Articles | Volume 15, issue 20
https://doi.org/10.5194/amt-15-6035-2022
https://doi.org/10.5194/amt-15-6035-2022
Research article
 | 
21 Oct 2022
Research article |  | 21 Oct 2022

DeepPrecip: a deep neural network for precipitation retrievals

Fraser King, George Duffy, Lisa Milani, Christopher G. Fletcher, Claire Pettersen, and Kerstin Ebell

Viewed

Total article views: 1,511 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,125 341 45 1,511 30 23
  • HTML: 1,125
  • PDF: 341
  • XML: 45
  • Total: 1,511
  • BibTeX: 30
  • EndNote: 23
Views and downloads (calculated since 16 Jun 2022)
Cumulative views and downloads (calculated since 16 Jun 2022)

Viewed (geographical distribution)

Total article views: 1,511 (including HTML, PDF, and XML) Thereof 1,423 with geography defined and 88 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 May 2024
Download
Short summary
Under warmer global temperatures, precipitation patterns are expected to shift substantially, with critical impact on the global water-energy budget. In this work, we develop a deep learning model for predicting snow and rain accumulation based on surface radar observations of the lower atmosphere. Our model demonstrates improved skill over traditional methods and provides new insights into the regions of the atmosphere that provide the most significant contributions to high model accuracy.