Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2393-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-2393-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Dec 2022
Research article |
| 08 Dec 2022
| 08 Dec 2022
Predicting power ramps from joint distributions of future wind speeds
Thomas Muschinski
CORRESPONDING AUTHOR
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Moritz N. Lang
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Georg J. Mayr
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Jakob W. Messner
MeteoServe Wetterdienst GmbH, Innsbruck, Austria
Achim Zeileis
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Thorsten Simon
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Department of Mathematics, Universität Innsbruck, Innsbruck, Austria
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Thomas Muschinski, Georg J. Mayr, Achim Zeileis, and Thorsten Simon
Nonlin. Processes Geophys., 30, 503–514, https://doi.org/10.5194/npg-30-503-2023, https://doi.org/10.5194/npg-30-503-2023, 2023
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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.
Helen Claire Ward, Mathias Walter Rotach, Alexander Gohm, Martin Graus, Thomas Karl, Maren Haid, Lukas Umek, and Thomas Muschinski
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This study examines how cities and their surroundings influence turbulent exchange processes responsible for weather and climate. Analysis of a 4-year observational dataset for the Alpine city of Innsbruck reveals several similarities with other (flat) city centre sites. However, the mountain setting leads to characteristic daily and seasonal flow patterns (valley winds) and downslope windstorms that have a marked effect on temperature, wind speed, turbulence and pollutant concentration.
Thomas Muschinski, Georg J. Mayr, Achim Zeileis, and Thorsten Simon
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Deborah Morgenstern, Isabell Stucke, Georg J. Mayr, Achim Zeileis, and Thorsten Simon
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Two thunderstorm environments are described for Europe: mass-field thunderstorms, which occur mostly in summer, over land, and under similar meteorological conditions, and wind-field thunderstorms, which occur mostly in winter, over the sea, and under more diverse meteorological conditions. Our descriptions are independent of static thresholds and help to understand why thunderstorms in unfavorable seasons for lightning pose a particular risk to tall infrastructure such as wind turbines.
Helen Claire Ward, Mathias Walter Rotach, Alexander Gohm, Martin Graus, Thomas Karl, Maren Haid, Lukas Umek, and Thomas Muschinski
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This study examines how cities and their surroundings influence turbulent exchange processes responsible for weather and climate. Analysis of a 4-year observational dataset for the Alpine city of Innsbruck reveals several similarities with other (flat) city centre sites. However, the mountain setting leads to characteristic daily and seasonal flow patterns (valley winds) and downslope windstorms that have a marked effect on temperature, wind speed, turbulence and pollutant concentration.
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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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Christian Mallaun, Andreas Giez, Georg J. Mayr, and Mathias W. Rotach
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Sebastian J. Dietz, Philipp Kneringer, Georg J. Mayr, and Achim Zeileis
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Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
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Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager
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Hugo Rubio, Martin Kühn, and Julia Gottschall
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Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn
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Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022, https://doi.org/10.5194/wes-7-1869-2022, 2022
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The sensitivity of the WRF mesoscale modeling framework in accurately representing and predicting wind-farm-level environmental variables for three extreme weather events over the Belgian North Sea is investigated in this study. The overall results indicate highly sensitive simulation results to the type and combination of physics parameterizations and the type of the weather phenomena, with indications that scale-aware physics parameterizations better reproduce wind-related variables.
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Short summary
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.
The power generated by offshore wind farms can vary greatly within a couple of hours, and...
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