Advances in Earth Science ›› 2011, Vol. 26 ›› Issue (11): 1162-1172. doi: 10.11867/j.issn.1001-8166.2011.11.1162

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Satellite Retrieval of Precipitation: An Overview

Liu Yuanbo 1, Fu Qiaoni 1,2, Song Ping 1, Zhao Xiaosong 1, Dou Cuicui 1,2   

  1. 1.State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing210008, China;
    2.School of Earth Sciences and Engineering, Hehai University, Nanjing210098, China
  • Received:2011-04-29 Revised:2011-08-29 Online:2011-11-10 Published:2011-11-10

Liu Yuanbo, Fu Qiaoni, Song Ping, Zhao Xiaosong, Dou Cuicui. Satellite Retrieval of Precipitation: An Overview[J]. Advances in Earth Science, 2011, 26(11): 1162-1172.

Precipitation is a fundamental component of the global water cycle. It is a key hydrologic variable of the water cycle in meteorology, climatology and hydrology. Accurate observation of precipitation and its regional, global distributions has long been a challenging scientific goal. With five-decade development of space-borne sensors, the approaches to retrieving precipitation appear mature. This paper briefly describes the principles and the main types of retrieval algorithms of precipitation using visible/infrared (VIS/IR), passive-microwave (PMW), precipitation radar (PR) data, or their combinations. The VIS/IR algorithms generally had relatively low retrieval accuracy, but it could provide better long-term retrieval due to better temporal sampling of geostationary data. The PMW algorithms were more accurate but more complicated than the VIS/IR algorithms in retrieval of instantaneous precipitation, and the PMW data had low spatial and temporal resolution. Among all the PMW algorithms, the Goddard Profiling Algorithm (GPROF) is the most widely applied one. The PR algorithm enabled capture of three-dimensional precipitation structure over the ocean and land. While the PR retrievals had accuracy on the order of ground-radar data, it had limited coverage of the Earth’s surface. The deficiencies of a single sensor algorithm were alleviated with the combination use of multi-sensors. A number of algorithms have been proposed with a particular combination of VIS/IR, PMW, and/or PR data. The commonly used algorithms include the Climate Prediction Center Morphing (CMORPH) algorithm, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) algorithm and the Global Satellite Mapping of Precipitation (GSMaP) algorithm. Currently, scientific efforts have been made to compare and evaluate the existing algorithms, for example, the Program to Evaluate High-Resolution Precipitation Products (PEHRPP). With the development of instruments and algorithms of precipitation, there are produced many regional and global data sets dedicated to precipitation monitoring. The widely spread global precipitation datasets include TRMM, GSMaP and Global Precipitation Climatology Project (GPCP). Each data set had its unique features in terms of spatial and temporal resolutions, typically in 3-hour and 0.25 degree. For future development, the launch of the Global Precipitation Measurement (GPM) mission will improve and extend the TRMM measurements to higher latitudes, with a more frequent sampling, and a higher sensitivity to light and heavy rainfalls. Furthermore, combination of observations at different wavelengths and from both low and geostationary-orbit satellites is a promising way to produce global precipitation. The International Precipitation Working Group (IPWG) is providing a focus on the study of satellite-based quantitative precipitation measurements. With the international efforts, we are approaching to a unique retrieval of a consistent global precipitation cross multi-sensors. 

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