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STREAM-Sat: A Novel Near-Realtime Quasi-global Satellite-Only Ensemble Precipitation Dataset
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  • Kaidi Peng,
  • Daniel Benjamin Wright,
  • Yagmur Derin,
  • Samantha Helen Hartke,
  • Zhe Li,
  • Jackson Tan
Kaidi Peng

Corresponding Author:[email protected]

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Daniel Benjamin Wright
University of Wisconsin-Madison
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Yagmur Derin
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Samantha Helen Hartke
National Center for Atmospheric Research
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Zhe Li
Colorado State University
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Jackson Tan
NASA Goddard Space Flight Center / Universities Space Research Association
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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.
28 Nov 2023Submitted to ESS Open Archive
03 Dec 2023Published in ESS Open Archive