1 G. Cessateur, T. Dudok de Wit, M. Kretzschmar, L. Vieira LPC2E, University of Orléans, France J. Lilensten LPG, University of Grenoble, France New Models and Observational Strategies for reconstructing the Solar Spectral Irradiance for Space Weather Applications G. Cessateur, T. Dudok de Wit, M. Kretzschmar, L. Vieira LPC2E, University of Orléans, France J. Lilensten LPG, 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 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 SORCE & TIMED data ( ) EUV Flux ≈ % FUV Flux < 10% MUV Flux < 1% Variability is wavelength-dependant Relative Variability [%] Wavelength (nm)

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

8 Models based on solar proxies 1. Reference Spectra + Variability measured by indices : F10.7, 81d 2. Linear Combination of solar proxies and indices EUV : Lyman α, Mg II, 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 Recent progress in automated image processing allows for tracking solar features in real-time (e.g. Barra et al, 2009) 1. Contrast defined empirically 2. Contrast defined semi-empirically, using the differential emission measure. Cook et al, 1980 Lean et al, 1982,1983,1984 Worden et al, 1996,1998 Warren et al, 1998a, 1998b, 2005

10 Models based on solar magnetograms 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) Solar variability in the UV, visible and IR is mainly driven by magnetic features (Quiet Sun, umbra, penumbra, faculae,...). Assign spectrum or coefficient to each region Solar spectrum is a combination of these spectra 1. Empirical Approach (Oral Presentation L. Vieira Friday 10h15) 2. Semi-Empirical Approach: SATIRE model (Krivova et al, 2003, 2006)

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

12 Observations vs Models: long time scales Important discrenpancies between models and observations (Haigh et al, 2010) 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 Observations Model

13 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 ? New Strategies ?

14 Observations with some PassbandsLYRA/PROBA2PREMOS/PICARDEUVS/GOES-13,14EUVS/GOES-R

15 Observations with some Passbands Different spectral 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) nm (H I) nm (Mg I)

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

17 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) Observations with some Passbands

18 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: Conclusions 1.Use solar proxies as substitutes (simple, but not accurate enough for upper atmosphere specification models) 2.Use segmented solar images (very promising, but calibration issues) 3.Use a simple dedicated monitors to observe a few passbands and reconstruct the spectrum from these.