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Space Weather: Modeling with Intelligent Systems
Henrik Lundstedt
Swedish Institute of Space Physics,
Solar-Terrestrial Physics Divisions, Lund, Sweden
[email protected] and www.irfl.lu.se
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
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)
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
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.
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).
x1
w1
x2
w2



wn
f(Swixi)
y
xn
x1 =+1


x2 =-1

x6 =+1
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.
 x3 =+ 1The

x5 =-1

x4 =+1
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.)
Supervised neural network
Recurrent neural network
Unsupervised neural network
Second generation neural nets
Spike neural networks
Neurons encode information by rate or
temporal coding.
Benefits of neural networks
A neural network, made up of interconnected
nonlinear neurons, is itself nonlinear.
n Neural networks are dynamical.
n A neural network can learn from examples to
construct an input-output mapping. This can
then be explained.
n They can describe the interaction between
microscopic and macroscopic phenomena.
n They are easily integrated with other AI
techniques into intelligent hybrid systems(IHS).
n
Modeling and Forecasting Solar Activity
Low solar activity
September 11, 2000
High solar activity
March 26, 2001
Prediction of the solar cycle 23 and 24 maximum
Group
Date
Ashmall & Moore (98) 01, 2000
Tian & Fan (98)
2000
Conway et al. (97)
2001
Calvo et al. (95)
2000,2001
Conway et al. (97)
2011
Amplitude
Neural Network
163
Elman (13:4:1) GPR
178
MLBP (3:2:2) GPR
130±30 MLBP (12:6:1)
167,161 MLBP (12:3:1)
140
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).
The Solar Mean and Solar Activity
Wilcox Solar
Observatory
GONG
SOHO - MDI
SOHO
MDI
Daily Wilcox Solar Observatory Mean Field
and
Wavelet Power Spectra
May 16, 1975 - March 13, 2001
One Minute SOHO/MDI Mean Field
March 1999 - August 2000
Solar Interior and
Solar Magnetic Activity
Halo Coronal Mass Ejection July, 14 2000
QuickTime och en
GIF-dekomprimerare
krävs för att kunna se bilden.
One Minute SOHO/MDI Mean Field
and
Wavelet Power Spectra
March 16 - April 10, 1999
A Sum of 53 Wavelet Power Spectra
of SOHO/MDI Mean Field
during times of CMEs and not
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.
Far-side activity gives model of activity (low/high) week ahead
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)
(---)
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
Satellite problems July 14-16, 2000
Proton flux (pfu) > 10 MeV,
24000 pfu (15 July, 12.30 UT).
Third largest!
Largest 43 000 pfu, (24 March 1991).
Second largest 40 000 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.
Aurora was seen
in Italy 6-7 April,
2000!
Aurora in Stockholm
Aurora in Italy
It also occurred
in July,15-16,
2000!
Solar wind - magnetosphere coupling model forecasts Dst and AE.
AE forecast model gives
dimension of magnetosphere
THE END
MDI Optical
Layout
Daily SOHO/MDI and WSO Mean Field
March 1999 - August 2000