Evaluation of GPM IMERG and its constellations in extreme events over the conterminous united states

Abstract

Improved quantification of extreme precipitation rates using observations has far-reaching implications for environmental sciences, especially for hydrometeorological studies. Yet, uncertainties still remain in satellite precipitation estimates, especially for a merged product. This study evaluates the performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) in extreme events over the conterminous US. Three approaches are followed to define and evaluate extreme events: (1) a percentile-based analysis, (2) an event-based analysis using the National Weather Service storm database, and (3) a frequency-based analysis using intensity–durationfrequency (IDF) curves. The IMERG Early Run (ER), Late Run (LR), and Final Run (FR) products and their original passive microwave and infrared (IR) sensors are intercompared against the National Centers for Environmental Predictions Stage IV ground-based radar precipitation data from 2015 to 2019. In particular, we break down the performance in three types of events (rain, snow, and hail). The results reveal that: (1) three types of extreme definitions converge toward an overall agreement - the degrees of underestimation of high-end extreme precipitation rates increases with data latency (FR > LR > ER) and FR delivers overall best performance; (2) passive microwave (PMW) estimates generally exhibits better detectability and quantification of extreme precipitation than IR estimates, especially in heavy rains; (3) Amongst PMW sensors, MHS (SAPHIR)-based estimates show the best (worst) extreme detection with CSI (Critical Success Index) equaling 0.15 (0.10) while AMSR and SSMIS outperform others for quantifying extreme rates. Lastly, different sensors (e.g., imagers and sounders in PMW and IR) deliver variable performance regarding different precipitation types. These findings reveal that IMERG is not a homogeneous precipitation product when it comes to estimating precipitation extremes. There are rooms for improvement to enhance homogeneity across precipitation estimates used in IMERG.