{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T18:30:09Z","timestamp":1767983409951,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CNRS, Sorbonne Universit\u00e9, UVSQ, CNES, Ecole Polytechnique and national research infrastructures Climeri-France and DATA TERRA"},{"name":"EUMETSAT member states through CM SAF"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multi-platform satellite-based precipitation gridded estimates are becoming widely available in support of climate monitoring and climate science. The characterization of the performances of these emerging Level-4 products is an active field of research. This study introduced a simple Gaussian mixture model (GMM) to characterize the distribution of uncertainty in these satellite products. The following three types of uncertainty were analyzed: constellation changes-induced uncertainties, sampling uncertainties and comparison with rain-gauges. The GMM was systematically compared with a single Gaussian approach and shown to perform well for the variety of uncertainties under consideration regardless of the precipitation levels. Additionally, GMM has also been demonstrated to be effective in evaluating the impact of Level-2 PMW rain estimates\u2019 detection threshold definition on the constellation changes-induced uncertainty characteristics at Level-4. This simple additive perspective opens future avenues for better understanding error propagation from Level-2 to Level-4.<\/jats:p>","DOI":"10.3390\/rs14153726","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T23:33:01Z","timestamp":1659569581000},"page":"3726","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Simple Statistical Model of the Uncertainty Distribution for Daily Gridded Precipitation Multi-Platform Satellite Products"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5090-1817","authenticated-orcid":false,"given":"R\u00f4mulo A. J.","family":"Oliveira","sequence":"first","affiliation":[{"name":"Laboratoire d\u2019Etudes en G\u00e9ophysique et Oc\u00e9anographie Spatiales, Universit\u00e9 de Toulouse III, CNRS, CNES, IRD, 31062 Toulouse, France"},{"name":"G\u00e9osciences Environnement Toulouse, Universit\u00e9 de Toulouse III, CNRS, CNES, IRD, 31062 Toulouse, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1843-0204","authenticated-orcid":false,"given":"R\u00e9my","family":"Roca","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Etudes en G\u00e9ophysique et Oc\u00e9anographie Spatiales, Universit\u00e9 de Toulouse III, CNRS, CNES, IRD, 31062 Toulouse, France"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","first-page":"20190458","article-title":"Earth\u2019s water reservoirs in a changing climate","volume":"476","author":"Stephens","year":"2020","journal-title":"Proc. R. Soc. A Math. Phys. Eng. 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