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

4. 1.Characteristics of SDO data 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,……

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

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

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

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

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. 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). 2.1 Long & mid-term forecasting

12. Long & mid-term forecasting

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

14. High spatial&temporal resolution = high quolity movies of multipal layers evolutions of solar magnetic parameters (flux, gradient, current dengsity, magnetic &current helicity,…) Short-term forecasting and nowcasting 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. 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. 2.3 Forecasting model

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

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

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 Number of singular points Length of neotral lines

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

22. Model testing results for M flares in 2001

23. Model testing results for SEPs in 2004

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

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

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

27. Prominences and cavity MLSO

28. SXT/YohkohXRT/Hinode http://solar.physics.montana.edu/canfield/sigmoids.shtmlhttp://solar.physics.montana.edu/press/XRT_Sigmoid.html

29. Photosphere Chromoshere and corona free energy building magnetic types, neotral lines, singgular points magnetic types, neotral lines, singgular points shear, strong gradient, magnetic & current helicity, shear, strong gradient, magnetic & current helicity,filaments,sigmoids,cavities,… What’s new from SDO Data? eruption triggering flux emerging & cancellation, shear and twist motion, flux emerging & cancellation, shear and twist motion, sunspot dynamics,… Filament oscillation, repetitive surges, … What’s new from SDO Data? 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 Global Circulation Irradiance Sources Far-side Imaging Solar Subsurface Weather Coronal Magnetic Field Magnetic Connectivity Sunspot Dynamics Magnetic Stresses Interior Structure NOAA 9393 Far- side Courtesy HMI Team and Y. Liu HMI+AIA+EVE HMI

32. Thanks !