Investigating the Representation of Extremes in High-Resolution, Long Period-of-Record Precipitation Products in the Continental US

Work is currently underway by multiple US agencies to update and improve estimates and understanding of extreme precipitation and its hydrologic impacts. These efforts include reassessing methods to estimate both pluvial and fluvial flood risk, updating precipitation climatologies, and modernizing the estimates of probable maximum precipitation used in the design and maintenance of critical infrastructure such as dams and power plants. All of these activities require high-spatiotemporal resolution estimates of precipitation with high fidelity and relatively long periods of record. Reliable representation of precipitation extremes is of particular importance. There exist several global and CONUS-wide precipitation datasets with relatively high resolution in both space and time and extended periods of record that meet the needs of these ongoing efforts, including (but not limited to) the Analysis of Record for Calibration (AORC), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and a 4-km long-term regional hydroclimate reanalysis over the conterminous United States (CONUS404). This study will compare the representation of extreme precipitation across such datasets to identify the products that can most effectively contribute to the goals of ongoing studies of extreme precipitation and its impacts. Findings will be demonstrated as a function of season, region, storm type, and the raw data that are included in the precipitation products. Several of the datasets being considered are either relatively new or have recently released new versions, and this work will also serve as a first look into how these new or updated products represent the characteristics of extreme precipitation in terms of intensity, placement, and spatiotemporal distribution. The results will inform the ongoing process of improving our understanding of and informing risk-based decision making regarding extreme precipitation.