Adaptive Variational Mode Decomposition Method for Eliminating Instrument Noise in Turbulence Detection

Abstract

AbstractNoise removal is a key issue in the retrieval of turbulence from meteorological radiosonde data using the method proposed by Thorpe. Only by reducing as much as possible the influence of noise in the potential temperature fluctuations can the retrieval results reflect the turbulence characteristics of the real atmosphere. In this paper, an adaptive variational mode decomposition (VMD) method is proposed that is used to remove noise fluctuations from the potential temperature profile, and particle swarm optimization and mutual information are used to optimize the preset VMD parameters. The Thorpe method is applied to the denoised potential temperature profile to identify and characterize turbulent regions. The results show that the adaptive VMD method is very effective for denoising the potential temperature profile in both simulation experiments and actual detection data. The real turbulence overturn can be selected from the inversions by optimal smoothing and statistical tests. This method is an improvement on the Wilson method and allows the Thorpe method to be applied to daytime sounding data, avoiding the confusion between noise and turbulence that results in the distortion of the turbulence scale.