Statistical Postprocessing of Numerical Weather Prediction Forecasts using Machine Learning

Nowadays, weather prediction is based on numerical models of the physics of the atmosphere. These models are usually run multiple times based on randomly perturbed initial conditions. The resulting so-called ensemble forecasts represent distinct scenarios of the future and provide probabilistic projections. However, these forecasts are subject to systematic errors such as biases and they are often unable to quantify the forecast uncertainty adequately. Statistical postprocessing methods aim to exploit structure in past pairs of forecasts and observations to correct these errors when applied to future forecasts. In this thesis, we develop statistical postprocessing methods based on the central paradigm of probabilistic forecasting, that is, to maximize the sharpness subject to calibration. A wide range of statistical and machine learning methods is presented with a focus on novel neural network-based postprocessing techniques. In particular, we analyze the aggregation of distributional forecasts from neural network ensembles and develop statistical postprocessing methods for ensemble forecasts of wind gusts, with a focus on European winter storms

The community economies of Esch-sur-Alzette: rereading the economy of Luxembourg

This article outlines the community economies of Esch-sur-Alzette, the ‘second city’ of Luxembourg. ‘Community economies’ – an approach outlined by J.K. Gibson-Graham – draws attention to alternative narratives of economic development and the representation of economic identity. Despite (the Grand Duchy of) Luxembourg’s reputation as a European Union centre, with substantial finance and tax activity, Esch-sur-Alzette is a post-industrial and multilingual melting pot. The alternative narrative here is of the multiple community-based organisations and movements in Esch-sur-Alzette: an energy cooperative, urban gardening, an upcycling clothing factory, a local food shop and restaurant, and vibrant civil society discussions and interventions in (inter)national politics. Civil society, while central to both understandings of grassroots environmental action and the community economies framework of Gibson-Graham, takes on quite a different flavour in Luxembourg. This article then takes the case of Luxembourg to reread the relationship of the state to the so-called third sector, in doing so defending the political possibilities of community economies

Post-Growth Geographies: Spatial Relations of Diverse and Alternative Economies

Post-Growth Geographies examines the spatial relations of diverse and alternative economies between growth-oriented institutions and multiple socio-ecological crises. The book brings together conceptual and empirical contributions from geography and its neighbouring disciplines and offers different perspectives on the possibilities, demands and critiques of post-growth transformation. Through case studies and interviews, the contributions combine voices from activism, civil society, planning and politics with current theoretical debates on socio-ecological transformation

Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks

Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts into reliable probabilistic forecast distributions. In this study, we examine the use of permutation-invariant neural networks for this task. In contrast to previous approaches, which often operate on ensemble summary statistics and dismiss details of the ensemble distribution, we propose networks which treat forecast ensembles as a set of unordered member forecasts and learn link functions that are by design invariant to permutations of the member ordering. We evaluate the quality of the obtained forecast distributions in terms of calibration and sharpness, and compare the models against classical and neural network-based benchmark methods. In case studies addressing the postprocessing of surface temperature and wind gust forecasts, we demonstrate state-of-the-art prediction quality. To deepen the understanding of the learned inference process, we further propose a permutation-based importance analysis for ensemble-valued predictors, which highlights specific aspects of the ensemble forecast that are considered important by the trained postprocessing models. Our results suggest that most of the relevant information is contained in few ensemble-internal degrees of freedom, which may impact the design of future ensemble forecasting and postprocessing systems.Comment: Submitted to Artificial Intelligence for the Earth System

Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting

In order to enable the transition towards renewable energy sources, probabilistic energy forecasting is of critical importance for incorporating volatile power sources such as solar energy into the electrical grid. Solar energy forecasting methods often aim to provide probabilistic predictions of solar irradiance. In particular, many hybrid approaches combine physical information from numerical weather prediction models with statistical methods. Even though the physical models can provide useful information at intra-day and day-ahead forecast horizons, ensemble weather forecasts from multiple model runs are often not calibrated and show systematic biases. We propose a post-processing model for ensemble weather predictions of solar irradiance at temporal resolutions between 30 min and 6 h. The proposed models provide probabilistic forecasts in the form of a censored logistic probability distribution for lead times up to 5 days and are evaluated in two case studies covering distinct physical models, geographical regions, temporal resolutions, and types of solar irradiance. We find that post-processing consistently and significantly improves the forecast performance of the ensemble predictions for lead times up to at least 48 h and is well able to correct the systematic lack of calibration

Classified AGV Material Flow and Layout Data Set for Multidisciplinary Investigation

Automated Guided Vehicles (AGV) are increasingly used in industry to automate material flow tasks. To efficiently operate systems of AGVs, researchers have proposed many different planning and control methods, e.g., for scheduling, dispatching, and routing. The performance of these methods depends on the characteristics of the system, such as transport distances and station operation frequencies. Even though these characteristics strongly influence the algorithms, no classified collection of layout data was found based on a scientific literature review. In this paper, a data set of 72 material flow and layout compositions from the scientific literature (42) and German industry (30) is presented. Each composition in the data set consists of a transport matrix and a distance matrix. To classify the compositions, a holistic taxonomy was established based on distinguishing criteria for material flow and layout compositions known from the scientific literature. The compositions were classified according to the taxonomy. An analysis of the station operation frequency and transport distance distribution data reveals typical characteristics of the compositions as well as variations between the classified compositions. The aim of this data set is to allow benchmarking of planning and control methods, thus increasing the transparency and traceability of scientific work. Furthermore, the analysis of the layouts and their taxonomy allows to compare the methods of different disciplines. By providing standardized, machine readable formats, automatic testing and optimization will be possible