PD Dr. Martin Schultz leads the group on Earth System Data Exploration (ESDE) at the Jülich Supercomputing Centre (JSC). He coordinates the DeepRain project 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.

Mr. Amirpasha Mozaffari is the data manager of the ESDE group at JSC and responsible for developing the data workflows and data management plans of the DeepRain project. 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 ground water flow before he joined the ESDE group in June 2019.

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 PhD student within the ESDE group. He holds a master’s degree in Phsilk 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 joined the ESDE group at the JSC in March 2020. During his work at the Meteorological Section of the Institute for Geosciences, University Bonn as a doctoral student, he implemented a hybrid approach for parameterizing deep convection in the ICOsahedral Non‐hydrostatic (ICON) model developed by the DWD and the MPI-M. He has profound expertise in numerical modeling of the atmosphere with special focus on the representation of precipitation processes. In the DeepRain project, his research focuses on the development of neuronal network architectures suitable for precipitation forecasts and their verification including the forecasts of the underlying weather prediction model.

Prof. Dr. Peter Baumann is Professor of Computer Science at Jacobs University, researching on datacube services and their application in science and engineering. With the rasdaman engine he and his team have pioneered datacubes and Array Databases, with over 160 scientific publications and international patents. The rasdaman datacube engine is successfully commercialized internationally and has received a series of innovation awards. Peter Baumann is editor of the core datacube standards in ISO and OGC. He coordinates WP2 and contributes to WP6.

Dr. Sebastian Villarroya is a postdoctoral researcher in L-SIS (Large-Scale Information Systems) laboratory at Jacobs University Bremen. His expertise is on the integrated representation and distributed processing of Raster and Vector large datasets. Currently his research effort is focused on the efficient integration of Machine Learning algorithms into Array Databases.

Dr. Dimitar Mišev is a postdoc researcher on array databases at Jacobs University. He has experience in several past research projects, most recently as a coordinator of BigDataCube, lead the specification of ISO SQL/MDA which extends SQL with support for multidimensional arrays, and oversees the technical development of the core rasdaman engine. In DeepRain he participates as a senior member from the rasdaman team supporting the data management and integration of ML methods in rasdaman.

Mr. Pascal Nieters is part of the Osnabrück team responsible for developing the neural network and machine learning model the project will be using to downscale precipitation data. He is trained in Cognitive Science and is pursuing research at the intersection of theoretical neuroscience and machine learning in the neuroinformatics group of Prof. Dr. Gordon Pipa. As a doctoral student and research associate, he has extensive experience in both basic research and the application of machine learning techniques to a wide variety of problems. He coordinates WP3.

Mr. Rüdiger Busche is pursuing his PhD in the Neuroinformatics group in Osnabrück. His task is to develop deep learning models for improving the resolution of rain forecasts. Rüdiger studied Cognitive Science, where he focused on machine learning and artificial intelligence. In his research he aims at applying machine learning to real world problems with a focus on the spatio-temporal domain.

Dr. Erik J. Schaffernicht current work is mainly concerned with the statistic post-processing of an ensemble prediction system that results from a numerical weather prediction model: different statistical methods are investigated to improve numerical weather predictions, such as the regression of numerical weather predictions with station-observed precipitation and radar measurements. As a member of the Hans Ertel Zentrum (HErZ), he is affiliated to the German weather service (Deutscher Wetterdienst, DWD) and the Department for Meteorology at the University of Bonn.  In addition to his climate modelling expertise, he possesses a profound understanding of Circulation Weather Type (CWT) classification and statistic dynamic downscaling approaches of climate models. He has developed a combined empirical orthogonal function decomposition which he has applied to different experiments of the Max Planck Institute Earth System Model (MPI-ESM). Using this method, he recognized and compared atmospheric patterns over the continental-scale areas (such as Europe and the North Atlantic) during glacial and warm climate regimes.

Dr. Bernhard Reichert leads a team for Central Postprocessing within the business unit Research and Development at Deutscher Wetterdienst (DWD) in Offenbach. For 15 years he has been working on strategic planning, conception, and development of meteorological applications in order to improve the weather forecast and warning process. He got his PhD on the simulation and interpretation of pre-industrial climate variability and future anthropogenic climate change at the Max-Planck-Institute for Meteorology, Hamburg.

Dr. Jan Keller has been working as a meteorologist for 15 years on the development of probabilistic methods in the field of numerical weather forecasting. Since 2012 he has been head of the topic “Climate Monitoring and Diagnostics” at the Hans Ertel Centre of the German Weather Service. Here he is responsible for the development of regional re-analyses and the research of evaluation techniques for weather and climate data. In addition, he researches post-processing and downscaling approaches for precipitation time series in relation to their application in climate monitoring. He coordinates WP4.

Prof. Dr. Andreas Hense is acting as university professor at the Institute of Geosciences, Sect. Meteorology University Bonn since 1990 and heads the group „Climate Dynamics“. He is working and teaching in fields of atmospheric and climate physics involving stochastic elements like natural climate variability on recent and paleo time scales, anthropogenic climate change over the past century, quantitative precipitation forecasts over few hours to days and climate extremes. Central to all work over the past 30 years is the joint analysis of models and observations including the verification of forecasts across all time scales from hours to decades and the detection and attribution of anthropogenic climate change. He coordinates WP5.

Dr. Rita Glowienka-Hense is working at the Institute of Geosciences, Sect. Meteorology University Bonn on the analysis of climate and weather variability including the verification of respective forecasts from weather and climate model simulations.   As such she published papers on the variability and structure of the North Atlantic Oscillation NAO and the assessment of skill of decadal climate predictions.  Part of that work included the preparation of a verification tool for the global medium range climate predictions over decades from the German MiKliP project.