In this work, we develop gradient boosting machines (GBMs) for forecasting the SYM‐H index multiple hours ahead using different combinations of solar wind and interplanetary magnetic field (IMF) parameters, derived parameters, and past SYM‐H values. Using Shapley Additive Explanation values to quantify the contributions from each input to predictions of the SYM‐H index from GBMs, we show that our predictions are consistent with physical understanding while also providing insight into the complex relationship between the solar wind and Earth's ring current. In particular, we found that feature contributions vary depending on the storm phase. We also perform a direct comparison between GBMs and neural networks presented in prior publications for forecasting the SYM‐H index by training, validating, and testing them on the same data. We find that the GBMs yield a statistically significant improvement in root mean squared error over the best published black‐box neural network schemes and the Burton equation.
Mendelian Randomization (MR) is a popular method in epidemiology and genetics that uses genetic variation as instrumental variables for causal inference. Existing MR methods usually assume most genetic variants are valid instrumental variables that identify a common causal effect. There is a general lack of awareness that this effect homogeneity assumption can be violated when there are multiple causal pathways involved, even if all the instrumental variables are valid. In this article, we introduce a latent mixture model MR-PATH that groups instruments that yield similar causal effect estimates together. We develop a Monte-Carlo EM algorithm to fit this mixture model, derive approximate confidence intervals for uncertainty quantification, and adopt a modified Bayesian Information Criterion (BIC) for model selection. We verify the efficacy of the Monte-Carlo EM algorithm, confidence intervals, and model selection criterion using numerical simulations. We identify potential mechanistic heterogeneity when applying our method to estimate the effect of high-density lipoprotein cholesterol on coronary heart disease and the effect of adiposity on type II diabetes.
Coronal mass ejections (CMEs) are large-scale eruptions of the solar coronal plasma and magnetic fields expelled into the solar wind. CMEs can create magnetic storms in the Earth's magnetosphere that are responsible for severe geomagnetic effects ranging from breakdown in radio communications to damage of sensitive electronics on satellites and even disrupting the power grid. Therefore it is imperative to obtain reliable long-term predictions of space weather events driven by CMEs.Current state-of-the-art modeling capabilities involve numerical simulations using coupled first-principles and/ or empirical models. A prominent example is the Space Weather Modeling Framework (SWMF) (Gombosi et al., 2021;Tóth et al., 2005Tóth et al., , 2012) that models domains from the upper solar chromosphere to the Earth's atmosphere and/or the outer heliosphere using efficient coupling between multiple models and is capable of full Sun-to-Earth simulations. Typically, as shown in Figure 1, the model chain consists of obtaining the background solar wind in Stage 1, generating and propagating a CME through the heliosphere to Earth in Stage 2, and finally calculating the magnetospheric impact via geospace models in Stage 3. Along the way, various observational data (in the blue boxes) are also available to calibrate or validate the model. The SWMF offers predictions for several macroscopic plasma quantities, including those that critically impact the magnetosphere and the resulting
Modeling the impact of space weather events such as coronal mass ejections (CMEs) is crucial to protecting critical infrastructure. The Space Weather Modeling Framework (SWMF) is a state-of-the-art framework that offers full Sun-to-Earth simulations by computing the background solar wind, CME propagation and magnetospheric impact. However, reliable long-term predictions of CME events require uncertainty quantification (UQ) and data assimilation (DA). We take the first steps by performing global sensitivity analysis (GSA) and UQ for background solar wind simulations produced by the Alfvén Wave Solar atmosphere Model (AWSoM) for two Carrington rotations: CR2152 (solar maximum) and CR2208 (solar minimum). We conduct GSA by computing Sobol indices that quantify contributions from model parameter uncertainty to the variance of solar wind speed and density at 1 au, both crucial quantities for CME propagation and strength. Sobol indices also allow us to rank and retain only the most important parameters, which aids in the construction of smaller ensembles for the reduced-dimension parameter space. We present an efficient procedure for computing the Sobol indices using polynomial chaos expansion (PCE) surrogates and space-filling designs. The PCEs further enable inexpensive forward UQ. Overall, we identify three important model parameters: the multiplicative factor applied to the magnetogram, Poynting flux per magnetic field strength constant used at the inner boundary, and the coefficient of the perpendicular correlation length in the turbulent cascade model in AWSoM.
This a preprint and has not been peer reviewed. Data may be preliminary.
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