Evaluation of extreme precipitation based on satellite retrievals over China

Abstract

The objective of this study is to evaluate satellite precipitation extremes of the Tropical Rainfall Measuring Mission (TRMM) 3B42 Version 7 product over China during the period of 2009–2013. Eight extreme indices are used to characterize precipitation extremes: monthly maximum 1-day precipitation (RX1day), monthly maximum consecutive 2-day precipitation (RX2day), monthly maximum 5-day consecutive precipitation (RX5day), simple daily intensity index (SDII), annual total precipitation amount for the wet days (PRCPTOT), annual wet days (R1), consecutive dry days (CDD), and consecutive wet days (CWD). The precipitation amount for indices RX1day, RX2day, RX5day, and PRCPTOT is well captured by TRMM 3B42-V7, as verified by lower mean relative bias and normalized root mean square error and the high spatial correlation coefficient. In contrast, the performance of TRMM 3B42-V7 in depicting the indices on intensity and duration (i.e., SDII, R1, CDD, and CWD) is not as good as its performance in depicting the precipitation amount indices. TRMM 3B42-V7 can reproduce extreme indices better in eastern China than in western China, and better in summer than in winter. Probability density function is also calculated better for RX1day, RX2day, RX5day, and PRCPTOT than for SDII, R1, CDD, and CWD. Investigation on the monthly time series of RX1day, RX2day, and RX5day at different spatial scales indicates that TRMM 3B42-V7 performs better at the large spatial scale than at the grid cell scale. Caution should be observed when the satellite-based extreme indices are used.

Keywords

satellite / extreme precipitation / TRMM / China

References

[1] AghaKouchak A, Behrangi A, Sorooshian S, Hsu K, Amitai E(2011). Evaluation of satellite-retrieved extreme precipitation rates across the central United States. J Geophys Res Atmos (1984–2012), 116(D2), doi:10.1029/2010JD014741

[2] Alexander L V, Zhang X, Peterson T C, Caesar J, Gleason B, Klein Tank A M G, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour A, Rupa Kumar K, Revadekar J, Griffiths G, Vincent L, Stephenson D B, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai P, Rusticucci M, Vazquez-Aguirre J L (2013). Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res Atmos, 111: D05109

[3] Allen C D, Macalady A K, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears D D, Hogg E H T, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim J H, Allard G, Running S W, Semerci A, Cobb N (2010). A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manage, 259(4): 660–684

[4] Asante K O, Macuacua R D, Artan G A, Lietzow R W, Verdin J P (2007). Developing a flood monitoring system from remotely sensed data for the Limpopo Basin. IEEE Trans Geosci Remote Sens, 45(6): 1709–1714

[5] Chen S, Hong Y, Gourley J J, Huffman G J, Tian Y, Cao Q, Kirstetter P E, Hu J, Hardy J, Xue X (2013a). Evaluation of the successive V6 and V7 TRMM multi-satellite precipitation analysis over the continental United States. Water Resour Res, 10 doi:10.1002/2012WR012795

[6] Chen S, Hong Y, Gourley J J, Kirstette P E, Yong B, Tian Y, Zhang Z, Hardy J (2013b). Similarity and difference of the two successive V6 and V7 TRMM multi-satellite precipitation analysis (TMPA) performance over China. J Geophys Res, 118 doi:10.1002/2013jd019964

[7] Chen Y, Ebert E E, Walsh K J, Davidson N E (2013). Evaluation of TMPA 3B42 daily precipitation estimates of tropical cyclone rainfall over Australia. J Geophys Res Atmos (1984–2012), 118(21): 11966–11978, doi:10.1002/2013JD020319

[8] De Boeck H J, Dreesen F E, Janssens I A, Nijs I (2011). Whole-system responses of experimental plant communities to climate extremes imposed in different seasons. New Phytol, 189(3): 806–817

[9] Ebert E E, Janowiak J E, Kidd C (2007). Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull Am Meteorol Soc, 88(1): 47–64

[10] Hanson S, Nicholls R, Ranger N, Hallegatte S, Corfee-Morlot J, Herweijer C, Chateau J A (2011). Global ranking of port cities with high exposure to climate extremes. Clim Change, 104(1): 89–111

[11] Hou A Y, Kakar R K, Neeck S, Azarbarzin A A, Kummerow C D, Kojima M, Oki R, Nakamura K, Iguchi T (2014). The global precipitation measurement mission. Bull Am Meteorol Soc, 95(5): 701–722

