Articles | Volume 28, issue 22
https://doi.org/10.5194/hess-28-4903-2024
https://doi.org/10.5194/hess-28-4903-2024
Research article
 | 
18 Nov 2024
Research article |  | 18 Nov 2024

Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5

Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner

Viewed

Total article views: 1,628 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,322 264 42 1,628 41 37
  • HTML: 1,322
  • PDF: 264
  • XML: 42
  • Total: 1,628
  • BibTeX: 41
  • EndNote: 37
Views and downloads (calculated since 24 Nov 2023)
Cumulative views and downloads (calculated since 24 Nov 2023)

Viewed (geographical distribution)

Total article views: 1,628 (including HTML, PDF, and XML) Thereof 1,488 with geography defined and 140 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 01 Apr 2025
Download
Short summary
This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a crucial water source for around 1.9 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions, including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows accuracy comparable to or better than existing benchmark datasets.
Share