Research On The Method Of Wind Profile Radar Wind Power Spectral Treatment Based On Wavelet Transform

The wind profile radar detects three-dimensional wind fields in high-altitude atmosphere, with greater spatial resolution when compared to other conventional equipment. Detection of atmospheric wind fields using the radar requires only a few minutes, and the spatial resolution can reach dozens of meters vertically. The wind radar generates high temporal and spatial resolution data. Researchers can subsequently use such data to explore and analyze related weather information, such as shear lines, gravity fluctuations, and atmospheric turbulence structure. As such, the wind profile radar is widely used in many fields of atmospheric research, such as weather warning and forecasting, urban atmospheric monitoring, aircraft flight safety, and missile and rocket launch testing.The wind profile radar detects and retrieves atmospheric wind fields by receiving electromagnetic waves that are scattered in the turbulent atmosphere. Therefore, compared with conventional weather radar that detects precipitation particles, the echo feedback from the wind radar is considerably weaker. In this case, the real atmospheric signal in the wind radar echo is easily submerged in the interference signal and the atmospheric background noise. Thus, it necessary to improve the detection efficiency and precision of the wind profile radar by separating the useful signal from undesirable clutter and noise.This study decodes the wind profile radar binary files using C++ library functions. The detection and echo charctersitics of the profile radar were analyzed. Extensive supression tests were performed on ground clutter and intermittent clutter on the spectrum data retrieved from the radar. Additional tests were performed with the aim of reducing noise through wavelet transformation. The OTSU algorithm has been adopted to realize the effective separation of useful signals and the background noise of atmospheric air. This realization was achieved through the analysis of distribution characteristics of the wind spectrum curve. The corresponding wind spectrum data was correlated using L-M algorithm. Afterwhich the above processing radar rendered surpressed noise and clutter. The atmospheric echo information has been highlighted. Finally, the data was analyzed and the results were evaluated by comparing the original spectrum and the spectrum after implementing the above method. Thereby verifying the effectiveness of the method.