STREAM-Sat: A Novel Near-Realtime Quasi-global Satellite-Only Ensemble
Precipitation Dataset
Abstract
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.