Turbulence for different background conditions using fuzzy logic and clustering

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, 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.

Wave influence on polar mesosphere summer echoes above Wasa: experimental and model studies

“…The T 0.5 quantity has an advantage over spectral width estimation since it removes the influence of horizontal winds on spectral width (Holdsworth et al, 2001;Satheesan and Kirkwood, 2010). Then the energy dissipation rate can be estimated as follows (Hocking, 1985): this expression.…”

Tracer–tracer relation in the Arctic stratosphere using fuzzy logic

“…Application of fuzzy methods for solving geophysical problems is becoming more and more popular (e.g. Kottayil et al, 2010;Satheesan and Kirkwood, 2010). The approach (applied to model turbulence) followed by Satheesan and Kirkwood (2010) is adapted here in exploring the applicability of fuzzy methods to the tracerÁtracer relation in the arctic stratosphere.…”

“…Lary et al (2004) used ANN to describe tracer correlations between CH 4 and N 2 O using data obtained from a chemical model. In this study, a non-linear method based on a combination of fuzzy logic and subtractive clustering techniques is used to find a relation between O 3 and N 2 O. Fuzzy-logic-based methods are one of the major techniques successful in non-linear system identification used in many areas such as communication, control systems, signal processing, chemical process control, biological processes, atmospheric parameter retrievals, turbulence classification etc (Sugeno, 1985;Center and Verma, 1998;Kottayil et al, 2010;Satheesan and Kirkwood, 2010). In this work, the potential of fuzzy logic combined with subtractive clustering for finding a robust relationship between O 3 and N 2 O in the arctic stratosphere using satellite data is demonstrated.…”

“…Powerful optimisation tools are usually required for modelling such complex processes in order to find a relationship between different variables (e.g. Satheesan and Kirkwood, 2010). Non-linear techniques such as genetic algorithm, artificial neural network (ANN) and so on have been found to be successful in many geophysical problems (e.g.…”

Adaptive decision fusion based framework for short-term wind speed and turbulence intensity forecasting: case study for North West of Turkey

“…For instance, Kucukali and Dinçkal [2] estimated time-average turbulence intensity with the function of the relative submergence of the reference point. In addition, Satheesan and Kirkwood [10] estimated turbulence intensities from radar observations by using data clustering and fuzzy logic. On the other hand, Yanovsky et al used the energy dissipation rate as an indicator of turbulence intensity and developed an algorithm to estimate energy dissipation rate based on the measured Dopplerpolarimetric radar data [11,12].…”