Multifeature Dynamic Weighting Moments Estimation for Boundary Layer Radar Wind Profiler

Weak atmospheric signals received by radar wind profilers (RWPs) often overlap with the clutter, making it difficult to identify pure atmospheric spectra. A solution with great potential is to develop RWPs with adaptive dynamic identification capabilities. We, therefore, propose a multifeature dynamic weighting (MFDW) method and specifically define a score function that includes spectral statistical features and their weights. This score function first calculates multiple spectral features and constructs a prediction function that maps the features to prediction values. Then, the weights are determined by Gaussian curve fitting and contributed to the predicted score calculation to extract pure atmospheric spectra. After that, the multiparameter cost function (MPCF) method is first improved by Doppler window settings and then utilized to estimate accurate spectral moments and profiles. The MFDW method is validated using RWP data from different regions and frequencies, and the extracted profiles are compared with GPS radiosonde (GPS RS) data. Research results confirm that dynamic weighting is adaptable to various overlap cases. Furthermore, Doppler wind profile estimation with this proposed method has a good match with GPS RS data. Both the detection accuracy and the running time excel many currently adopted methods. In brief, the chief contributions of this article lie in our innovative MFDW method and feature- and weights-involved score function.