Earth System Data Exploration

Earth System Data Exploration


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 revolutionize research on air quality and weather.

We are proud partners of the AtmoRep initiative, host of the ERC Advanced Grant IntelliAQ and partners in the EuroHPC project MAELSTROM , the BMU-funded KI:STE project, the BMBF-funded WestAI sevice center and Warmworld project. Furthermore, we contribute to a Destination Earth use case on air quality.

Research Topics

  • Develop machine learning tools and methods for the interpolation, forecasting and quality control of global air pollution data including uncertainty analysis,
  • Investigate the use of high-end deep learning methods for weather forecasting and downscaling of weather model output,
  • Build and maintain a world-leading data infrastructure for global air quality observations with web-based analysis and visualisation capabilities,
  • Develop FAIR and scalable workflow solutions for extreme data management and dissemination in collaboration with leading weather and climate centres.


PD Dr. Martin Schultz


Building 14.14 / Room 4010

+49 2461/61-96870


Research Topics

Deep Learning for Weather and Air Quality

Team lead: Michael Langguth

Data Infrastructure and Workflows

Team lead: Sabine Schröder


Team lead: Michael Langguth




Dr. Wing Yi LiNoneBuilding 14.14 / Room 4002+49 2461/61-85470

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Last Modified: 24.06.2024