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

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<p>Phnomene der Sonne</p> <div><p>Assimilation of solar spectral irradiance data within the SOLID projectMargit HaberreiterPMOD/WRC, Davos, Switzerland</p></div> <div><p>1</p><p>TO do:</p><p>Sonnenbild von Davos, Sonne+haze, Strandbild Kauai....</p></div> <div><p>Thanks to the SOLID Team</p><p>Kick-off Meeting at PMOD/WRC</p><p>February 12-14, 2013</p><p>Annual Meeting in Orleans, France</p><p>http://projects.pmodwrc.ch/solid/</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>Overview</p><p>Motivation</p><p>Goals</p><p>Approach</p><p>First Results</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>SOLID Motivation I Scattered SSI Data</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>SOLID Motivation II Overlapping data and models do not agree</p><p>Ermolli et al., 2013, ACP</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>Understand the physical processes behind solar spectral irradiance variations</p><p>5</p></div> <div><p>Instrumental accuracy (210 nm)</p><p>Courtesy Micha Schoell</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>Importance for other research fields</p><p>Stellar Physics</p><p>to be able to link the Sun to other solar like stars the variation in SSI is important</p><p>Sun-Earth connection</p><p>Investigate solar long-term changes in spectral irradiance and their influence on the Earths climate</p><p>Influence on other planets</p><p>Effect on other planetary atmospheres (link to exoplanets)</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>SOLID in a nutshell</p><p>SOLID: First European Comprehensive SOLar Irradiance Data exploitation </p><p>Goal: provide a consistent SSI time series from the EUV to IR with error bars</p><p>Approach: use all existing SSI and proxy data complemented with models to fill temporal and spectral gaps</p><p>Coordinator: PMOD/WRC (W. Schmutz, Projekt Manager M. Haberreiter)</p><p>Partners: 9 institutes from 7 Countries (CH, FR, BE, UK, DE, EL, IT) </p><p>+ 1 US collaborator (LASP)</p><p>Start: December 2012; Duration: 3 years</p><p>8</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>Understand the physical processes behind solar spectral irradiance variations</p><p>8</p></div> <div><p>SOLID What exactly do we do</p><p>Solar spectral irradiance data</p><p>Reconstruction models of SSI</p><p>Stitch it all together</p><p>i.e. find an objective method to obtain the most correct SSI time series</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>SOLID Type of observations</p><p>Reference Spectra</p><p>ATLAS1, ATLAS3, SOLAR/ISS/SOLSPEC, (Thuillier et al., 2006, 2013, 2014)</p><p>SSI time series</p><p>e.g. SORCE/SIM, SORCE/SOLSTICE, PROBA2/LYRA, UARS/SOLSITCE, UARS/SUSIM, </p><p>Proxy time series</p><p>E.g. Mg II index, F10.7</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>SOLID Modelling to fill the gaps</p><p>Empirical modelling</p><p>Principal Component Analysis, Single Value Decomposition</p><p>Semi-Empirical Modelling</p><p>Combine synthetic spectra based on solar image analysis</p><p>SATIRE, COSI, SOLMOD, OAR</p><p>Including an update of atomic lines</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>EUV Reconstruction versus SOHO/SEM</p><p>SOLMOD Reconstruction</p><p>Error of the modelled SSI is of the order of the observation</p><p>Haberreiter et al., 2014, accepted, J. Space Weather Space Climate</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>Accuracy, Precision, Stability</p><p>Accuracy (confidence in absolute level)</p><p>Previous issues, e.g. TIM-VIRGO off-set)</p><p>Precision (confidence in short-term uncertainty)</p><p>Repeatability of the measurement (influenced e.g. by readout noise of the instrument) </p><p>Stability (confidence in long-term uncertainty)</p><p>Instrument degradation (covered by instrument teams)</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>Sanity Check of each SSI Data set </p><p>Noise estimate for each data set</p><p>One homogeneous data format</p><p>Missing data interpolated via single value decomposition</p><p>(Error introduced is estimated)</p><p>Outliers are removed and flagged </p><p>Solar outliers are kept in the data set!</p><p>Data processing tracable</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>How to stitch it together?