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Phnomene der Sonne

Assimilation of solar spectral irradiance data within the SOLID projectMargit HaberreiterPMOD/WRC, Davos, Switzerland

1

TO do:

Sonnenbild von Davos, Sonne+haze, Strandbild Kauai....

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

Overview

Motivation

Goals

Approach

First Results

ESPM-14Margit Haberreiter

Dublin, 8.-12.2014

SOLID Motivation I Scattered SSI Data

ESPM-14Margit Haberreiter

Dublin, 8.-12.2014

SOLID Motivation II Overlapping data and models do not agree

Ermolli et al., 2013, ACP

ESPM-14Margit Haberreiter

Dublin, 8.-12.2014

Understand the physical processes behind solar spectral irradiance variations

5

Instrumental accuracy (210 nm)

Courtesy Micha Schoell

ESPM-14Margit Haberreiter

Dublin, 8.-12.2014

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 Earths climate

Influence on other planets

Effect on other planetary atmospheres (link to exoplanets)

ESPM-14Margit Haberreiter

Dublin, 8.-12.2014

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

Understand the physical processes behind solar spectral irradiance variations

8

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

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

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

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

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

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

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

Our SOLID Approach

ESPM-14Margit Haberreiter

Dublin, 8.-12.2014

Wit the Bayesian approach:

Derive the composite that is most compatible with the observations

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)

(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

Example: Uncertainty in the UV

UV Spectrum

Uncertainty SOLSTICE

Uncertainty SUSIM

ESPM-14Margit Haberreiter

Dublin, 8.-12.2014

Courtesy Kretzschmar

First Studies Mg II index

Courtesy Thierry Dudok de Wit

First Studies Mg II index

Courtesy Thierry Dudok de Wit

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

SOLID Webpage

http://projects.pmodwrc.ch/solid/

Database

Wiki

Deliverables

Poster on the SOLID Project

Haberreiter et al.

22

SOLID Webpage and Database

http://projects.pmodwrc.ch/solid-visualization/makeover/

ESPM-14Margit Haberreiter

Dublin, 8.-12.2014

Small Scale Heating Events in the solar corona

Coronal heating events identified from 3D MHD simulations

Poster: Guerreiro, Haberreiter, Hansteen, Schmutz,

ESPM-14Margit Haberreiter

Dublin, 8.-12.2014

Questions

SOLID How do we address the problem

Newsletter

http://projects.pmodwrc.ch/solid/

Email:

Margit.habereiter@pmodwrc.ch

Tourpali@auth.gr

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

This is how we see our role in SOLID.

This is you, all other working groups. This part is responsible for producing data, TSI and SSI irraidainces, proxies and etc.

In the other side, such as climate modelling communities, groups working in the iososphere/thermosphere jut to name a few.

We are the middlemean, the interface if you wish, a separate entity which established connections between SOLID and users.

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.

28

AGU2013(T.(Dudok(de(Wit

Step 4Compute the composite that is the most compatible with the observations, given prior information

Use a Bayesian approach

12

P(c|data, I) = P(data|c, I) P(c|I)P(data|I)

compositeprior(informa.on

AGU2013(T.(Dudok(de(Wit

Ste p 4

Compute the composite that is the most compatible

with the observations, given prior info rmation

Use a Ba yesian app roach

12

P(c|data,I)=

P(data|c,I)P(c|I)

P(data|I)

composite

prior(informa.on

AGU2013(T.(Dudok(de(Wit

1995 1997 2000 2002 2005 2007 2010 20120.26

0.265

0.27

0.275

0.28

0.285

year

MgI

I ind

ex [a

.u.]

The Bayes composite

14

AGU2013(T.(Dudok(de(Wit

Bayes composite: 2 excerpts

15

Bayes(composite((1

09/2008 10/2008 11/2008

0.262

0.263

0.264

0.265

0.266

date

MgI

I ind

ex [a

.u.]

10/2009 11/2009 12/2009 01/2010 02/20100.263

0.264

0.265

0.266

0.267

0.268

0.269

date

MgI

I ind

ex [a

.u.]

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