Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 11
Articles | Volume 16, issue 11
https://doi.org/10.5194/gmd-16-3241-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3241-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 11
Development and technical paper
 | 
09 Jun 2023
Development and technical paper |  | 09 Jun 2023

Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0

Peter Ukkonen and Robin J. Hogan

Viewed

Total article views: 1,380 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,089 242 49 1,380 35 32
  • HTML: 1,089
  • PDF: 242
  • XML: 49
  • Total: 1,380
  • BibTeX: 35
  • EndNote: 32
Views and downloads (calculated since 09 Dec 2022)
Cumulative views and downloads (calculated since 09 Dec 2022)

Viewed (geographical distribution)

Total article views: 1,380 (including HTML, PDF, and XML) Thereof 1,340 with geography defined and 40 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
Read more