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Solar Energetic Particles (SEPs) form a critical component of Space Weather.The complex, intertwined dynamics of SEP sources, acceleration, and transportmake their forecasting very challenging. Yet, information about SEP arrival andtheir properties (e.g., peak flux) is crucial for space exploration on manyfronts. We have recently introduced a novel probabilistic ensemble model calledthe Multivariate Ensemble of Models for Probabilistic Forecast of SolarEnergetic Particles (MEMPSEP). Its primary aim is to forecast the occurrenceand physical properties of SEPs. The occurrence forecasting, thoroughlydiscussed in a preceding paper (Chatterjee et al., 2023), is complemented bythe work presented here, which focuses on forecasting the physical propertiesof SEPs. The MEMPSEP model relies on an ensemble of Convolutional NeuralNetworks, which leverage a multi-variate dataset comprising full-discmagnetogram sequences and numerous derived and in-situ data from varioussources. Skill scores demonstrate that MEMPSEP exhibits improved predictions onSEP properties for the test set data with SEP occurrence probability above 50%,compared to those with a probability below 50%. Results present a promisingapproach to address the challenging task of forecasting SEP physicalproperties, thus improving our forecasting capabilities and advancing ourunderstanding of the dominant parameters and processes that govern SEPproduction.

MEMPSEP II. -- Forecasting the Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach