Deep Learning Emulators for Accessible Climate Projections

Climate change has shifted from a purely scientific topic to a deeply politicized issue. To combat climate change we need to create mutual understanding on the links between policies, global warming, and city-scale impacts. Climate models have been incredibly helpful in generating this causal understanding, but running them requires supercomputers and is only accessible to the minority of researchers. This thesis explores how emulating climate models with deep learning can make them more accessible and, at the same time, raise novel challenges in deep learning on physical, long-term time-series, and high-dimensional data. This dissertation shows that deep learning can decrease runtime in dynamical models, increase accuracy in local climate projections, and generate visualizations of climate impacts. Specifically, this thesis contributes a hybrid model, called multiscale neural operator, that corrects fast low-resolution simulations by learning a hard-to-model parametrization term. This achieves to cut runtime complexity from quadratic to quasilinear which can result in a 1000x faster model on selected equations in multiscale dynamics. This thesis also contributes satellite imagery of the future that visualizes climate data using physically-consistent deep generative vision models. The thesis contributions are framed in an envisioned online tool that rapidly emulates the city-scale impacts of various climate policies. In the future, such an emulator could accelerate local climate risk analyses, attribution of extreme events, and the understanding of causal links between between impacts and policies.Ph.D

Airport2030 - Lösungen für den Lufttransport der Zukunft

Im Flightpath 2050, in der Hightech-Strategie der Bundesregierung und der Strategie des Hamburger Luftfahrtclusters werden Ziele für die Effizienz des Lufttransports hinsichtlich Nachhaltigkeit, Komfort, Reisezeit und Intermodalität benannt. Im Verbundprojekt Airport2030 werden am Beispiel des Flughafen Hamburg ausgewählte Technologien und Maßnahmen zu Flughafenanbindung, Terminalbetrieb, Flughafenprozesssteuerung, Flugzeugkonfigurationen und Bodeninfrastrukturen untersucht und bewertet. Es werden hier ein Überblick über das Verbundprojekt gegeben und ausgewählte Ergebnisse der bisherigen Projektlaufzeit vorgestellt. Das Verbundprojekt Airport2030 bildet den Leuchtturm III innerhalb der Spitzencluster-Förderung des Luftfahrtclusters Metropolregion Hamburg durch das Bundesministerium für Bildung und Forschung

GEO-Bench: Toward Foundation Models for Earth Monitoring

Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society

Einflussfaktoren auf Check-in-Wartezeiten am Beispiel des Flughafens Hamburg

Passagiere empfinden Wartezeiten während ihrer Reise als verlorene Zeit. Auf Flugreisen treten Wartezeiten jedoch häufig an verschiedenen Prozessstellen innerhalb eines Flughafens auf und können Unzufriedenheit oder sogar Stress auslösen. Am Beispiel des Flughafens Hamburg werden Einflussfaktoren analysiert, die speziell beim Check-in zu zeitlichen Prozessverzögerungen führen. Auf Basis der Ergebnisse einer Beobachtungsstudie werden Empfehlungen aufgezeigt, wie einerseits Wartezeiten und anderseits Konfusion und Stress unter den Passagieren am Check-in vermieden werden können