Hybrid AI models improve weather forecasts using learned memory variables
Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation
Machine Learning
Summary
Weather and climate models often miss important slower processes because they only look at the current state and ignore past information. This paper shows how to add a kind of memory to AI models that predict weather patterns by learning compact variables that capture past information. They use math equations to describe how these memory variables change over time, making forecasts more realistic. Their method improves predictions of rain patterns and the daily cycle of tropical weather compared to models without memory.
What this means in practice
- •For climate model developers: Add memory-informed variables to coarse climate models for more accurate convection and precipitation predictions.
- •For meteorological simulation engineers: Integrate symbolic latent variable equations to improve the temporal realism of weather forecasts at coarse resolution.
Authors
Jurij Schönfeld, Tom Beucler, Julien Savre, Steven Sherwood, Veronika Eyring
Abstract
Hybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data. However, this so far mostly involves local-in-time, diagnostic parameterizations, in which the subgrid state depends only on the current coarse state with no memory of previous states, which is unrealistic for processes such as convection that have intrinsic persistence. To address this, we enhance local-in-time parameterizations by learning prognostic variables that compactly carry important, additional past information where no explicit sub-grid information is available. First we compress past information into a low-dimensional latent space using an autoencoder, which then informs a neural network trained to parameterize targeted subgrid-scale processes. We then replace the autoencoder with symbolic equations that govern the time evolution of the latent variables, yielding additional prognostic memory variables that can be integrated alongside the resolved atmospheric state. We evaluate this approach on two systems: the Lorenz-96 model (online) and surface precipitation from high-resolution atmospheric simulations (offline). A forced multivariate linear ordinary differential equation recovers most of the added value achieved by the autoencoder-based approach in both experiments. Benchmarked against diagnostic parameterizations without memory, our memory-informed approach improves climate statistics and temporal structure, including a realistic diurnal cycle of tropical land precipitation.