Climate reanalyses provide a description the of past weather by retrospectively assimilating reprocessed observational datasets ranging from surface stations and satellites with an up-to-date Numerical Weather Prediction (NWP) model. The resulting time series of the atmospheric state is both dynamically consistent and close to observations. A reanalysis typically provides a broad set of atmospheric parameters, containing near surface parameters, (as e.g. temperature and precipitation), as well as parameters at several altitudes (as e.g. wind).

Regional reanalyses are now available for Europe and specific sub-domains, e.g. produced by national meteorological services. Global and regional reanalyses are an important element of the Copernicus Climate Change Services.

The interest in extracting climate information from reanalysis is rising and they are used in a wide range of applications. In recent years, it has become apparent that reanalyses are a popular basis for training in machine learning methods that enable successful AI-based weather forecasts, for example.

This session invites papers that:
• Present the status of reanalysis activities in Europe and beyond.
• Explore and demonstrate the capability of global and regional reanalysis data for climate applications, including energy applications.
• Illustrate the role of reanalysis data for machine learning and artificial intelligence.
• Compare different reanalysis (global, regional) with each other and/or observations
• Improve recovery, quality control and uncertainty estimation of related observations
• Analyse the uncertainty budget of the reanalyses and relate to user applications

Depending on the submitted contributions, the session could also provide a platform for discussions about the requirements of reanalysis producers towards data providers.