title: Predicting dose-time profiles of solar energetic particle events using Bayesian forecasting methods.
abstract: Bayesian inference techniques, coupled with Markov chain Monte Carlo sampling methods, are used to predict dose-time profiles for energetic solar particle events. Inputs into the predictive methodology are dose and dose-rate measurements obtained early in the event. Surrogate dose values are grouped in hierarchical models to express relationships among similar solar particle events. Models assume nonlinear, sigmoidal growth for dose throughout an event. Markov chain Monte Carlo methods are used to sample from Bayesian posterior predictive distributions for dose and dose rate. Example predictions are provided for the November 8, 2000, and August 12, 1989, solar particle events.
authors.affiliation: University of Tennessee, Knoxville, TN 37996, USA. nealjs1@ornl.gov.
authors.forename: J S
authors.initials: JS
authors.lastname: Neal
authors.forename: L W
authors.initials: LW
authors.lastname: Townsend
mesh_terms: D001499:Bayes Theorem; D005544:Forecasting / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D011522:Protons; D011829:Radiation Dosage; D011834:Radiation Monitoring / Q000379:methods / Q000706:statistics & numerical data*; D019445:Solar Activity; D013026:Space Flight; D013997:Time Factors; D014867:Water
chemical_list: D011522:Protons; D014867:Water