Sonny: Breaking the Compute Wall in Medium-Range Weather Forecasting
Weather forecasting is a fundamental problem for protecting lives and infrastructure from high-impact atmospheric events. Recently, data-driven weather forecasting methods based on deep learning have demonstrated strong performance, often reaching accuracy levels competitive with operational numerical systems. However, many existing models rely on large-scale training regimes and compute-intensive architectures, which raises the practical barrier for academic groups with limited compute resources. Here we introduce Sonny, an efficient hierarchical transformer that achieves competitive medium-range forecasting performance while remaining feasible within reasonable compute budgets. At the c...
| Authors | Minjong Cheon |
|---|---|
| Primary category | cs.LG |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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