This paper describes an experimental protocol focused on sudden stratospheric warmings to be carried out by subseasonal forecast modeling centers. These will allow for inter-model comparisons of these major disruptions to the stratospheric polar vortex and their impacts on the near-surface flow. The protocol will lead to new insights into the contribution of the stratosphere to subseasonal forecast skill and new approaches to the dynamical attribution of extreme events.

This work presents the on-line coupled meteorology-chemistry transport model BOLCHEM. The paper describes the meteorological and chemical modules, and presents simulation results on the European domain for one year run. For all considered pollutants (O₃, NO₂, PM₁₀, PM_2.5) the model performances are close to those achieved by the current state-of-the-art model system dedicated to air quality study, e.g. Copernicus CAMS products.

The ensemble reforecasts of the CNR-ISAC and ECMWF forecasting systems, both participating to the S2S project, have been combined in a multimodel ensemble. Tercile probability predictions of wintertime 2 m temperature produced through logistic regression outperform the probability estimation based on the direct count of ensemble members, in terms of RPSS and reliability diagrams. Also, it is argued that the logistic regression would not yield further improvements if a larger dataset were used.

Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. This work shows the application of a technique to improve wind forecasting. The study area is southern Italy.

In the HyMeX framework, an intercomparison study of two different configurations of the ISPRA hydro-meteo-marine forecasting system (SIMM) has been performed to assess the forecast skill in predicting high precipitations and very intense storm surges over the northern Adriatic Sea (acqua alta). The results show an objective added value of the use of high-resolution meteorological models, and they suggest the opportunity to develop a time-lagged multi-model ensemble for the acqua alta prediction.