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arXiv:2611.08255v1 Announce Type: new Abstract: High-resolution tropical cyclone surface wind speeds derived from synthetic aperture radar (SAR) observations can support storm structure and intensity...
arXiv:2607. 27152v1 Announce Type: new Abstract: Accurate intraday forecasts of offshore wind are becoming increasingly important for power system operation and the integration of growing shares of offshore wind energy.
arXiv:2607. 16080v1 Announce Type: new Abstract: Precipitation nowcasting over the immediate 10-90 min period is important for flood management and real-time decision-making in urban regions.
arXiv:2510. 16031v2 Announce Type: replace-cross Abstract: Machine learning-based precipitation nowcasting relies on high-fidelity radar reflectivity sequences to model the short-term evolution of convective storms.
The study evaluates machine learning techniques for detecting weather fronts using satellite data, comparing a model that relies solely on geostationary infrared observations with one that also incorporates 28 ERA5 reanalysis fields. The satellite-only model achieves a neighbourhood critical success index (CSI@150) of 0.402, while the combined satellite and reanalysis model improves this metric to 0.575, a gain of 0.173. The analysis indicates that low‑level winds are poorly predicted from satellite imagery alone (R² = 0.23) and that excluding wind or moisture variables from the model leads to the most significant drops in detection performance.
The paper presents Laxmi, a retrained version of the AIFS weather model that uses satellite-based precipitation observations instead of ERA5 reanalysis data. Laxmi achieves a 19% improvement in global probabilistic accuracy, reduces drizzle overprediction by 33%, and boosts the 95th percentile Brier skill score by 57%. In a case study of 10 Indian tropical storms, Laxmi accurately forecasted 150 mm event-total precipitation in 7 events, outperforming both the original AIFS and the leading physical model IFS.