Performance of the AROME Model for Forecasting Heavy Precipitation Events in Portugal

Precipitation forecasting remains challenging due to the complexity of its driving mechanisms and model limitations in resolving short temporal scales and subgrid processes. This study provides a comprehensive assessment of the performance of the Application of Research to Operations at Mesoscale (AROME) model in forecasting precipitation over mainland Portugal, using observations from a network of automatic weather stations for the period 2022–2023. Forecast skill is evaluated using a combination of categorical metrics derived from contingency tables and spatial verification approaches, enabling a multi-scale analysis of model performance. Results reveal a decrease in model skill with increasing precipitation thresholds. However, model performance improves for longer accumulation periods and when evaluated over larger spatial neighbourhoods, highlighting the importance of phase errors. To identify skilful predictors of heavy precipitation events, convective conditions were analysed during two illustrative extreme events in the Douro (Northern Portugal) and Alentejo (Southern Portugal) wine regions. Thunderstorm diagnostic parameters derived from the AROME model show good agreement with observed lightning activity, demonstrating skill in identifying favourable conditions for deep convection. Additionally, the consistency across forecast lead times suggests that these indices can support early identification of convective activity. These findings highlight the potential of combining precipitation forecasts with thunderstorm diagnostic parameters to improve operational early warning systems and the implementation of suitable risk reduction measures.

Acknowledgements: The authors acknowledge National Funds by FCT – Portuguese Foundation for Science and Technology, under the projects UID/04033/2025: Centre for the Research and Technology of Agro-Environmental and Biological Sciences (https://doi.org/10.54499/UID/04033/2025) and LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020). Project WATERKNOW – Infraestrutura de Conhecimento Geoespacial para a Gestão Inteligente dos Recursos Hídricos (NORTE2030-FEDER-01392400)