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

Viewed

Total article views: 1,519 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,096 374 49 1,519 46 34
  • HTML: 1,096
  • PDF: 374
  • XML: 49
  • Total: 1,519
  • BibTeX: 46
  • EndNote: 34
Views and downloads (calculated since 06 Apr 2022)
Cumulative views and downloads (calculated since 06 Apr 2022)

Viewed (geographical distribution)

Total article views: 1,519 (including HTML, PDF, and XML) Thereof 1,441 with geography defined and 78 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 22 May 2024
Download
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.