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<sub>fca</sub>, a diagnostic of turbulence is clustered in te...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2010-08-01
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| Series: | Annales Geophysicae |
| Online Access: | https://www.ann-geophys.net/28/1475/2010/angeo-28-1475-2010.pdf |
| Summary: | 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<sub>fca</sub>, 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<sub>fca</sub> 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<sub>fca</sub> for different wind conditions at different altitudes is studied. |
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| ISSN: | 0992-7689 1432-0576 |