Non-stationary large-scale statistics of precipitation extremes in central Europe ...
Extreme precipitation shows non-stationarity, meaning that its distribution can change with time or other large-scale variables. For a classical frequency-intensity analysis this effect is often neglected. Here, we propose a model including the influence of North Atlantic Oscillation, time, surface...
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ftdatacite:10.17169/refubium-40060 2023-12-03T10:26:56+01:00 Non-stationary large-scale statistics of precipitation extremes in central Europe ... Fauer, Felix S. Rust, Henning W. 2023 https://dx.doi.org/10.17169/refubium-40060 https://refubium.fu-berlin.de/handle/fub188/40339 unknown Freie Universität Berlin https://doi.org/10.1007/s00477-023-02515-z https://dx.doi.org/10.1007/s00477-023-02515-z https://doi.org/10.1007/s00477-023-02515-z Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Extreme precipitation Generalized extreme value distribution Maximum likelihood Non-stationary climate Large scale Intensity-duration-frequency 500 Naturwissenschaften und Mathematik550 Geowissenschaften, Geologie551 Geologie, Hydrologie, Meteorologie ScholarlyArticle Text article-journal Wissenschaftlicher Artikel 2023 ftdatacite https://doi.org/10.17169/refubium-4006010.1007/s00477-023-02515-z 2023-11-03T10:39:01Z Extreme precipitation shows non-stationarity, meaning that its distribution can change with time or other large-scale variables. For a classical frequency-intensity analysis this effect is often neglected. Here, we propose a model including the influence of North Atlantic Oscillation, time, surface temperature and a blocking index. The model features flexibility to use annual maxima as well as seasonal maxima to be fitted in a generalized extreme value setting. To further increase the efficiency of data usage, maxima from different accumulation durations are aggregated so that information for extremes on different time scales can be provided. Our model is trained to individual station data with temporal resolutions ranging from one minute to one day across Germany. Models are chosen with a stepwise BIC model selection and verified with a cross-validated quantile skill index. The verification shows that the new model performs better than a reference model without large-scale information. Also, the new model ... Text North Atlantic North Atlantic oscillation DataCite Metadata Store (German National Library of Science and Technology) |
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Extreme precipitation Generalized extreme value distribution Maximum likelihood Non-stationary climate Large scale Intensity-duration-frequency 500 Naturwissenschaften und Mathematik550 Geowissenschaften, Geologie551 Geologie, Hydrologie, Meteorologie |
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Extreme precipitation Generalized extreme value distribution Maximum likelihood Non-stationary climate Large scale Intensity-duration-frequency 500 Naturwissenschaften und Mathematik550 Geowissenschaften, Geologie551 Geologie, Hydrologie, Meteorologie Fauer, Felix S. Rust, Henning W. Non-stationary large-scale statistics of precipitation extremes in central Europe ... |
topic_facet |
Extreme precipitation Generalized extreme value distribution Maximum likelihood Non-stationary climate Large scale Intensity-duration-frequency 500 Naturwissenschaften und Mathematik550 Geowissenschaften, Geologie551 Geologie, Hydrologie, Meteorologie |
description |
Extreme precipitation shows non-stationarity, meaning that its distribution can change with time or other large-scale variables. For a classical frequency-intensity analysis this effect is often neglected. Here, we propose a model including the influence of North Atlantic Oscillation, time, surface temperature and a blocking index. The model features flexibility to use annual maxima as well as seasonal maxima to be fitted in a generalized extreme value setting. To further increase the efficiency of data usage, maxima from different accumulation durations are aggregated so that information for extremes on different time scales can be provided. Our model is trained to individual station data with temporal resolutions ranging from one minute to one day across Germany. Models are chosen with a stepwise BIC model selection and verified with a cross-validated quantile skill index. The verification shows that the new model performs better than a reference model without large-scale information. Also, the new model ... |
format |
Text |
author |
Fauer, Felix S. Rust, Henning W. |
author_facet |
Fauer, Felix S. Rust, Henning W. |
author_sort |
Fauer, Felix S. |
title |
Non-stationary large-scale statistics of precipitation extremes in central Europe ... |
title_short |
Non-stationary large-scale statistics of precipitation extremes in central Europe ... |
title_full |
Non-stationary large-scale statistics of precipitation extremes in central Europe ... |
title_fullStr |
Non-stationary large-scale statistics of precipitation extremes in central Europe ... |
title_full_unstemmed |
Non-stationary large-scale statistics of precipitation extremes in central Europe ... |
title_sort |
non-stationary large-scale statistics of precipitation extremes in central europe ... |
publisher |
Freie Universität Berlin |
publishDate |
2023 |
url |
https://dx.doi.org/10.17169/refubium-40060 https://refubium.fu-berlin.de/handle/fub188/40339 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
https://doi.org/10.1007/s00477-023-02515-z https://dx.doi.org/10.1007/s00477-023-02515-z https://doi.org/10.1007/s00477-023-02515-z |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.17169/refubium-4006010.1007/s00477-023-02515-z |
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1784276414119280640 |