Data from: STREAM-Sat: a novel near-realtime quasi-global satellite-only ensemble precipitation

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.