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地球科学进展  2011, Vol. 26 Issue (11): 1162-1172    DOI: 10.11867/j.issn.1001-8166.2011.11.1162
综述与评述     
卫星遥感反演降水研究综述
刘元波1,傅巧妮1,2,宋平1,赵晓松1,豆翠翠1,2
1.中国科学院南京地理与湖泊研究所湖泊与环境国家重点实验室,江苏南京210008;2.河海大学地球科学与工程学院,江苏南京210098
Satellite Retrieval of Precipitation: An Overview
Liu Yuanbo1, Fu Qiaoni1,2, Song Ping1, Zhao Xiaosong1, Dou Cuicui1,2
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
 全文: PDF(1068 KB)  
摘要:

降水是地球水循环的基本组成部分,具有重要的气象、气候和水文学意义。精准地测量降水及其区域和全球分布,长期以来一直是一个颇具挑战性的科学研究目标。经过近50年的发展,基于可见光、红外和微波等各类卫星传感器的降水反演算法也逐渐发展成熟起来。简要回顾可见光/红外、被动微波、雷达和多传感器联合反演等卫星遥感降水反演的基本原理、主要反演算法以及存在的难点和前沿性研究问题,介绍了应用降水反演算法制成的3种主要全球降水数据集,包括热带雨林观测卫星(TRMM)、全球降水卫星制图(GSMaP)和全球降水气候项目(GPCP)数据集,并结合目前存在的问题探讨卫星降水反演研究发展趋势。

关键词: 降水反演算法可见光/红外遥感主被动微波遥感全球数据集    
Abstract:

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. 

Key words: Precipitation retrieval algorithm    Visible/Infrared remote sensing    Microwave remote sensing    Global precipitation dataset.
收稿日期: 2011-04-29 出版日期: 2011-11-10
:  P412.27  
基金资助:

中国科学院“百人计划”择优支持项目“基于定量遥感的湖泊蓄水量变化驱动机制研究”;中国科学院知识创新工程重要方向项目“鄱阳湖流域气候—水文—物质输移过程与湖泊水安全研究”(编号:KZCX2-YW-337)资助.

通讯作者: 刘元波(1969-),男,山东济宁人,研究员,主要从事水文遥感研究.      E-mail: ybliu@niglas.ac.cn
作者简介: 刘元波(1969-),男,山东济宁人,研究员,主要从事水文遥感研究. E-mail:ybliu@niglas.ac.cn
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引用本文:

刘元波,傅巧妮,宋平,赵晓松,豆翠翠. 卫星遥感反演降水研究综述[J]. 地球科学进展, 2011, 26(11): 1162-1172.

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

链接本文:

http://www.adearth.ac.cn/CN/10.11867/j.issn.1001-8166.2011.11.1162        http://www.adearth.ac.cn/CN/Y2011/V26/I11/1162

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