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Predictive inference for big, spatial, non-Gaussian data: MODIS cloud data and its change-of-support

Journal Article


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Abstract


  • Remote sensing of the earth with satellites yields datasets that can be massive in size,

    nonstationary in space, and non-Gaussian in distribution. To overcome computational

    challenges, we use the reduced-rank spatial random effects (SRE) model in a statistical

    analysis of cloud-mask data from NASA’s Moderate Resolution Imaging Spectroradiometer

    (MODIS) instrument on board NASA’s Terra satellite. Parameterisations of cloud processes

    are the biggest source of uncertainty and sensitivity in different climate models’ future

    projections of Earth’s climate. An accurate quantification of the spatial distribution of clouds,

    as well as a rigorously estimated pixel-scale clear-sky-probability process, is needed to

    establish reliable estimates of cloud-distributional changes and trends caused by climate

    change. Here we give a hierarchical spatial-statistical modelling approach for a very large

    spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud-mask

    data, and we use spatial change-of-support relationships to estimate cloud fraction at coarser

    resolutions. Our model is non-Gaussian; it postulates a hidden process for the clear-sky

    probability that makes use of the SRE model, EM-estimation, and optimal (empirical Bayes)

    spatial prediction of the clear-sky-probability process. Measures of prediction uncertainty

    are also given.

Authors


  •   Sengupta, Aritra (external author)
  •   Cressie, Noel A.
  •   Kahn, Brian H. (external author)
  •   Frey, Richard (external author)

Publication Date


  • 2016

Citation


  • Sengupta, A., Cressie, N., Kahn, B. H. & Frey, R. (2016). Predictive inference for big, spatial, non-Gaussian data: MODIS cloud data and its change-of-support. Australian and New Zealand Journal of Statistics, 58 (1), 15-45.

Scopus Eid


  • 2-s2.0-84963795661

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=6707&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5679

Number Of Pages


  • 30

Start Page


  • 15

End Page


  • 45

Volume


  • 58

Issue


  • 1

Place Of Publication


  • Australia

Abstract


  • Remote sensing of the earth with satellites yields datasets that can be massive in size,

    nonstationary in space, and non-Gaussian in distribution. To overcome computational

    challenges, we use the reduced-rank spatial random effects (SRE) model in a statistical

    analysis of cloud-mask data from NASA’s Moderate Resolution Imaging Spectroradiometer

    (MODIS) instrument on board NASA’s Terra satellite. Parameterisations of cloud processes

    are the biggest source of uncertainty and sensitivity in different climate models’ future

    projections of Earth’s climate. An accurate quantification of the spatial distribution of clouds,

    as well as a rigorously estimated pixel-scale clear-sky-probability process, is needed to

    establish reliable estimates of cloud-distributional changes and trends caused by climate

    change. Here we give a hierarchical spatial-statistical modelling approach for a very large

    spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud-mask

    data, and we use spatial change-of-support relationships to estimate cloud fraction at coarser

    resolutions. Our model is non-Gaussian; it postulates a hidden process for the clear-sky

    probability that makes use of the SRE model, EM-estimation, and optimal (empirical Bayes)

    spatial prediction of the clear-sky-probability process. Measures of prediction uncertainty

    are also given.

Authors


  •   Sengupta, Aritra (external author)
  •   Cressie, Noel A.
  •   Kahn, Brian H. (external author)
  •   Frey, Richard (external author)

Publication Date


  • 2016

Citation


  • Sengupta, A., Cressie, N., Kahn, B. H. & Frey, R. (2016). Predictive inference for big, spatial, non-Gaussian data: MODIS cloud data and its change-of-support. Australian and New Zealand Journal of Statistics, 58 (1), 15-45.

Scopus Eid


  • 2-s2.0-84963795661

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=6707&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5679

Number Of Pages


  • 30

Start Page


  • 15

End Page


  • 45

Volume


  • 58

Issue


  • 1

Place Of Publication


  • Australia