Articles | Volume 5, issue 1
https://doi.org/10.5194/ascmo-5-87-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-5-87-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Skewed logistic distribution for statistical temperature post-processing in mountainous areas
Manuel Gebetsberger
CORRESPONDING AUTHOR
Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
LuftBlick, Innsbruck, Austria
Division for Biomedical Physics, Medical University of Innsbruck, Innsbruck, Austria
Reto Stauffer
Department of Statistics, University of Innsbruck, Innsbruck, Austria
Georg J. Mayr
Institute of Atmospheric and Cryospheric Sciences, University of Innsbruck, Innsbruck, Austria
Achim Zeileis
Department of Statistics, University of Innsbruck, Innsbruck, Austria
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
Fiona Fix, Georg Mayr, Achim Zeileis, Isabell Stucke, and Reto Stauffer
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Atmospheric deserts (ADs) are air masses that are transported away from hot, dry regions. Our study introduces this new concept. ADs can suppress or boost thunderstorms and potentially contribute to the formation of heat waves, which makes them relevant for forecasting extreme events. Using a novel detection method, we follow an AD directly from North Africa to Europe for a case in June 2022, allowing us to analyse the air mass at any time and investigate how it is modified along the way.
Thomas Muschinski, Georg J. Mayr, Achim Zeileis, and Thorsten Simon
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Statistical post-processing is necessary to generate probabilistic forecasts from physical numerical weather prediction models. To allow for more flexibility, there has been a shift in post-processing away from traditional parametric regression models towards modern machine learning methods. By fusing these two approaches, we developed model output statistics random forests, a new post-processing method that is highly flexible but at the same time also very robust and easy to interpret.
Deborah Morgenstern, Isabell Stucke, Georg J. Mayr, Achim Zeileis, and Thorsten Simon
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Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Achim Zeileis, and Thorsten Simon
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The power generated by offshore wind farms can vary greatly within a couple of hours, and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.
Deborah Morgenstern, Isabell Stucke, Thorsten Simon, Georg J. Mayr, and Achim Zeileis
Weather Clim. Dynam., 3, 361–375, https://doi.org/10.5194/wcd-3-361-2022, https://doi.org/10.5194/wcd-3-361-2022, 2022
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Wintertime lightning in central Europe is rare but has a large damage potential for tall structures such as wind turbines. We use a data-driven approach to explain why it even occurs when the meteorological processes causing thunderstorms in summer are absent. In summer, with strong solar input, thunderclouds have a large vertical extent, whereas in winter, thunderclouds are shallower in the vertical but tilted and elongated in the horizontal by strong winds that increase with altitude.
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Short summary
This article presents a method for improving probabilistic air temperature forecasts, particularly at Alpine sites. Using a nonsymmetric forecast distribution, the probabilistic forecast quality can be improved with respect to the common symmetric Gaussian distribution used. Furthermore, a long-term training approach of 3 years is presented to ensure the stability of the regression coefficients. The research was based on a PhD project on building an automated forecast system for northern Italy.
This article presents a method for improving probabilistic air temperature forecasts,...