EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Emulating radiative transfer in a numerical weather prediction model

Matthew Chantry1, Peter Ukkonen2, Robin Hogan1,3, and Peter Dueben1
Matthew Chantry et al.
  • 1ECMWF
  • 2Danish Meterological Institute
  • 3University of Reading

Machine learning, and particularly neural networks, have been touted as a valuable accelerator for physical processes. By training on data generated from an existing algorithm a network may theoretically learn a more efficient representation and accelerate the computations via emulation. For many parameterized physical processes in weather and climate models this being actively pursued. Here, we examine the value of this approach for radiative transfer within the IFS, an operational numerical weather prediction model where both accuracy and speed are vital. By designing custom, physics-informed, neural networks we achieve outstanding offline accuracy for both longwave and shortwave processes. In coupled testing we find minimal changes to forecast scores at near operational resolutions. We carry out coupled inference on GPUs to maximise the speed benefits from the emulator approach.

How to cite: Chantry, M., Ukkonen, P., Hogan, R., and Dueben, P.: Emulating radiative transfer in a numerical weather prediction model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3256,, 2023.