  Geostationary satellites collect high-resolution weather data comprising a
series of images which can be used to estimate wind speed and direction at
different altitudes. The Derived Motion Winds (DMW) Algorithm is commonly used
to process these data and estimate atmospheric winds by tracking features in
images taken by the GOES-R series of the NOAA geostationary meteorological
satellites. However, the wind estimates from the DMW Algorithm are sparse and
do not come with uncertainty measures. This motivates us to statistically model
wind motions as a spatial process drifting in time. We propose a covariance
function that depends on spatial and temporal lags and a drift parameter to
capture the wind speed and wind direction. We estimate the parameters by local
maximum likelihood. Our method allows us to compute standard errors of the
estimates, enabling spatial smoothing of the estimates using a Gaussian kernel
weighted by the inverses of the estimated variances. We conduct extensive
simulation studies to determine the situations where our method performs well.
The proposed method is applied to the GOES-15 brightness temperature data over
Colorado and reduces prediction error of brightness temperature compared to the
DMW Algorithm.
