论文

GPM卫星降水产品在台风极端降水过程的误差评估

  • 肖柳斯 ,
  • 张阿思 ,
  • 闵超 ,
  • 陈生
展开
  • 中山大学大气科学学院, 广东 珠海 519080;广州市气象台, 广东 广州 511430;广东省气候变化与自然灾害研究重点实验室, 广东 广州 510275

收稿日期: 2018-07-03

  网络出版日期: 2019-10-28

基金资助

国家自然科学基金项目(41875182);中山大学"百人计划二期"急需青年杰出人才项目(74110-52601108);广州市科技计划项目(201904010162);广东省气象局科研项目(GRMC2018Q12);中国气象局预报员专项(CMAYBY2018-052)

Evaluation of GPM Satellite-based Precipitation Estimates during Three Tropical-related Extreme Rainfall Events

  • XIAO Liusi ,
  • ZHANG Asi ,
  • MIN Chao ,
  • CHEN Sheng
Expand
  • School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519082, Guangdong, China;GuangzhouMeteorological Observatory, Guangzhou 511430, Guangdong, China;Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University, Guangzhou 510275, Guangdong, China

Received date: 2018-07-03

  Online published: 2019-10-28

摘要

以雨量站观测数据为基准,利用相关系数(CC)、相对偏差(RB)、均方根误差(RMSE)以及分级评分指标(探测率POD、误报率FAR、临界成功指数CSI),对全球降水计划GPM多卫星融合产品4.4版本准实时产品IMERG_ER(简称IMERG)在2017年接连登陆广东的3个台风"天鸽"、"帕卡"和"玛娃"极端降水过程性能进行评估。广东省内"天鸽"、"帕卡"和"玛娃"的CC分别为0.80,0.68和0.47,RB为-12.00%,-47.06%和-29.10%,RMSE达33.00,40.03和26.40 mm。雨区的CC分别为0.59,0.48和0.33,RB为2.21%,-43.58%和-25.94%,RMSE为44.34,51.04和40.64 mm。IMERG低估了"天鸽"、"帕卡"、"玛娃"的总体降水强度,主要源自于对雨区的低估。从散点分布来看,IMERG高估了强降水,低估了弱降水的强度,对极端强降水的估测能力存在较大的不稳定性。降水量时序变化特征表明,IMERG较好体现了降水峰值和谷值的数量及变化趋势,但时间和强度有偏差。误差来源于复杂地形、PMW观测时间分辨率不足和IR反演降水准确度不足对卫星估测降水的影响。分级检验结果显示,相同量级内,雨区的POD更大,FAR更小,CSI评分更高,IMERG对雨区的反演能力更强。IMERG对"天鸽"估测效果最好,POD较高,FAR较小,CSI较高;"帕卡" POD较低,暴雨及以上的FAR较高,CSI下降显著;"玛娃" POD比"帕卡"高,但FAR也高,CSI中等。可见IMERG对小量级降水具有较好的估测能力,强降水估测显著偏高,暴雨及以上的降水误差起了主要贡献。

本文引用格式

肖柳斯 , 张阿思 , 闵超 , 陈生 . GPM卫星降水产品在台风极端降水过程的误差评估[J]. 高原气象, 2019 , 38(5) : 993 -1003 . DOI: 10.7522/j.issn.1000-0534.2018.00143

Abstract

Extremely heavy rain storms caused by typhoon usually lead to significant losses in economic infrastructure and human life in urban agglomeration. Accurate precipitation estimation from satellites is of great importance for hydrology, meteorology, climate change, and ecological research due to its extensive spatial coverage, consistent measurements over land and oceanic areas. In this research, gauge observations are used as ground true to evaluated the capacity of near real-time precipitation estimates from Integrated Multi-satellite Retrievals for GPM extent (IMERG_ER, IMERG hereafter) during three heavy rainfall storms. These storms were brought by typhoon Hato, Pakhar and Mawar, which landed Guangdong Province successively in August and September of 2017. Six commonly used skill scores are used for quantitative analysis. These scores include Correlation Coefficient (CC), Relative Bias (RB), Root-Mean-Squared Error (RMSE), Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI). Results show that:(1) IMERG generally captures overall characteristics of storm-based accumulated precipitation with CC about 0.80, 0.68, and 0.47, respectively, underestimates rainfall in three cases in different degrees with different RB (-12.00%, -47.06% and -29.10%), and show different uncertainties with RMSE about 33.00, 40.03 and 26.40 mm, which may be caused by complex terrain and rapid variation of moisture transport; (2) IMERG's ability on estimation of extreme heavy rain is unstable according to result of CC(0.59, 0.48 and 0.33), RB(2.21%, -43.58% and -25.94%), RMSE(44.34, 51.04 and 40.64 mm) and scatter diagram; (3) IMERG grasps the magnitude of hourly rainfall caused by typhoons themselves, but underestimates precipitation derived from southwest monsoon trough, owing to limitation of PMW's low temporal resolution and IR's low accuracy; (4) IMERG has better value of POD, FAR and CSI in rainfall center. Despite the apparent differences with gauge measurements, the conclusions in this study highlight that IMERG has great potential to provide high-resolution precipitation information around the whole earth area, especially applied to tropical storms. The analysis result shows that bias of heavy rain plays main roles on the whole deviation of typhoon events.

