The Annals of Applied Statistics

Interpolation of nonstationary high frequency spatial–temporal temperature data

Joseph Guinness and Michael L. Stein

Full-text: Open access

Abstract

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.

Article information

Source
Ann. Appl. Stat., Volume 7, Number 3 (2013), 1684-1708.

Dates
First available in Project Euclid: 3 October 2013

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1380804812

Digital Object Identifier
doi:10.1214/13-AOAS633

Mathematical Reviews number (MathSciNet)
MR3127964

Zentralblatt MATH identifier
06237193

Keywords
Nonstationary process spatial–temporal modeling evolutionary spectrum spatial–temporal jumps

Citation

Guinness, Joseph; Stein, Michael L. Interpolation of nonstationary high frequency spatial–temporal temperature data. Ann. Appl. Stat. 7 (2013), no. 3, 1684--1708. doi:10.1214/13-AOAS633. https://projecteuclid.org/euclid.aoas/1380804812


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