Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 23
Articles | Volume 15, issue 23
https://doi.org/10.5194/amt-15-6907-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-6907-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 1,183 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
845 311 27 1,183 29 16
  • HTML: 845
  • PDF: 311
  • XML: 27
  • Total: 1,183
  • BibTeX: 29
  • EndNote: 16
Views and downloads (calculated since 06 Apr 2022)
Cumulative views and downloads (calculated since 06 Apr 2022)

Viewed (geographical distribution)

Total article views: 1,183 (including HTML, PDF, and XML) Thereof 1,120 with geography defined and 63 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 02 Oct 2023
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.
Read more