Process for generating spatially continuous wind profiles from wind profiler measurements

Abstract

A neural network process for improving wind retrievals from wind profiler measurements is described. In this invention, a neural network is trained to retrieve missing or incomplete upper level winds from ground based wind profiler measurements. Radiosonde measurements in conjunction with wind profiler ground measurements for specific geographical locations are used as training sets for the neural network. The idea is to retrieve timely and spatially continuous upper level wind information from fragmented or incomplete wind profiler measurements.

1 . A process for generating spatially continuous vertical wind profiles from fragmented and/or incomplete wind profiler measurements comprising the steps of:
 (a) transmitting and receiving wind profiler radar and/or sodar and/or lidar signals;   (b) obtaining coincident or near coincident radiosonde measurements of winds and other related meteorological parameters such as synoptic weather conditions, temperature, pressure, height, and moisture;   (c) collecting a series of coincident wind profiler and radiosonde measurements to be used as a training set for a neural network.   (d) Screening data files to remove incomplete and erroneous data records.   (e) Selecting coincident or nearest neighborhood data sets for specific geographical locations.   (f) Assembling neural network training and testing sets from archived wind profiler and radiosonde measurements.   (g) Training the wind profiler neural network for specific geographical locations.   (h) Computing the weighting coefficients for each level of interest using wind information at other levels as input.   (i) Computing root mean square (RMS) errors using test cases of wind profiler measurements.   (j) Archiving computed weighting coefficients and the associated RMS errors.   (k) Applying neural network to operational wind profiler measurements where profiles are fragmented or incomplete.