Validation of GPM IMERG Extreme Precipitation in the Maritime Continent by Station and Radar Data. Issue 7 (8th July 2021)

• Record Type: Journal Article
• Authors: Da Silva, Nicolas A.; Webber, Benjamin G. M.; Matthews, Adrian J.; Feist, Matthew M.; Stein, Thorwald H. M.; Holloway, Christopher E.; Abdullah, Muhammad F. A. B.
• Is Part Of: Earth and space science. Volume 8:Issue 7(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-08
• Subjects: precipitation evaluation -- IMERG -- Maritime Continent -- South East Asia -- radar precipitation; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2021EA001738
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: 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. Plain Language Summary: Extreme rainfall is a major hazard in many parts of the tropics, leading to flooding and social and economic impacts. Accurate weather forecasting of extreme rainfall events is needed by national and regional government planners and disaster relief organizations, as well as by agriculture and industry. The skill of weather forecast computer models needs to be tested against a reliable data set of observed rainfall, so that scientists can improve the models to give better forecasts of extreme rainfall. Observed rainfall data sets need to be evaluated prior to their use for testing models. Here, we evaluate the reliability of the Integrated Multi‐Satellite Retrieval for Global Precipitation Measurement (IMERG) rainfall data set for this purpose. IMERG is based on satellite and rain gauge measurements of rainfall from across the planet. We focus on the area known as the western Maritime Continent. After comparing IMERG rainfall against local measurements of rainfall from weather radar in Malaysia, and weather station data across the region, the recommendation is that IMERG can be used as a reliable measure of fairly extreme rainfall (the top 5% of daily rainfall totals), but tends to overestimate and therefore should be used with caution for very extreme rainfall (the top 1% of daily rainfall totals). Key Points: Spatial sampling error severely affects the comparison of Integrated Multi‐Satellite Retrieval for Global Precipitation Measurement (IMERG) data with pointwise precipitation The 95th percentile is the optimum choice for comparison of numerical weather prediction precipitation extremes against IMERG Above the 95th percentile, IMERG overestimates daily precipitation rates compared with rain gauges