The Earth System Data Exploration group – home of the IntelliAQ project team 2019 (from left to right):  Clara Betancourt, Bing Gong, Jessica Ahring, Sabine Schröder, Mathilde Romberg, Najmeh Kaffazahdeh, Martin Schultz, Felix Kleinert, Lukas Leufen, and Amirpasha Mozaffari

PD Dr Martin Schultz leads the group on Earth System Data Exploration (ESDE) at the Jülich Supercomputing Centre (JSC). He holds the ERC grant for IntelliAQ and leads the activities on high-performance data provisioning. Schultz is a well-known expert in atmospheric science and numerical simulations. His research interests now focus on high performance data services and machine learning applications in the fields of meteorology and air quality.

Clara Betancourt is a doctoral researcher and team member of the ESDE group. She graduated in the University of Cologne and holds a master’s degree in Physics of the Earth and Atmosphere. Her research focuses on mapping station metadata to air quality metrics for the interpolation of air quality data with neural networks.

Dr Bing Gong is a postdoctoral researcher of the ESDE group at JSC.  She joined the ESDE group in January 2019. Her current duties in the group are developing state-of-art scalable deep learning neural networks with a focus on time series prediction and video frame prediction in weather and air quality applications. She obtained her Ph.D. in the field of artificial intelligence in the application of environmental science and energy from the Technical University of Madrid, Spain, in July 2017.

Felix Kleinert is a doctoral researcher within the ESDE group. He holds a master’s degree in Physics of Earth and Atmosphere from the Rheinische Friedrich-Wilhelms-University of Bonn. His research interests focus on the development and application of machine learning techniques to improve local meteorological point forecasts.

Michael Langguth holds a Master degree in Physics of the Earth and Atmosphere from the Rheinische Friedrich-Wilhelms-University of Bonn. Before he joined the ESDE group in March 2020, he implemented a hybrid parametrization scheme for deep convection in the ICOsahedral Non‐hydrostatic (ICON) model developed by the DWD and the MPI-M as part of his dissertation. Now, his academic focus lies on the development of neural networks for atmospheric Earth system applications combined with expertise from numerical modelling.

Lukas H. Leufen is a doctoral researcher within the ESDE group. During his master studies in meteorology at the Karlsruhe Institute of Technology, he focused on the adaptability of neural networks on boundary layer meteorology and Monin-Obukhov theory. Beside his studies, he applied machine learning in the scope of European power markets. His current academic focus is on forecasting air quality data time series based on station measurements. In particular, he is interested in teaching underlying physical laws to neural networks (physical-guided machine learning) and the potential of time-related filtering during data preprocessing to improve the network’s forecast accuracy.

Amirpasha Mozaffari is the data manager of the ESDE group at JSC and responsible for developing the data management and workflows plans. Mozaffari has been trained in terrestrial earth system science and worked on numerical and statistical analysis of environmental data on supercomputers as well as numerical simulations and inversions of groundwater flow before he joined the ESDE group in June 2019.

Sabine Schröder is one of the main developers of the TOAR database ( Before she joined the ESDE group at JSC in 2019, she worked as an application programmer in the field of Atmospheric Chemistry with focus on climate modelling on the HPC systems in Jülich.

Dr Scarlet Stadtler is a postdoctoral researcher in the ESDE group at JSC. She obtained her Ph.D. in the field of meteorology in the development of multi-phase chemistry in a global climate model from the University of Bonn in July 2018 and joined the ESDE group in February 2020 after a year abroad in Tokyo, Japan. She is involved in different research projects for using machine learning and deep learning in the context of the atmosphere. Her main task is to contribute to the development of a workflow for deep learning based weather prediction. This deep learning framework should be a base for a spatial temporal air quality forecast for IntelliAQ. Within IntelliAQ she contributes to the pattern recognition in air quality data. Besides, she is principal investigator of a project for the application of AI on earth system data.

Former team members

Dr Najmeh Kaffashzadeh