Abstract
Abstract. 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.
Highlights
Turbulence in the atmosphere is a phenomena affecting the transport and diffusion of trace gases
Turbulence is always high when the wind direction is from north and west sides of the radar and speed is above 25 m/s
Radar observed turbulence has been studied for different background conditions using fuzzy clustering
Summary
Turbulence in the atmosphere is a phenomena affecting the transport and diffusion of trace gases. In the present work, using a nonlinear technique, turbulence observed by radar is clustered for different background conditions. The nonlinear method used in this study is based on the combination of fuzzy logic and data clustering techniques. Fuzzy logic is one of the major approaches towards nonlinear system identification and has been applied successfully in the areas of communication, control systems, signal processing, chemical process control, biological processes, and atmospheric parameter retrievals (Center and Verma, 1998; Sugeno, 1985; Ajil et al, 2010). In the fuzzy based method, data clustering is applied to classify the predictants depending on their homogeneity. Fuzzy rules are applied to establish a relationship between predictors and the clustered
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
