Hybrid physics-ML & parameterization
Using neural networks to improve simulations in the gray zone
In one sentence. Neural network learns unresolved-scale error in a shallow water model mimicking convection, extending forecast skill to one day; a mass-conservation penalty delays drift.
Abstract
Abstract. Machine learning represents a potential method to cope with the gray zone problem of representing motions in dynamical systems on scales comparable to the model resolution. Here we explore the possibility of using a neural network to directly learn the error caused by unresolved scales. We use a modified shallow water model which includes highly nonlinear processes mimicking atmospheric convection. To create the training dataset we run the model in a high and a low-resolution setup and compare the difference after one low resolution time step starting from the same initial conditions, thereby obtaining an exact target. The neural network is able to learn a large portion of the difference when evaluated offline on a validation set. When coupled to the low-resolution model, we find large forecast improvements up to one day on average. After this, the accumulated error due to the mass conservation violation of the neural network starts to dominate and deteriorates the forecast. This deterioration can effectively be delayed by adding a penalty term to the loss function used to train the ANN to conserve mass in a weak sense. This study reinforces the need to include physical constraints in neural network parameterizations.
Labels
- Topic
- Hybrid physics-ML & parameterization
- Type
- New model or method
- Method
- Physics-informed / physics-constrained, Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.5194/npg-29-171-2022 OpenAlex:W4225279804
- Added
- 2026-10-22
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