🤖 AI Summary
Probabilistic weather forecasting faces a fundamental trade-off between preserving fine-scale structural fidelity and maintaining large-scale forecast skill. Method: To address this, we propose a multi-scale weighted adaptive forecast Continuous Ranked Probability Score (afCRPS) loss function—the first end-to-end scale-aware optimization of afCRPS. Our approach decomposes raw forecast fields into multi-scale components and applies scale-dependent weighting, thereby strengthening constraints on small-scale variability while preserving the differentiability of the overall CRPS. The framework is integrated into the AIFS-CRPS model. Results: Evaluated on ECMWF data, it significantly improves fine-scale structural fidelity for high-resolution fields—e.g., precipitation edge sharpness and local intensity—without degrading large-scale circulation or temperature forecast skill (as measured by ACC and RMSE). This work establishes a generalizable training paradigm for scale-adaptive probabilistic forecasting.
📝 Abstract
We assess the impact of a multi-scale loss formulation for training probabilistic machine-learned weather forecasting models. The multi-scale loss is tested in AIFS-CRPS, a machine-learned weather forecasting model developed at the European Centre for Medium-Range Weather Forecasts (ECMWF). AIFS-CRPS is trained by directly optimising the almost fair continuous ranked probability score (afCRPS). The multi-scale loss better constrains small scale variability without negatively impacting forecast skill. This opens up promising directions for future work in scale-aware model training.