1. Assimilation of solar spectral irradiance data within the SOLID project Margit Haberreiter PMOD/WRC, Davos, Switzerland

2. Thanks to the SOLID Team Kick-off Meeting at PMOD/WRC February 12-14, 2013 Annual Meeting in Orleans, France http://projects.pmodwrc.ch/solid/ ESPM-14Margit Haberreiter Dublin, 8.-12.2014

3. Overview Motivation Goals Approach First Results ESPM-14Margit Haberreiter Dublin, 8.-12.2014

4. SOLID Motivation I – Scattered SSI Data ESPM-14Margit Haberreiter Dublin, 8.-12.2014

5. SOLID Motivation II – Overlapping data and models do not agree Ermolli et al., 2013, ACP ESPM-14Margit Haberreiter Dublin, 8.-12.2014

6. Instrumental accuracy (210 nm) Courtesy Micha Schoell ESPM-14Margit Haberreiter Dublin, 8.-12.2014

7. Importance for other research fields Stellar Physics to be able to link the Sun to other solar like stars the variation in SSI is important Sun-Earth connection Investigate solar long-term changes in spectral irradiance and their influence on the Earth’s climate Influence on other planets Effect on other planetary atmospheres (link to exoplanets) ESPM-14Margit Haberreiter Dublin, 8.-12.2014

8. SOLID in a nutshell SOLID: First European Comprehensive SOLar Irradiance Data exploitation Goal: provide a consistent SSI time series from the EUV to IR with error bars Approach: use all existing SSI and proxy data complemented with models to fill temporal and spectral gaps Coordinator: PMOD/WRC (W. Schmutz, Projekt Manager M. Haberreiter) Partners: 9 institutes from 7 Countries (CH, FR, BE, UK, DE, EL, IT) + 1 US collaborator (LASP) Start: December 2012; Duration: 3 years 8 ESPM-14Margit Haberreiter Dublin, 8.-12.2014

9. SOLID – What exactly do we do Solar spectral irradiance data Reconstruction models of SSI “Stitch it all together” i.e. find an objective method to obtain the most correct SSI time series ESPM-14Margit Haberreiter Dublin, 8.-12.2014

10. SOLID – Type of observations Reference Spectra –ATLAS1, ATLAS3, SOLAR/ISS/SOLSPEC, (Thuillier et al., 2006, 2013, 2014) SSI time series e.g. SORCE/SIM, SORCE/SOLSTICE, PROBA2/LYRA, UARS/SOLSITCE, UARS/SUSIM, Proxy time series –E.g. Mg II index, F10.7 ESPM-14Margit Haberreiter Dublin, 8.-12.2014

11. SOLID – Modelling to fill the gaps Empirical modelling –Principal Component Analysis, Single Value Decomposition Semi-Empirical Modelling –Combine synthetic spectra based on solar image analysis –SATIRE, COSI, SOLMOD, OAR –Including an update of atomic lines ESPM-14Margit Haberreiter Dublin, 8.-12.2014

12. EUV Reconstruction versus SOHO/SEM SOLMOD Reconstruction Error of the modelled SSI is of the order of the observation Haberreiter et al., 2014, accepted, J. Space Weather Space Climate ESPM-14Margit Haberreiter Dublin, 8.-12.2014

13. Accuracy, Precision, Stability Accuracy (confidence in absolute level) –Previous issues, e.g. TIM-VIRGO off-set) Precision (confidence in short-term uncertainty) –Repeatability of the measurement (influenced e.g. by readout noise of the instrument) Stability (confidence in long-term uncertainty) –Instrument degradation (covered by instrument teams) ESPM-14Margit Haberreiter Dublin, 8.-12.2014

14. Sanity Check of each SSI Data set Noise estimate for each data set One homogeneous data format Missing data interpolated via single value decomposition (Error introduced is estimated) Outliers are removed and flagged –Solar outliers are kept in the data set! Data processing tracable ESPM-14Margit Haberreiter Dublin, 8.-12.2014

15. How to stitch it together? Mean – introduces discontinuities (not a good idea) Median Take your favorite data set (very subjective) Bayesian approach ESPM-14Margit Haberreiter Dublin, 8.-12.2014

16. Our SOLID Approach ESPM-14Margit Haberreiter Dublin, 8.-12.2014 Wit the Bayesian approach: Derive the composite that is most compatible with the observations

17. Our SOLID Appraoch Bayesian: Provides the most compatible value (based on objective prior information) Perform the data fusion scale-wise E.g. daily, 28 day, 81-day, annual, solar cycle Requirement: time-dependent uncertainties (confidence intervals) ε(t, scale) Independent data sets (no inbreeding) Proof of concept: new TSI composite (Dudok de Wit et al., in prepration; Kopp et al., in preparation) ESPM-14Margit Haberreiter Dublin, 8.-12.2014

18. Example: Uncertainty in the UV UV Spectrum Uncertainty SOLSTICE Uncertainty SUSIM ESPM-14Margit Haberreiter Dublin, 8.-12.2014 Courtesy Kretzschmar

19. First Studies – Mg II index Courtesy Thierry Dudok de Wit

20. First Studies – Mg II index Courtesy Thierry Dudok de Wit

21. Goal: Unprecedented SSI time series Approach will be applied to all SSI data sets (incl models) homogeneous dataset i.e. uniform time and wavelength grid from EUV to IR Realistic error bars (important for the composite) time and wavelength dependent confidence intervals for each ESPM-14Margit Haberreiter Dublin, 8.-12.2014

22. SOLID Webpage http://projects.pmodwrc.ch/solid/ Database Wiki Deliverables

23. SOLID – Webpage and Database http://projects.pmodwrc.ch/solid-visualization/makeover/ ESPM-14Margit Haberreiter Dublin, 8.-12.2014

24. Small Scale Heating Events in the solar corona Coronal heating events identified from 3D MHD simulations ESPM-14Margit Haberreiter Dublin, 8.-12.2014

25. Questions

26. SOLID – How do we address the problem

27. Newsletter http://projects.pmodwrc.ch/solid/ Email: Margit.habereiter@pmodwrc.ch Tourpali@auth.gr

28. SSI and TSI WP2 WP3 WP4 WP5 proxies EUV/UV spectra data modeling groups WP6 Interaction with communities WP8 Dissemination end-users atmospheric research photobiology space weather ionosphere thermosphere Interaction with user communities