Optimal generation of radar wind profiler spectra

Radar wind profilers (RWPs) sense the mean and turbulent motion of the clea r air through Doppler shifts induced along several (3-5) upward-looking bea ms. RWP signals, like all radars signals, are often contaminated. The conta mination is clearly evident in the associated Doppler spectra, and automati c routines designed to extract meteorological quantities from these spectra often yield inaccurate results. Much of the observed contamination is due to an aliasing of higher frequency signals into the clear-air portion of th e spectrum and a broadening of the spectral contaminants caused by the rela tively short: time series used to generate Doppler spectra. In the past, th is source of contamination could not be avoided because of limitations on t he size and speed of RWP processing computers. Today's computers, however, are able to process larger amounts of data at greatly increased speeds. Her e it is shown how standard and well-known spectral processing methods can b e applied to significantly longer time series to reduce contamination in th e radar spectra and thereby improve the accuracy and the reliability of met eorological products derived from RWP systems. In particular, spectral proc essing methods to identify and remove contamination that is often aliased i nto the clear-air portion of the spectrum are considered. Optimal technique s for combining multiple spectra to produce averaged spectra are also discu ssed.