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.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS208 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
