Systematic assessment of GPM IMERG V06 precipitation products with dense rain gauge observations over Zhejiang Province, China

被引:2
|
作者
Bi, Zaoying [1 ]
Sun, Shanlei [1 ]
Shen, Huayu [2 ]
Liu, Yi [3 ]
Ren, Yongjian [4 ]
Li, Jinjian [5 ]
Lin, Bin [6 ]
机构
[1] Nanjing Univ Informat Sci & Technol NUIST, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Key Lab Meteorol Disaster, Minist Educ,Int Joint Res Lab Climate & Environm, Nanjing, Peoples R China
[2] Ningbo Meteorol Bur, Ningbo, Peoples R China
[3] Univ New South Wales, Sch Civil & Environm Engn, Sydney, NSW, Australia
[4] Reg Climate Ctr Wuhan, Wuhan, Peoples R China
[5] Chengdu Univ Informat Technol, Sch Atmospher Sci, Chengdu, Peoples R China
[6] 7th Inst Geol & Mineral Explorat Shandong Prov, Linyi, Shandong, Peoples R China
基金
中国国家自然科学基金;
关键词
assessment; GPM; IMERG V06; precipitation; Zhejiang Province; SATELLITE PRECIPITATION; PASSIVE MICROWAVE; COMPLEX TERRAIN; TRMM; 3B42; TMPA; PERFORMANCE; ERRORS; VALIDATION; NETWORK; INFORMATION;
D O I
10.1002/joc.7838
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
This study systematically assessed the performance of the Integrated Multi-satellitE Retrievals (IMERG) for Global Precipitation Measurement V06, including the near-real-time "Late Run" (IMERG-L) and the post-real-time "Final Run" (IMERG-F), over Zhejiang Province (ZJP), China. The evaluation was conducted at daily and hourly timescales for a full year and for each season, based on dense rain gauge observations and continuous and categorical validation statistics. For the full year and for each season, IMERG-F outperformed IMERG-L in representing the spatial pattern of multiyear mean precipitation. For regional mean of ZJP, IMERG-F and IMERG-L overestimated the daily/hourly precipitation for the full year by 6.51 and 4.98%, respectively. Among seasons, the regional mean relative biases for IMERG-F were between 5.65 and 8.63%; however, for IMERG-L, they exhibited notable variations with a maximum of 11.09% in fall and a minimum of 0 in spring. Bias composition suggested that the regional mean overestimations were largely due to false bias for the full year and for each season, except in winter, wherein it was due to hit bias. Spatially, the biases for the full year and for each season commonly arose from false and hit biases at daily timescale, and from false and miss biases at hourly timescale. Based on the remaining continuous metrics (i.e., root-mean-square-error [RMSE], correlation coefficient [CC], and Kling-Gupta Efficiency [KGE]) and all categorical metrics, the IMERG daily/hourly performance was acceptable on regional and grid scales throughout the year and in all seasons. From a region-average perspective, IMERG-F outperformed IMERG-L according to CC, RMSE, and KGE, but both products showed the same performance overall based on all categorical metrics; most grids also share these characteristics. This study provides a valuable reference for IMERG developers to improve product accuracy from the perspective of the final postprocessing step and for potential users in ZJP.
引用
收藏
页码:9471 / 9493
页数:23
相关论文
共 50 条
  • [1] Evaluation of the GPM IMERG V06 products for light rain over Mainland China
    Li, Xiaoying
    Sungmin, O.
    Wang, Na
    Liu, Lichen
    Huang, Yinzhou
    [J]. ATMOSPHERIC RESEARCH, 2021, 253
  • [2] Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China
    Li, Peng
    Xu, Zongxue
    Ye, Chenlei
    Ren, Meifang
    Chen, Hao
    Wang, Jingjing
    Song, Sulin
    [J]. REMOTE SENSING, 2021, 13 (07)
  • [3] Validation of GPM IMERG V05 and V06 Precipitation Products over Iran
    Hosseini-Moghari, Seyed-Mohammad
    Tang, Qiuhong
    [J]. JOURNAL OF HYDROMETEOROLOGY, 2020, 21 (05) : 1011 - 1037
  • [4] A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China
    Yu, Linfei
    Leng, Guoyong
    Python, Andre
    Peng, Jian
    [J]. REMOTE SENSING, 2021, 13 (06)
  • [5] Evaluation of GPM IMERG precipitation products with the point rain gauge records over Sichuan, China
    Yang, Mengxi
    Liu, Guodong
    Chen, Ting
    Chen, Yu
    Xia, Chengcheng
    [J]. ATMOSPHERIC RESEARCH, 2020, 246
  • [6] Assessment of IMERG v06 Satellite Precipitation Products in the Canadian Great Lakes Region
    Zhao, B. O.
    Hudak, D.
    Rodriguez, P.
    Mekis, E.
    Brunet, D.
    Eckert, E.
    Melo, S.
    [J]. JOURNAL OF HYDROMETEOROLOGY, 2023, 24 (06) : 1017 - 1037
  • [7] SPATIAL DOWNSCALING OF GPM IMERG V06 GRIDDED PRECIPITATION USING MACHINE LEARNING ALGORITHMS
    Sathianarayanan, Manikandan
    Hsu, Pai-Hui
    [J]. GEOINFORMATION WEEK 2022, VOL. 48-4, 2023, : 327 - 332
  • [8] Evaluation of the GPM-IMERG V06 Final Run Products for Monthly/Annual Precipitation under the Complex Climatic and Topographic Conditions of China
    Zhang, Ying
    Zheng, Xiao
    LI, Xiufen
    Lyu, Jiaxin
    Zhao, Lanlin
    [J]. JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY, 2023, 62 (08) : 929 - 946
  • [9] A comprehensive evaluation of GPM-IMERG V06 and MRMS with hourly ground-based precipitation observations across Canada
    Moazami, S.
    Najafi, M. R.
    [J]. JOURNAL OF HYDROLOGY, 2021, 594
  • [10] Event-Based Bias Correction of the GPM IMERG V06 Product by Random Forest Method over Mainland China
    Liu, Zhenyu
    Hou, Haowen
    Zhang, Lanhui
    Hu, Bin
    [J]. REMOTE SENSING, 2022, 14 (16)