Weather and climate prediction are high-performance computing application with outstanding societal and economic impact ranging from the daily decision-making of citizens to that of civil services for emergency response, and from predicting weather drivers in food, agriculture and energy markets as well as for risk and loss management by insurances.

 

Forecasts are based on millions of observations made every day around the globe and physically based numerical models that represent processes acting on scales from hundreds of metres to thousands of kilometres in the atmosphere, the ocean, the land surface and the cryosphere. Forecast production and product dissemination to users is always time critical and forecast output data volumes already reach petabytes per week.

Meeting the future requirements for forecast reliability and timeliness needs 100-1000 times bigger highperformance computing and data management resources than today – towards what’s generally called ‘exascale’. To meet these needs, the weather and climate prediction community is undergoing one of its biggest revolutions since its foundation in the early 20th century.

 

This revolution encompasses a fundamental redesign of mathematical algorithms and numerical methods, the adaptation to new programming models, the implementation of dynamic and resilient workflows and the efficient post-processing and handling of big data. Due to these enormous computing and data challenges, artificial intelligence methods offer significant potential for gaining efficiency and for making optimal use of the generated information for European society.