A self-affine multi-fractal wave turbulence discrimination method using data from single point fast response sensors in a nocturnal atmospheric boundary layer

Abstract

We present DA, a self-affine, multi-fractal which may become the first routine wave/turbulence discriminant for time series data. Using nocturnal atmospheric data, we show the advantages of D A over self-similar fractals and standard turbulence measures such as FFTs, Richardson number, Brunt-Vaisala frequency, buoyancy length scale, variances, turbulent kinetic energy, and phase averaging. DA also shows promise in resolving "wave-break" events. Since it uses local basis functions, DA may be an ideal tool to detect intermittent turbulence, coherent structures, and discrete wave trains in general. DA may also be a measure of chaos in general.