Wavelet Entropy Energy Measure (WEEM): A multiscale measure to grade a geophysical system's predictability

<p>Model-free gradation of predictability of a geophysical system is essential to quantify how much inherent information is contained within the system and evaluate different forecasting methods' performance to get the best possible prediction. We conjecture that Multiscale Information enclosed in a given geophysical time series is the only input source for any forecast model. In the literature, established entropic measures dealing with grading the predictability of a time series at multiple time scales are limited. Therefore, we need an additional measure to quantify the information at multiple time scales, thereby grading the predictability level. This study introduces a novel measure, Wavelet Entropy Energy Measure (WEEM), based on Wavelet entropy to investigate a time series's energy distribution. From the WEEM analysis, predictability can be graded low to high. The difference between the entropy of a wavelet energy distribution of a time series and entropy of wavelet energy of white noise is the basis for gradation. The metric quantifies the proportion of the deterministic component of a time series in terms of energy concentration, and its range varies from zero to one. One corresponds to high predictable due to its high energy concentration and zero representing a process similar to the white noise process having scattered energy distribution. The proposed metric is normalized, handles non-stationarity, independent of the length of the data. Therefore, it can explain the evolution of predictability for any geophysical time series (ex: precipitation, streamflow, paleoclimate series) from past to the present. WEEM metric's performance can guide the forecasting models in getting the best possible prediction of a geophysical system by comparing different methods. </p>

On the Ensemble Transfer Entropy Analysis of Non-Stationary Geophysical Time Series: The Case of Magnetospheric Response

<p>The fundamental question what causes what has always been the motivating motto for natural sciences, being the study of causality a crucial point for characterizing dynamical relationships. In the framework of complex dynamical systems, both linear statistical tools and Granger causality models drastically fail to detect causal relationships between time series, while a powerful model-free statistical framework is offered by the information theory. </p><p>Here we discuss how to deal with the problem of measuring causal information in non-stationary complex systems by considering a local estimation of the information-theoretic functionals via an ensemble-based statistics. Then, its application for investigating the dynamical coupling and relationships between the solar wind and the Earth’s magnetosphere is also presented. </p>

Exploring the Relationship between Geomagnetic Activity and Human Heart Rate Variability

Both geomagnetic and solar activity fluctuate over time and have been proposed to affect human physiology. One physiological measurement that has been previously investigated in this context, heart rate variability (HRV), has substantial health implications regarding the ability to adapt to stressors and has been shown to be altered in many cardiovascular and neurological disorders. Intriguingly, previous work found significant, strong correlations between HRV and geomagnetic/solar activity. In an attempt to replicate these findings, we simultaneously measured HRV from 20 healthy participants during a thirty-day period. In agreement with previous work, we found several significant correlations between HRV and geophysical time-series. However, after correction for autocorrelation, which is inherent in time-series, the only significant results were an increase in very low frequency during higher local geomagnetic activity and a geomagnetic anticipatory decrease in heart rate a day before higher global geomagnetic activity. Both correlations were very low. The loss of most significant effects after this correction suggests that previous findings may be a result of autocorrelation. A further note of caution is required since our and the previous studies in the field do not correct for multiple comparisons given the exploratory analysis strategy. We thus conclude that the effects of geomagnetic and solar activity are (if present) most likely of very small effect size and question the validity of the previous studies given the methodological concerns we have uncovered in our work.

Quantifying changes in spatial patterns of surface air temperature dynamics over several decades

We study daily surface air temperature (SAT) reanalysis in a grid over the Earth's surface to identify and quantify changes in SAT dynamics during the period 1979–2016. By analysing the Hilbert amplitude and frequency we identify the regions where relative variations are most pronounced (larger than ±50 % for the amplitude and ±100 % for the frequency). Amplitude variations are interpreted as due to changes in precipitation or ice melting, while frequency variations are interpreted as due to a northward shift of the inter-tropical convergence zone (ITCZ) and to a widening of the rainfall band in the western Pacific Ocean. The ITCZ is the ascending branch of the Hadley cell, and thus by affecting the tropical atmospheric circulation, ITCZ migration has far-reaching climatic consequences. As the methodology proposed here can be applied to many other geophysical time series, our work will stimulate new research that will advance the understanding of climate change impacts.