Articles | Volume 15, issue 23
https://doi.org/10.5194/amt-15-6907-2022
https://doi.org/10.5194/amt-15-6907-2022
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

Data sets

Gauge data used in 'An improved near-real-time precipitation retrieval for Brazil' by Pfreundschuh et al. S. Pfreundschuh https://doi.org/10.5281/zenodo.7354897

GPM DPR and GMI Combined Precipitation L2B 1.5 h 5 km V06 W. Olson https://doi.org/10.5067/GPM/DPRGMI/CMB/2B/06

GPM GMI (GPROF) Radiometer Precipitation Profiling L2A 1.5 hours 13 km V07 C. Kummerow https://doi.org/10.5067/GPM/GMI/GPM/GPROF/2A/07

GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree $\times$ 0.1 degree V06, Greenbelt, MD G. J. Huffman, E. F. Stocker, D. T. Bolvin, E. J. Nelkin, and J. Tan https://doi.org/10.5067/GPM/IMERG/3B-HH/06

Model code and software

Hydronn S. Pfreundschuh https://doi.org/10.5281/zenodo.6371712

Video supplement

Satellite measurements of rain over Brazil S. Pfreundschuh https://doi.org/10.5281/zenodo.7117246

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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.