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Estimating atmospheric motion winds from satellite image data using space‐time drift models

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  • Indranil Sahoo
  • Joseph Guinness
  • Brian J. Reich

Abstract

Geostationary weather satellites collect high‐resolution data comprising a series of images. The Derived Motion Winds (DMW) 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 us to statistically model 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, we estimate the parameters by local maximum likelihood. Our method allows us to compute standard errors of the local 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.

Suggested Citation

  • Indranil Sahoo & Joseph Guinness & Brian J. Reich, 2023. "Estimating atmospheric motion winds from satellite image data using space‐time drift models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.
  • Handle: RePEc:wly:envmet:v:34:y:2023:i:8:n:e2818
    DOI: 10.1002/env.2818
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    References listed on IDEAS

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    1. Gregory P. Bopp & Benjamin A. Shaby, 2017. "An exponential–gamma mixture model for extreme Santa Ana winds," Environmetrics, John Wiley & Sons, Ltd., vol. 28(8), December.
    2. Felipe Tagle & Stefano Castruccio & Paola Crippa & Marc G. Genton, 2019. "A Non‐Gaussian Spatio‐Temporal Model for Daily Wind Speeds Based on a Multi‐Variate Skew‐t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(3), pages 312-326, May.
    3. Michael L. Stein, 2005. "Statistical methods for regular monitoring data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 667-687, November.
    4. Wenceslao González‐Manteiga & Rosa M. Crujeiras & Danny Modlin & Montserrat Fuentes & Brian Reich, 2012. "Circular conditional autoregressive modeling of vector fields," Environmetrics, John Wiley & Sons, Ltd., vol. 23(1), pages 46-53, February.
    5. Anderes, Ethan B. & Stein, Michael L., 2011. "Local likelihood estimation for nonstationary random fields," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 506-520, March.
    6. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    7. Ashton Wiens & Douglas Nychka & William Kleiber, 2020. "Modeling spatial data using local likelihood estimation and a Matérn to spatial autoregressive translation," Environmetrics, John Wiley & Sons, Ltd., vol. 31(6), September.
    8. Daniela Castro-Camilo & Raphaël Huser, 2020. "Local Likelihood Estimation of Complex Tail Dependence Structures, Applied to U.S. Precipitation Extremes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1037-1054, July.
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