B0426
Title: Estimating atmospheric motion winds from satellite image data using spacetime drift models
Authors: Indranil Sahoo - Virginia Commonwealth University (United States) [presenting]
Joseph Guinness - NC State University (United States)
Brian Reich - North Carolina State University (United States)
Abstract: Geostationary weather satellites collect high-resolution data comprising a series of images. The derived motion winds algorithm is commonly used to process these data and estimate atmospheric winds by tracking features in the images. However, the wind estimates from the DMW algorithm are often missing and do not come with uncertainty measures. Also, the DMW algorithm estimates can only be half integers, since the algorithm requires the original and shifted data to be at the same locations, in order to calculate the displacement vector between them. This motivates the statistical model of wind motions as a spatial process drifting in time. Using a covariance function that depends on spatial and temporal lags and a drift parameter to capture the wind speed and wind direction, the parameters are estimated by local maximum likelihood. The method allows the computation of standard errors of the local estimates, enabling spatial smoothing of the estimates using a Gaussian kernel weighted by the inverses of the estimated variances. Extensive simulation studies are conducted to determine the situations where the method performs well. The proposed method is applied to the GOES-15 brightness temperature data over Colorado and reduces the prediction error of brightness temperature compared to the DMW algorithm.
