Performance of Multi-Radar Multi-Sensor (MRMS) product in monitoring precipitation under extreme events in Harris County, Texas

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Journal of Hydrology


With an increase in intensity and frequency of extreme precipitations as a result of climate change, it is necessary to develop effective strategies for emergency flood management plan. It is critical to integrate the use of high-resolution hydro-meteorological data (e.g., precipitation) as input for hydrologic modeling to accurately predict and reduce the negative impact of floods. The Multi-Radar Multi-Sensor (MRMS) system has been developed by the National Severe Storms Laboratory (NSSL) to produce high-resolution spatio-temporal precipitation data. While the MRMS data are available at relatively high spatial (1 km) and temporal (2 min) resolutions across the continental United States (CONUS), MRMS's accuracy in measuring actual precipitation needs to be investigated across some urban areas such as Harris County, TX. Therefore, the objectives of this study are to evaluate i) the performance of the MRMS system compared to other precipitation products (rain gauge network, Multisensor Precipitation Estimator (MPE)) at different spatial (5, 10, 15, 30 km) and temporal aggregations (5, 10,15, 30, 60 min) during four major flooding events of May 2015 (Memorial Day flood), April 2016 (Tax Day Flood), August 2017 (Hurricane Harvey), and September 2019 (Tropical Storm Imelda) in Harris County, Texas; and ii) the effects of temporal and spatial aggregation scales on the performance of the MRMS system using a suite of statistical parameters. Point-to-grid comparisons were conducted between 142 rain gauges and MRMS system data during four extreme flood events. Overall, the MRMS system captured precipitation reasonably well with a coefficient of determination (R2) of 0.78, correlation coefficient (CC) of 0.88, root mean square error (RMSE) of 1.21 mm, critical success index (CSI) of 0.65, probability of detection (POD) of 0.98, and false alarm ratio (FAR) of 0.34 over Harris County at 15 min and 15 km temporal and spatial resolutions. The results indicate that MRMS product tends to underestimate higher precipitation rates and overestimate light precipitation. Coarser temporal resolutions from 5 min to 1 h resolved some of the overestimation issues. Temporal aggregation increased R2, CC, CSI, and error variances and decreased FAR. However, increasing spatial resolution from 1 to 30 km increased R2, CC, and CSI and reduced RMSE and FAR. A comparison of MPE QPE and MRMS products at hourly temporal resolution with gauge observations showed that both products estimate rainfall accurately for the four events. Still, on average, MRMS product has a slightly better agreement with rain gauge observations at 1-hr temporal resolution.



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