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

Nicolas A Da Silva,Benjamin G M Webber,Christopher E Holloway,Adrian J Matthews,Muhammad F A B Abdullah,Thorwald H M Stein,Matthew M Feist

doi:10.1029/2021ea001738

Nicolas A Da Silva, Benjamin G M Webber + Show 5 more

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https://doi.org/10.1029/2021ea001738

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Abstract

AbstractThe 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.

Highlights

Precipitation has a considerable impact on human society
To ascertain whether the very large Integrated Multi-satellitE Retrievals for GPM (IMERG)–Global Historical Climatology Network (GHCN) precipitation differences can be attributed to the spatial sampling error, we examine the equivalent probability density function (PDF) for differences between the two different spatial resolutions of the Subang radar data
For extreme precipitation (95th and higher), the green line is below the 1:1 line, indicating that the (e.g.,) 95th percentile of radar precipitation on the native high resolution grid is larger than the 95th percentile of radar precipitation on the coarser IMERG grid

Summary

Introduction

Precipitation has a considerable impact on human society. Precipitation produces devastating floods that have a high destructive capacity for both infrastructure and human lives. Separates the Indian Ocean from the Pacific and encompasses the countries of Indonesia, Malaysia and the Philippines, among others. This region experiences significant extreme precipitation (Hai et al, 2017; Warlina & Guinensa, 2019), which, combined with the high vulnerability of the local population (Takama et al, 2017; Karki, 2019; Abd Majid et al., 2019; Cabrera & Lee, 2020), can lead to severe consequences. Accurate prediction of extreme precipitation in the MC is of crucial importance for society. Numerical weather prediction (NWP) models still struggle to correctly predict such extreme events in the MC

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