Visualizing climate variability with time-dependent probability density functions, detecting it using information theory

0Citations
Citations of this article
20Readers
Mendeley users who have this article in their library.

Abstract

A framework for visualizing and detecting climate variability and change based on time-dependent probability density functions (PDFs) is developed. A set of information-theoretic statistics based on the Shannon Entropy and the Kullback-Leibler Divergence (KLD) are defined to assess PDF complexity and temporal variability. The KLD-based measures quantify the representativeness of a thirty year sampling window of a larger climatic record, how well a long sample can predict a smaller sample's PDF, and how well one thirty year sample matches a similar sample shifted in time. These techniques are applied the the Central England Temperature record, the longest continuous meteorological observational record. © 2012 Published by Elsevier Ltd.

Cite

CITATION STYLE

APA

Larson, J. W. (2012). Visualizing climate variability with time-dependent probability density functions, detecting it using information theory. In Procedia Computer Science (Vol. 9, pp. 917–926). Elsevier B.V. https://doi.org/10.1016/j.procs.2012.04.098

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free