud rainfall. These results are practically consistent with rain gauge measurements in both rainfall area division and rainfall intensity grade estimation. Furthermore, this study demonstrates that the temporal resolution of satellite detection is important and necessary in improving the precision of satellite rainfall retrieval.

Corresponding author address: Fan Yu, School of Atmospheric Sciences, Nanjing University, Nanjing 210093, Jiangsu Province, China. E-mail: yufan@mail.nju.edu.cn

Abstract

This study develops a method for both precipitation area and intensity retrievals based on multispectral geostationary satellite images. This method can be applied to continuous observation of large-scale precipitation so as to solve the problem from the measurements of rainfall radar and rain gauge. Satellite observation is instantaneous, whereas the rain gauge records accumulative data during a time interval. For this reason, collocated 10-min rain gauge measurements and infrared (IR) and visible (VIS) data from the FengYun-2C (FY-2C) geostationary satellite are employed to improve the accuracy of satellite rainfall retrieval. First of all, the rainfall probability identification matrix (RPIM) is used to distinguish rainfall clouds from nonrainfall clouds. This RPIM is more efficient in improving the retrieval accuracy of rainfall area than previous threshold combination screening methods. Second, the multispectral segmented curve-fitting rainfall algorithm (MSCFRA) is proposed and tested to estimate the 10-min rain rates. Rainfall samples taken from June to August 2008 are used to assess the performance of the rainfall algorithm. Assessment results show that the MSCFRA improves the accuracy of rainfall estimation for both stratiform cloud rainfall and convective cloud rainfall. These results are practically consistent with rain gauge measurements in both rainfall area division and rainfall intensity grade estimation. Furthermore, this study demonstrates that the temporal resolution of satellite detection is important and necessary in improving the precision of satellite rainfall retrieval.

Corresponding author address: Fan Yu, School of Atmospheric Sciences, Nanjing University, Nanjing 210093, Jiangsu Province, China. E-mail: yufan@mail.nju.edu.cn
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