Research on the Wavelet Denoising Algorithm for Thorpe Analysis Based on the Radiosonde Data

Thorpe analysis is a commonly used method to study turbulence, an important research object in the field of atmospheric science. However, using Thorpe analysis to retrieve turbulence parameters from radiosonde data can reduce inversion accuracy due to instrument noise. Therefore, this paper presents a new wavelet denoising method to determine the optimal decomposition layer based on adaptative Monte Carlo simulation, and the denoising effect of this method is tested by simulation data and real detection data. Numerical experiments shows that the stability and accuracy of the proposed new method are better than those of the previous methods.