Publications

@article{Azad2004,
title = {Symbol sequence analysis of climatic time signals},
author = {R Azad, J Subba Rao, and R Ramaswamy},
url = {https://doi.org/10.1016/j.nonrwa.2003.11.003},
doi = {10.1016/j.nonrwa.2003.11.003},
year = {2004},
date = {2004-07-01},
journal = {Nonlinear Analysis: Real World Applications },
volume = {5},
number = {3},
pages = {487-500},
abstract = {The conditional entropy of two symbolic sequences encoded faithfully from different chaotic time signal data is minimised at zero relative shift of the two signals if the signals have their origin in same underlying dynamics. We show that this minimum in conditional entropies E(Z/X) and E(X/Z) obtained after suitably ‘coarse–graining’ the time signals of, say, variables X and Z have marked differences depending upon the degree of dynamical coupling of the variables in the model. This technique has also been shown to be useful in studying delayed dependences of time signals. Application is made to climate data of four meteorological stations in India, namely New Delhi, Jaipur, Sundernagar and Chennai in order to determine (a) the commonality of underlying dynamics, (b) relative strength of dynamical coupling of different variables and (c) the delay implicit in the dynamics. The method appears robust to measurement noise.},
keywords = {climate, dynamical coupling, entropy, Time Delay},
pubstate = {published},
tppubtype = {article}
}

@article{Mohan1989,
title = {Dimension analysis of climatic data},
author = {T R Krishna Mohan, J Subba Rao and R Ramaswamy},
url = {https://doi.org/10.1175/1520-0442(1989)002%3C1047:DAOCD%3E2.0.CO;2},
doi = {10.1175/1520-0442},
year = {1989},
date = {1989-09-01},
journal = {Journal of Climate},
volume = {2},
number = {9},
pages = {1047–1057},
abstract = {It has been conjectured that the unpredictability of climatic systems is due to strange attractors (SAs) in the configuration space dynamics. One climatic record, the oxygen isotope ratio data from deep-sea cores that pertains to long periods on the order of one million years and provides direct correlation with the glaciation-deglaciation periods, seemed to indicate (under earlier analysis) a low dimensional attractor of correlation dimension D2 ≈ 3.1. Our present reanalysis of this data in light of recent methods suggested by Broomhead and King (BK) is at variance with that result. Two (model) four-variable systems that support chaotic strange attractors are examined using an analysis similar to BK to investigate the practical drawbacks of using a short time-series vis-a-vis the estimation of attractor dimension.},
keywords = {climate, fractal dimension},
pubstate = {published},
tppubtype = {article}
}