Multiregional Satellite Precipitation Products Evaluation over Complex Terrain

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Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Autonomous ballooning allows for energy-efficient long-range missions but introduces significant challenges for planning and control algorithms, due to their single degree of actuation: vertical rate control through either buoyancy or vertical thrust. Late ...

Lightning is directly or indirectly responsible for significant human casualties and property damage worldwide. A timely prediction of its occurrence can enable authorities and the public to take necessary precautionary actions resulting in diminishing the ...

Data-driven algorithms—such as signal processing and artificial neural networks—are required to process and extract meaningful information from the massive amounts of data currently being produced in the world. This processing is, however, limited by the t ...

The principle of tailoring material properties to improve the mechanical behaviour of soils through compaction or cement grouting dates to the 60s. The increasing trends of urbanization worldwide require new solutions for the development of resilient and s ...

On average every 2 years, the stratospheric polar vortex exhibits extreme perturbations known as sudden stratospheric warmings (SSWs). The impact of these events is not limited to the stratosphere: but they can also influence the weather at the surface of ...

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring resu ...

Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model a ...

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...

Stable water isotopes (SWIs) contain valuable information on the past climate and phase changes in the hydrologic cycle. Recently, vapor measurements in the polar regions have provided new insights into the effects of snow-related and atmospheric processes ...

Wind energy potential in complex terrain is still poorly understood and difficult to quantify. With Switzerland’s current efforts to shift to renewable energy resources, it is now becoming even more crucial to investigate the hidden potential of wind energ ...

Deep neural networks may easily memorize noisy labels present in real-world data, which degrades their ability to generalize. It is therefore important to track and evaluate the robustness of models against noisy label memorization. We propose a metric, ca ...

In this thesis we address various factors that contribute both theoretically and practically to mitigating supply demand mismatches. The thesis is composed of three chapters, where each chapter is an independent scientific paper. In the first paper, we dev ...

The high-impact nature and increasing occurrence of severe weather phenomena pushes forward research on their occurrence and improving our understanding thereof. Supercell thunderstorms are the focus of much severe convective research, as they represent on ...

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

Mapping the neural patterns that drive human behavior is a key challenge in neuroscience. Even the simplest of our everyday actions stem from the dynamic and complex interplay of multiple neural structures across the central nervous system (CNS). Yet, most ...

This thesis presents work at the junction of statistics and climate science. We first provide methodology for use by climate scientists when performing fast event attribution using extreme value theory, and then describe two interdisciplinary projects in c ...

The governing hydrological processes are expected to shift under climate change in the alpine regions of Switzerland. This raises the need for more adaptive and accurate methods to estimate river flow. In high-altitude catchments influenced by snow and gla ...

The surface mass balance (SMB) of large polar ice sheets and of snow and ice surfaces in general are incompletely understood because of the complexity of processes involved. One such process, drifting and blowing snow, has only been considered in a very si ...

Clouds are omnipresent in the Earth's atmosphere. Their phase composition significantly modulates their interaction with solar and terrestrial radiation, as well as precipitation formation. Particularly for clouds containing both phases, known as mixed-pha ...

Multiregional Satellite Precipitation Products Evaluation over Complex Terrain

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Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

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Wind, Hail, and Climate Extremes: Modelling and Attribution Studies for Environmental Data

Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network

CRYOWRF—Model Evaluation and the Effect of Blowing Snow on the Antarctic Surface Mass Balance

The role of secondary ice production in mixed-phase clouds