The ESDE group explores the use of advanced deep learning methods and large data workflows for analysing and forecasting atmospheric data with a focus on air quality and weather.

Our ability to analyse air quality, weather and climate data is fundamentally important to save lives, for example during extreme weather events, to protect nature and biodiversity and to create and preserve economic value through science-based decision making on mitigation and protection measures. Modern machine learning can play an important role to complement or even substitute traditional simulation models and to extract more information from the huge amount of environmental monitoring data that has become available in recent years. The handling, processing and distribution of such data with modern high-performance computing technology abiding to open, federated and FAIR principles is a necessary requirement for building sustainable tools for the analysis of the environment, but also an interesting research topic in itself. The ESDE group works on end-to-end solutions and interacts with many international partners to revolutionise research on air quality, climate and weather.

Within the Program-oriented Funding (PoF IV) of Helmholtz Information, this group contributes to Program 1 “Engineering Digital Futures”, Topic 1 “Enabling Computational- & Data-Intensive Science and Engineering” and is part of the Joint Lab Exascale Earth System Modelling (ExaESM) and of the Center for Advanced Simulation and Analytics (CASA).