Progress in space weather predictions and applications

Abstract The methods of today’s predictions of space weather and effects are so much more advanced and yesterday’s statistical methods are now replaced by integrated knowledge-based neuro-computing models and MHD methods. Within the ESA Space Weather Programme Study a real-time forecast service has been developed for space weather and effects. This prototype is now being implemented for specific users. Today’s applications are not only so many more but also so much more advanced and user-oriented. A scientist needs real-time predictions of a global index as input for an MHD model calculating the radiation dose for EVAs. A power company system operator needs a prediction of the local value of a geomagnetically induced current. A science tourist needs to know whether or not aurora will occur. Soon we might even be able to predict the tropospheric climate changes and weather caused by the space weather.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Toshiki Tajima,et al.  Neural net forecasting for geomagnetic activity , 1993 .

[3]  Peter Wintoft,et al.  A neural network study of the mapping from solar magnetic fields to the daily average solar wind velocity , 1999 .

[4]  Françoise Fessant,et al.  On the prediction of solar activity using different neural network models , 1996, Annales Geophysicae.

[5]  C. Russell,et al.  An empirical relationship between interplanetary conditions and Dst , 1975 .

[6]  P. Wintoft,et al.  Prediction of geomagnetic storms from solar wind data with the use of a neural network , 1994 .

[7]  Henrik Lundstedt,et al.  Solar Wind Variations Related to Fluctuations of the North Atlantic Oscillation , 2002 .

[8]  Xianping Zhao,et al.  Magnetic cloud B s events and their dependence on cloud parameters , 2001 .

[9]  LiMin Fu,et al.  Neural networks in computer intelligence , 1994 .

[10]  Ljiljana R. Cander,et al.  Twenty‐four hour predictions of f0F2 using time delay neural networks , 2000 .

[11]  H. Gleisner.  Solar Wind and Geomagnetic Activity - Predictions Using Neural Networks , 2000 .

[12]  F. Boberg,et al.  Coronal mass ejections detected in solar mean magnetic field , 2000 .

[13]  M. Mundt,et al.  Chaos in the sunspot cycle: Analysis and Prediction , 1991 .

[14]  J. Hoeksema.  Structure and evolution of the large scale solar and heliospheric magnetic fields. Ph.D. Thesis , 1984 .

[15]  H. Lundstedt,et al.  Prediction of geomagnetic storms from solar wind data using Elman Recurrent Neural Networks , 1996 .

[16]  M. Juan,et al.  SHORT TERM FORECAST OF SOLAR PROTON EVENT , 2003 .

[17]  A. J. Conway,et al.  Prediction of solar and geomagnetic activity data using neural networks , 1995 .

[18]  K. Schatten,et al.  A new look at wolf sunspot numbers in the late 1700's , 1992 .

[19]  R. Calvo,et al.  Neural Network Prediction of Solar Activity , 1995 .

[20]  Peter Wintoft,et al.  Real time Kp predictions from solar wind data using neural networks , 2000 .

[21]  P. Wintoft,et al.  Operational forecasts of the geomagnetic Dst index , 2002 .

[22]  Jacek M. Zurada,et al.  Knowledge-based neurocomputing , 2000 .

[23]  A. J. Conway,et al.  A neural network prediction of solar cycle 23 , 1998 .

[24]  Richard Fozzard,et al.  A Connectionist Expert System that Actually Works , 1988, NIPS.

[25]  H. Gleisner,et al.  Auroral electrojet predictions with dynamic neural networks , 2001 .

[26]  Henrik Lundstedt,et al.  A neural network-based local model for prediction of geomagnetic disturbances , 2001 .

[27]  V. Bothmer,et al.  The structure and origin of magnetic clouds in the solar wind , 1997 .

[28]  Takahiro Obara,et al.  Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms , 2002 .