全球降雨计划GSMaP与IMERG卫星降雨产品在陕西地区的精度评估

doi:10.12118/j.issn.1000–6060.2021.027

Abstract

Abstract:

The Global Precipitation Measurement (GPM) mission is an international network of satellites that provide next-generation global observations of rain and snow. GPM was initiated by NASA and JAXA as a global successor to the Tropical Rainfall Measurement Mission (TRMM). In the GPM era, IMERG and GSMaP are two main satellite-based precipitation products. Compared with TRMM, GPM has wider coverage and higher spatiotemporal resolution. Understanding the accuracy and reliability of these products is important to further promote the application of new high-resolution satellite precipitation products. Therefore, this study intends to evaluate and compare the accuracy of IMERG and GSMaP in Shaanxi Province, China. In this paper, with surface precipitation observation datasets acquired from the China Meteorological Data Network used as reference, six evaluation indices, namely, correlation coefficient, root mean squared error, relative bias, false alarm ratio, probability of detection, and critical success index, were employed to evaluate and compare their accuracy at different temporal scales. Their performance in monitoring heavy rainfall events was also analyzed. Results show the following: (1) At an annual scale, the accuracy of GSMaP is higher than that of IMERG. GSMaP is highly correlated with the ground observation, while IMERG is moderately correlated with the ground observation. GSMaP overestimates the precipitation at an annual scale, whereas IMERG underestimates the precipitation. (2) At the seasonal scale, both data have the highest accuracy during summer. Overall, IMERG has a higher data accuracy than GSMaP. (3) At a monthly scale, the two products show a high correlation with the ground observation, but a certain degree of overestimation is observed. IMERG has a relatively higher accuracy than GSMaP. (4) At a daily scale, the accuracy of GSMaP is higher than that of IMERG. (5) The accuracy of satellite precipitation products is related to total precipitation volume, which is generally overestimated when the precipitation volume is small and underestimated when the volume is large. (6) The accuracy of satellite precipitation product data shows obvious regional differences. GSMaP underestimates the precipitation in Shaanxi Province as a whole, especially in northern Shaanxi. IMERG slightly overestimates the precipitation in Guanzhong, but significantly underestimates the precipitation in northern and southern Shaanxi. (7) An analysis of four heavy precipitation events shows that the monitoring capability for heavy precipitation events of GSMap is slightly stronger than that of IMERG. This study is expected to provide a reference for the selection and application of precipitation data in meteorological and hydrological studies in this area.

Key words: satellite precipitation products, global precipitation observation plan (GPM), heavy precipitation events, accuracy assessment

LI Yanni,HUANG Chang,PANG Guowei. Accuracy assessment of GSMaP and IMERG satellite precipitation products in Shaanxi Province[J].Arid Land Geography, 2022, 45(1): 80-90.

