A stochastic ensemble forecast model for geosynchronous relativistic electron fluxes

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.

Name thesis:degree_name

Electrical Engineering

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Year

2010

Author dc:creator

• Nelson, Steven

Contributors dc:contributor

• Gilmore, Mark
• Simpson, Jamesina
• Christodoulou, Christos

• Ionospheric storms--Forecasting--Statistical methods
• Atmospheric electricity--Mathematical models
• Stochastic models.

Language dc:language

English

Repository record dc:identifier

https://digitalrepository.unm.edu/ece_etds/191

OAI identifier oai:identifier

oai:digitalrepository.unm.edu:ece_etds-1190