Multi-Scale Forecasting of Severe Wind Events: Insights from Two Contrasting Cases in Zagreb

Severe weather events can arise from fundamentally different atmospheric processes, posing distinct challenges for forecasting and warning systems. This study compares two high-impact events in Zagreb: a large-scale windstorm in March 2026 and a convective storm in July 2023, both of which caused significant damage in an urban area despite their different origins. In both cases, the damage in Zagreb was primarily caused by wind. In March 2026, this occurred in combination with heavy rainfall, although wind speeds did not reach record values. In contrast, the July 2023 event featured a prolonged episode of extreme wind gusts, reaching record values for the Zagreb area, where such intensities are uncommon.
The March 2026 event was associated with a well-developed synoptic-scale system, characterized by strong pressure gradients and widespread wind fields. Such conditions were captured by global and regional numerical weather prediction (NWP) models, with ALADIN-HR providing a reliable baseline for forecasting precipitation and wind intensity as well as spatial extent several days in advance. In contrast, the July 2023 event was driven by deep convection, featuring localized but extremely intense wind gusts and rapid storm evolution, which proved significantly more difficult to predict using standard NWP approaches. For the convective case, short-term forecasting tools and observationally driven systems, such as INCA, can play a crucial role in capturing storm development and evolution at relevant spatial and temporal scales. Additional value for both can be provided by post-processing approaches, including machine-learning-based methods, that help refine local-scale impact estimates.
By analysing these two contrasting cases, the study highlights how the usefulness of forecasting tools depends strongly on the dominant atmospheric processes. The results demonstrate the importance of a multi-scale forecasting framework that combines information from global and regional models, nowcasting systems, and post-processing methods, while acknowledging the inherent limitations of each approach.