Real-time space weather forecasts based on neural networks

Transcription

1 Real-time space weather forecasts based on neural networks Henrik Lundstedt and Peter Wintoft Swedish Institute of Space Physics, Lund, Sweden Yurdanur Tulunay and Ersin Tulunay Middle East Technical University, Ankara/Turkey

2 Outline Space weather concept: stp/prediction & sw/user/forecast Workshops on AI Applications in Solar-Terrestrial Physics Artificial neural networks - Hybrids Developed solar-terrestrial physics predictions Operational forcast space weather service for users

3 Space weather concept Its origin and use Space weather was first mentioned in NASA Technical Document , (i.e. by engineers) US Air Force started to use it in 1970 Scientists Bob McPherron & John Freeman claim they coined it in late 1980! Definition IRF-Lund uses Prediction within STP Forecasting within space weather operational services for users In late 1980 power companies (user) contact us in Sweden on space weather forecasts. NSF/NSWP definition 1995, Report of the Assessment Committee for the NSWP FCM-R24, 2006: (LWS,Heliophysics RM 2035) (CISM,Transitioning scientific STP models into operational tools 10 years ahead) Space weather activities at ESA starts in 1995 i.e. more that 10 years ago. (SWprogramme, SDAs..), EU/COST724

4 Workshops arranged by IRF-Lund Workshops on Artificial Intelligence Applications in Solar-Terrestrial Physics were held in Lund 1993 and A third workshop: Solar Activity: Exploration, Understanding and Prediction was held in September 19-21, 2005

5 Artificial neural networks MLBP Neural networks differ by how the neurons (process units) work, how the neurons are connected (topology) and by which learning algorithm that has been chosen.

6 Elman recurrent neural network Download Lund Dst model in Java and Matlab

7 Self Organizing Maps Self Organizing Maps Synoptic field structures were clasified using SOM Structures in solar wind, related to CMEs and CH were clasified with SOM

8 Neurofuzzy prediction A. Gholipour et al., 2005

9 Outline

10 Solar-Terrestrial Physics Predictions Solar-Terrestrial Physics Predictions Input parameters Output KBNM method Reference Daily sunspot number Daily sunspot number SOM and Liszka 93;97 Monthly sunspot number Date of solar cycle max and amplitude and Elman, Neurofuzzy Macpherson et al., 95, Conway et al, 98 Maris&Oncica, 06, Gholipour, 05 Monthly sunspot number and aa Date of solar cycle max and amplitude Elman Ashmall and Moore, 98 Yearly sunspot number Date of solar cycle max and amplitude Calvo et al., 95 McIntosh sunspot class & MW magn complex. X class solar flare expert system Bradshaw et al., 89 Flare location, duration X-ray and radio flux;x-ray flux Proton events, Neurofuzzy Xue et al., 97; Gabriel et al., 00, Tulunay et al 07 Photospheric magnetic field expansion factor Solar wind velocity 1-3 days ahead RBF, PFM MHD Wintoft & Lundstedt 97;99 Photospheric magnetic synoptic field Solar magnetic total flux MLB Lundstedt et al., 07

11 Solar-Terrestrial Physics Predictions Solar-Terrestrial Physics Predictions Input parameters Output KBNM method Reference Solar wind n, V, Bz Relativistic electrons in Earth magnetosphere hour ahead Wintoft and Lundstedt, 00 Solar wind n,v, Bz, Dst Relativistic electrons one hour ahead, MHD, MSFM Freeman et al., 93 ΣKp Relativistic electrons day ahead Stringer and McPherron, 93 Solar wind V from photospheric B Daily geomagnetic Ap index Detman et al., 00 Ap index Ap index Thompson, 93 Solar wind n, V, Bz Kp index 3 hours ahead Boberg et al., 00 Solar wind n, V, B,Bz Dst 1-8 hours ahead, Elman Lundstedt, 91; Wu and Lundstedt, 97 Solar wind n, V, B,Bz AE 1 hour ahead Elman, Gleisner and Lundstedt,00,Gavrishchaka et al.,00, 01

12 Solar-Terrestrial Physics Prediction Solar-Terrestrial Physics Prediction Input parametrs Output KBNM method References Solar wind V 2 B s, (nv 2 ) 1/2, LT, local geomag Δx e, ΔY w Local geomagnetic field ΔX, ΔY and RBF Gleisner and Lundstedt 00 Solar wind n,v, Bz None, weak or strong aurora (NAO month ahead) Lundstedt et al., 00 Boberg and Lundstedt 02,03 fof2 fof2 1 hour ahead Wintoft and Lundstedt, 99 AE, local time, seasonal information fof hours ahead Wintoft and Cander, 00 fof2, TEC, Ap, F10.7 cm 24 hours ahead Wintoft and Cander, 99 Tulunay, E., Ozkaptan, C., 00. Tulunay, E., Senalp, E.T., Radicella, S.M., Tulunay, Y.,06 ΣKp Satellite anomalies Wintoft and Lundstedt 00 Solar wind n, V, Bz dbx/dt, GIC Elman, Kronfeldt et al., 01 and Weigel et al.,02

13 Transitioning Solar-Terrestrial Predictions into operational Space Weather Forecasts

14 Real-time space weather forecasts and warnings Real-time space weather forecasts and warnings Solar input data Scientific missions Ends 2011 Solar observations with SOHO make warnings 1-3 days ahead possible. Solar wind observations with ACE make accurate forecasts 1-3 hours ahead possible.

15 ESA/Lund Space Weather Forecast Service Package

16 Latest information on forecasts of Kp, Dst, AE and GIC

17 ESA GIC Pilot Project Real-time predictions of GIC

18 Real-time forecasts of GIC November 7, events

19 NN Forecasts from British Geological Survey NN Forecasts from British Geological Survey

20 Operation neural network space weather forecasts Operation neural network space weather forecasts Flare activity based vector magnetogram and NN (Aviation, polar flights, SDO) US Air Force MSM (Freeman) forecasts of magnetospheric conditions for satellites (NN Kp + MHD model) (satellites) Daily Ap NN forecasts (BGS, UK) (Oil companies, power companies) 3hr Kp NN forecasts (RWC-Sweden, Lund) (STP) Dst NN forecasts (RWC-Sweden, Lund; RWC-Japan, Tokyo, NICT Space Environment Information Service) (STP) GIC NN forecasts (RWC-Sweden, Lund) (Swedish power companies)

21 Coordinate the forecast service with ISES? Coordinate the forecast service with ISES? ISES Director: D. Boteler Deputy Director: H. Lundstedt Secr. for World days: H. Coffey Secr. Space Weather: J. Kunches WWW for Satellites: J. King