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dc.contributor.authorSahoo, Indranil
dc.contributor.authorGuinness, Joseph
dc.contributor.authorReich, Brian J.
dc.date.accessioned2019-12-29T11:21:02Z
dc.date.available2019-12-29T11:21:02Z
dc.date.issued2019-02-25
dc.identifier.urihttp://hdl.handle.net/10754/660849
dc.description.abstractGeostationary 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 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.
dc.description.sponsorshipThe data analyzed in this paper were provided by Jessica Matthews of the North Carolina Institute for Climate Studies. This work was supported by the National Science Foundation (DMS - 1613219), the National Institutes of Health (R01ES027892) and King Abdullah University of Science and Technology (3800.2)
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/1902.09653
dc.rightsArchived with thanks to arXiv
dc.titleEstimating Atmospheric Motion Winds from Satellite Image Data using Space-time Drift Models
dc.typePreprint
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Mathematics and Statistics, Wake Forest University
dc.contributor.institutionDepartment of Statistics and Data Science, Cornell University
dc.contributor.institutionDepartment of Statistics, North Carolina State University
dc.identifier.arxivid1902.09653
dc.versionv1
refterms.dateFOA2019-12-29T11:21:24Z


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