参考文献

[1]Chen F R, Li X, 2016. Evaluation of IMERG and TRMM 3B43 monthly precipitation products over mainland China[J]. Remote Sensing, 8(6), 472. DOI:10.3390/rs8060472.
[2]Chen S, Hong Y, Cao Q, et al, 2013a. Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China[J]. Journal of Geophysical Research:Atmospheres, 118:13060-13074. DOI:10.1002/2013JD019964.
[3]Chen S, Hong Y, Cao Q, et al, 2013b. Performance evaluation of radar and satellite rainfalls for Typhoon Morakot over Taiwan:Are remote-sensing products ready for gauge denial scenario of extreme events?[J]. Journal of Hydrology, 506(25):4-13. DOI:10.1016/j.jhydrol. 2012.12.026.
[4]Chen S, Liu H, You Y, et al, 2014. Evaluation of high-resolution precipitation estimates from satellites during July 2012 Beijing flood event using dense rain gauge observations[J]. Plos One, 9(4):e89681. DOI:10.1371/journal. pone. 0089681.
[5]Guo H, Chen S, Bao A, et al, 2015a. Comprehensive evaluation of high-resolution satellite-based precipitation products over China[J]. Atmosphere, 7(1):6.
[6]Guo H, Chen S, Bao A, et al, 2015b. Inter-comparison of high-resolution satellite precipitation products over Central Asia[J]. Remote Sensing, 7(6):7181-7211. DOI:10.3390/rs70607181.
[7]Guo H, Chen S, Bao A, et al, 2016. Early assessment of integrated multi-satellite retrievals for global precipitation measurement over China[J]. Atmospheric Research, 176:121-133. DOI:10.1016/j.atmosres. 2016.02.020.
[8]Habib E, Henschke A, 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[J]. Atmospheric Research, 94:373-388. DOI:10.1016/j.atmosres. 2009.06.015.
[9]Hong Y, Hsu K, Sorooshian S, et al, 2004. Precipitation estimation from remotely sensed imagery using an Artificial Neural Network Cloud Classification System[J]. Journal of Applied Meteorology, 43:1834-1853.
[10]Hou A Y, Kakar R K, Neeck S, et al, 2014. The global precipitation measurement mission[J]. Bulletin of the American Meteorological Society, 95(5):701-722.
[11]Huang Y, Chen S, Cao Q, et al, 2014. Evaluation of version-7 TRMM multi-satellite precipitation analysis product during the Beijing extreme heavy rainfall event of 21 July 2012[J]. Water, 6:32-44. DOI:10.3390/w6010032.
[12]Huffman G J, Adler R F, Bolvin D T, et al, 2007. The TRMM Multisatellite Precipitation Analysis (TMPA):Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales[J]. Journal of Hydrometeorology, 8(1):38-55. DOI:10.1175/JHM560.1.
[13]Huffman G J, Bolvin D T, Braithwaite D, et al, 2014. Algorithm Theoretical Basis Document (ATBD) version 4.4 for the NASA Global Precipitation Measurement(GPM) Integrated Multi-satellite Retrievals for GPM (IMERG)[G]. NASA/GSFC Code, 612:1-26.
[14]Huffman G J, Bolvin D T, Nelkin E J, et al, 2017. Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation[G]. NASA/GSFC Code, 612:1-54.
[15]Joyce R J, Janowiak J E, Arkin P A, et al, 2004. CMORPH:A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution[J]. Journal of Hydrometeorology, 5(3):487-503.
