Learning Prognostic Variables for AI Convective Parameterizations via Symbolic Distillation
Researchers learn symbolic prognostic variables so AI convective parameterizations retain memory and improve climate statistics.
Hybrid AI-physics climate models usually use diagnostic parameterizations that depend only on the current coarse state. The authors compress past information with an autoencoder, then replace it with symbolic equations—a forced multivariate linear ODE—that evolve latent memory variables alongside the resolved atmosphere. Tested online on Lorenz-96 and offline on high-resolution surface precipitation, the approach recovers most of the autoencoder’s gains and improves climate statistics, temporal structure, and the diurnal cycle of tropical land precipitation versus memory-free baselines.
- Autoencoder compresses past states into a low-dimensional latent memory
- Symbolic distillation replaces the encoder with a forced linear ODE
- Evaluated online on Lorenz-96 and offline on precipitation
- Improves climate statistics and the tropical land diurnal cycle
From the original article
1 min read at arxiv.orgHybrid AI-physics climate modeling aims to improve coarse (~100km-resolution) Earth system models by learning to parameterize subgrid processes from high-fidelity data.
By Jurij Schönfeld · arxiv.org
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