Solar wind plasma parameter variability across solar cycles 23 and 24 : from turbulence to extremes

Solar wind variability spans a wide range of amplitudes and timescales, from turbulent fluctuations to the 11 year solar cycle. We apply the data quantile-quantile (DQQ) method to NASA/Wind observations spanning solar cycles 23 and 24, to study how the uniqueness of each cycle maximum and minimum manifests in the changing statistical distribution of plasma parameters in fast and slow solar wind. The DQQ method allows us to discriminate between two distinct components of the distribution: the core region simply tracks the solar cycle in its moments but shows little sensitivity to solar wind state or the specific activity of each cycle. This would be consistent with an underlying in situ process such as turbulence driving the evolution of fluctuations up to an outer scale. In contrast, the tail component of the distribution is sensitive both to the differences between the maxima and minima of cycles 23 and 24, and the fast or slow state of the solar wind. The tail component varies over the solar cycle in such a way as to maintain a constant functional form, with only its moments varying with solar activity. Finally, after isolating the core region of the distribution, we test its lognormality over the solar cycle in each solar wind state and find the lognormal provides a more robust description of the statistics in slow wind than fast; however, in both states the goodness of fit is significantly reduced at solar maximum

Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models

Long-Range Dependence (LRD) and heavy-tailed distributions are ubiquitous in natural and socio-economic data. Such data can be self-similar whereby both LRD and heavy-tailed distributions contribute to the self-similarity as measured by the Hurst exponent. Some methods widely used in the physical sciences separately estimate these two parameters, which can lead to estimation bias. Those which do simultaneous estimation are based on frequentist methods such as Whittle’s approximate maximum likelihood estimator. Here we present a new and systematic Bayesian framework for the simultaneous inference of the LRD and heavy-tailed distribution parameters of a parametric ARFIMA model with non-Gaussian innovations. As innovations we use the α-stable and t-distributions which have power law tails. Our algorithm also provides parameter uncertainty estimates. We test our algorithm using synthetic data, and also data from the Geostationary Operational Environmental Satellite system (GOES) solar X-ray time series. These tests show that our algorithm is able to accurately and robustly estimate the LRD and heavy-tailed distribution parameters

Norms of Presentational Force

This is the author's accepted manuscript, made available with permission of the American Forensic Association.Can style or presentational devices reasonably compel us to believe, agree, act? I submit that they can, and that the normative pragmatic project explains how. After describing a normative pragmatic approach to presentational force, I analyze and evaluate presentational force in Susan B. Anthony's "Is it a Crime for a U. S. Citizen to Vote" as it apparently proceeds from logic, emotion, and style. I conclude with reflections on the compatibility of the normative pragmatic approach with the recently-developed pragma-dialectical treatment of presentational devices