Rescene: band-limited stochastic forcing turns a frozen neural weather operator into a climate emulator
Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has produced deterministic models whose medium-range skill matches or exceeds that of the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES). However, when these models are integrated freely beyond the horizon they were trained for, they blow up, drift, or lose their seasonal cycle, and retraining them for stability is expensive. We therefore ask what can be recovered from a strictly frozen backbone. We present Rescene, a 0.4 M-parameter wrapper around a frozen 1.5 degree, 6-hourly vision-transformer operator, developed using ERA5 reanalysis data...
| Authors | Minjong Cheon |
|---|---|
| Primary category | physics.ao-ph |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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