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References 33

[1]	Skofronick-Jackson G, Kirschbaum D, Petersen W, et al. The global precipitation measurement (GPM) mission’s scientific achievements and societal contributions: Reviewing four years of advanced rain and snow observations[J]. Quarterly Journal of the Royal Meteorological Society, 2018, 144(51):27-48. doi: 10.1002/qj.3313 pmid: 31213729
[2]	Shawky M, Moussa A, Hassan Q K, et al. Performance assessment of sub-daily and daily precipitation estimates derived from GPM and GSMaP products over an arid environment[J]. Remote Sensing, 2019, 11(23):2840, doi: 10.3390/rs11232840. doi: 10.3390/rs11232840
[3]	Kidd C. Satellite rainfall climatology: A review[J]. International Journal of Climatology, 2001, 21(9):1041-1066. doi: 10.1002/(ISSN)1097-0088
[4]	Testik F Y, Gebremichael M. Rainfall: State of the science[M]. United States: American Geophysical Union, 2010: 127-158.
[5]	Liu Z, Ostrenga D, Teng W, et al. Tropical rainfall measuring mission (TRMM) precipitation data and services for research and applications[J]. Bulletin of the American Meteorological Society, 2012, 93(9):1317-1325. doi: 10.1175/BAMS-D-11-00152.1
[6]	Chen S, Hong Y, Cao Q, et al. Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China[J]. Journal of Geophysical Research, 2013, 118(23):13060-13074.
[7]	唐国强, 龙笛, 万玮, 等. 全球水遥感技术及其应用研究的综述与展望[J]. 中国科学: 技术科学, 2015, 45(10):1013-1023.
	[Tang Guoqiang, Long Di, Wan Wei, et al. An overview and outlook of global water remote sensing technology and applications[J]. Scientia Sinica Technologica, 2015, 45(10):1013-1023. ]
[8]	Tian Y, Peters-Lidard C D, Adler R F, et al. Evaluation of GSMaP precipitation estimates over the contiguous United States[J]. Journal of Hydrometeorology, 2010, 11(2):566-574. doi: 10.1175/2009JHM1190.1
[9]	陈洪滨, 尹红刚, 何文英. 星载主动微波遥感云和降水技术与应用[M]. 北京: 科学出版社, 2020: 30-31.
	[Chen Hongbin, Yin Honggang, He Wenying. Technology and application of satellite borne active microwave remote sensing of cloud and precipitation[M]. Beijing: Science Press, 2020: 30-31. ]
[10]	李政, 吴静, 李纯斌, 等. TRMM降水产品在中国草地的适用性研究[J]. 中国草地学报, 2020, 42(6):75-81.
	[Li Zheng, Wu Jing, Li Chunbin, et al. Applicability study of TRMM precipitation products to China grassland[J]. Chinese Journal of Grassland, 2020, 42(6):75-81. ]
[11]	Prakash S, Mitra A K, Pai D S, et al. From TRMM to GPM: How well can heavy rainfall be detected from space?[J]. Advances in Water Resources, 2016, 88:1-7. doi: 10.1016/j.advwatres.2015.11.008
[12]	Guo H, Chen S, Bao A, et al. Early assessment of integrated multi-satellite retrievals for global precipitation measurement over China[J]. Atmospheric Research, 2016, 176-177:121-133. doi: 10.1016/j.atmosres.2016.02.020
[13]	Hou A Y, Kakar R K, Neeck S, et al. The global precipitation measurement mission[J]. Bulletin of the American Meteorological Society, 2014, 95(5):701-722. doi: 10.1175/BAMS-D-13-00164.1
[14]	曾岁康, 雍斌. 全球降雨计划IMERG和GSMaP反演降雨在四川地区的精度评估[J]. 地理学报, 2019, 74(7):1305-1318. doi: 10.11821/dlxb201907003
	[Zeng Suikang, Yong Bin. Evaluation of the GPM-based IMERG and GSMaP precipitation estimates over the Sichuan region[J]. Acta Geographica Sinica, 2019, 74(7):1305-1318. ] doi: 10.11821/dlxb201907003
[15]	Beria H, Nanda T, Bisht D S, et al. Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale[J]. Hydrology and Earth System Sciences Discussions, 2017, 21(12):6117-6134.
[16]	Ning S, Song F, Parmeshwar U, et al. Error analysis and evaluation of the latest GSMap and IMERG precipitation products over eastern China[J]. Advances in Meteorology, 2017(11):1-16.
[17]	陈汉清, 鹿德凯, 周泽慧, 等. GPM降雨产品评估研究综述[J]. 水资源保护, 2019, 35(1):27-34.
	[Chen Hanqing, Lu Dekai, Zhou Zehui, et al. An overview of assessments on global precipitation measurement (GPM) precipitation products[J]. Water Resources Protection, 2019, 35(1):27-34. ]
[18]	王思梦, 王大钊, 黄昌. GPM卫星降雨数据在黑河流域的适用性评价[J]. 自然资源学报, 2018, 33(10):1847-1860.
	[Wang Simeng, Wang Dazhao, Huang Chang. Evaluating the applicability of GPM satellite precipitation data in Heihe River Basin[J]. Journal of Natural Resources, 2018, 33(10):1847-1860. ]
[19]	李媛媛, 宁少尉, 丁伟, 等. 最新GPM降雨数据在黄河流域的精度评估[J]. 国土资源遥感, 2019, 31(1):164-170.
	[Li Yuanyuan, Ning Shaowei, Ding Wei, et al. The evaluation of latest GPM-Era precipitation data in Yellow River Basin[J]. Remote Sensing for Land & Resources, 2019, 31(1):164-170. ]
