سابِق|تال

Extreme precipitation monitoring capability of the multi-satellite jointly retrieval precipitation products of Global Precipitation Measurement（GPM） mission

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, China

    College of Hydrology and Water resources, Hohai University, Nanjing 210098, China

  • Email：dingmz1997@163.com
  • Introduction：丁明泽，研究方向为GIS在水文气象中的应用。E-mail： dingmz1997@163.com
DING Mingze12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, China

    College of Hydrology and Water resources, Hohai University, Nanjing 210098, China

  • Email：yongbin@hhu.edu.cn
  • Introduction：雍斌，研究方向为GIS在水文气象中的应用。E-mail： yongbin@hhu.edu.cn
YONG Bin12*,  
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211000, China

YANG Zekang3

ملخص

This study aims to comprehensively evaluate the four sets of pure satellite precipitation data of IMERG and GSMaP under Global Precipitation Measurement (GPM). The evaluation content is divided into three aspects: extreme precipitation index, extreme precipitation detection ability, and accuracy evaluation of extreme precipitation events with different durations.Based on the grid data set of automatic stations and CMORPH, the study area is divided into three regions according to the threshold of extreme precipitation events. Five evaluation indexes, namely, CC, BIAS, RMSE, POD, and FAR, are used to quantitatively study the performance of satellite precipitation in extreme precipitation. The precipitation products of IMERG and GSMaP are affected by factors such as terrain, precipitation intensity, and inversion algorithm, showing similar error characteristics and obvious accuracy differences. The correction of similar error characteristics will be the focus and direction of extreme precipitation inversion in the future. The discussion of accuracy difference can provide a reference for the improvement of the precipitation satellite inversion algorithm. (1) In the RX1 extreme precipitation index, IMERG and GSMaP data are obviously overestimated in the complex terrain area affected by complex terrain and underestimated in other areas. In the R95pTOT index, four sets of satellite data perform well and have a high correlation with ground-based datasets. (2) In terms of detection capability of extreme precipitation events, the performance of four pure satellite products in Northeast China is better than that of other regions. GSMaP performs better than does IMERG data with a lower false alarm rate, but the retrieval accuracy for extreme precipitation is low. (3) In the accuracy evaluation of extreme precipitation events with different durations, IMERG and GSMaP satellite precipitation products have better performance and higher precision in long-term extreme precipitation events. For extreme daily precipitation events, the error of satellite precipitation products under high rain intensity is very obvious, which is much higher than that of complex terrain on the accuracy of satellite precipitation retrieval, resulting in the performance of satellite precipitation in complex terrain area Ⅲ is better than other regions. Overall, the IMERG products have the better ability to monitor extreme precipitation in the study area than GSMaP products, and IMERG_Late data performs best. The retrieval error of extreme precipitation from IMERG and GSMaP satellite precipitation products has obvious regional characteristics in China, and the error characteristic of underestimation of high rainfall intensity is significant. The four types of satellite precipitation products can show the extreme precipitation region characteristics in study area but underestimate the precipitation in most parts of the study area. The error correction of satellite precipitation products for rainfall intensity remains one of the key and difficult points in future extreme precipitation retrieval.

مفهوم

remote sensing;IMERG;GSMaP;satellite precipitation;extreme precipitation;GPM;error characteristics

