Minseo Yoon

Mapping coarse-resolution history directly to future high-resolution weather trajectories requires forecasting, spatial downscaling and temporal densification at once, yet existing low-rank fine-tuning does not separate reusing pretrained principal directions from correcting outside them under a fixed rank budget. PROLA splits that budget into a principal branch and an orthogonal branch and rescales rank-1 optimizer updates with gradient signals; adapting Aurora on WeatherBench 2 with 3.92M trainable parameters, it reaches a 3-hour Z500 RMSE of 50.58, below AdaLoRA's 53.40.

Mapping coarse-resolution history directly to future high-resolution weather trajectories requires forecasting, spatial downscaling and temporal densification at once, yet existing low-rank fine-tuning does not separate reusing pretrained principal directions from correcting outside them under a fixed rank budget. PROLA splits that budget into a principal branch and an orthogonal branch and rescales rank-1 optimizer updates with gradient signals; adapting Aurora on WeatherBench 2 with 3.92M trainable parameters, it reaches a 3-hour Z500 RMSE of 50.58, below AdaLoRA's 53.40.