[16]Li N, Tang G Q, Zhao P, et al, 2017. Statistical assessment and hydrological utility of the latestmulti-satellite precipitation analysis IMERG in Ganjiang River basin[J]. Atmospheric Research, 183:212-223. DOI:10.1016/j.atmosres. 2016.07.020.
[17]Ma Y Z, Tang G Q, Long D, et al, 2016. Similarity and error intercomparison of the GPM and its predecessor-TRMM multisatellite precipitation analysis using the best available hourly gauge network over the Tibetan Plateau[J]. Remote Sensing, 8:569. DOI:10.3390/rs8070569.
[18]Prakash S, Mitra A K, Pai D S, et al, 2016. From TRMM to GPM:How well can heavy rainfall be detected from space?[J]. Advances in Water Resources, 88:1-7. DOI:10.1016/j.advwatres. 2015.11.008.
[19]Sorooshian S, Hsu K L, Gao X, et al, 2000. Evaluation of PRESIANN system satellite-based estimates of tropical rainfall[J]. Bulletin of the American Meteorological Society, 81:2035-2046.
[20]Takuji K, Shoichi S, Hiroshi H, et al, 2007. Global precipitation map using satellite-borne microwave radiometers by the GSMaP project:production and validation[J]. IEEE Transactions on Geoscience and Remote Sensing, 45(7):2259-2275.
[21]Tang G Q, Zeng Z Y, Long D, et al, 2015. 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?[J]. Journal of Hydrometeorology, 17:121-137. DOI:10.1175/JHM-D-15-0059.1.
[22]Yang Y, Tang G Q, Lei X H, et al, 2018. Can satellite precipitation products estimate probable maximum precipitation a comparative investigation with gauge data in the Dadu River Basin[J]. Remote Sensing, 10:41. DOI:10.3390/rs10010041.
[23]Zhang A S, Xiao L S, Min C, et al, 2018. Evaluation of latest GPM-Era high-resolution satellite precipitation products during the May 2017 Guangdong extreme rainfall event[J]. Atmospheric Research, 216:76-85. DOI:10.1016/j.atmosres. 2018.09.018.
[24]黄嘉佑, 李庆祥, 2014.气象数据统计分析方法[M].北京:气象出版社, 28-47.
[25]金晓龙, 邵华, 张弛, 等, 2016. GPM卫星降水数据在天山山区的适用性分析[J].自然资源学报, 31(12):2074-2085.
[26]李伶杰, 胡庆芳, 黄勇, 等, 2018.近实时卫星降水数据对南京"20170610"极端性强降水过程的监测分析[J].高原气象, 37(3):806-814. DOI:10.7522/j.issn.1000-0534.2017.00080.
[27]李蒙, 秦天玲, 刘少华, 等, 2017.怒江上游TRMM 3B42V7降水产品资料时空验证及降水特征分析[J].高原气象, 36(4):950-959. DOI:10.7522/j.issn.1000-0534.2016.00071.
[28]李庆祥, 2011.气候资料均一性研究导论[M].北京:气象出版社.
[29]林良勋, 冯业荣, 黄忠, 等, 2009.广东省天气预报技术手册[M].北京:气象出版社.
[30]邱金晶, 余贞寿, 陈锋, 2017.2000-2013年5-10月TRMM测雨产品3B42RT在浙江地区的评估检验[J].暴雨灾害, 36(5):467-474.
[31]任福民, 向纯怡, 2017.登陆热带气旋降水预报研究回顾与展望[J].海洋气象学报, 37(4):8-18.
[32]任芝花, 王改利, 邹风玲, 等, 2003.中国降水测量误差的研究[J].气象学报, 61(5):621-627.
[33]唐国强, 万玮, 曾子悦, 等, 2015.全球降水测量(GPM)计划及其最新进展综述[J].遥感技术与应用, 30(4):607-615.
[34]王磊, 陈仁升, 宋耀选, 2017.高寒山区面降水量获取方法及影响因素研究进展[J].高原气象, 36(6):1546-1556. DOI:10.7522/j.issn.1000-0534.2017.00007.
[35]王黎娟, 高辉, 刘伟辉, 2011.西南季风与登陆台风耦合的暴雨增幅诊断及其数值模拟[J].大气科学学报, 34(6):662-671.
[36]王皘, 钱传海, 张玲, 2018.2017年西北太平洋和南海台风活动概述[J].海洋气象学报, 38(2):1-11.
[37]吴伯雄, 包澄澜, 1963.登陆台风的过程总降水量分布规律及其应用[J].南京大学学报(气象学), Z1:57-70.
[38]中华人民共和国国家标准, 2012. GB/T 28592-2012降水量等级[S].北京: 中国标准出版社.
文章导航

/