Validation of GPM IMERG Extreme Precipitation in the Maritime Continent by Station and Radar Data

被引:35
|
作者
Da Silva, Nicolas A. [1 ]
Webber, Benjamin G. M. [1 ]
Matthews, Adrian J. [2 ]
Feist, Matthew M. [3 ]
Stein, Thorwald H. M. [3 ]
Holloway, Christopher E. [3 ]
Abdullah, Muhammad F. A. B. [4 ]
机构
[1] Univ East Anglia, Sch Environm Sci, Ctr Ocean & Atmospher Sci, Norwich, Norfolk, England
[2] Univ East Anglia, Sch Environm Sci & Sch Math, Ctr Ocean & Atmospher Sci, Norwich, Norfolk, England
[3] Univ Reading, Dept Meteorol, Reading, Berks, England
[4] Malaysian Meteorol Dept, Petaling Jaya, Malaysia
关键词
GLOBAL PRECIPITATION; RAINFALL PRODUCTS; TRMM; 3B42; SATELLITE; TMPA; PERFORMANCE; NETWORK; ERRORS; BALI;
D O I
10.1029/2021EA001738
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
The Maritime Continent (MC) is a region subject to high impact weather (HIW) events, which are still poorly predicted by numerical weather prediction (NWP) models. To improve predictability of such events, NWP needs to be evaluated against accurate measures of extreme precipitation across the whole MC. With its global spatial coverage at high spatio-temporal resolution, the Global Precipitation Measurement (GPM) data set is a suitable candidate. Here we evaluate extreme precipitation in the Integrated Multi-Satellite Retrieval for GPM (IMERG) V06B product against station data from the Global Historical Climatology Network in Malaysia and the Philippines. We find that the high intragrid spatial variability of precipitation extremes results in large spatial sampling errors when each IMERG grid box is compared with individual co-located precipitation measurements, a result that may explain discrepancies found in earlier studies in the MC. Overall, IMERG daily precipitation is similar to station precipitation between the 85th and 95th percentile, but tends to overestimate above the 95th. IMERG data were also compared with radar data in western Peninsular Malaysia for sub-daily timescales. Allowing for uncertainties in radar data, the analysis suggests that the 95th percentile is still suitable for NWP evaluation of extreme sub-daily precipitation, but that the rainfall rates diverge at higher percentiles. Hence, our overall recommendation is that the 95th percentile be used to evaluate NWP forecasts of HIW on daily and sub-daily time scales against IMERG data, but that higher percentiles (i.e., more extreme precipitation) be treated with caution.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Capability of GPM IMERG Products for Extreme Precipitation Analysis over the Indonesian Maritime Continent
    Ramadhan, Ravidho
    Marzuki, Marzuki
    Yusnaini, Helmi
    Muharsyah, Robi
    Suryanto, Wiwit
    Sholihun, Sholihun
    Vonnisa, Mutya
    Battaglia, Alessandro
    Hashiguchi, Hiroyuki
    [J]. REMOTE SENSING, 2022, 14 (02)
  • [2] Ground Validation of GPM IMERG Precipitation Products over Iran
    Maghsood, Fatemeh Fadia
    Hashemi, Hossein
    Hosseini, Seyyed Hasan
    Berndtsson, Ronny
    [J]. REMOTE SENSING, 2020, 12 (01)
  • [3] Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales
    Ramadhan, Ravidho
    Yusnaini, Helmi
    Marzuki, Marzuki
    Muharsyah, Robi
    Suryanto, Wiwit
    Sholihun, Sholihun
    Vonnisa, Mutya
    Harmadi, Harmadi
    Ningsih, Ayu Putri
    Battaglia, Alessandro
    Hashiguchi, Hiroyuki
    Tokay, Ali
    [J]. REMOTE SENSING, 2022, 14 (05)
  • [4] A Spatial-Temporal Extreme Precipitation Database from GPM IMERG
    Zhou, Yaping
    Nelson, Kevin
    Mohr, Karen I.
    Huffman, George J.
    Levy, Robert
    Grecu, Mircea
    [J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2019, 124 (19) : 10344 - 10363
  • [5] Can GPM IMERG Capture Extreme Precipitation in North China Plain?
    Zhang, Dasheng
    Yang, Mingxiang
    Ma, Meihong
    Tang, Guoqiang
    Wang, Tsechun
    Zhao, Xun
    Ma, Suying
    Wu, Jin
    Wang, Wei
    [J]. REMOTE SENSING, 2022, 14 (04)
  • [6] Improving an Extreme Rainfall Detection System with GPM IMERG data
    Mazzoglio, Paola '
    Laio, Francesco
    Balbo, Simone
    Boccardo, Piero
    Disabato, Franca
    [J]. REMOTE SENSING, 2019, 11 (06)
  • [7] Correcting GPM IMERG precipitation data over the Tianshan Mountains in China
    Lu, Xinyu
    Tang, Guoqiang
    Wang, Xiuqin
    Liu, Yan
    Jia, Lihong
    Xie, Guohui
    Li, Shuai
    Zhang, Yingxin
    [J]. JOURNAL OF HYDROLOGY, 2019, 575 : 1239 - 1252
  • [8] An Analysis for the Applicability of Global Precipitation Measurement Mission (GPM) IMERG Precipitation Data in Typhoons
    Fan, Nengzhu
    Lin, Xiaohong
    Guo, Hong
    [J]. ATMOSPHERE, 2023, 14 (08)
  • [9] Evaluation of IMERG for GPM satellite-based precipitation products for extreme precipitation indices over Turkiye
    Aksu, Hakan
    Taflan, Gaye Yesim
    Yaldiz, Sait Genar
    Akgul, Mehmet Ali
    [J]. ATMOSPHERIC RESEARCH, 2023, 291
  • [10] Oceanic Validation of IMERG-GMI Version 6 Precipitation Using the GPM Validation Network
    Watters, Daniel C.
    Gatlin, Patrick N.
    Bolvin, David T.
    Huffman, George J.
    Joyce, Robert
    Kirstetter, Pierre
    Nelkin, Eric J.
    Ringerud, Sarah
    Tan, Jackson
    Wang, Jianxin
    Wolff, David
    [J]. JOURNAL OF HYDROMETEOROLOGY, 2024, 25 (01) : 125 - 142