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, ν fca , a diagnostic of turbulence is clustered in terms of hourly wind speed, direc...
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ftdoajarticles:oai:doaj.org/article:ee1bff9bc123435dadf5e26cb12d20c9 2023-05-15T15:03:06+02:00 Turbulence for different background conditions using fuzzy logic and clustering K. Satheesan S. Kirkwood 2010-08-01T00:00:00Z https://doi.org/10.5194/angeo-28-1475-2010 https://doaj.org/article/ee1bff9bc123435dadf5e26cb12d20c9 EN eng Copernicus Publications https://www.ann-geophys.net/28/1475/2010/angeo-28-1475-2010.pdf https://doaj.org/toc/0992-7689 https://doaj.org/toc/1432-0576 doi:10.5194/angeo-28-1475-2010 0992-7689 1432-0576 https://doaj.org/article/ee1bff9bc123435dadf5e26cb12d20c9 Annales Geophysicae, Vol 28, Pp 1475-1481 (2010) Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 article 2010 ftdoajarticles https://doi.org/10.5194/angeo-28-1475-2010 2022-12-31T14:47:50Z 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, ν fca , 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 ν fca 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 ν fca for different wind conditions at different altitudes is studied. Article in Journal/Newspaper Arctic Kiruna Directory of Open Access Journals: DOAJ Articles Arctic Kiruna Annales Geophysicae 28 8 1475 1481 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 K. Satheesan S. Kirkwood Turbulence for different background conditions using fuzzy logic and clustering |
topic_facet |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
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, ν fca , 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 ν fca 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 ν fca for different wind conditions at different altitudes is studied. |
format |
Article in Journal/Newspaper |
author |
K. Satheesan S. Kirkwood |
author_facet |
K. Satheesan S. Kirkwood |
author_sort |
K. Satheesan |
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://doaj.org/article/ee1bff9bc123435dadf5e26cb12d20c9 |
geographic |
Arctic Kiruna |
geographic_facet |
Arctic Kiruna |
genre |
Arctic Kiruna |
genre_facet |
Arctic Kiruna |
op_source |
Annales Geophysicae, Vol 28, Pp 1475-1481 (2010) |
op_relation |
https://www.ann-geophys.net/28/1475/2010/angeo-28-1475-2010.pdf https://doaj.org/toc/0992-7689 https://doaj.org/toc/1432-0576 doi:10.5194/angeo-28-1475-2010 0992-7689 1432-0576 https://doaj.org/article/ee1bff9bc123435dadf5e26cb12d20c9 |
op_doi |
https://doi.org/10.5194/angeo-28-1475-2010 |
container_title |
Annales Geophysicae |
container_volume |
28 |
container_issue |
8 |
container_start_page |
1475 |
op_container_end_page |
1481 |
_version_ |
1766335003701018624 |