</p><p>Mean introduces discontinuities (not a good idea)</p><p>Median</p><p>Take your favorite data set (very subjective) </p><p>Bayesian approach</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>Our SOLID Approach</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p><p>Wit the Bayesian approach: </p><p>Derive the composite that is most compatible with the observations</p></div> <div><p>Our SOLID Appraoch</p><p>Bayesian: Provides the most compatible value (based on objective prior information)</p><p>Perform the data fusion scale-wise </p><p>E.g. daily, 28 day, 81-day, annual, solar cycle</p><p>Requirement: </p><p>time-dependent uncertainties (confidence intervals) </p><p>(t, scale)</p><p>Independent data sets (no inbreeding)</p><p>Proof of concept: new TSI composite (Dudok de Wit et al., in prepration; Kopp et al., in preparation)</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>Example: Uncertainty in the UV</p><p>UV Spectrum</p><p>Uncertainty SOLSTICE</p><p>Uncertainty SUSIM</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p><p>Courtesy Kretzschmar</p></div> <div><p>First Studies Mg II index</p><p>Courtesy Thierry Dudok de Wit</p></div> <div><p>First Studies Mg II index</p><p>Courtesy Thierry Dudok de Wit</p></div> <div><p>Goal: Unprecedented SSI time series</p><p>Approach will be applied to all SSI data sets (incl models)</p><p>homogeneous dataset</p><p>i.e. uniform time and wavelength grid</p><p>from EUV to IR</p><p>Realistic error bars (important for the composite)</p><p>time and wavelength dependent confidence intervals for each</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>SOLID Webpage</p><p>http://projects.pmodwrc.ch/solid/</p><p>Database</p><p>Wiki</p><p>Deliverables</p><p>Poster on the SOLID Project</p><p>Haberreiter et al.</p></div> <div><p>22</p></div> <div><p>SOLID Webpage and Database</p><p>http://projects.pmodwrc.ch/solid-visualization/makeover/</p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>Small Scale Heating Events in the solar corona</p><p>Coronal heating events identified from 3D MHD simulations</p><p>Poster: Guerreiro, Haberreiter, Hansteen, Schmutz, </p><p>ESPM-14Margit Haberreiter</p><p>Dublin, 8.-12.2014</p></div> <div><p>Questions</p></div> <div><p>SOLID How do we address the problem</p></div> <div><p>Newsletter</p><p>http://projects.pmodwrc.ch/solid/</p><p>Email:</p><p>Margit.habereiter@pmodwrc.ch</p><p>Tourpali@auth.gr</p></div> <div><p>SSI and TSI</p><p>WP2</p><p>WP3</p><p>WP4</p><p>WP5</p><p>proxies</p><p>EUV/UV spectra</p><p>data</p><p>modeling groups</p><p>WP6</p><p>Interaction with communities</p><p>WP8</p><p>Dissemination</p><p>end-users</p><p>atmospheric research</p><p>photobiology</p><p>space weather</p><p>ionosphere</p><p>thermosphere</p><p>Interaction with user communities</p></div> <div><p>This is how we see our role in SOLID.</p><p>This is you, all other working groups. This part is responsible for producing data, TSI and SSI irraidainces, proxies and etc.</p><p>In the other side, such as climate modelling communities, groups working in the iososphere/thermosphere jut to name a few.</p><p>We are the middlemean, the interface if you wish, a separate entity which established connections between SOLID and users.</p><p> So far, interaction between solar physics and climate modelling communities was poorly established and solar data were produced according to the needs of solar physics and then handed out to climate scientists.</p><p>28</p></div> <div><p>AGU2013(T.(Dudok(de(Wit</p><p>Step 4Compute the composite that is the most compatible with the observations, given prior information</p><p> Use a Bayesian approach</p><p>12</p><p>P(c|data, I) = P(data|c, I) P(c|I)P(data|I)</p><p>compositeprior(informa.on</p></div> <p>AGU2013(T.(Dudok(de(Wit</p> <p>Ste p 4</p> <p>Compute the composite that is the most compatible </p> <p>with the observations, given prior info rmation</p> <p> Use a Ba yesian app roach</p> <p>12</p> <p>P(c|data,I)=</p> <p>P(data|c,I)P(c|I)</p> <p>P(data|I)</p> <p>composite</p> <p>prior(informa.on</p> <div><p>AGU2013(T.(Dudok(de(Wit</p><p>1995 1997 2000 2002 2005 2007 2010 20120.26</p><p>0.265</p><p>0.27</p><p>0.275</p><p>0.28</p><p>0.285</p><p>year</p><p>MgI</p><p>I ind</p><p>ex [a</p><p>.u.]</p><p>The Bayes composite</p><p>14</p></div> <div><p>AGU2013(T.(Dudok(de(Wit</p><p>Bayes composite: 2 excerpts</p><p>15</p><p>Bayes(composite((1</p><p>09/2008 10/2008 11/2008</p><p>0.262</p><p>0.263</p><p>0.264</p><p>0.265</p><p>0.266</p><p>date</p><p>MgI</p><p>I ind</p><p>ex [a</p><p>.u.]</p><p>10/2009 11/2009 12/2009 01/2010 02/20100.263</p><p>0.264</p><p>0.265</p><p>0.266</p><p>0.267</p><p>0.268</p><p>0.269</p><p>date</p><p>MgI</p><p>I ind</p><p>ex [a</p><p>.u.]</p></div>