@article {4460,
title = {Visualizing Climate Variability with Time-Dependent Probability Density Functions, Detecting it with Information Theory},
journal = {Procedia Computer Science},
volume = {9},
year = {2012},
month = {06/2012},
pages = {917-926},
abstract = {A framework is presented for visualizing and detecting climate variability and change based on time-dependent probability density functions (PDFs). The PDFs show how the distribution of values in the sample window changes over time and show more detail than do timeseries of windowed moments. 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 30-year sampling window of a larger climatic record: how well a long sample can predict a smaller samples PDF, and how well one 30-year sample matches a similar sample shifted in time. These information-theoretic statistics constitute a new type of climate variability, informatic variability. These techniques are applied to the Central England Temperature record, the longest continuous meteorological observational record.},
url = {http://www.sciencedirect.com/science/article/pii/S1877050912002190},
author = {J. W. Larson}
}