Articles | Volume 15, issue 23
Research article
01 Dec 2022
Research article |  | 01 Dec 2022

An improved near-real-time precipitation retrieval for Brazil

Simon Pfreundschuh, Ingrid Ingemarsson, Patrick Eriksson, Daniel A. Vila, and Alan J. P. Calheiros

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Cited articles

Adler, R. F. and Negri, A. J.: A satellite infrared technique to estimate tropical convective and stratiform rainfall, J. Appl. Meteorol. Clim., 27, 30–51, 1988. a
Arkin, P. A. and Meisner, B. N.: The relationship between large-scale convective rainfall and cold cloud over the western hemisphere during 1982–84, Mon. Weather Rev., 115, 51–74, 1987. a
Chollet, F.: Xception: Deep learning with depthwise separable convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 1251–1258, Honolulu, HI, USA, 21–26 July 2017,, 2017. a
de Siqueira, R. A. and Vila, D.: Hybrid methodology for precipitation estimation using Hydro-Estimator over Brazil, Int. J. Remote Sens., 40, 4244–4263,, 2019. a, b, c, d, e
Fohla De S. Paulo​​​​​​​: Chuva causa morte, derruba casas e deixa famílias desalojadas em Duque de Caxias, no RJ, (last access: 31 January 2022), 2020. a, b
Short summary
We used methods from the field of artificial intelligence to train an algorithm to estimate rain from satellite observations. In contrast to other methods, our algorithm not only estimates rain, but also the uncertainty of the estimate. Using independent measurements from rain gauges, we show that our method performs better than currently available methods and that the provided uncertainty estimates are reliable. Our method makes satellite-based measurements of rain more accurate and reliable.