Passive Aquatic Listeners (PALs) have been increasingly deployed to collect minute-scale surface oceanic rainfall and wind information, with a sampling area similar to the spaceborne sensor footprints. This provides an unprecedented opportunity to validate satellite precipitation products over oceans. This study evaluates the Global Precipitation Climatology Project (GPCP) daily products, including the widely-used GPCP v1.3 and the newly released GPCP v3.2, over oceans using 58 PALs as references. The study shows that the GPCP performance depends on time scale, region, and rainfall intensity. The two versions of GPCP perform similarly at multi-year and monthly scales, while GPCP v3.2 shows substantial improvements in representing rain occurrence and rain intensity at daily scale. The results also highlight the challenge of precipitation measurement over certain regions such as the tropical Northeastern Pacific and extratropical North Pacific, where both versions of the GPCP products perform similarly but exhibit noticeable differences compared to PAL observations.

Satellite-based precipitation observations provide near-global coverage with high spatiotemporal resolution in near-realtime. Their utility, however, is hindered by oftentimes large errors that vary substantially in space and time. Since precipitation uncertainty is, by definition, a random process, probabilistic expression of satellite-based precipitation product uncertainty is needed to advance their operational applications. Ensemble methods, in which uncertainty is depicted via multiple realizations of precipitation fields, have been widely used in other contexts such as numerical weather prediction, but rarely in satellite contexts. Creating such an ensemble dataset is challenging due to the complexity of errors and the scarcity of “ground truth” to characterize it. This challenge is particularly pronounced in ungauged regions, where the benefits of satellite-based precipitation data could otherwise provide substantial benefits. In this study, we propose the first quasi-global (covering all continental land masses within 50°N-50°S) satellite-only ensemble precipitation dataset, derived entirely from NASA’s Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (IMERG) and GPM’s radar-radiometer combined precipitation product (2B-CMB). No ground-based measurements are used in this generation and it is suitable for near-realtime use, limited only by the latency of IMERG. We compare the results against several precipitation datasets of distinct classes, including global satellite-based, rain gauge-based, atmospheric reanalysis, and merged products. While our proposed approach faces some limitations and is not universally superior to the datasets it is compared to in all respects, it does hold relative advantages due to its combination of accuracy, resolution, latency, and utility in hydrologic and hazard applications.

The usefulness of satellite multi-sensor precipitation (SMP) and other satellite-informed precipitation products in water resources modeling can be hindered by substantial errors which vary considerably with spatiotemporal scale. One approach to cope with these errors is by combining SMPs with ensemble generation methods, such that each ensemble member reflects one plausible realization of the true—but unknown—precipitation. This requires replicating the spatiotemporal autocorrelation structure of SMP errors. The climatology of this structure is unknown for most locations due to a lack of ground reference observations, while the unique anisotropy and nonstationarity within any particular precipitation system limit the relevance of this climataology to the depiction of error in individual storm systems. Characterizing and simulating this autocorrelation across spatiotemporal scales has thus been called a grand challenge within the precipitation community. We introduce the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which combines anisotropic and nonstationary SMP spatiotemporal correlation structures with a pixel-scale precipitation error model to stochastically generate ensemble precipitation fields that resemble “ground truth” precipitation. We generate STREAM precipitation ensembles at high resolution (1-hour, 0.1˚) with minimal reliance on ground-reference data, and evaluate these ensembles at multiple scales. STREAM ensembles consistently “bracket” ground-truth observations and replicate the autocorrelation structure of ground-truth precipitation fields. STREAM is compatible with pixel-scale error/uncertainty formulations beyond those presented here, and could be applied globally to other precipitation sources such as numerical weather predictions or “blended” products. In combination with recent work in SMP uncertainty characterization, STREAM could be run without any ground data.