Dr. Scarlet Stadtler

Scarlet Stadtler

PostDoc at Jülich Supercomputing Centre

Division Federated Systems and Data

Group Member of Earth System Data Exploration

Principal Investigator of the KI:STE project

Artificial Intelligence and Machine Learning Research

Explainable AI

Uncertainty Quantification

Mapping of Geographical Data to Air Quality Data


Every year, Forschungszentrum Jülich organizes a festive lecture evening to mark the end of the year. This year, Jülich’s young investigators give an insight into their exciting and forward-looking work. I was lucky to be among them! Johannes Laube uses weather balloons to send measuring instruments into the little researched stratosphere, where he collects data on its composition. I am talking about modelling environmental and Earth system data using artificial intelligence.

AI Strategy for Earth System Data Project KI:STE

Environmental data consists of heterogeneous, big datasets encoding not fully understood spatio-temporal processes. These datasets pose unique challenges to Earth scientists decoding natural processes to solve global environmental challenges. The recent algorithmic developments and impressing capabilities of artificial intelligence led to first real world applications using environmental data. Under JSC leadership, the KI:STE project aims to facilitate the application of large scale machine learning on HPC systems for environmental data by using a sophisticated strategy that combines the development of an Earth-AI-Platform with strong training and network concept. The Earth-AI-Platform will create 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.

kiste overview
KI:STE Project EGU Presentation

KI gegen den Klimawandel: Bundesumweltministerin Svenja Schulze im Forschungszentrum

Jülich, 28. Juni 2021 – Bundesumweltministerin Svenja Schulze machte auf ihrer Sommerreise heute Station im Forschungszentrum Jülich. Im Zentrum des Besuchs standen Informationen über energieeffizientes Supercomputing und der Einsatz Künstlicher Intelligenz (KI) für den Klima- und Umweltschutz. Die Jülicher Forscherinnen und Forscher wollen Methoden der KI nutzen, um Gefahren durch den Klimawandel frühzeitig zu erkennen. Mit JUWELS können sie dafür auf einen äußerst energieeffizienten und den aktuell schnellsten Superrechner Europas zurückgreifen.

Copyright: Forschungszentrum Jülich / Ralf-Uwe Limbach

Source: FZ-Jülich

AQ-Bench: a benchmark dataset for machine learning on global air quality metrics

Abstract. With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air quality based on this metadata. AQ-Bench contains a unique collection of aggregated air quality data from the years 2010–2014 and metadata at more than 5500 air quality monitoring stations all over the world, provided by the first Tropospheric Ozone Assessment Report (TOAR). It focuses in particular on metrics of tropospheric ozone, which has a detrimental effect on climate, human morbidity and mortality, as well as crop yields. The purpose of this dataset is to produce estimates of various long-term ozone metrics based on time-independent local site conditions. We combine this task with a suitable evaluation metric. Baseline scores obtained from a linear regression method, a fully connected neural network and random forest are provided for reference and validation. AQ-Bench offers a low-threshold entrance for all machine learners with an interest in environmental science and for atmospheric scientists who are interested in applying machine learning techniques. It enables them to start with a real-world problem relevant to humans and nature. The dataset and introductory machine learning code are available in this paper. (Betancourt et al., 2020) and https://gitlab.version.fz-juelich.de/esde/machine-learning/aq-bench (Betancourt et al., 2021). AQ-Bench thus provides a blueprint for environmental benchmark datasets as well as an example for data re-use according to the FAIR principles.
https://doi.org/10.5194/essd-13-3013-2021, 2021

C. Betancourt et al.: AQ-Bench

Can deep learning beat numerical weather prediction?

Abstract. The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology. There is some evidence that better weather forecasts can be produced by introducing big data mining and neural networks into the weather prediction workflow. Here, we discuss the question of whether it is possible to completely replace the current numerical weather models and data assimilation systems with DL approaches. This discussion entails a review of state-of-the-art machine learning concepts and their applicability to weather data with its pertinent statistical properties. We think that it is not inconceivable that numerical weather models may one day become obsolete, but a number of fundamental breakthroughs are needed before this goal comes into reach. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.
https://doi.org/10.1098/rsta.2020.0097, 2021

M. G. Schultz, C. Betancourt, B. Gong, F. Kleinert, M. Langguth, L. H. Leufen, A. Mozaffari and S. Stadtler