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Correlation Models for Temperature Fields

delete2011-11-15
delete68
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OA
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G
Gerald R. North *
王
王珏 (Jue Wang)
M
Marc G. Genton
DOI:10.1175/2011JCLI4199.1delete
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Abstract

Abstract

En 中文
This paper presents derivations of some analytical forms for spatial correlations of evolving random fields governed by a white-noise-driven damped diffusion equation that is the analog of autoregressive order 1 in time and autoregressive order 2 in space. The study considers the two-dimensional plane and the surface of a sphere, both of which have been studied before, but here time is introduced to the problem. Such models have a finite characteristic length (roughly the separation at which the autocorrelation falls to 1/e) and a relaxation time scale. In particular, the characteristic length of a particular temporal Fourier component of the field increases to a finite value as the frequency of the particular component decreases. Some near-analytical formulas are provided for the results. A potential application is to the correlation structure of surface temperature fields and to the estimation of large area averages, depending on how the original datastream is filtered into a distribution of Fourier frequencies (e.g., moving average, low pass, or narrow band). The form of the governing equation is just that of the simple energy balance climate models, which have a long history in climate studies. The physical motivation provided by the derivation from a climate model provides some heuristic appeal to the approach and suggests extensions of the work to nonuniform cases.
Keywords:
GLOBAL MEAN TEMPERATURE
2ND-MOMENT STATISTICS
CLIMATE
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Journal

Journal of Climate cover
Journal of Climate
IF:
4
Papers:
1.4W
Citations:
5.9W

Organization

T
Texas A&M University System
Scholars:
4.4W
Papers: 4.0W
Citations: 4.0K
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