[20]	冯克鹏, 洪阳, 田军仓, 等. 多源降水数据的小流域水文模拟效用评估[J]. 干旱区地理, 2020, 43(5):1179-1191.
	[Feng Kepeng, Hong Yang, Tian Juncang, et al. Evaluating runoff simulation of multi-source precipitation data in small watersheds[J]. Arid Land Geography, 2020, 43(5):1179-1191. ]
[21]	万相均, 任志远, 张翀. 陕西省气温与降雨变化时空分布研究[J]. 干旱区资源与环境, 2013, 27(6):140-147.
	[Wan Xiangjun, Ren Zhiyuan, Zhang Chong. Research on spatial-temporal distribution of temperature and precipitation changes in Shaanxi[J]. Journal of Arid Land Resources and Environment, 2013, 27(6):140-147. ]
[22]	任亮, 王晓峰, 曾昭昭. 陕西秦巴山区TRMM 3B42卫星降水数据精度评价[J]. 陕西师范大学学报(自然科学版), 2017, 45(1):87-97.
	[Ren Liang, Wang Xiaofeng, Zeng Zhaozhao. The accuaracy evaluation of TRMM 3B42 precipitation data in Shaanxi Qinling-Daba Mountains[J]. Journal of Shaanxi Normal University (Natural Science Edition), 2017, 45(1):87-97. ]
[23]	曾昭昭, 王晓峰, 任亮. 基于GWR模型的陕西秦巴山区TRMM降水数据降尺度研究[J]. 干旱区地理, 2017, 40(1):26-36.
	[Zeng Zhaozhao, Wang Xiaofeng, Ren Liang. Spatial downscaling of TRMM rainfall data based on GWR model for Qinling-Daba Mountains in Shaanxi Province[J]. Arid Land Geography, 2017, 40(1):26-36. ]
[24]	刘政鸿. 陕西省近50年来降雨量时空变化特征分析[J]. 水土保持研究, 2015, 22(2):107-112.
	[Liu Zhenghong. Analysis of spatiotemporal variation characteristics of precipitation in the past five decades in Shaanxi Province[J]. Research of Soil and Water Conservation, 2015, 22(2):107-112. ]
[25]	黄少妮, 许新田, 王丹. 陕西降雨季节及季节内振荡的气候特征[J]. 干旱气象, 2014, 32(1):46-51.
	[Huang Shaoni, Xu Xintian, Wang Dan. Climatological feature of seasonal and intraseasonal oscillation of rainfall in Shaanxi[J]. Journal of Arid Meteorology, 2014, 32(1):46-51. ]
[26]	Sungmin O, Foelsche U, Kirchengast G, et al. Evaluation of GPM IMERG early, late, and final rainfall estimates using WegenerNet gauge data in southeastern Austria[J]. Hydrology & Earth System Sciences Discussions, 2017, 21:6559-6572.
[27]	Xie P, Akiyo Y, Chen M, et al. A gauge-based analysis of daily precipitation over East Asia[J]. Journal of Hydrometeorology, 2007, 8(3):607-626. doi: 10.1175/JHM583.1
[28]	胡庆芳, 杨大文, 王银堂, 等. 赣江流域高分辨率卫星降雨数据的精度特征与时空变化规律[J]. 中国科学: 技术科学, 2013, 43(4):447-459.
	[Hu Qingfang, Yang Dawen, Wang Yintang, et al. Precision characteristics and temporal and spatial variation of high resolution satellite rainfall data in Ganjiang River Basin[J]. Scientia Sinica Technologica, 2013, 43(4):447-459. ]
[29]	高玥, 徐慧, 刘国. GSMaP遥感降雨产品对典型极端降雨事件监测能力评估[J]. 遥感技术与应用, 2019, 34(5):1121-1132.
	[Gao Yue, Xu Hui, Liu Guo. Evaluation of the GSMaP estimates on monitoring extreme precipitation events[J]. Remote Sensing Technology and Application, 2019, 34(5):1121-1132. ]
[30]	Tian Y D, Peters-Lidard C D, Eylander J B, et al. Component analysis of errors in satellite-based precipitation estimates[J]. Journal of Geophysical Research, 2009, 114(D24):D011949, doi: 10.1029/2009JD011949. doi: 10.1029/2009JD011949
[31]	汪梓彤, 李石宝, 张志友. GPM近实时降水产品在青藏高原的多尺度精度评价[J]. 人民黄河, 2021, 43(4):43-49, 116.
	[Wang Zitong, Li Shibao, Zhang Zhiyou. Multi-scale accuracy evaluation of GPM precipitation products over the Qinghai-Tibet Plateau[J]. Yellow River, 2021, 43(4):43-49, 116. ]
[32]	李相虎, 张奇, 邵敏. 基于TRMM数据的鄱阳湖流域降雨时空分布特征及其精度评价[J]. 地理科学进展, 2012, 31(9):1164-1170.
	[Li Xianghu, Zhang Qi, Shao Min. Spatio-temporal distribution of precipitation in Poyang Lake Basin based on TRMM data and precision evaluation[J]. Progress in Geography, 2012, 31(9):1164-1170. ]
[33]	Xu R, Tian F, Yang L, et al. Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high-density rain gauge network[J]. Journal of Geophysical Research Atmospheres, 2017, 122(2):910-924. doi: 10.1002/2016JD025418

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卫星降雨产品	时间分辨率/h	空间分辨率/(°)	覆盖范围	数据来源
IMERG	0.5	0.1	90°N~90°S	NASA
GSMaP	1.0	0.1	60°N~60°S	JAXA

卫星降雨产品	相关系数（CC）	均方根误差（RMSE）/mm	相对偏差（BIAS）/%
IMERG	0.56	77.69	-11.41
GSMaP	0.89	92.65	13.60

卫星降雨产品	CC	RMSE/mm	BIAS/%
IMERG	0.95	2.77	4.29
GSMaP	0.94	7.41	11.46

卫星降雨产品	CC	RMSE/mm	BIAS/%
IMERG	0.50	3.75	-27.23
GSMaP	0.62	2.38	-17.27

区域	卫星降雨产品	命中率（POD）	误报率（FAR）	关键成功指数（CSI）
陕北	IMERG	0.36	0.29	0.31
陕北	GSMaP	0.55	0.54	0.33
关中	IMERG	0.67	0.33	0.50
关中	GSMaP	0.80	0.20	0.67
陕南	IMERG	0.64	0.22	0.54
陕南	GSMaP	0.72	0.35	0.52

Accuracy assessment of GSMaP and IMERG satellite precipitation products in Shaanxi Province

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