Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Thomas Muschinski"'
Publikováno v:
Meteorologische Zeitschrift, Vol 30, Iss 2, Pp 153-168 (2021)
Interactions between foehn and mountain-valley cold air pools (CAPs) strongly influence severe weather and air quality at the valley bottom, but have seen limited research compared to the fully established foehn phase. The Penetration and Interruptio
Externí odkaz:
https://doaj.org/article/ea01694b335046ad9e46fc4e1fec5afd
Publikováno v:
eISSN
Physical numerical weather prediction models have biases and miscalibrations that can depend on the weather situation, which makes it difficult to postprocess them effectively using the traditional model output statistics (MOS) framework based on par
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a85a4c56fa8f947b9e47c38d6a5bf10
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1021/
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1021/
Autor:
Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Achim Zeileis, Thorsten Simon
Power ramps are sudden changes in turbine power and must be accurately predicted to minimize costly imbalances in the electrical grid. Doing so requires reliable wind speed forecasts, which can be obtained from ensembles of physical numerical weather
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::35c475c331fbd0097cb037f6819d0cb7
https://doi.org/10.5194/wes-2022-48
https://doi.org/10.5194/wes-2022-48
Autor:
Helen Claire Ward, Mathias Walter Rotach, Alexander Gohm, Martin Graus, Thomas Karl, Maren Haid, Lukas Umek, Thomas Muschinski
We present the first detailed analysis of multi-seasonal near-surface turbulence observations for an urban area in highly complex terrain. Using four years of eddy covariance data collected over the Alpine city of Innsbruck, Austria, we assess the im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::418ffba00cd27ce31189f55ebb5f16df
https://doi.org/10.5194/egusphere-egu22-6065
https://doi.org/10.5194/egusphere-egu22-6065
Autor:
Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Thorsten Simon, Achim Zeileis
Efficient wind farm operation requires reliable probabilistic forecasts of power ramps. These are sudden fluctuations in power production which, if unanticipated, can lead to significant imbalances in the electrical grid. The power produced by a turb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c6cdfdd702e3b2ad03ac1d0aac2bb35
https://doi.org/10.5194/egusphere-egu22-2176
https://doi.org/10.5194/egusphere-egu22-2176
Autor:
Helen Claire Ward, Mathias Walter Rotach, Alexander Gohm, Martin Graus, Thomas Karl, Maren Haid, Lukas Umek, Thomas Muschinski
This study represents the first detailed analysis of multi-year near-surface turbulence observations for an urban area located in highly complex terrain. Using four years of eddy covariance measurements over the Alpine city of Innsbruck, Austria, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a1b560e524b27debee9480e5ab798e63
https://acp.copernicus.org/preprints/acp-2021-1073/
https://acp.copernicus.org/preprints/acp-2021-1073/
To obtain reliable joint probability forecasts, multivariate postprocessing of numerical weather predictions (NWPs) must take into account dependencies among the univariate forecast errors—across different forecast horizons, locations or atmospheri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ddf6c78e34213908f08c36777897258
https://doi.org/10.5194/egusphere-egu21-9840
https://doi.org/10.5194/egusphere-egu21-9840
Distributional regression is extended to Gaussian response vectors of dimension greater than two by parameterizing the covariance matrix $\Sigma$ of the response distribution using the entries of its Cholesky decomposition. The more common variance-c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02e02da83b6e744aadd70a6cd69d738e