A 1ST-GUESS FEATURE-BASED ALGORITHM FOR ESTIMATING WIND-SPEED IN CLEAR-AIR DOPPLER RADAR SPECTRA

Algorithms for deriving winds from profiler range-gated spectra curren tly rely on consensus averaging to remove outliers from the subhourly velocity estimates. For persistent ground clutter in the echo return t hat is stronger than the atmospheric component, consensus averaging of the spectral peak power densities fails because the peak power densit y is derived from the ground clutter and not the atmosphere. To negate the deleterious effects of persistent ground clutter, as well as to a ttempt to improve performance during periods of poor signal-to-noise r atio, an algorithm was developed that uses the local maxima in power d ensity in each spectrum to build multiple profiles of possible radial velocity estimates from the first to last range gate. To build profile s of radial velocity estimates from a set of spectra, the spectra are smoothed, the local power density maxima are identified, chains are fo rmed across range gates by connecting those local power density maxima that satisfy a continuity constraint, and finally profiles are built from a combination of chains by maximizing an energy function based on continuity, gate separation, and summed power density. Features based on power density and power density after half-plane subtraction are t hen constructed for each profile and a backpropagation neural network is subsequently used to classify the profile most likely reflecting th e atmospheric state. It was found that use of this technique significa ntly reduced ground clutter contamination in the horizontal beam veloc ity estimates and improved performance at low signal-to-noise ratios f or all velocity estimates.