Neural networks in auroral data assimilation |
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| Affiliation: | 1. University of Waterloo (UofW), Waterloo, ON, Canada 2NL 3G1;2. National Institute for Space Research (INPE), São José dos Campos, SP, 12227-010, Brazil;3. Instituto Tecnológico de Aeronáutica, Praça Marechal Eduardo Gomes, 50, CEP 12228-900, São José dos Campos, Brazil;1. INAF-IAPS, via Fosso del Cavaliere 100, I-00133 Rome, Italy;2. ASI-SSDC, via del Politecnico snc, I-00133 Rome, Italy;3. INAF-OAR, via di Frascati 33, 00040 Monte Porzio Catone (RM), Italy;4. LESIA, Observatoire de Paris/CNRS/Université Pierre et Marie Curie/Université Paris-Diderot, F-92195 Meudon, France;1. Faculty of Information Technology, Monash University, Melbourne, Australia;2. School of Computing, Telkom University, Bandung, Indonesia;1. Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor’s Circle, Winnipeg, MB R3T 5V6, Canada;2. Center for Nonlinear Science, University of North Texas, 1155 Union Circle, #311427, Denton, TX 76203-5017, USA;3. Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB R3B 2E9, Canada |
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| Abstract: | ![]()
Data assimilation is an essential step for improving space weather forecasting by means of a weighted combination between observational data and data from a mathematical model. In the present work data assimilation methods based on Kalman filter (KF) and artificial neural networks are applied to a three-wave model of auroral radio emissions. A novel data assimilation method is presented, whereby a multilayer perceptron neural network is trained to emulate a KF for data assimilation by using cross-validation. The results obtained render support for the use of neural networks as an assimilation technique for space weather prediction. |
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