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Activity Number: 620 - Spatial and Spatiotemporal Modeling in Climate and Meteorology
Type: Contributed
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #305024
Title: Estimating Atmospheric Motion Winds from Satellite Image Data Using Space-Time Drift Models
Author(s): Indranil Sahoo* and Joseph Guinness and Brian Reich
Companies: Wake Forest University and Cornell University and North Carolina State University
Keywords: Derived motion winds; Satellite image data; GOES-15; Profile likelihood; Smoothing

Geostationary satellites collect high resolution weather data comprising a series of images which can be used to estimate wind motion at various altitudes. The Derived Motion Winds (DMW) Algorithm is commonly used to 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 motion. 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 simulations to determine the situations where our method should perform 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.

Authors who are presenting talks have a * after their name.

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