Multiple solar particle event dose time profile predictions using Bayesian inference

Abstract

The prediction of solar particle event occurrence and the resulting effects on humans and electronics continues to be a mission and/or life-threatening concern for the National Aeronautics and Space Administration and military and commercial satellite operators. While the frequency of events generally follows the solar cycle, individual event occurrence is sporadic and the prediction of resulting effects prior to the event onset is difficult. In one approach to space weather prediction, the forecaster begins to make predictions after the onset of an event. Previous work proved the efficacy of a forecasting methodology that used Bayesian inference and dose and/or dose rate information obtained early after the onset of an event to make predictions of dose and dose rate time profiles out to 120 h beyond onset. The previous work, however, was restricted to predictions for single-event solar particle events. Some of the largest recorded events, including the October 1989 and August 1972 events, were actually multiple events. In this study, we present an analysis of nine large events, some single and some multiple. This work ties together particle flux and fluence data with dose rate and dose calculations in an effort to develop a criterion for characterising an event as multiple and thus, generalising the Bayesian methodology to allow predictions for all events. Dose time profile predictions are made for the four separate events that made up the October 1989 event.

Solar Particle Event Dose Forecasting Using Regression Techniques

“…Efforts to develop such a tool began two decades ago (Zapp et al, ). Initially, efforts focused on using Bayesian inference (Neal & Townsend, , , ; L. W. Townsend & Neal, ) and artificial neural networks (Hoff et al, ). Bayesian methods showed great promise in making dose forecasts, but the computational effort, which required the use of Markov chain Monte Carlo methods to perform the multidimensional integrations, meant that it could not be used for real‐time or near‐real‐time forecasting.…”