Understanding the role of synoptic and mesoescalar models in the context of severe weather forecasts in Basque Country

In Basque Country a region, with complex topography, located in the North of Iberian Peninsula, accurate prediction models for severe weather phenomena forecast are crucial. The Basque meteorology agency (Euskalmet) works with synoptic and mesoscale meteorological models for operational prediction purposes. These models help anticipate and mitigate the impacts of severe events like extreme high temperatures, thunderstorms, heavy rainfall or flash floods, which can be intensified by terrain characteristics. These prediction models enhance decision making to protect the population and infrastructure from severe weather hazards.

Synoptic and mesoscale modelling are essential for accurate weather forecasting. Synoptic-scale models capture large-scale weather patterns, while mesoscale models focus on smaller localized features like orographically driven phenomena, mesoscale convective systems or convective scale processes. Combining both scales improves predictions, vital for mitigating damages in such areas. Understanding the role of synoptic and mesoscale models in weather forecasting requires validating these models across various scenarios. One key aspect is to assess their performance in accurately representing precipitation and temperature patterns. By evaluating model accuracy under different conditions, forecasters can adjust and enhance forecasting techniques. This validation process is crucial for ensuring reliable predictions.

During this work, we have used some models currently operational at Euskalmet, including synoptic models like GFS and ECMWF, as well as own mesoscale model configurations and external mesoscale models such as WRF, MM5 or AROME. Our analysis has focused on severe weather episodes versus non-severe ones, assessing severity based on the official weather warning system of the Basque meteorological agency.

To carry out the validation and analysis process, several indexes and graphs are prepared. In the set of graphs, we have worked with scatter plots and Taylor diagram for comparing the quality and accuracy among weather models. Indices are calculated using three approaches: continuous, categorical, and areal. Within the set of continuous we have the usual indices RMSE, bias, correlation, etc. In the categorical approach, contingency tables are used to understand dichotomous (yes/no) forecasts based on different severity criteria. Severe events are a common example of this type of forecast. Several validation scores can be obtained from contingency tables: Proportion Correct score (PC), Probability of Detection (POD), False Alarm Rate (FAR), Critical Success Index (CSI), etc. An object-based quality measure (SAL) is applied for areal verification of precipitation forecasts.

Conclusions and recommendations for forecasters are obtained from the results of the validation process. The results of this work are intended to be a fundamental (key, essential) step towards the understanding and modelling of atmospheric hazards and severe weather phenomena in the operational context of Euskalmet.