{"id":{"repo_id":"unm","oai_identifier":"oai:digitalrepository.unm.edu:ece_etds-1190"},"canonical_url":"https://search.dev.ndltd.org/etd/unm/oai:digitalrepository.unm.edu:ece_etds-1190","repository":{"repo_id":"unm","name":"University of New Mexico","base_url":"https://digitalrepository.unm.edu/do/oai/"},"display":{"title":"A stochastic ensemble forecast model for geosynchronous relativistic electron fluxes","abstract":"A stochastic ensemble model composed of three functional forecasting models has been developed to forecast >2 MeV electron flux at geosynchronous (GEO) orbit. The REFM model is based on a statistical link between electron flux and solar wind speed using empirically derived linear filter coefficients, the Li model solves a radial diffusion equation with a diffusion coefficient that is a function of the solar wind velocity and interplanetary magnetic field, and the Fluxpred model is a multi-layer feed-forward neural network with electron flux and summed Kp as input. Individual model results were combined using a multivariate regression to produce significantly better predictive results than any of the individual models alone. A stochastic model is then developed to forecast the probability that a fluence threshold will be exceeded. The regression technique, model optimization, and calculation of forecast probability will be discussed in reference to the ensemble model.","abstract_html":"A stochastic ensemble model composed of three functional forecasting models has been developed to forecast >2 MeV electron flux at geosynchronous (GEO) orbit. The REFM model is based on a statistical link between electron flux and solar wind speed using empirically derived linear filter coefficients, the Li model solves a radial diffusion equation with a diffusion coefficient that is a function of the solar wind velocity and interplanetary magnetic field, and the Fluxpred model is a multi-layer feed-forward neural network with electron flux and summed Kp as input. Individual model results were combined using a multivariate regression to produce significantly better predictive results than any of the individual models alone. A stochastic model is then developed to forecast the probability that a fluence threshold will be exceeded. The regression technique, model optimization, and calculation of forecast probability will be discussed in reference to the ensemble model.","abstract_has_math":false,"creators":["Nelson, Steven"],"institution":null,"degree_name":"Electrical Engineering","degree_level":"Thesis","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Gilmore, Mark","Simpson, Jamesina","Christodoulou, Christos"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-06-25T07:00:00Z","date_published":"2010-06-25T07:00:00Z","updated_at":"2026-07-24T05:27:25Z","subjects":["Ionospheric storms--Forecasting--Statistical methods","Atmospheric electricity--Mathematical models","Stochastic models."],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalrepository.unm.edu/ece_etds/191","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gilmore, Mark","Simpson, Jamesina","Christodoulou, Christos"]},{"key":"dc:creator","label":"Author","values":["Nelson, Steven"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis","Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Electrical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ionospheric storms--Forecasting--Statistical methods","Atmospheric electricity--Mathematical models","Stochastic models."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalrepository.unm.edu/ece_etds/191"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A stochastic ensemble model composed of three functional forecasting models has been developed to forecast >2 MeV electron flux at geosynchronous (GEO) orbit. The REFM model is based on a statistical link between electron flux and solar wind speed using empirically derived linear filter coefficients, the Li model solves a radial diffusion equation with a diffusion coefficient that is a function of the solar wind velocity and interplanetary magnetic field, and the Fluxpred model is a multi-layer feed-forward neural network with electron flux and summed Kp as input. Individual model results were combined using a multivariate regression to produce significantly better predictive results than any of the individual models alone. A stochastic model is then developed to forecast the probability that a fluence threshold will be exceeded. The regression technique, model optimization, and calculation of forecast probability will be discussed in reference to the ensemble model."]},{"key":"dc:title","label":"Title","values":["A stochastic ensemble forecast model for geosynchronous relativistic electron fluxes"]}]}],"canonical_facts":{"dc:contributor":["Gilmore, Mark","Simpson, Jamesina","Christodoulou, Christos"],"dc:creator":["Nelson, Steven"],"dc:description.abstract":["A stochastic ensemble model composed of three functional forecasting models has been developed to forecast >2 MeV electron flux at geosynchronous (GEO) orbit. The REFM model is based on a statistical link between electron flux and solar wind speed using empirically derived linear filter coefficients, the Li model solves a radial diffusion equation with a diffusion coefficient that is a function of the solar wind velocity and interplanetary magnetic field, and the Fluxpred model is a multi-layer feed-forward neural network with electron flux and summed Kp as input. Individual model results were combined using a multivariate regression to produce significantly better predictive results than any of the individual models alone. A stochastic model is then developed to forecast the probability that a fluence threshold will be exceeded. The regression technique, model optimization, and calculation of forecast probability will be discussed in reference to the ensemble model."],"dc:identifier":["https://digitalrepository.unm.edu/ece_etds/191"],"dc:language":["English"],"dc:subject":["Ionospheric storms--Forecasting--Statistical methods","Atmospheric electricity--Mathematical models","Stochastic models."],"dc:title":["A stochastic ensemble forecast model for geosynchronous relativistic electron fluxes"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["Thesis","Masters"],"thesis:degree_name":["Electrical Engineering"]},"updated_at":"2026-07-24T05:27:25Z"}