KI:STE Project − AI Strategy for Earth System Data

Abstract

Artificial intelligence (AI) methods currently experience rapid development and are also used more and more frequently in environmental and Earth system sciences. To date however, this is often done in the context of isolated rather than systematic solutions. In particular, for researchers there is often a discrepancy between the requirements of a solid and technically sound environmental data analysis and the availability of modern AI methods such as deep learning. Their systematic use is not yet established in environmental and Earth system sciences. The recently started KI:STE project bridges this gap with a dedicated strategy that combines both, the development of AI applications and a strong training and network concept, thereby covering different relevant aspects of environmental and Earth system research. It creates the technical prerequisites to make high-performance AI applications on environmental data portable for future users and to establish environmental AI as a key technology. Specifically, within KI:STE an AI-platform is envisioned which unifies machine learning (ML) workflows designed to study five core Earth system topics: cloud variability, hydrology, earth surface processes, vegetation health and air quality. All of them are strongly coupled and will profit from ML, e.g. to extend locally available information into global maps, or the track the interplay of spatio-temporal variability on different scales along process cascades. Besides being already connected across disciplines in the classical sense, KI:STE aims to furthermore bridge between these different topics by jointly addressing cutting edge ML research questions beyond pure algorithmic approaches. In particular, we will put emphasize on an explainable AI approach, which itself is a yet to be explored highly relevant topic within the Earth system sciences. It has the potential to connect the interdisciplinary work on yet another level. KI:STE will also launch an e-learning platform in order to support the usage of the AI-platform as well as to communicate the knowledge to adequately use ML techniques within the different Earth system science domains.

Authors 8

1. Scarlet Stadtler corresponding

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany

2. Julia Kowalski Aachen Aachen Institute for Advanced Study in Computational Engineering Science (AICES)

   Aachen Institute for Advanced Study in Computational Engineering Science · RWTH Aachen University

   Affiliation as printed

   Aachen Institute for Advanced Study in Computational Engineering Science, RWTH Aachen, Aachen, Germany

3. Markus Abel
   Affiliation as printed

   Ambrosys GmbH - Gesellschaft für Management komplexer Systeme, Potsdam, Germany

4. Ribana Roscher corresponding

   University of Bonn

   Affiliation as printed

   Institute for Geodesy and Geoinformation Bonn, University of Bonn, Bonn, Germany

5. Susanne Crewell corresponding

   University of Cologne

   Affiliation as printed

   Institute for Geophysics and Meteorology, Cologne University, Cologne, Germany

6. Benedikt Gräler corresponding

   52°North Spatial Information Research

   Affiliation as printed

   52°North Initiative for Geospatial Open Source Software GmbH, Muenster, Germany

7. Stefan J Kollet

   Forschungszentrum Jülich

   Affiliation as printed

   Insitute for Bio- and Geosciences, Forschungszentrum Jülich, Jülich, Germany

8. Martin G. Schultz corresponding

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany