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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 that 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 a few ensemble-internal degrees of freedom, which may impact the design of future ensemble forecasting and postprocessing systems., Comment: in press

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biassed and underdispersed, meteorologists post-process the ensembles. This post-processing can successfully correct the biasses in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post-processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post-processing method and evaluate four possible strategies: only using the raw ensembles without post-processing, a one-step strategy where only the weather ensembles are post-processed, a one-step strategy where we only post-process the power ensembles and a two-step strategy where we post-process both the weather and power ensembles. Results show that post-processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness whilst only post-processing the weather ensembles does not necessarily lead to increased forecast performance., Funding Agencies|Deutsche Forschungsgemeinschaft [EXC 2064/1, RTG 2153, SFB/TRR 165]; Helmholtz-Gemeinschaft

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biassed and underdispersed, meteorologists post-process the ensembles. This post-processing can successfully correct the biasses in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post-processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post-processing method and evaluate four possible strategies: only using the raw ensembles without post-processing, a one-step strategy where only the weather ensembles are post-processed, a one-step strategy where we only post-process the power ensembles and a two-step strategy where we post-process both the weather and power ensembles. Results show that post-processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness whilst only post-processing the weather ensembles does not necessarily lead to increased forecast performance., Funding Agencies|Deutsche Forschungsgemeinschaft [EXC 2064/1, RTG 2153, SFB/TRR 165]; Helmholtz-Gemeinschaft

The Madden–Julian Oscillation (MJO) is a major source of predictability on the sub-seasonal (10 to 90 d) timescale. An improved forecast of the MJO may have important socioeconomic impacts due to the influence of MJO on both tropical and extratropical weather extremes. Although in the last decades state-of-the-art climate models have proved their capability for forecasting the MJO exceeding the 5-week prediction skill, there is still room for improving the prediction. In this study we use multiple linear regression (MLR) and a machine learning (ML) algorithm as post-processing methods to improve the forecast of the model that currently holds the best MJO forecasting performance, the European Centre for Medium-Range Weather Forecasts (ECMWF) model. We find that both MLR and ML improve the MJO prediction and that ML outperforms MLR. The largest improvement is in the prediction of the MJO geographical location and intensity., The authors thank Linus Magnusson for providing access to the data. This work received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska–Curie Actions agreement no. 813844. Cristina Masoller also acknowledges funding by the Spanish Ministerio de Ciencia, Innovacion y Universidades (PGC2018-099443- B-I00) and the ICREA ACADEMIA program of Generalitat de Catalunya. Sebastian Lerch acknowledges support by the Vector Stiftung through the Young Investigator Group “Artificial Intelligence for Probabilistic Weather Forecasting”., Peer Reviewed, Postprint (published version)

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biassed and underdispersed, meteorologists post-process the ensembles. This post-processing can successfully correct the biasses in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post-processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post-processing method and evaluate four possible strategies: only using the raw ensembles without post-processing, a one-step strategy where only the weather ensembles are post-processed, a one-step strategy where we only post-process the power ensembles and a two-step strategy where we post-process both the weather and power ensembles. Results show that post-processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness whilst only post-processing the weather ensembles does not necessarily lead to increased forecast performance., Funding Agencies|Deutsche Forschungsgemeinschaft [EXC 2064/1, RTG 2153, SFB/TRR 165]; Helmholtz-Gemeinschaft

Ensemble weather forecasts based on multiple runs of numerical weather prediction models typically show systematic errors and require post-processing to obtain reliable forecasts. Accurately modeling multivariate dependencies is crucial in many practical applications, and various approaches to multivariate post-processing have been proposed where ensemble predictions are first post-processed separately in each margin and multivariate dependencies are then restored via copulas. These two-step methods share common key limitations, in particular the difficulty to include additional predictors in modeling the dependencies. We propose a novel multivariate post-processing method based on generative machine learning to address these challenges. In this new class of nonparametric data-driven distributional regression models, samples from the multivariate forecast distribution are directly obtained as output of a generative neural network. The generative model is trained by optimizing a proper scoring rule which measures the discrepancy between the generated and observed data, conditional on exogenous input variables. Our method does not require parametric assumptions on univariate distributions or multivariate dependencies and allows for incorporating arbitrary predictors. In two case studies on multivariate temperature and wind speed forecasting at weather stations over Germany, our generative model shows significant improvements over state-of-the-art methods and particularly improves the representation of spatial dependencies.

