Operational forecasts of Dst

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Presentation transcript:

Operational forecasts of Dst Henrik Lundstedt Hans Gleisner Peter Wintoft

Main goal – The Lund Dst model Use an Elman recurrent neural network to predict Dst from solar wind data. Find the network that solves the problem with as few neurons as possible. Implement for real time forecasting. Publish models on the Internet in Java and Matlab code.

Model Inputs: [By(t)], Bz(t), n(t), V(t). Output: Dst(t+1). Data OMNI set from 1963 – current. >10 days of continuous data (data gaps max 2 hours).

Elman Neural Network

Statistical evaluation RMSE (nT) Correlation Lund Dst model 10.3 0.88 O’Brien and McPherron 12.3 0.83 Fenrich and Luhmann 15.3 0.78 Burton et al. 16.4 0.76

Statistical evaluation cont.

Statistical evaluation cont.

Example

Interpretation of weights Burton: Elman network:

Interpretation of weights cont.

Web pages Regional Warning Center – Sweden Dst real time forecasts www.lund.irf.se/rwc Dst real time forecasts www.lund.irf.se/rwc/dst Dst Matlab and Java models www.lund.irf.se/rwc/dst/models