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We present a novel algorithm that predicts the probability that the time derivative of the horizontal component of the ground magnetic field $dB/dt$ exceeds a specified threshold at a given location. This quantity provides important information that is physically relevant to Geomagnetically Induced Currents (GIC), which are electric currents { associated to} sudden changes in the Earths magnetic field due to Space Weather events. The model follows a gray-box approach by combining the output of a physics-based model with machine learning. Specifically, we combine the University of Michigans Geospace model that is operational at the NOAA Space Weather Prediction Center, with a boosted ensemble of classification trees. We discuss the problem of re-calibrating the output of the decision tree to obtain reliable probabilities. The performance of the model is assessed by typical metrics for probabilistic forecasts: Probability of Detection and False Detection, True Skill Statistic, Heidke Skill Score, and Receiver Operating Characteristic curve. We show that the ML enhanced algorithm consistently improves all the metrics considered.
Motivated by low-altitude cusp observations of small-scale (~ 1 km) field-aligned currents (SSFACs) interpreted as ionospheric Alfven resonator modes, we investigated the effects of Alfven wave energy deposition on thermospheric upwelling and the for
We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning (ML) tools to gain utility from those data. We have compiled, curated, analyzed, and mad
The forecast of the time of arrival of a coronal mass ejection (CME) to Earth is of critical importance for our high-technology society and for any future manned exploration of the Solar System. As critical as the forecast accuracy is the knowledge o
In Bayesian classification, it is important to establish a probabilistic model for each class for likelihood estimation. Most of the previous methods modeled the probability distribution in the whole sample space. However, real-world problems are usu
We perform a probabilistic analysis of onion routing. The analysis is presented in a black-box model of anonymous communication in the Universally Composable framework that abstracts the essential properties of onion routing in the presence of an act