H.N. Wang Key Laboratory of Solar Activity National Astronomical Observatory

1. H.N. Wang Key Laboratory of Solar Activity National Astronomical Observatory Chinese Academy of Sciences SDO data for solar activity forecasts

2. Although missions such as SOHO and TRACE have taught us much about the solar influences on space weather, we still do not fully understand all sources of space weather nor can we reliably predict energetic particle eruptions or solar wind variations. Likewise, although we have learned much about the structure and dynamics of the solar interior and the evolution of active region magnetic fields, we still don't understand the solar dynamo and can't reliably predict the size of the next solar cycle or the emergence of the next active region. ---http://sdo.gsfc.nasa.gov/

3. Outline 1. Characteristics of SDO data 2. Requirements from forecasting operation 3. SDO data and space weather

4. Multipl wave ranges High spatial, temporal and spectral resolution Image and spectrum Physical parameters derived from observational data: Intensity field, velocity field, magnetic field temperature,density, electric current,…… Characteristics of SDO data

5. wave bands 10:EUV, UV,WL wave range 0.1nm-105nm centralcave length: 617.3 nm FeI spatial res.: ~1” time intervel: Doppler and LOS field 45s Vector field 135s FOV: >full disk spectral res.: wave range depandence cadence: 10s spatial res.: 1.5” cadence: 10s FOV: >full disk image spectrum image

6. 94A 131A 171A 193A 211A

7. 304A 1600A 335A 1-70A SAM rotation image 6173A WH-4500A

9. Soft X-ray –EUV spectrum Courtesy EVE Team LOS magnetic field Courtesy HMI Team and Y. Liu Vector magnetic field Courtesy HMI Team and K. Hayashi

10. 2. Requirements from forecasting operation • Long & mid-term forecasting • (longer than 3days) • Daily or monthly fluctuations are smoothed • Short-term forecasting • (shorter than 3days) and nowcasting • SDO data with multipl wave ranges,high spatial, temporal and spectral resolution will provide precursors of solar eruptions. • Forecasting model • Accumulated data are very helpful

11. 2.1 Long & mid-term forecasting HMI data provide key parameters for solar dynamo model. HMI data can be used for detecting sunspot dynamics and solar far-side active regions. EVE data describe solar EUV irradiance variations due to solar rotation (days), and solar cycle (years).

12. Long & mid-term forecasting

13. 2.2 Short-term forecasting and nowcasting Multipl wave ranges =multipl layers in solar atmosphere

14. Short-term forecasting and nowcasting High spatial&temporal resolution = high quolity movies of multipal layers evolutions of solar magnetic parameters (flux, gradient, current dengsity, magnetic &current helicity,…) Courtesy HMI Team and Y. Liu

15. Short-term forecasting and nowcasting High spectral&temporal resolution = EUV irradiance variations due to solar flares. May 7, 2010; C2.0 – Long (courtesy: Chamberlin)

16. 2.3 Forecasting model We believe that SDO data will play important role in modeling for solar activity forecast. Previous space and gruand based observational data have been widely used in forecasting model. A part of models is prensented here.

17. 活动区磁场等效距离参数 : Ed = (Rn+Rs)/Rns = 2.0969 Rs (Xs,Ys) Rns (Xn,Yn) Rn Guo J., Chumak O. et al., 2005, 2006

18. Forecasting Coronal Mass Ejections from Magnetograms Length of strong-gradient main Neutral Line: a measures of active region complexity that is promising as a predictor of CMEs Falconer, et al, 2002, 2003, 2006

19. Magnetic complexity of photospheric field Cui et al, 2006, 2007 Horizontal gradient AR 9574（8/11/2001） 1unit =1 pixel Number of singgular point Length of neotal line HSOS magnetogram

20. Solar flare productivity and magnetic measures samples: 1997-2004, number >23,000 （Cui, Y. M. et al , 2006; Wang, H. N. et al, 2009） Maximun of horizontal gradient Length of neotral lines Number of singular points

21. Modeling with artificial intelligence (NAOC) Li, R. et al, 2007; Wang, H. N., 2008, Yu, D. R., et al, 2009, 2010 Training samples Testing samples Physical parameters Physical parameters Magnetic complexity Training model Magnetic complexity Artificial intelligence   Test Results

22. Model testing results for M flares in 2001

23. Model testing results for SEPs in 2004

24. Precursors of solar eruptions from theoretical models Kusano et al., 2008 Lin & Forbes 2000

25. Precursors of solar eruptions Photosphere: Morphology of magnetic field (magnetic types, neotral lines, singgular points) Non-potentiality of magnetic field (shear, strong gradient, magnetic & current helicity) Evolution of magnetic field (flux emerging & cancellation, shear and twist motion )

26. Chromoshere and corona Filaments, filament oscillation, repetitive surges, cavities, sigmoids Chen, P. f., et al. 2008

27. Prominences and cavity MLSO

28. SXT/Yohkoh XRT/Hinode http://solar.physics.montana.edu/canfield/sigmoids.shtml http://solar.physics.montana.edu/press/XRT_Sigmoid.html

29. Precursors of solar eruptions

30. 3. SDO and space weather • Convection-zone dynamics and solar dynamo • origin and evolution of sunspots, active regions and • complexes of activity (HMI); • Sources and drivers of solar activity and disturbances(HMI); • Links between the internal processes and dynamics of the • corona and heliosphere(HMI,AIA,EVE); • The irradiance of the Sun that produces the ionosphere • (AIA,EVE); • The sources of radiation and how they evolve. (EVE ,AIA); • Precursors of solar disturbances for space-weather • forecasts(HMI,AIA,EVE).

31. SDO data and space weather Solar Dynamo Sunspot Dynamics Global Circulation Magnetic Connectivity Interior Structure Irradiance Sources Coronal Magnetic Field Far-side Imaging NOAA 9393 Far-side Solar Subsurface Weather Magnetic Stresses HMI HMI HMI HMI+AIA+EVE HMI+AIA+EVE HMI HMI HMI HMI Courtesy HMI Team and Y. Liu

32. Thanks !