References

  1. 1.
    Bai L Y and Rong Y S. 2015. Reanalysis of the characteristics of extreme rainfall in the Yangtze River basin during recent 50 years. Journal of Water Resources Research, 4(1): 88-100
  2. 2.
    Chen H Q, Yong B, Shen Y, Liu J F, Hong Y and Zhang J Y. 2020. Comparison analysis of six purely satellite-derived global precipitation estimates. Journal of Hydrology, 581: 124376
  3. 3.
    Chen S, Hong Y, Cao Q, Kirstetter P E, Gourley J J, Qi Y C, Zhang J, Howard K, Hu J J and Wang J. 2013. Performance evaluation of radar and satellite rainfalls for Typhoon Morakot over Taiwan: are remote-sensing products ready for gauge denial scenario of extreme events? Journal of Hydrology, 506: 4-13
  4. 4.
    de Sousa Neto A L and Barbosa M H. 2012. Incidentes transfusionais imediatos: revisão integrativa da literatura. Acta Paulista de Enfermagem, 25(1): 146-150
  5. 5.
    Guo R F and Liu Y B. 2015. Multi-satellite retrieval of high resolution precipitation: an overview. Advances in Earth Science, 30(8): 891-903
  6. 6.
    Habib E, Henschke A and Adler R F. 2009. Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA. Atmospheric Research, 94(3): 373-388
  7. 7.
    Huffman G J, Adler R F, Morrissey M M, Bolvin D T, Curtis S, Joyce R, McGavock B and Susskind J. 2001. Global precipitation at one-degree daily resolution from multisatellite observations. Journal of Hydrometeorology, 2(1): 36-50
  8. 8.
    Huffman G J, Bolvin D T, Braithwaite D, Hsu K, Joyce R and Xie P P. 2014. NASA Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG). National Aeronautics and Space Administration
  9. 9.
    Kidd C. 2010. Satellite rainfall climatology: a review. International Journal of Climatology, 21(9): 1041-1066.
  10. 10.
    Liu G, Zhu Z W and Tan X H. 2017. Assessments of high resolution multi-satellite precipitation products on extreme storm event monitoring: a case study of typhoon “Rammasun” in 2014. Journal of Subtropical Resources and Environment, 12(4): 39-48
  11. 11.
    Meehl G A, Karl T, Easterling D R, Changnon S, Pielke R, Changnon D, Evans J, Groisman P Y, Knutson T R, Kunkel K E, Mearns L O, Parmesan C, Pulwarty R, Root T, Sylves R T, Whetton P and Zwiers F. 2000. An introduction to trends in extreme weather and climate events: observations, socioeconomic impacts, terrestrial ecological impacts, and model projections. Bulletin of the American Meteorological Society, 81(3): 413-416
  12. 12.
    Prakash S, Mitra A K, Pai D S and AghaKouchak A. 2016. From TRMM to GPM: how well can heavy rainfall be detected from space? Advances in Water Resources, 88: 1-7
  13. 13.
    Prigent C. 2010. Precipitation retrieval from space: an overview. Comptes Rendus Geoscience, 342(4/5): 380-389
  14. 14.
    Qian W H, Fu J L, Zhang W W and Lin X. 2007. Changes in mean climate and extreme cliamte in China during the last 40 years. Advances in Earth Science, 22(7): 673-684
  15. 15.
    Ren Y J, Yong B, Lu D K and Chen H Q. 2019. Evaluation of the Integrated Multi-satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM) mission over the mainland china at multiple scales. Journal of Lake Sciences, 31(2): 560-572
  16. 16.
    Shen Y, Pan Y, Yu J J, Zhao P and Zhou Z J. 2013. Quality assessment of hourly merged precipitation product over China. Journal of Nanjing Institute of Meteorology, 36(1): 37-46
  17. 17.
    Shen Y, Zhao P, Pan Y and Yu J J. 2014. A high spatiotemporal gauge-satellite merged precipitation analysis over China. Journal of Geophysical Research, 119(6): 3063-3075
  18. 18.
    Skofronick-Jackson G, Petersen W A, Berg W, Kidd C, Stocker E F, Kirschbaum D B, Kakar R, Braun S A, Huffman G J, Iguchi T, Kirstetter P E, Kummerow C, Meneghini R, Oki R, Olson W S, Takayabu Y N, Furukawa K and Wilheit T. 2017. The Global Precipitation Measurement (GPM) mission for science and society. Bulletin of the American Meteorological Society, 98(8): 1679-1695
  19. 19.
    Su J B, Lü H S, Zhu Y H, Wang X Y and Wei G H. 2018. Component analysis of errors in four GPM-based precipitation estimations over mainland China. Remote Sensing, 10(9): 1420.
  20. 20.
    Tang G Q, Zeng Z Y, Long D, Guo X L, Yong B, Zhang W H and Hong Y. 2016. Statistical and hydrological comparisons between TRMM and GPM level-3 products over a midlatitude basin: is day-1 IMERG a good successor for TMPA 3b42v7? Journal of Hydrometeorology, 17(1): 121-137
  21. 21.
    Tian Y D, Peters-Lidard C D, Adler R F, Kubota T and Ushio T. 2010. Evaluation of GSMaP Precipitation Estimates over the Contiguous United States. Journal of Hydrometeorology, 11(2): 566-574
  22. 22.
    Ushio T, Sasashige K, Kubota T, Shige S, Okamoto K, Aonashi K, Inoue T, Takahashi N, Iguchi T, Kachi M, Oki R, Morimoto T and Kawasaki Z I. 2009. A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. Journal of the Meteorological Society of Japan, 87A: 137-151
  23. 23.
    Wang H L, Shao Z H, Gao T, Zou T, Liu J and Yuan H B. 2017. Extreme precipitation event over the Yellow Sea western coast: is there a trend? Quaternary International, 441: 1-17
  24. 24.
    Wang Z. 2019. Spatial and temporal variation of extreme precipitation indices in Sichuan province from 1971 to 2018. Open Journal of Nature Science, 7(4): 333-348
  25. 25.
    Westra S, Alexander L V and Zwiers F W. 2013. Global increasing trends in annual maximum daily precipitation. Journal of Climate, 26(11): 3904-3918
  26. 26.
    Wilheit T, Adler R, Avery S, Barrett E, Bauer P, Berg W, Chang A, Ferriday J, Grody N, Goodman S, Kidd C, Kniveton D, Kummerow C, Mugnai A, Olson W, Petty G, Shibata A and Smith E. 1994. Algorithms for the retrieval of rainfall from passive microwave measurements. Remote Sensing Reviews, 11(1/4): 163-194
  27. 27.
    Wilheit T, Kummerow C D, Ferraro R. 2003. NASDARainfall algorithms for AMSR-E. IEEE Transactions on Geoscience and Remote Sensing, 41(2): 204-214
  28. 28.
    Xie P P and Xiong A Y. 2011. A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses. Journal of Geophysical Research, 116(21): D21106
  29. 29.
    Zeng S K and Yong B. 2019. Evaluation of the GPM-based IMERG and GSMaP precipitation estimates over the Sichuan region. Acta Geographica Sinica, 74(7): 1305-1318
  30. 30.
    Zou Y C, Yang X Q, Sun X G, Tang J P, Fang J B and Liao Y F. 2009. Seasonal difference of the spatio-temporal variation of the number of the extreme precipitation processes in China. Journal of Nanjing University (Natural Sciences), 45(1): 98-109

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website