Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 20
Articles | Volume 15, issue 20
https://doi.org/10.5194/amt-15-6035-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-6035-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 20
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: 2,662 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,068 514 80 2,662 83 77
  • HTML: 2,068
  • PDF: 514
  • XML: 80
  • Total: 2,662
  • BibTeX: 83
  • EndNote: 77
Views and downloads (calculated since 16 Jun 2022)
Cumulative views and downloads (calculated since 16 Jun 2022)

Viewed (geographical distribution)

Total article views: 2,662 (including HTML, PDF, and XML) Thereof 2,471 with geography defined and 191 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 19 Nov 2025
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.
Read more
Share