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The Solar and Space Weather Reseach Group in Lund. Space weather Solar activity - the driver Modelling and forecasting space weather and effects using KBN ESA/Lund Space Weather Forecast Service Regional Warning Center Sweden in Lund www.irfl.lu.se. Space weather.
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The Solar and Space Weather Reseach Group in Lund • Space weather • Solar activity - the driver • Modelling and forecasting space weather and effects using KBN • ESA/Lund Space Weather Forecast Service • Regional Warning Center Sweden in Lund • www.irfl.lu.se
Space weather • The solar activity causes conditions that can influence the Earth’s atmosphere, technological systems, and endanger human health and life. • We call that space weather
SOHO • Solar Heliospheric Observatory was launched on December 2, 1995 • SOHO carries three instruments observing the solar interior, six the solar corona and three the solar wind
MDI/SOHO reveals the interior MDI shows how the dynamo changes Sunspots are footpoints of emerging magnetic flux tubes MDI shows how magnetic elements form sunspots
Sunspots observed on solar surface Schwabe found the 11- year sunspot solar cycle. R = k(10g + f) The two peaks of solar activity, 1.3 years separated!
Gleissberg solar cycle in sunspot group number Gleissberg found an amplitude modulation of the 11-year cycle of 80-90 years, as seen by the sunspot group number
High solar activity and warm climate, low and cold climate The solar activity during 5 000 years derived from C14 measurments in old trees. Cold climate in England, Holland during Little Ice Age. Vikings settled down in Greenland during warm Middle Age.
Possible physical explanation of solar climate relation High solar activity Cosmic ray decreases Cloud coverage decreases Higher temperature The solar coronal magnetic flux has increased with 131% since 1901. Clouds effect climate 100 times more effectively than CO2.
CMEs cause the most severe space weather effects • Halo CMEs are most geoeffective • Mass: 5-50 billion tons • Frequency: 3.5/day (max), 0.2/day (min) • Speed: 200-2000 km/s
Wavelet power spectra of MDI magnetic mean field Upper panel shows for 53 CME events. Lower panel shows for times without CMEs.
Probing CMEs with a solar radar 15 000 antennas with sensors and emitters, organized in 100 clusters distributed within a circular region of about 350 km diameter. Frequency 10-250 MHz (30-1.5m). The total data rate will be 2.5 Tbits/s.
Modelling and forecasting with KBN • 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 (generalize), if the network is properlydesigned (architecture, weights) and trained (learning algorithm). • Both architecture and weights can be determined from differential equations which describe the causal relations between the physical variables (the solution of the diff equation is approximated with a RBF). The network (KBN) is then trained with observations. • The architecture (number of input and hidden nodes) can also be determined from dynamic system analysis (reconstruction of attractor from time series gives dimension). • Neural network can discover laws from regularities in data (e.g. Newton’s law). If one construct a hierachy of neural networks where networks at each level can learn knowledge at some level of abstraction, even more advanced laws can be dicovered.
Workshops arranged by us Workshops on ”Artificial Intelligence Applications in Solar-Terrestrial Physics” were held in Lund 1993 and 1997.
Real-time forecasts and warnings based on KBN Solar input data Solar observations with SOHO make warnings 1-3 days ahead possible. Solar wind observations with ACE make accurate forecasts 1-3 hours ahead possible.
