Gian Franco Marras

This work presents a new dataset for recent climate developed within the Highlander project by dynamically downscaling ERA5 reanalysis, originally available at ≃31 km horizontal resolution, to ≃2.2 km resolution (i.e., convection permitting scale). Dynamical downscaling was conducted through the COSMO Regional Climate Model (RCM). The temporal resolution of output is hourly (like for ERA5). Runs cover the whole Italian territory (and neighboring areas according to the necessary computation boundary) to provide a very detailed (in terms of space–time resolution) and comprehensive (in terms of meteorological fields) dataset of climatological data for at least the last 30 years (01/1989-12/2020). These types of datasets can be used for (applied) research and downstream services (e.g., for decision support systems).

At the national level, in Italy, observational and forecast data are collected by various public bodies and are often kept in various small, heterogeneous and non‐interoperable repositories, released under different licenses, thus limiting the usability for external users. In this context, MISTRAL (the Meteo Italian SupercompuTing PoRtAL) was launched as the first Italian meteorological open data portal, with the aim of promoting the reuse of meteorological data sets available at national level coverage. The MISTRAL portal provides (and archives) meteorological data from various observation networks, both public and private, and forecast data that are generated and post‐processed within the Consortium for Small‐scale Modeling‐Limited Area Model Italia (COSMO‐LAMI) agreement using high performance computing (HPC) facilities. Also incorporated is the Italy Flash Flood use case, implemented with the collaboration of European Centre for Medium‐Range Weather Forecasts (ECMWF), which exploits cutting edge advances in HPC‐based post‐processing of ensemble precipitation forecasts, for different model resolutions, and applies those to deliver novel blended‐resolution forecasts specifically for Italy. Finally, in addition to providing architectures for the acquisition and display of observational data, MISTRAL also delivers an interactive system for visualizing forecast data of different resolutions as superimposed multi‐layer maps.