The usefulness of satellite multisensor precipitation products such as NASA’s 30-minute, 0.1° Integrated Multi-satellitE Retrievals for the Global Precipitation Mission (IMERG) is hindered by their associated errors. Reliable estimates of uncertainty would mitigate this limitation, especially in near-real time. Creating such estimates is challenging, however, due both to the complex discrete-continuous nature of satellite precipitation errors and to the lack of “ground truth” data precisely in the places—including complex terrain and developing countries—that could benefit most from satellite precipitation estimates. In this work, we use swath-based precipitation products from the Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR) as an alternative to ground-based observations to facilitate IMERG uncertainty estimation. We compare the suitability of two DPR derived products, 2ADPR and 2BCMB, against higher-fidelity Ground Validation Multi-Radar Multi-Sensor (GV-MRMS) ground reference data over the contiguous United States. 2BCMB is selected to train mixed discrete-continuous error models based on Censored Shifted Gamma Distributions. Uncertainty estimates from these error models are compared against alternative models trained on GV-MRMS. Using information from NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis, we also demonstrate how IMERG uncertainty estimates can be further constrained using additional precipitation-related predictors. Though several critical issues remain unresolved, the proposed method shows promise for yielding robust uncertainty estimates in near-real time for IMERG and other similar precipitation products at their native resolution across the entire globe.

Improvements in remote sensing capability and improvements in artificial intelligence have created significant opportunities to advance understanding of precipitation processes. While highly advanced Machine Learning (ML) techniques improve the accuracy of precipitation retrievals, how these observations contribute to our understanding of precipitation processes remains an underexplored research question. In a companion manuscript, a precipitation type prognostic ML model is developed by deriving predictors from the Advanced Baseline Imager (ABI) sensor onboard Geostationary Observing Environmental Satellite (GOES)-16. In this study, these predictors are linked to different precipitation processes. It is observed that satellite observations are important in separating Rain and No-Rain areas. For stratiform precipitation types, predictors related to atmospheric moisture content, such as relative humidity and precipitable water, are the most important predictors, while for convective types, predictors such as 850-500hPa lapse-rate and Convective Available Potential Energy (CAPE) are more important. The diagnostic analysis confirms the benefit of spatial textures derived from ABI observations to improve the classification accuracy. It is recommended to combine the heritage water vapor channel T6.2 with the IR T11.2 channel for improved precipitation classification. There is more than 10% improvement in detection of stratiform and tropical precipitation types compared to using T11.2 alone.

Precipitation is an essential climate and forcing variable for modeling the global water cycle. Particularly, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product retrospectively provides unprecedented two-decades of high-resolution satellite precipitation estimates (0.1-deg, 30-min) globally. The primary goal of this study is to examine the similarities and differences between the two latest and also arguably most popular GPM IMERG Early and Final Run (ER and FR) products systematically over the globe. The results reveal that: (1) ER systematically estimates 13.0% higher annual rainfall than FR, particularly over land (13.8%); (2) ER and FR show less difference with instantaneous rates (Root Mean Squared Difference: RMSD=2.38 mm/h and normalized RMSD: RMSD_norm=1.10), especially in Europe (RMSD=2.16 mm/h) and cold areas (RMSD_norm=0.87); and (3) with similar detectability of extreme events and timely data delivery, ER is favored for use in hydrometeorological applications, especially in early warning of flooding. Throughout this study, large discrepancies between ER and FR are found in inland water bodies, (semi) arid regions, and complex terrains, possibly owing to morphing differences and gauge corrections while magnified by surface emissivity and precipitation dynamics. The exploration of their similarities and differences provides a first-order global assessment of various hydrological utilities: FR is designed to be more suitable for retrospective hydroclimatology and water resource management, while the earliest available ER product, though not bias-corrected by ground gauges, shows suitable applicability in operational modeling setting for early rainfall-triggered hazard alerts.