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20232025
most citedComparison of Model Output Statistics and Neural Networks to Postprocess Wind Gusts

4 citations · 6 across the 8 of their papers we have counts for

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5 papers · 1 filter

stat.AP20251 cited

Probabilistic measures afford fair comparisons of AIWP and NWP model output

Tilmann Gneiting, Tobias Biegert, Kristof Kraus +3

We introduce a new measure for fair and meaningful comparisons of single-valued output from artificial intelligence based weather prediction (AIWP) and numerical weather prediction…

stat.AP2025

Probabilistic intraday electricity price forecasting using generative machine learning

Jieyu Chen, Sebastian Lerch, Melanie Schienle +2

The growing importance of intraday electricity trading in Europe calls for improved price forecasting and tailored decision-support tools. In this paper, we propose a novel generat…

stat.AP2024

Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning

Nina Horat, Sina Klerings, Sebastian Lerch

Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting, where a cascade of physics-based models is used in a model chain approac…

stat.AP20244 cited

Comparison of Model Output Statistics and Neural Networks to Postprocess Wind Gusts

Cristina Primo, Benedikt Schulz, Sebastian Lerch +1

Wind gust prediction plays an important role in warning strategies of national meteorological services due to the high impact of its extreme values. However, forecasting wind gusts…

stat.AP2023

Direction Augmentation in the Evaluation of Armed Conflict Predictions

Johannes Bracher, Lotta Rüter, Fabian Krüger +2

In many forecasting settings, there is a specific interest in predicting the sign of an outcome variable correctly in addition to its magnitude. For instance, when forecasting arme…