Spectrally High resolution Infrared measurements for the characterisation of Volcanic Ash (SHIVA): a new way to study

volcanic processes

Elisa Carboni

Identify ash composition from information contained in infrared spectra

to study their change during an eruption,

to better understand the volcanic process that control eruptive activity.

How we do it:

Developing algorithms using new ash optical properties to retrieve ash

characteristics from data collected with three different hyperspectral observing

systems: ground-based absorption spectra FTIR and satellite-based emission

spectra (from the limb MIPAS and from nadir IASI).

Validating the results, with geochemical and petrological analysis of ash samples

and with reference to laboratory refractive index measurements of ash samples.

Objective

Component and morphological analyses of the erupted ash, and comparison of these data with those from other monitoring techniques, demonstrates a clear relationship between ash features and styles of explosive activity.

Scientific Background

. Basaltic Andesitic Rhyolitic

Increasing SiO2 %

Extinction coefficients obtained for ice, volcanic ash (Peters, 2012), pumice, andesite, H2SO4, quartz, obsidian.

Increasing explosivity of volcanic activity

Scientific Background

IASI observations of three dust storms and seven volcanic ash plumes (Kasatochi 8/2008, Eyjafjallajoküll 4/2010, Sarychev 6/2009, Montserrat 2/2010, Kliuchevskoi 6/2010 and Chaitén 4/2008) from Clarisse et al. (2010).

Method

FTIR IASI MIPAS

Optimal estimation approach (Rodgers, 2000) to retrieve ash composition and possibly size from infrared spectral measurements. We will study ash formed from different magmas and at different stages of evolution within a volcanic plume.

+ Ash sample analysis

ground and satellite retrievals ash type/composition (with different size distributions) to be compared with the analysis done on ash sample for the same volcanic plume.

one-to-one correspondence between refractive index spectra, compositions and remote sensing measurements.

Ash optical properties – WP1

literature survey + Dan meas. + TEM, SEM meas. ash refractive index, morphology and size

spectral extinction coefficient, spectral single scattering albedo andphase function

simulations of spectrometer measurements

Mie (spherical particles) or

T-matrix (non-spherical)

- Log-normal size distributions, 2 or more components- External or internal mixing- Coating of the ash particles with water, ice or sulphuric acid

Spectral extinction coefficient for different effective radius, obtained using the same volcanic ash refractive index (Peters, 2013).

We can consider:

Opt. prop.

Deliverable: Database of spectral ash and volcanic aerosol optical properties available through the project web page.

one to one

Ground transmittance analysis (FTIR) – WP2

Fourier transform infrared spectroscopy (FTIR) transmittance measurements from 800 to 5000 cm-1.

Retrieved parameters: ash optical depth AOD and effective radius Reff and possibly other (mode radius, spread and components) depending on the information content

Institute Volcano

INGV Etna (2002/2003), Iceland (April 2010)

Earth Science, Cambridge Soufriere Hills, Kilauea,

Geography, Cambridge Erebus, Yasur, Nyiragongo, Erta' Ale, Masaya, Poas, Villarrica, Soufriere Hills

Project partners and measurements available.

Deliverable: (1) Forward model to reproduce the FTIR measurements and retrieval code for analysis. (2) A publication describing the ash/aerosol retrieval using FTIR measurements and including the results of the analysis.

Forward Model Options:- Thin plume -> a single scattering model- Thicker plume -> DISORT (include multiple scattering and thermal emission) - others?

Problems: transmittance? Radiometric drift? Different sources?

IASI – WP3

The RTTOV output for a clean atmosphere (containing gas but not cloud or aerosol/ash) will be combined with an the ash/cloud layer using the ORAC scheme

Retrieved parameters: ash optical depth (at a reference wavelength tbd), ash effective radius, ash type and layer altitude.

We will assess the ability of the retrieval to distinguish the type of layer over a range of ash-aerosol-cloud mixtures

Comparison with: (1) independent IASI retrieval (project partner: University of Bristol),(2) ASTER ash retrieval (project partner: University of Bristol)(3) ORAC ash and cloud retrievals from the SEVIRI and AATSR.(4) independent ash retrieval from SEVIRI and MODIS (project partner INGV)(5) CALIPSO measurements.

Deliverables: (1) retrieval code for IASI ash; (2) a publication.

We have a forward model that simulate IASI spectra with ash and SO2 and can include two separate layers (e.g. ash above cloud).

