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

Data sets

Gauge data used in 'An improved near-real-time precipitation retrieval for Brazil' by Pfreundschuh et al. S. Pfreundschuh

GPM DPR and GMI Combined Precipitation L2B 1.5 h 5 km V06 W. Olson

GPM GMI (GPROF) Radiometer Precipitation Profiling L2A 1.5 hours 13 km V07 C. Kummerow

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

Model code and software

Hydronn S. Pfreundschuh

Video supplement

Satellite measurements of rain over Brazil S. Pfreundschuh

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.