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biassed and underdispersed, meteorologists post-process the ensembles. This post-processing can successfully correct the biasses in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post-processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post-processing method and evaluate four possible strategies: only using the raw ensembles without post-processing, a one-step strategy where only the weather ensembles are post-processed, a one-step strategy where we only post-process the power ensembles and a two-step strategy where we post-process both the weather and power ensembles. Results show that post-processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness whilst only post-processing the weather ensembles does not necessarily lead to increased forecast performance., Funding Agencies|Deutsche Forschungsgemeinschaft [EXC 2064/1, RTG 2153, SFB/TRR 165]; Helmholtz-Gemeinschaft

Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing. Even state-of-the-art post-processing methods based on neural networks often solely rely on location-specific predictors that require an interpolation of the physical weather model's spatial forecast fields to the target locations. However, potentially useful predictability information contained in large-scale spatial structures within the input fields is potentially lost in this interpolation step. Therefore, we propose the use of convolutional autoencoders to learn compact representations of spatial input fields which can then be used to augment location-specific information as additional inputs to post-processing models. The benefits of including this spatial information is demonstrated in a case study of 2-m temperature forecasts at surface stations in Germany., Comment: Accepted as conference paper at ICLR 2022 - AI for Earth and Space Science Workshop, https://ai4earthscience.github.io/iclr-2022-workshop

The importance of accurately quantifying forecast uncertainty has motivated much recent research on probabilistic forecasting. In particular, a variety of deep learning approaches has been proposed, with forecast distributions obtained as output of neural networks. These neural network-based methods are often used in the form of an ensemble based on multiple model runs from different random initializations, resulting in a collection of forecast distributions that need to be aggregated into a final probabilistic prediction. With the aim of consolidating findings from the machine learning literature on ensemble methods and the statistical literature on forecast combination, we address the question of how to aggregate distribution forecasts based on such deep ensembles. Using theoretical arguments, simulation experiments and a case study on wind gust forecasting, we systematically compare probability- and quantile-based aggregation methods for three neural network-based approaches with different forecast distribution types as output. Our results show that combining forecast distributions can substantially improve the predictive performance. We propose a general quantile aggregation framework for deep ensembles that shows superior performance compared to a linear combination of the forecast densities. Finally, we investigate the effects of the ensemble size and derive recommendations of aggregating distribution forecasts from deep ensembles in practice.

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biassed and underdispersed, meteorologists post-process the ensembles. This post-processing can successfully correct the biasses in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post-processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post-processing method and evaluate four possible strategies: only using the raw ensembles without post-processing, a one-step strategy where only the weather ensembles are post-processed, a one-step strategy where we only post-process the power ensembles and a two-step strategy where we post-process both the weather and power ensembles. Results show that post-processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness whilst only post-processing the weather ensembles does not necessarily lead to increased forecast performance., Funding Agencies|Deutsche Forschungsgemeinschaft [EXC 2064/1, RTG 2153, SFB/TRR 165]; Helmholtz-Gemeinschaft

The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford workshop on Machine Learning for Weather and Climate, this paper also presents thoughts on medium-term goals to advance such use of AI, which include assuring that algorithms are trustworthy and interpretable, adherence to FAIR data practices to promote usability, and development of techniques that leverage our physical knowledge of the atmosphere. The coauthors propose several actionable items and have initiated one of those: a repository for datasets from various real weather and climate problems that can be addressed using AI. Five such datasets are presented and permanently archived, together with Jupyter notebooks to process them and assess the results in comparison with a baseline technique. The coauthors invite the readers to test their own algorithms in comparison with the baseline and to archive their results.

The amount, size, and complexity of astronomical data-sets and databases are growing rapidly in the last decades, due to new technologies and dedicated survey telescopes. Besides dealing with poly-structured and complex data, sparse data has become a field of growing scientific interest. A specific field of Astroinformatics research is the estimation of redshifts of extra-galactic sources by using sparse photometric observations. Many techniques have been developed to produce those estimates with increasing precision. In recent years, models have been favored which instead of providing a point estimate only, are able to generate probabilistic density functions (PDFs) in order to characterize and quantify the uncertainties of their estimates. Crucial to the development of those models is a proper, mathematically principled way to evaluate and characterize their performances, based on scoring functions as well as on tools for assessing calibration. Still, in literature inappropriate methods are being used to express the quality of the estimates that are often not sufficient and can potentially generate misleading interpretations. In this work we summarize how to correctly evaluate errors and forecast quality when dealing with PDFs. We describe the use of the log-likelihood, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) to characterize the calibration as well as the sharpness of predicted PDFs. We present what we achieved when using proper scoring rules to train deep neural networks as well as to evaluate the model estimates and how this work led from well calibrated redshift estimates to improvements in probabilistic weather forecasting. The presented work is an example of interdisciplinarity in data-science and illustrates how methods can help to bridge gaps between different fields of application., Comment: Presented at ADASS XXX 2020

The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford workshop on Machine Learning for Weather and Climate, this paper also presents thoughts on medium-term goals to advance such use of AI, which include assuring that algorithms are trustworthy and interpretable, adherence to FAIR data practices to promote usability, and development of techniques that leverage our physical knowledge of the atmosphere. The coauthors propose several actionable items and have initiated one of those: a repository for datasets from various real weather and climate problems that can be addressed using AI. Five such datasets are presented and permanently archived, together with Jupyter notebooks to process them and assess the results in comparison with a baseline technique. The coauthors invite the readers to test their own algorithms in comparison with the baseline and to archive their results.

Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing, that can be divided in three groups: State of the art postprocessing techniques from statistics (ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression), established machine learning methods (gradient-boosting extended EMOS, quantile regression forests) and neural network-based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using six years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer.

Accurate and reliable forecasting of total cloud cover (TCC) is vital for many areas such as astronomy, energy demand and production, or agriculture. Most meteorological centres issue ensemble forecasts of TCC, however, these forecasts are often uncalibrated and exhibit worse forecast skill than ensemble forecasts of other weather variables. Hence, some form of post-processing is strongly required to improve predictive performance. As TCC observations are usually reported on a discrete scale taking just nine different values called oktas, statistical calibration of TCC ensemble forecasts can be considered a classification problem with outputs given by the probabilities of the oktas. This is a classical area where machine learning methods are applied. We investigate the performance of post-processing using multilayer perceptron (MLP) neural networks, gradient boosting machines (GBM) and random forest (RF) methods. Based on the European Centre for Medium-Range Weather Forecasts global TCC ensemble forecasts for 2002-2014 we compare these approaches with the proportional odds logistic regression (POLR) and multiclass logistic regression (MLR) models, as well as the raw TCC ensemble forecasts. We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor. Compared to the raw ensemble, all calibration methods result in a significant improvement in forecast skill. RF models provide the smallest increase in predictive performance, while MLP, POLR and GBM approaches perform best. The use of precipitation forecast data leads to further improvements in forecast skill and except for very short lead times the extended MLP model shows the best overall performance., Comment: 24 pages, 7 figures

Was tun, wenn alle Selbstoptimierungen nichts geholfen haben? Sich in Bescheidenheit üben? Das Überleben zur Lebenskunst erklären? Es mit einer neuen Identität versuchen? Der Beitrag bietet Momentaufnahmen von Suchbewegungen in Zeiten unsicherer Lebensverhältnisse.

Der Beitrag fragt nach den Bedingungen von Lernen im Alter, diskutiert die Zielperspektiven Gesundheit sowie Lebensqualität und bezieht dabei die Debatte der ungarischen Erwachsenenbildung ein. Zentrale Frage ist, inwieweit sich Altenbildung in Deutschland und Ungarn perspektivisch und inhaltlich verändern muss, um einen nachhaltigen Beitrag für lebenslanges Lernen zu leisten.

Die Frage nach einem guten, glücklichen und gelingenden Leben ist eine relevante Frage der Erwachsenenbildung. Dieses gelingende Leben ist aber mehr als individuelles Glück, es bezieht auch gutes Handeln ein und gewinnt dadurch eine gesellschaftliche Dimension...

Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear relationships between arbitrary predictor variables and forecast distribution parameters that are automatically learned in a data-driven way rather than requiring pre-specified link functions. In a case study of 2-meter temperature forecasts at surface stations in Germany, the neural network approach significantly outperforms benchmark post-processing methods while being computationally more affordable. Key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings. Furthermore, the trained neural network can be used to gain insight into the importance of meteorological variables thereby challenging the notion of neural networks as uninterpretable black boxes. Our approach can easily be extended to other statistical post-processing and forecasting problems. We anticipate that recent advances in deep learning combined with the ever-increasing amounts of model and observation data will transform the post-processing of numerical weather forecasts in the coming decade.

Dieser Beitrag beleuchtet das Verschränkungsverhältnis von Begriffsgeschichte und Erwachsenenbildung. Im Mittelpunkt steht dabei die Frage nach dem disziplinären und eigenen Selbstverständnis, das durch begriffsgeschichtliche Annäherungen an Tiefe gewinnen kann. Nach grundlegenden bildungsphilosophischen Überlegungen zu den Möglichkeiten begriffsgeschichtlicher Betrachtungen für die Erwachsenenbildung werden unterschiedliche Perspektiven und Ebenen zur Analyse vorgestellt und abschließend auf ihre Reichweite für die Erwachsenenbildung hin diskutiert.

Der Beitrag widmet sich der -in der Erwachsenenbildung im Zuge der Subjektivierung von Erwerbsarbeit an Bedeutung gewinnenden- Kategorie der Selbstkompetenz(en). Im Zentrum der theoretischen und empirischen Analyse steht dabei eine "Klärung" des Begriffs mit seinen Wirkungsweisen im Hinblick auf das Subjekt. Nach einer Beschreibung grundlegender Entwicklungen, die das Emporkommen der Thematik bedingen, erfolgt eine theoretische und empirische Modellierung der (Selbst-)Kompetenzen. Abschließend werden daraus Konsequenzen für Theorie und Praxis der Weiterbildung formuliert.