Polar‐low track prediction using machine‐learning methods

Abstract Polar lows (PLs) are intense mesoscale cyclone systems that rapidly develop and pose risks to coastal infrastructure, shipping, and maritime operations. Hence, making accurate predictions of PL trajectories is crucial. Due to the high dynamics and small scales of PLs (100 km), numerical dynamical models require a high resolution to properly resolve these systems, thereby increasing computational costs. However, such high‐resolution models still face challenges in accurate predictions. To address these issues, this study explores the application of machine‐learning (ML) models, including temporal models and spatiotemporal sequence models for 12‐hour forecasts of PL trajectories. Such algorithms are a considerably cheaper alternative to numerical models. We train the ML models on a high‐resolution reanalysis. The spatiotemporal models trained with key meteorological variables perform better than the temporal models at the first two lead three‐hour time steps. At later steps, spatiotemporal models trained with historical data show growing errors that surpass those of the temporal type. Encouragingly, however, spatiotemporal models trained with historical and future data, especially the combination of lower‐ and upper‐level geopotential height fields, achieve the best predictive accuracy, consistently maintaining the lowest mean distance error (MDE) and outperforming a benchmark error (67.2 km) constituting the error of the reanalysis relative to an expert‐derived PL list (the Noer list). Moreover, introducing a physical constraint loss function associated with the MDE as well as an ensemble method further improves prediction accuracy. These findings demonstrate that ML models can generate fast and accurate PL trajectory forecasts, producing results more quickly than numerical weather prediction models. Incorporating future meteorological variables from numerical models, along with high‐quality trajectory data, further enhances the prediction accuracy of ML models, suggesting potential for improving the operational forecasting of PLs, based on a combination of numerical and ML models.