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.
Turbulence in the atmosphere is a phenomena affecting the transport and diffusion of trace gases. It also affects the aviation safety. Modelling and prediction of turbulence is a challenge to the scientific community. This is due to the fact that turbulence cannot be measured directly and it is usually not possible to link occurrence of turbulence to any visible phenomena. Moreover, the theory and physical mechanisms that produce turbulence in the atmosphere are not understood well. MST radar is a useful tool for estimating turbulence. Vertical eddy diffusivity (Kz) is commonly used as a measure of turbulence. There are various methods to estimate turbulence using MST radar. Some commonly used methods are the the power method, doppler spectral width method, and variance method. The assumptions involved and the strengths and weaknesses of various methods are explained elsewhere (Wilson, 2004; Satheesan and Krishna Murthy, 2002, 2004). Turbulence in the atmosphere is affected by the background conditions. For example, generation of turbulence in the boundary layer is strongly influenced by the wind direction due to boundary layer heterogeneity (Klipp, 2007). Nastrom and Eaton (2005) found that there is significant correlation between turbulent parameters and wind speed while Kirkwood et al. (2010) have shown that turbulence in the free troposphere can be caused by the interplay of synoptic wind shear and mountain waves. Long records of radar observations can be used to study the climatology of turbulence and its relation to the background wind conditions. In the present work, using a nonlinear technique, turbulence observed by radar is clustered for different background conditions. Non linear system identification methods are used in many geophysical problems (Basu et al., 2005a,b).
Publisher : Copernicus Publications
By : K. Satheesan and S. Kirkwood
File Information: English Language/ 7 Page / size: 3,035 KB
سال : 2010
ناشر : Copernicus Publications
کاری از : K. Satheesan and S. Kirkwood
اطلاعات فایل : زبان انگلیسی / 7صفحه / حجم : KB 3,035
لینک دانلود : روی همین لینک کلیک کنید