SCAL: A Station-Calibrated Asymmetric Loss for Extreme-Aware Spatiotemporal Wind Forecasting

Forecasting extreme wind speed is difficult: tail events are sparse, their errors carry a systematic direction, and each station behaves differently. We propose the Station-Calibrated Asymmetric Loss (SCAL), which sets the penalty in the extreme range, the upper 10% of each station’s training distribution, from that station’s own under-prediction rate during training, with hyperparameters shared rather than tuned per station. We combine it with a spatiotemporal Transformer that turns the preceding 24 h of the wide-area ERA5 field into per-station representations, forecasting the next 24 h at 18 stations operated by the Korea Meteorological Administration. Applied to the Self-adaptive Extreme Penalized Loss (EPL) over three random seeds on the 2023–2024 test period, SCAL moves the extreme-range bias from −0.905 to +0.056 m s⁻¹ and the under-prediction rate from 0.728 to 0.482, against a neutral value of 0.5. Across objectives trained under one backbone and schedule, the size of the extreme-range error and its direction prove separable: those whose asymmetry is fixed in advance lower the error yet still fall below the observation at 62% to 78% of extreme observations. Of those, only quantile regression at the 0.9 level brings the rate close to neutral, and at a higher whole-range cost than SCAL (0.817 against 0.681 in normalized mean absolute error). The extreme-range error falls 13.9% relative to EPL while the whole-range error rises 32.8%.