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Full-Text Articles in Physical Sciences and Mathematics
Fast, Resolution-Consistent Spatial Prediction Of Global Processes From Satellite Data, Hsin-Cheng Huang, Noel A. Cressie, John Gabrosek
Fast, Resolution-Consistent Spatial Prediction Of Global Processes From Satellite Data, Hsin-Cheng Huang, Noel A. Cressie, John Gabrosek
Professor Noel Cressie
Polar orbiting satellites remotely sense the earth and its atmosphere, producing datasets that give daily global coverage. For any given day, the data are many and measured at spatially irregular locations. Our goal in this article is to predict values that are spatially regular at different resolutions; such values are often used as input to general circulation models (GCMs) and the like. Not only do we wish to predict optimally, but because data acquisition is relentless, our algorithm must also process the data very rapidly. This article applies a multiresolution autoregressive tree-structured model, and presents a new statistical prediction methodology …
Flexible Spatial Models For Kriging And Cokriging Using Moving Averages And The Fast Fourier Transform (Fft), Jay M. Ver Hoef, Noel A. Cressie, Ronald P. Barry
Flexible Spatial Models For Kriging And Cokriging Using Moving Averages And The Fast Fourier Transform (Fft), Jay M. Ver Hoef, Noel A. Cressie, Ronald P. Barry
Professor Noel Cressie
Models for spatial autocorrelation and cross-correlation depend on the distance and direction separating two locations, and are constrained so that for all possible sets of locations, the covariance matrices implied from the models remain nonnegative-definite. Based on spatial correlation, optimal linear predictors can be constructed that yield complete maps of spatial fields from incomplete and noisy spatial data. This methodology is called kriging if the data are of only one variable type, and it is called cokriging if it is of two or more variable types. Historically, to satisfy the nonnegative-definite condition, cokriging has used coregionalization models for cross-variograms, even …