Solar Magnetic Field Data
Indicators of Solar Magnetic Activity
Exploration with Wavelet Methods Theoretical Understanding Solar Output Reconstructions

Predictions using
Computer Intelligence

Since solar magnetic activity can be described as a non-linear chaotic dynamic system, methods such as wavelets and neural networks are very suitable. We focus on studies of transient events, trends and cyclic variation. Wavelet methods make it also possible to interpret indicators, such as sunspot number and C14 production rate, into physical quantities by comparing them with recent accurrate SOHO/MDI solar magnetic field data. Studies of mid-, and long-term solar magnetic activity are also carried out. We study dynamical systems representing the solar dynamo using topological methods.

The research is expected to give new results within basic solar physics, but also give improved predictions of the influence of the space weather on satellites, conditions of the radio communication, and power systems. Studies of long-term variations of solar magnetic activity are also of great importance for understanding the role of the Sun in climate changes.

We are grateful to the teams of ESA/NASA for the use of the data and images, especially the MDI Stanford team. We are also grateful for the support from the Swedish National Space Board.

Solar Magnetic Activity Analysis
(WP 11000, WG1, EU COST Action 724 Space Weather)
Henrik Lundstedt is the research leader at the Swedish Institute of Space Physics, Scheelev. 17, SE-223 70 Lund, Sweden

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