  The Atmospheric Radiation Measurement program is a U.S. Department of Energy
project that collects meteorological observations at several locations around
the world in order to study how weather processes affect global climate change.
As one of its initiatives, it operates a set of fixed but irregularly-spaced
monitoring facilities in the Southern Great Plains region of the U.S. We
describe methods for interpolating temperature records from these fixed
facilities to locations at which no observations were made, which can be useful
when values are required on a spatial grid. We interpolate by conditionally
simulating from a fitted nonstationary Gaussian process model that accounts for
the time-varying statistical characteristics of the temperatures, as well as
the dependence on solar radiation. The model is fit by maximizing an
approximate likelihood, and the conditional simulations result in
well-calibrated confidence intervals for the predicted temperatures. We also
describe methods for handling spatial-temporal jumps in the data to interpolate
a slow-moving cold front.
