1. Space Weather: Modeling with Intelligent Systems
Henrik Lundstedt
Swedish Institute of Space Physics,
Solar-Terrestrial Physics Divisions, Lund, Sweden
and

2. Space Weather: Modeling with Intelligent Systems
n Space Weather
n Intelligent Systems
n Modeling and Forecasting Solar Activity
n Modeling and Forecasting Geomagnetic Activity
n A Prototype Based on AI (ESA SWP Study, Alcatel Team)

3. Definition of Space Weather Solar and space conditions
affecting the society.
Conditions caused by the Sun,
that influence Earth atmosphere,
technological systems and human
health.
Space weather refers to conditions
on the sun and in the solar wind,
magnetosphere, ionosphere that
can influence the performance and
reliability of space-borne and
ground-based technological
systems and can endanger human
life or health. (NSWP, USA)

4. Intelligent Systems
n Symbolic approach: in which knowledge is explicity
expressed in words and symbols (expert systems)
n Numerical approach: such as neural networks,
genetic algorithm, fuzzy systems.
n Many Intelligent Systems are hybrids

5. Mathematical Modeling
A model is constructed to explain a hypothesis or simulate a real world system.
The basis of using neural networks as mathematical models is ”mapping”. Given a dynamic system, a neural network can model it on the basis of a set of examples encoding the input/output behavior of the system. It can learn the mathematical function underlying the system operation, if the network is designed (architechure, weights) and trained properly (learning algorithm).
Both architechure and weights can be determined from differential equations which describe the causal relations between the physical variables (solution of diff eq is approximized by a RBF). The network is then trained with observations.
Neural networks process information from their inputs to their outputs, therefore
an information theoretical approach is natural.

6. Artificial Neural Networks (ANN)
The basic element of every ANN is an artificial neuron or simply a neuron (which is an abstract model of a natural neuron).
The neuron receives an input vector x
and then computes the output y=f(Swixi).
The value y is the state of the neuron. If
f=sgn then the state of the neuron is (+1,-1).
Neural networks are dynamical (i.e. change with time). The state at time t for the general
nework to the left is described by the state
vector X(t)=(+1,-1,+1,+1,-1,+1)T.
The sequence of states as times evolves, is
called a trajectory. The endpoints of the trajectories are called fundamental memories or attractors (strange chaotics e.g.)

7. Supervised neural network Recurrent neural network
Unsupervised neural network
Second generation neural nets
Spike neural networks
Neurons encode information by rate or
temporal coding.

8. Benefits of neural networks
A neural network, made up of interconnected nonlinear neurons, is itself nonlinear.
Neural networks are dynamical.
A neural network can learn from examples to construct an input-output mapping. This can then be explained.
They can describe the interaction between microscopic and macroscopic phenomena.
They are easily integrated with other AI techniques into intelligent hybrid systems(IHS).

9. Modeling and Forecasting Solar Activity
Low solar activity
September 11, 2000
High solar activity
March 26, 2001

10. Prediction of the solar cycle 23 and 24 maximum
Group Date Amplitude Neural Network
Ashmall & Moore (98) 01, Elman (13:4:1) GPR
Tian & Fan (98) MLBP (3:2:2) GPR
Conway et al. (97) ±30 MLBP (12:6:1)
Calvo et al. (95) , , MLBP (12:3:1)
Conway et al. (97) MLBP (12:6:1)
Mundt et al., (1991, JGR): The sunspot cycle is chaotic. To extend
predictability beyond the 4 years threshold extra information is
needed (e.g. about precursors) to be incorporated (IHS).

11. The Solar Mean and Solar Activity
Wilcox Solar
Observatory
GONG
SOHO - MDI

12. SOHO MDI

13. Daily Wilcox Solar Observatory Mean Field and Wavelet Power Spectra
May 16, March 13, 2001

14. One Minute SOHO/MDI Mean Field
March August 2000

15. Solar Interior and Solar Magnetic Activity

16. Halo Coronal Mass Ejection July, 14 2000

17. One Minute SOHO/MDI Mean Field and Wavelet Power Spectra
March 16 - April 10, 1999

18. A Sum of 53 Wavelet Power Spectra
of SOHO/MDI Mean Field
during times of CMEs and not

19. Modelling and Predicting Solar Activity Using the Solar Mean field
A) By decomposing a time series into time-frequency space,
and then determine both the dominant modes of variability
and how those modes vary in time. Neural networks are then
trained with this information.
B) Neural networks are trained with magnetograms as input.

20. Far-side activity gives model of activity (low/high) week ahead

21. Prediction of solar wind velocity from daily solar WSO magnetograms
Input
A time-series
fs (t - 4),..fs (t) of the
expansion factor fs (t),
fs = (Rps/Rss)2 Bps/Bss.
Output
Daily solar wind velocity V(t + 2)
(---)

22. SOHO warns us of effects of solar and solar wind activity
ACE makes real-time predictions of effects of solar and solar wind activity possible

23. Satellite problems July 14-16, 2000
Proton flux (pfu) > 10 MeV,
24000 pfu (15 July, UT).
Third largest!
Largest pfu, (24 March 1991). Second largest pfu
(20 October 1989).
The proton event caused problem for ACE (36 hrs), SOHO (solar panel 1 year older), WIND (2 days), GOES , Ørsted, Akebono (Japanese satellite, electronics damaged),”star trackers” on board commercial satellites, and ASCA (Japanese X-ray satellite) stopped working.

24. It also occurred in July,15-16, 2000!
Aurora was seen in Italy 6-7 April, 2000!
Aurora in Stockholm
It also occurred in July,15-16, 2000!
Aurora in Italy

25. Solar wind - magnetosphere coupling model forecasts Dst and AE.
AE forecast model gives dimension of magnetosphere

31. THE END

32. MDI Optical Layout

33. Daily SOHO/MDI and WSO Mean Field
March August 2000