Retrieval of Precipitation by Using Himawari-8 Infrared Images

被引：0
|
作者：
Sun S. [1 ,2 ]
Li W. [1 ]
Huang Y. [1 ]
机构：
[1] Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing
[2] 93313 Troop, Changchun
来源：
Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis | 2019年 / 55卷 / 02期
关键词：
Brightness temperature; Brightness temperature difference; Rain rate; Retrieval; Satellite remote sensing;
D O I：
10.13209/j.0479-8023.2018.101
中图分类号：
学科分类号：
摘要：
Using the matched data between infrared images of the geostationary meteorological satellite Himawari-8 and the product 2AGPROFGMI of Global Precipitation Measurement (GPM), the matched infrared brightness temperature (BT), brightness temperature difference (BTD) and surface precipitation are connected by pixel to pixel. Further more, because of the advantage of the more channels of geostationary meteorological satellite, two-dimensional and three-dimensional lookup tables of rain rate (RR) are established with the matched infrared brightness temperature, brightness temperature difference and surface precipitation. The lookup tables which can identify the rain rate of different grades are found by the retrieval tests. The three-dimensional lookup tables (BT10.4, BTD12.4-10.4, BTD6.2-7.3) show higher probability of detection (POD=0.8817) and lower false alarm rate (FAR=0.4042) when detecting the rain, so it can identify the areas of rainfall. © 2019 Peking University.
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页码：215 / 226
页数：11
相关论文
共 19 条
  • [1] Behrangi A., Andreadis K., Fisher J.B., Et al., Satellite-based precipitation estimation and its application for streamflow prediction over mountainous western U. S. Basins, Journal of Applied Meteorology & Climatology, 53, 12, pp. 2823-2842, (2014)
  • [2] Prigent C., Precipitation retrieval from space: an overview, Comptes Rendus Geoscience, 342, 4, pp. 380-389, (2010)
  • [3] Ba M.B., Gruber A., GOES multispectral rainfall algorithm (GMSRA), Journal of Applied Meteorology, 40, 8, pp. 1500-1514, (2001)
  • [4] Adler R.F., Negri A.J., A satellite infrared technique to estimate tropical convective and stratiform rainfall, Journal of Applied Meteorology, 27, 1, pp. 30-51, (1988)
  • [5] Rosenfeld D., Gutman G., Retrieving microphysical properties near the tops of potential rain clouds by multispectral analysis of AVHRR data, Atmospheric Research, 34, pp. 259-283, (1994)
  • [6] Behrangi A., Hsu K.L., Lmam B., Et al., PERSIANN-MSA: a precipitation estimation method from satellite- based multispectral analysis, Journal of Hydrometeorology, 10, 6, pp. 1414-1429, (2009)
  • [7] Schmit T.J., Gunshor M.M., Menzel W.P., Et al., Introducing the next-generation advanced baseline imager on GOES-R, Bulletin of the American Meteorological Society, 86, 8, pp. 1079-1096, (2005)
  • [8] Tao Y., Hsu K., Gao X., Et al., A two-stage deep neural network framework for precipitation estimation from bispectral satellite information, Journal of Hydrometeorology, 19, 2, pp. 393-408, (2018)
  • [9] Zhuge X., Yu F., Zhang C., Rainfall retrieval and nowcasting based on multispectral satellite images. Part I: Retrieval Study on Daytime 10-Minute Rain Rate, Journal of Hydrometeorology, 12, 6, pp. 1255-1270, (2011)
  • [10] Bessho K., Date K., Hayashi M., Et al., An introduction to Himawari-8/9- Japan's new-generation geostationary meteorological satellites, Journal of the Meteorological Society of Japan, 94, 2, pp. 151-183, (2016)
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