Machine learning emulation of gravity wave drag in numerical weather forecasting

Abstract

We assess the value of machine learning as an accelerator for the parameterisation schemes of operational weather forecasting systems, specifically the parameterisation of non-orographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, more complex networks produce more accurate emulators. By training on an increased complexity version of the existing parameterisation scheme we build emulators that produce more accurate forecasts. {For medium range forecasting we find evidence our emulators are more accurate} than the version of the parametrisation scheme that is used for operational predictions. Using the current operational CPU hardware our emulators have a similar computational cost to the existing scheme, but are heavily limited by data movement. On GPU hardware our emulators perform ten times faster than the existing scheme on a CPU.

References in corpus (7)

• Deep learning to represent sub-grid processes in climate models
• Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
• Using machine learning to parameterize moist convection: potential for modeling of climate, climate change and extreme events
• Data-driven medium-range weather prediction with a Resnet pretrained on climate simulations: A new model for WeatherBench
• Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse-graining
• Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision
• Predicting atmospheric optical properties for radiative transfer computations using neural networks

Cited by in corpus (4)

• Deep Learning Based Cloud Cover Parameterization for ICON
• A posteriori learning for quasi-geostrophic turbulence parametrization
• Machine Learning Emulation of 3D Cloud Radiative Effects
• Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime