Probable Maximum Storms (PMS) are synthetic design storms represented by idealized hyetographs. They play a critical role in assessing extreme rainfall events over extended durations and are widely applied in the hydraulic design of infrastructure such as dams, culverts, and bridges. PMS provide essential input for estimating Probable Maximum Floods (PMF), vital for analyzing worst-case flood scenarios with the potential to cause catastrophic loss of life and property. Despite their importance, the estimation of design storms at ungauged locations, particularly across synoptic scales, remains a major scientific and engineering challenge. This study addresses this gap by utilizing the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) dataset, which provides near-global estimated precipitation coverage. IMERG’s 24 h design storm hyetographs (expressed as cumulative percentage of precipitation throughout a 24 h period) were modeled and compared with similar reference data from NOAA Atlas 14 across twenty-eight regions and seven larger zones covering most of the conterminous United States (CONUS). Across the regions, the average root mean square error (RMSE) was 3.7%, with a mean relative bias (RB) of 1.4%. The mean normalized storm loading index (NSLI) from NOAA Atlas 14 was −7.7%, indicating that 57.7% of the total precipitation was received during the first 12 h of the storm, whereas IMERG storms exhibited a mean NSLI of −4.1%, suggesting they are also frontloaded but to a lesser extent. Across the broader zones, the mean RMSE was 4.8% and the mean RB was 1.1%. The mean NSLI values were −9.7% for NOAA Atlas 14 and −5.7% for IMERG, again indicating that IMERG storms are less frontloaded. When design storm families were estimated corresponding with different degrees of frontloading (corresponding to the 10, 20, …, 90% deciles of NSLI), the 40th to 60th percentile range exhibited the strongest agreement between IMERG and NOAA Atlas 14 hyetographs.
Ekpetere et al. (Wed,) studied this question.