Impact of Missing Passive Microwave Sensors on Multi-Satellite Precipitation Retrieval Algorithm

被引:4
|
作者
Yong, Bin [1 ,2 ]
Chen, Bo [1 ]
Hong, Yang [3 ]
Gourley, Jonathan J. [4 ]
Li, Zhe [5 ]
机构
[1] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Jiangsu, Peoples R China
[2] SOA, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Zhejiang, Peoples R China
[3] Univ Oklahoma, Sch Civil Engn & Environm Sci, Norman, OK 73019 USA
[4] NOAA, Natl Severe Storms Lab, Norman, OK 73072 USA
[5] Tsinghua Univ, Dept Hydraul Engn, Beijing 100084, Peoples R China
来源
REMOTE SENSING | 2015年 / 7卷 / 01期
基金
中国国家自然科学基金;
关键词
satellite precipitation; TRMM; GPM; IMERG; TMPA; hydrological application; REAL-TIME; LOW LATITUDES; PRODUCTS; RAINFALL; BASINS; TRMM; VALIDATION; LAND;
D O I
10.3390/rs70100668
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The impact of one or two missing passive microwave (PMW) input sensors on the end product of multi-satellite precipitation products is an interesting but obscure issue for both algorithm developers and data users. On 28 January 2013, the Version-7 TRMM Multi-satellite Precipitation Analysis (TMPA) products were reproduced and re-released by National Aeronautics and Space Administration (NASA) Goddard Space Flight Center because the Advanced Microwave Sounding Unit-B (AMSU-B) and the Special Sensor Microwave Imager-Sounder-F16 (SSMIS-F16) input data were unintentionally disregarded in the prior retrieval. Thus, this study investigates the sensitivity of TMPA algorithm results to missing PMW sensors by intercomparing the "early" and "late" Version-7 TMPA real-time (TMPA-RT) precipitation estimates (i.e., without and with AMSU-B, SSMIS-F16 sensors) with an independent high-density gauge network of 200 tipping-bucket rain gauges over the Chinese Jinghe river basin (45,421 km(2)). The retrieval counts and retrieval frequency of various PMW and Infrared (IR) sensors incorporated into the TMPA system were also analyzed to identify and diagnose the impacts of sensor availability on the TMPA-RT retrieval accuracy. Results show that the incorporation of AMSU-B and SSMIS-F16 has substantially reduced systematic errors. The improvement exhibits rather strong seasonal and topographic dependencies. Our analyses suggest that one or two single PMW sensors might play a key role in affecting the end product of current combined microwave-infrared precipitation estimates. This finding supports algorithm developers' current endeavor in spatiotemporally incorporating as many PMW sensors as possible in the multi-satellite precipitation retrieval system called Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission (IMERG). This study also recommends users of satellite precipitation products to switch to the newest Version-7 TMPA datasets and the forthcoming IMERG products whenever they become available.
引用
收藏
页码:668 / 683
页数:16
相关论文
共 50 条
  • [41] Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data
    Derksen, C.
    Toose, P.
    Rees, A.
    Wang, L.
    English, M.
    Walker, A.
    Sturm, M.
    REMOTE SENSING OF ENVIRONMENT, 2010, 114 (08) : 1699 - 1709
  • [42] A prototype precipitation retrieval algorithm over land using passive microwave observations stratified by surface condition and precipitation vertical structure
    You, Yalei
    Wang, Nai-Yu
    Ferraro, Ralph
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2015, 120 (11) : 5295 - 5315
  • [43] ERROR ANALYSIS OF ENSEMBLE MULTI-SATELLITE PRECIPITATION DATASETS OVER THE TIBETAN PLATEAU
    Liu, Ronghua
    Ma, Yingzhao
    Yang, Yuan
    Han, Zhongying
    Tang, Guoqiang
    Liu, Qi
    Hong, Yang
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 4684 - 4687
  • [44] Validation of TRMM multi-satellite precipitation analysis (TMPA) products in the Peruvian Andes
    Mantas, V. M.
    Liu, Z.
    Caro, C.
    Pereira, A. J. S. C.
    ATMOSPHERIC RESEARCH, 2015, 163 : 132 - 145
  • [45] Overview of Chlorophyll-a Concentration Retrieval Algorithms from Multi-Satellite Data
    Park, Ji-Eun
    Park, Kyung-Ae
    Park, Young-Je
    Han, Hee-Jeong
    JOURNAL OF THE KOREAN EARTH SCIENCE SOCIETY, 2019, 40 (04): : 315 - 328
  • [46] Evaluating the Performance of Merged Multi-Satellite Precipitation Products Over a Complex Terrain
    Golian, Saeed
    Moazami, Saber
    Kirstetter, Pierre-Emmanuel
    Hong, Yang
    WATER RESOURCES MANAGEMENT, 2015, 29 (13) : 4885 - 4901
  • [47] Evaluating the Performance of Merged Multi-Satellite Precipitation Products Over a Complex Terrain
    Saeed Golian
    Saber Moazami
    Pierre-Emmanuel Kirstetter
    Yang Hong
    Water Resources Management, 2015, 29 : 4885 - 4901
  • [48] A novel Selection-Learning algorithm for multi-satellite scheduling problems
    Zhang, Yan
    Yang, Feng
    Huang, YongXuan
    2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, : 1318 - +
  • [49] CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations
    Mugnai, A.
    Smith, E. A.
    Tripoli, G. J.
    Bizzarri, B.
    Casella, D.
    Dietrich, S.
    Di Paola, F.
    Panegrossi, G.
    Sano, P.
    NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2013, 13 (04) : 887 - 912
  • [50] COMMUNITY SATELLITE PROCESSING PACKAGE (CSPP) - PROVIDING HYPERSPECTRAL SOUNDING RETRIEVAL FROM MULTI-SATELLITE/SENSOR
    Huang, Allen
    Goldberg, Mitch
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 5086 - 5089