EGU21-7678, updated on 08 Apr 2024
EGU General Assembly 2021
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Machine learning emulation of gravity wave drag in numerical weather forecasting

Matthew Chantry1, Sam Hatfield2, Peter Duben2, Inna Polichtchouk2, and Tim Palmer1
Matthew Chantry et al.
  • 1University of Oxford, AOPP, Physics, Oxford, United Kingdom of Great Britain – England, Scotland, Wales (
  • 2European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

We assess the value of machine learning as an accelerator for a kernel of an operational weather forecasting system, specifically the parameterisation of non-orographic gravity wave drag. Emulators of this scheme can be trained that produce stable and accurate results up to seasonal forecasting timescales. By training on an increased complexity version of the parameterisation scheme we build emulators that produce more accurate forecasts than the existing parameterisation scheme. Leveraging the differentiability of neural networks we generate tangent linear and adjoint versions of our parameterisation, key components in 4D-var data-assimilation. We test our tangent linear and adjoint codes within an operational-like 4D-var setup and find no degradation in skill vs hand-written tangent-linear and adjoint codes.

How to cite: Chantry, M., Hatfield, S., Duben, P., Polichtchouk, I., and Palmer, T.: Machine learning emulation of gravity wave drag in numerical weather forecasting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7678,, 2021.

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