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March 2009 Spatial interpolation of high-frequency monitoring data
Michael L. Stein
Ann. Appl. Stat. 3(1): 272-291 (March 2009). DOI: 10.1214/08-AOAS208


Climate modelers generally require meteorological information on regular grids, but monitoring stations are, in practice, sited irregularly. Thus, there is a need to produce public data records that interpolate available data to a high density grid, which can then be used to generate meteorological maps at a broad range of spatial and temporal scales. In addition to point predictions, quantifications of uncertainty are also needed. One way to accomplish this is to provide multiple simulations of the relevant meteorological quantities conditional on the observed data taking into account the various uncertainties in predicting a space-time process at locations with no monitoring data. Using a high-quality dataset of minute-by-minute measurements of atmospheric pressure in north-central Oklahoma, this work describes a statistical approach to carrying out these conditional simulations. Based on observations at 11 stations, conditional simulations were produced at two other sites with monitoring stations. The resulting point predictions are very accurate and the multiple simulations produce well-calibrated prediction uncertainties for temporal changes in atmospheric pressure but are substantially overconservative for the uncertainties in the predictions of (undifferenced) pressure.


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Michael L. Stein. "Spatial interpolation of high-frequency monitoring data." Ann. Appl. Stat. 3 (1) 272 - 291, March 2009.


Published: March 2009
First available in Project Euclid: 16 April 2009

zbMATH: 1160.62094
MathSciNet: MR2668708
Digital Object Identifier: 10.1214/08-AOAS208

Keywords: Gaussian process , Meteorology , Space–time process , spectral analysis

Rights: Copyright © 2009 Institute of Mathematical Statistics


Vol.3 • No. 1 • March 2009
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