Outline

1. New Models and Observational Strategies for reconstructing the Solar Spectral Irradiance for Space Weather Applications G. Cessateur, T. Dudok de Wit, M. Kretzschmar, L. VieiraLPC2E, University of Orléans, France J. LilenstenLPG, University of Grenoble, France

2. Outline • Why is solar UV radiation important for Space Weather ? • Observations and Modelling of the Solar Spectral Irradiance • News strategies of observation • Solar radio telescopes

3. Solar UV Radiation • 1-300 nm range ≈ 1% of the Total Solar Irradiance (TSI) • Main source of energy for aeronomic processes

4. Solar UV Radiation • Various time scale • 11-years cycle • 27-days solar rotation • min to hours: impulsive events SEM/SoHO

5. Solar UV Radiation Variability is wavelength-dependant EUV Flux ≈ 10-100% FUV Flux < 10% MUV Flux < 1% Relative Variability [%] Wavelength (nm) SORCE & TIMED data (2003-2010)

6. Why is solar UV radiation important for space Weather ? • Heating of the upper atmosphere (EUV) satellite drag • Formation of the ionosphere (XUV-EUV) • Photolysis (EUV-MUV) climate satellite communications

7. Which Observations are Available ? Instrumental Challenge • Lack of measurements • Instrument degradation (e.g. Floyd et al, 1999) • Intercalibration issues (e.g. Deland & Cebula, 2008) SDO UV Models Spectral Irradiance Observations (Δλ≤ 1nm): Instrument satellite-based

8. Models based on solar proxies 1.Reference Spectra + Variability measured by indices: F10.7, < F10.7> 81d 2. Linear Combination of solar proxies and indices EUV: Lyman α, Mg II, F10.7, < F10.7> 81d, He I(1058nm), E10.7, … FUV/MUV: Mg II index (Facules) and Sunspots index SERF / HFG (Hinteregger et al, 1981) EUVAC / HEUVAC (Richards et al, 1994, 2006) Radio Background Nusisov (1984) Woods and Rottman (2002) + SERF2, EUV91/97, SOLAR 2000 Tobiska et al, 1988,1991,1998,2000,2006 NRLEUV: Warren et al, 2003, 2006; Lean et al 2003 Lean et al, 1997, 2000 Method Convenient but not reliable for Space weather purposes

9. Models based on EUV-UV images Solar variability in the EUV, FUV/MUV is driven by different features (Quiet Sun, sunspot, active network, enhanced network, ...). Filling factor for different regions 1. Contrast defined empirically 2. Contrast defined semi-empirically, using the differential emission measure. Cook et al, 1980Lean et al, 1982,1983,1984Worden et al, 1996,1998 Warren et al, 1998a, 1998b, 2005 Recent progress in automated image processing allows for tracking solar features in real-time(e.g. Barra et al, 2009)

10. Models based on solar magnetograms Solar variability in the UV, visible and IR is mainly driven by magnetic features (Quiet Sun, umbra, penumbra, faculae, ...). Solar spectrum is a combination of these spectra Assign spectrum or coefficient to each region • 1. Empirical Approach (Oral Presentation L. Vieira Friday 10h15) • 2. Semi-Empirical Approach: SATIRE model (Krivova et al, 2003, 2006) • Is solar spectral variability entirely driven by solar magnetism ? • The quiet Sun contribution plays a crucial role • The XUV and EUV ranges cannot be properly described but solar atmosphere NLTE models are steadily improving (Shapiro et al, 2010)

11. Observations VS Models: short time scales Model (NRLEUV) Observations (TIMED SEE) EUV (10-120 nm) Woods et al, 2005 Short time scales are well reconstructed, within 40% of the data Similar results for the FUV/MUV range (120-300 nm) (Lean et al, 2005)

12. Observations vs Models: long time scales Observations Important discrenpancies between models and observations (Haigh et al, 2010) Model 1) Calibration or instrument degradation? 2) Anomalous declining phase of the solar cycle 23 Different characteristics of the solar spectrum ? Models are not well constrained

13. New Strategies ? • No missions planned after SORCE to continuously monitor spectral irradiance above 120 nm • Instrumental challenge: instrument degradation Technology more robust: diamond detectors developed for radiometers • Present and Futures Missions: is high spectral resolution really needed or can we just focus on a few spectral bands ?

14. Observations with some Passbands LYRA/PROBA2 PREMOS/PICARD EUVS/GOES-13,14 EUVS/GOES-R

15. Observations with some Passbands Differentspectral lines evolve remarkably coherently they can be reconstructed from a combination of a few others(Dudok de Wit et al, 2005; Kretszchmar et al, 2006) 30.5 nm (He I) 121.5 nm (H I) 250.5 nm (Mg I)

16. Observations with some Passbands Statistical approach: Multidimensional scaling 4 passbands should suffice to reconstruct the solar UV spectral variability

17. Observations with some Passbands • Empirical Approach • Each spectral line can be reconstructed from a linear combination of four passbands • Reconstruction error is • comparable to instrumental error. • (Cessateur et al, submitted)

18. Conclusions • Continuous monitoring of the solar spectral irradiance is crucial for space weather and for space climate • But data are highly fragmented and no observation of the full UV spectrum will exist after SORCE ends (2013) • Lack of continuous observations: • Use solar proxies as substitutes (simple, but not accurate enough for upper atmosphere specification models) • Use segmented solar images (very promising, but calibration issues) • Use a simple dedicated monitors to observe a few passbands and reconstruct the spectrum from these.