[12] Huffman G J, Bolvin D T (2007). TRMM and other data precipitation data set documentation. Laboratory for Atmospheres, NASA Goddard Space Flight Center and Science Systems and Applications, Available online ftp://meso-a.gsfc.nasa.gov/pub/trmmdocs/ 3B42_3B43_doc.pdf

[13] Huffman G J, Bolvin D T (2013). Real-Time TRMM Multi-Satellite Precipitation Analysis Data Set Documentation. ftp://meso-a.gsfc.nasa.gov/pub/trmmdocs/rt/ 3B4XRT_doc_V7.pdf

[14] Huffman G J, Bolvin D T, Braithwaite D, Hsu K, Joyce R, Xie P (2014). GPM Integrated Multi-Satellite Retrievals for GPM (IMERG) Algorithm Theoretical Basis Document (ATBD) Version 4.4. PPS, NASA/GSFC, 30 pp.<http:// pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V4.4.pdf>

[15] Huffman G J, Bolvin D T, Nelkin E J, Wolff D B, Adler R F, Gu G, Hong Y, Bowman K P, Stocker E F (2007). The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol, 8(1): 38–55

[16] Joyce R J, Janowiak J E, Arkin P A, Xie P (2004). CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol, 5(3): 487–503

[17] Kidd C, Bauer P, Turk J, Huffman G J, Joyce R, Hsu K L, Braithwaite D (2012). Intercomparison of high-resolution precipitation products over northwest Europe. J Hydrometeorol, 13(1): 67–83

[18] Klein Tank A M G, Wijngaard J B, Können G P, Böhm R, Demarée G, Gocheva A, Mileta M, Pashiardis S, Hejkrlik L, Kern-Hansen C, Heino R, Bessemoulin P, Müller-Westermeier G, Tzanakou M, Szalai S, Pálsdóttir T, Fitzgerald D, Rubin S, Capaldo M, Maugeri M, Leitass A, Bukantis A, Aberfeld R, van Engelen A F V, Forland E, Mietus M, Coelho F, Mares C, Razuvaev V, Nieplova E, Cegnar T, Antonio López J, Dahlström B, Moberg A, Kirchhofer W, Ceylan A, Pachaliuk O, Alexander L V, Petrovic P (2002). Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int J Climatol, 22(12): 1441–1453

[19] Li N, Tang G, Zhao P, Hong Y, Gou Y, Yang K (2017). Statistical assessment and hydrological utility of the latest multi-satellite precipitation analysis IMERG in Ganjiang River basin. Atmos Res, 183: 212–223

[20] Liu Z (2015). Comparison of precipitation estimates between Version 7 3-hourly TRMM Multi-Satellite Precipitation Analysis (TMPA) near-real-time and research products. Atmos Res, 153: 119–133

[21] Lockhoff M, Zolina O, Simmer C, Schulz J (2014). Evaluation of satellite-retrieved extreme precipitation over Europe using Gauge Observations. J Clim, 27(2): 607–623

[22] Menne M J, Durre I, Vose R S, Gleason B E, Houston T G (2012). An overview of the global historical climatology network-daily database. J Atmos Ocean Technol, 29(7): 897–910

[23] Michaelides S, Levizzani V, Anagnostou E, Bauer P, Kasparis T, Lane J E (2009). Precipitation: measurement, remote sensing, climatology and modeling. Atmos Res, 94(4): 512–533

[24] Moberg A, Jones P D (2005). Trends in indices for extremes in daily temperature and precipitation in central and western Europe, 1901–99. Int J Climatol, 25(9): 1149–1171

[25] Monier E, Gao X (2014). Climate change impacts on extreme events in the United States: an uncertainty analysis. Clim Change, 131(1):67–81

[26] Nastos P T, Kapsomenakis J, Douvis K C (2013). Analysis of precipitation extremes based on satellite and high-resolution gridded data set over Mediterranean basin. Atmos Res, 131: 46–59

[27] Peterson T, Daan H, Jones P (1997). Initial selection of a GCOS surface network. Bull Am Meteorol Soc, 78(10): 2145–2152

[28] Prakash S, Mitra A K, Pai D S,AghaKouchak A (2016). From TRMM to GPM: how well can heavy rainfall be detected from space? Adv Water Resour, doi:10.1016/j.advwatres.2015.11.008

