1. Operational forecasts of Dst
Henrik Lundstedt Hans Gleisner Peter Wintoft

2. 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.

3. 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).

4. Elman Neural Network

5. 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

6. Statistical evaluation cont.

7. Statistical evaluation cont.

8. Example

9. Interpretation of weights
Burton:
Elman network:

10. Interpretation of weights cont.

11. Web pages Regional Warning Center – Sweden Dst real time forecasts
Dst real time forecasts
Dst Matlab and Java models