Turbulence for different background conditions using fuzzy logic and clustering
Wind and turbulence estimated from MST radar observations in Kiruna, in Arctic Sweden are used to characterize turbulence in the free troposphere using data clustering and fuzzy logic. The root mean square velocity, νfca, a diagnostic of turbulence is clustered in terms of hourly wind speed, directi...
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00028657 2023-05-15T15:02:53+02:00 Turbulence for different background conditions using fuzzy logic and clustering Satheesan, K. Kirkwood, S. 2010-08 electronic https://doi.org/10.5194/angeo-28-1475-2010 https://noa.gwlb.de/receive/cop_mods_00028657 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00028612/angeo-28-1475-2010.pdf https://angeo.copernicus.org/articles/28/1475/2010/angeo-28-1475-2010.pdf eng eng Copernicus Publications Annales Geophysicae -- http://www.bibliothek.uni-regensburg.de/ezeit/?1458425 -- https://www.ann-geophys.net/ -- https://www.ann-geophys.net/volumes.html -- http://link.springer.com/journal/585 -- 1432-0576 https://doi.org/10.5194/angeo-28-1475-2010 https://noa.gwlb.de/receive/cop_mods_00028657 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00028612/angeo-28-1475-2010.pdf https://angeo.copernicus.org/articles/28/1475/2010/angeo-28-1475-2010.pdf uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2010 ftnonlinearchiv https://doi.org/10.5194/angeo-28-1475-2010 2022-02-08T22:48:06Z Wind and turbulence estimated from MST radar observations in Kiruna, in Arctic Sweden are used to characterize turbulence in the free troposphere using data clustering and fuzzy logic. The root mean square velocity, νfca, a diagnostic of turbulence is clustered in terms of hourly wind speed, direction, vertical wind speed, and altitude of the radar observations, which are the predictors. The predictors are graded over an interval of zero to one through an input membership function. Subtractive data clustering has been applied to classify νfca depending on its homogeneity. Fuzzy rules are applied to the clustered dataset to establish a relationship between predictors and the predictant. The accuracy of the predicted turbulence shows that this method gives very good prediction of turbulence in the troposphere. Using this method, the behaviour of νfca for different wind conditions at different altitudes is studied. Article in Journal/Newspaper Arctic Kiruna Niedersächsisches Online-Archiv NOA Arctic Kiruna Annales Geophysicae 28 8 1475 1481 |
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English |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Satheesan, K. Kirkwood, S. Turbulence for different background conditions using fuzzy logic and clustering |
topic_facet |
article Verlagsveröffentlichung |
description |
Wind and turbulence estimated from MST radar observations in Kiruna, in Arctic Sweden are used to characterize turbulence in the free troposphere using data clustering and fuzzy logic. The root mean square velocity, νfca, a diagnostic of turbulence is clustered in terms of hourly wind speed, direction, vertical wind speed, and altitude of the radar observations, which are the predictors. The predictors are graded over an interval of zero to one through an input membership function. Subtractive data clustering has been applied to classify νfca depending on its homogeneity. Fuzzy rules are applied to the clustered dataset to establish a relationship between predictors and the predictant. The accuracy of the predicted turbulence shows that this method gives very good prediction of turbulence in the troposphere. Using this method, the behaviour of νfca for different wind conditions at different altitudes is studied. |
format |
Article in Journal/Newspaper |
author |
Satheesan, K. Kirkwood, S. |
author_facet |
Satheesan, K. Kirkwood, S. |
author_sort |
Satheesan, K. |
title |
Turbulence for different background conditions using fuzzy logic and clustering |
title_short |
Turbulence for different background conditions using fuzzy logic and clustering |
title_full |
Turbulence for different background conditions using fuzzy logic and clustering |
title_fullStr |
Turbulence for different background conditions using fuzzy logic and clustering |
title_full_unstemmed |
Turbulence for different background conditions using fuzzy logic and clustering |
title_sort |
turbulence for different background conditions using fuzzy logic and clustering |
publisher |
Copernicus Publications |
publishDate |
2010 |
url |
https://doi.org/10.5194/angeo-28-1475-2010 https://noa.gwlb.de/receive/cop_mods_00028657 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00028612/angeo-28-1475-2010.pdf https://angeo.copernicus.org/articles/28/1475/2010/angeo-28-1475-2010.pdf |
geographic |
Arctic Kiruna |
geographic_facet |
Arctic Kiruna |
genre |
Arctic Kiruna |
genre_facet |
Arctic Kiruna |
op_relation |
Annales Geophysicae -- http://www.bibliothek.uni-regensburg.de/ezeit/?1458425 -- https://www.ann-geophys.net/ -- https://www.ann-geophys.net/volumes.html -- http://link.springer.com/journal/585 -- 1432-0576 https://doi.org/10.5194/angeo-28-1475-2010 https://noa.gwlb.de/receive/cop_mods_00028657 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00028612/angeo-28-1475-2010.pdf https://angeo.copernicus.org/articles/28/1475/2010/angeo-28-1475-2010.pdf |
op_rights |
uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/angeo-28-1475-2010 |
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Annales Geophysicae |
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28 |
container_issue |
8 |
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1475 |
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1481 |
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