A New Algorithm for Wind-Vector Retrieval From Scatterometers

Abstract

A new efficient algorithm for retrieving wind-vector solutions from scatterometers is developed based on a criterion of minimum normalized standard deviation (NSD) of wind speed derived from backscatter measurements using a geophysical model function (GMF). Its performance has been evaluated through simulations using QSCAT-1 GMF and the QuikSCAT observational geometry. The present algorithm, named the NSD algorithm, is found to be computationally more efficient (two to three times) besides being at par with the maximum-likelihood estimator (MLE) algorithm in terms of retrieval skill, retrieval errors, and distribution of solutions, on the basis of simulations as well as comparison of limited QuikSCAT-data-derived winds with National Centers for Environmental Prediction and European Centre for Medium-Range Weather Forecasts model winds. Simulation results and analysis of sample QuikSCAT data are presented.