Block-Entropy Analysis of Climate Data

We explore the use of block entropy as a dynamics classifier for meteorological timeseries data. The block entropy estimates define the entropy growth curve H(L) with respect to word length L. For a finitary process, the entropy growth curve tends to an asymtotic linear regime H(L) = E + hμL, with entropy rate hμ and excess entropy E. These quantities apportion the system’s information content into “memory” (E) and randomness (hμ). We discuss the challenges inherent in analyzing weather data using symbolic techniques. We apply the block entropy-based techniques to Australian daily precipitation data from the Patched Point Dataset station record collection and version 3 of the Australian Water Availability Project analysis dataset. Preliminary results demonstrate hμand E are viable climatological classifiers for precipitation, with station records from similar climatic regimes possessing similar values of hμand E. The entropy rates of convergence analysis rules out finite order Markov processes for orders falling within the range of block sizes considered. Differences between entropy parameters associated with station records and analyses at station locations may provide clues regarding analysis error sources.