Wind and turbulence estimated from MST radar observations in Kiruna, inArctic Sweden are used to characterize turbulence in the free troposphereusing data clustering and fuzzy logic. The root mean square velocity,νfca, a diagnostic of turbulence is clustered in terms of hourlywind speed, direction, vertical wind speed, and altitude of the radarobservations, which are the predictors. The predictors are graded over aninterval of zero to one through an input membership function. Subtractivedata clustering has been applied to classify νfca depending on itshomogeneity. Fuzzy rules are applied to the clustered dataset to establish arelationship between predictors and the predictant. The accuracy of thepredicted turbulence shows that this method gives very good prediction ofturbulence in the troposphere. Using this method, the behaviour of νfca for different wind conditions at different altitudes is studied.
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