IASI FAST FORWARD MODEL

ba c d

Iac

Iac

Rc, e

c, T

c ,B

c

Rs, e

s, B

s

Tac

Ibc

,Tbc

atm. above

aerosol layer

atm. belowe

surface

We need to divide the contributions between atmosphere and aerosol layer components

LUTs for aerosol layer parameters

The suffix ‘c’ refers to aerosol layer

Other atmospheric parameters (radiances above/below aerosol layer going up/down) are computed with RTTOV using ECMWF atmospheric profiles.

Ash Re=2 [mm], h=3 [km]

Dust Re=2 [mm], h=3 [km]

Water CloudRe=10 [mm], h=3 [km]

IASI forward model include aerosol and cloud.

Obtained with ‘Aso’ refractive index measured by Daniel Peters.

Ash and cloud IASI forward model

MIPAS – WP4

The spatial distribution of measurements from MIPAS is more sparse than IASI, but provides accurate information on the ash or cloud top layer altitude (Hurley et al. 2011, Spang et al. ACP 2012).

Two approaches for ash retrieval will be tested:

(1) Fitting the measurements using an ash MIPAS forward model (to be developed) and the optical properties from WP1.

(2) Using a singular vector decomposition (SVD) technique, as successfully applied for cloud retrieval (Hurley et al., 2011).

Deliverables: (1) retrieval code for MIPAS ash; (2) A publication describing the ash/aerosol retrieval using MIPAS measurements and including the results of the analysis.

Validation/comparison and interpretation – WP5

- Test consistency of retrieved composition from different sensors (IASI, MIPAS, FTIR)

- Compare remotely sensed composition (ash type) with samples taken on ground.

- Case studies to observe how ash/aerosol size distribution and composition changes as a function of time (or others parameters) to obtain information on the volcanic processes.

- Improve prediction of ash characteristcs by studying relationships between ash concentration and size with other parameters:

- measures of volcanic activity (plume height/seismicity etc) - the involvement of external water sources., - the gas (e.g. SO2) flux etc.

Wish list: output of the day ?

How we can define a finite number of ash/aerosol model (3-10?) that correspond to ash composition/optical properties to be used in the retrievals?(possibly distinguishable ash model)

How to link satellite and ground base measurements?Can we relate FTIR measurements with satellite? With ash sample measurements?

What is more interesting to retrieve/study? What can tell us something about volcanic processes? (aerosol type/ash composition, amount, dimension, variation in time?)

Define tasks, deadline and who will work on them.(tasks should become papers!?!)

Define how we will be in contact after the workshop:mail list? Teleconference? Others?

c2 between simulated IASI spectra with dust and ash, as function of different dust AOD and altitude

c2 = (ydust-mash)T Sash-1 (ydust-mash).

(1) ash cloud can be discriminated from other dust or aerosol clouds?

: Aod=[0.2,0.5,1,1.5,2]; Reff=[0,2,0.5,1, 1.5,2,3,4,6,10] micron; H=[0,2,6,9,12,15] km.

All simulations of IASI ash spectra have been used to compute the covariance matrix Sash and the mean spectra mash of ash.

Values higher then 1700 (dark blu) have probability less the 0.04 to be obtained (with the number of channel considered e.g. 1601), it means that the dust spectra is not fitted by the ash model, so ash and dust (with the assumed refractive index and optical properties) are distinguishable.

For the same state vector the ash and dust case have different spectral shape of the jacobian. (IASI FM)

Initial state estimate: x0 A priori: xa

Run forward model: f(xi)

Compare to J = [y - f(xi)]Se-1[y - f(xi)] +

measurements (y): [xi - xa]Sa-1[xi - xa]

Update state: xi → xi+1 (Levenburg-Marquardt)

Stop when: J is small , or when i is large.

OPTIMAL ESTIMATION [Rodgers 2000]

NB Optimal estimation method provides quality control and error estimate

RETRIEVAL METHOD

The optimal estimate of x taking into account total measurement error may be computed as:

Sytot is computed considering an appropriate ensemble of N measured spectra to construct an estimate of total measurement error variance-covariance Syobs

[Rodger 2000]

OE detection theory

[ Walker, Dudhia, Carboni, Atmos. Meas. Tech. Discuss., 2010 ]

Thermal infrared spectra

Wavenumber [cm-1]

Brig

htne

ss T

empe

ratu

re [

k]

SO2 absorption bands

1 3

ash

AEROSOL OPTICAL PROPERTIES

Microphysical properties Every component is characterized by:

Spectral refractive indexm(l) + i k(l)

Mode radius rm and spread s

Changing the mixing ratio between component we obtain the optical properties corresponding to different effective radius

Kext()

() < 1

P(, )

Aerosol class with more components

Volcanic ash: 1 component only

Changing the mode radius rm (= tranlation of the log normal)