[29] Qiao L, Hong Y, Chen S, Zou C B, Gourley J J, Yong B (2014). Performance assessment of the successive Version 6 and Version 7 TMPA products over the climate-transitional zone in the southern Great Plains, USA. J Hydrol (Amst), 513: 446–456

[30] Rhee J, Im J, Carbone G J (2010). Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sens Environ, 114(12): 2875–2887

[31] Romilly T G, Gebremichael M (2011). Evaluation of satellite rainfall estimates over Ethiopian river basins. Hydrol Earth Syst Sci, 15(5): 1505–1514

[32] Shen Y, Feng M, Zhang H, Gao F (2010a). Interpolation methods of China daily precipitation data. J Appl Meteor Sci, 21(3): 279–281 (in Chinese)

[33] Shen Y, Xiong A, Wang Y, Xie P (2010b). Performance of high-resolution satellite precipitation products over China. J Geophys Res (1984–2012), 115(D2), doi:10.1029/2009JD012097

[34] Su F, Hong Y, Lettenmaier D P (2008). Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and its utility in hydrologic prediction in the La Plata Basin. J Hydrometeorol, 9(4): 622–640

[35] Tang G, Ma Y, Long D, Zhong L, Hong Y (2016). Evaluation of GPM Day-1 IMERG and TMPA version-7 legacy products over mainland China at multiple spatiotemporal scales. J Hydrol (Amst), 533: 152–167

[36] Thibeault J M, Seth A (2014). Changing climate extremes in the Northeast United States: observations and projections from CMIP5. Clim Change, 127(2): 273–287

[37] Tian Y, Peters-Lidard C D, Eylander J B, Joyce R J, Huffman G J, Adler R F, Hsu K, Turk FJ, Garcia M, Zeng J (2009). Component analysis of errors in satellite-based precipitation estimates. J Geophys Res Atmos (1984-2012), 114(D24), doi:10.1029/2009JD011949

[38] Vila D, Ferraro R, Joyce R (2007). Evaluation and improvement of AMSU precipitation retrievals. J Geophys Res Atmos (1984-2012), 112(D20), doi:10.1029/2007JD008617

[39] Westra S, Alexander L V, Zwiers F W (2013). Global increasing trends in annual maximum daily precipitation. J Clim, 26(11): 3904–3918

[40] WMO (2014). Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes 1970–2012. Accessed on http://reliefweb.int/sites/reliefweb.int/files/resources/2014.06.12-wmo1123_Atlas_120614.pdf

[41] Yong B, Chen B, Gourley J J, Ren L, Hong Y, Chen X, Wang W, Chen S, Gong L (2014). Intercomparison of the Version-6 and Version-7 TMPA precipitation products over high and low latitudes basins with independent gauge networks: Is the newer version better in both real-time and post-real-time analysis for water resources and hydrologic extremes? J Hydrol (Amst), 508: 77–87

[42] Yong B, Liu D, Gourley J J, Tian Y, Huffman G J, Ren L L, Hong Y (2015). Global view of real-time TRMM multi-satellite precipitation analysis: implication to its successor global precipitation measurement mission. Bull Amer Meteor Soc, doi: 10.1175/BAMS-D-14-00017.1

[43] You Q, Kang S, Aguilar E, Pepin N, Flügel W A, Yan Y, Xu Y, Zhang Y, Huang J (2011). Changes in daily climate extremes in China and their connection to the large scale atmospheric circulation during 1961–2003. Clim Dyn, 36(11–12): 2399–2417

[44] Zhao T, Yatagai A (2014). Evaluation of TRMM 3B42 product using a new gauge-based analysis of daily precipitation over China. Int J Climatol, 34(8): 2749–2762

[45] Zulkafli Z, Buytaert W, Onof C, Manz B, Tarnavsky E, Lavado W, Guyot J L (2014). A comparative performance analysis of TRMM 3B42 (TMPA) versions 6 and 7 for hydrological applications over Andean–Amazon River basins. J Hydrometeorol, 15(2): 581–592

Acknowledgments

This study is supported by the National Natural Science Foundation of China (Grant No. 51379224), the Fundamental Research Funds for the Central Universities (No. 15lgjc), and the Water Science and Technology Innovative Project of Guangdong Province (No. 2014-11).

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