Using a time series of geomagnetic storm events between 1957 and 2019, obtained by selecting storms where −50 nT, we have analyzed the probability of occurrence of moderate, intense, and severe events. Considering that geomagnetic storms can be modeled as stochastic processes with a log‐normal probability distribution over their minimum index, the dataset was separated according to solar cycle (SC) and SC phases, and the distributions of events were fitted through maximum likelihood method in order to characterize the occurrence of storms in each cycle and phase, and then compare those occurrences to the SC24. Our results show that there is a strong dependence between the occurrence of intense storms, with −100 nT, and the strength of the SC measured by the sunspot numbers. In particular, SC24 is very similar to SC20. However, when comparing the occurrence of storms by SC phases, events tend to show similar activity toward the minimum phase and have significant differences in the maximum phases. By looking at the value—the fit log‐normal distribution “width” parameter—characteristic of the occurrence rate of storms, we have found that the (the sigma value in the descending phase of one cycle) shows the highest correlation () with (the sigma value in the maximum phase of the next cycle) which allows us to estimate the occurrence rate of storms for SC25 to be similar to those of SC21 and SC22, suggesting a more intense cycle than the one that just ended.

The construction of a relativistic thermodynamics theory is still controversial after more than 110 years. To the date there is no agreement on which set of relativistic transformations of thermodynamic quantities is the correct one, or if the problem even has a solution. Starting from Planck and Einstein, several authors have proposed their own reasoning, concluding that a moving body could appear cooler, hotter or at the same temperature as measured by a local observer. In this article we present a review of the main theories of relativistic thermodynamics, with an special emphasis on the physical assumptions adopted by each one. We also present a set of relativistic transformations that we have derived by assuming the laws of Thermodynamics to be covariant. We found that under such assumptions a moving body appears to be hotter. Since relativistic thermodynamics is a topic that can be treated as part of an undergraduate course of classical thermodynamics or modern physics, the review and our own derivations presented here aim to encourage undergraduate physics students to open a discussion on the fundamental assumptions in thermodynamics and to engage in research activities early in their scientific career.

Using Van Allen Probes Energetic Particle, Composition, and Thermal Plasma‐Relativistic Electron‐Proton Telescope (ECT‐REPT) observations, we performed a statistical study on the effect of geomagnetic storms on relativistic electrons fluxes in the outer radiation belt for 78 storms between September 2012 and June 2016. We found that the probability of enhancement, depletion, and no change in flux values depends strongly on L and energy. Enhancement events are more common for ∼2 MeV electrons at L ∼ 5, and the number of enhancement events decreases with increasing energy at any given L shell. However, considering the percentage of occurrence of each kind of event, enhancements are more probable at higher energies, and the probability of enhancement tends to increases with increasing L shell. Depletion are more probable for 4–5 MeV electrons at the heart of the outer radiation belt, and no‐change events are more frequent at L < 3.5 for E ∼ 3 MeV particles. Moreover, for L > 4.5 the probability of enhancement, depletion, or no‐change response presents little variation for all energies. Because these probabilities remain relatively constant as a function of radial distance in the outer radiation belt, measurements obtained at geosynchronous orbit may be used as a proxy to monitor E≥1.8 MeV electrons in the outer belt.

Geomagnetically induced currents (GIC) can drive power outages and damage power grid components while also affecting pipelines and train systems. Developing the ability to predict local GICs is important to protecting infrastructure and limiting the impact of geomagnetic storms on public safety and the economy. While GIC data is not readily available, variations in the magnetic field, dB/dt, measured by ground magnetometers can be used as a proxy for GICs. We are developing a set of neural networks to predict the east and north components of the magnetic field, B_E and B_N, from which the horizontal component, B_H, and its variation in time, dB_H/dt, are calculated. We apply two techniques for time series analysis to study the connection of solar wind and interplanetary magnetic field properties obtained from the OMNI dataset to the ground magnetic field perturbations. The analysis techniques include a feed-forward artificial neural network (ANN) and a long-short term memory (LSTM) neural network. Here we present a comparison of both models' performance when predicting the B_H component of the Ottawa (OTT) ground magnetometer for the year 2011 and 2015 and then when attempting to reconstruct the time series of B_H for two geomagnetic storms that occurred on 5 August 2011 and 17 March 2015.