A new scheme for sulfur dioxide retrieval from IASI measurements:

application to recent volcanic eruptions.

Elisa Carboni1,

Roy Grainger1, Joanne Walker1, Anu Dudhia1, Richard Siddans2

(1) University of Oxford, Oxford, United Kingdom.(2) Rutherford Appleton Laboratory, Didcot, United Kingdom.

IASI measurements introduction: IR spectra - SO2 absorption band

SO2 retrieval: Optimal Estimation and covariance matrix Fast forward model Sensitivity to ash and cloud

Preliminary results: Evolutions of Eyjafjallajökull eruption (April-May 2010)

Grimsvötn eruption (May 2011) Puyehue-Cordón Caulle eruption (June 2011)

Summary and further work

Outline

Thermal infrared spectra

Wavenumber [cm-1]

Brig

htne

ss T

empe

ratu

re [

k]

SO2 absorption bands

1 3

ash

stratosphere

stratosphere

troposphere

troposphereB

TD

(s

td.a

tm.

- pl

ume)

- IASI is sensitive to both the amount of SO2 and the altitude of the plume

- amount and altitude have different spectral signature

=> we attempt to retrieve both

- Note that getting the altitude correct is important not just for itself, but also in order to get the correct amount of SO2, since the signal depends strongly on altitude.

- often near real time algorithm make assumption about height of the plume, and this can lead to large error in the retrieved amount

this applies to both UV and IR methods

y is the measurement vector, x the state vector, F forward model, Sy measurement error vector

Usually: y would contain the IASI measured radiancesSy would represent noise on those measurementsx would contain all atmospheric + surface parameters that affect these radiances and

are imperfectly known - in this case including H2O,T, cloud and many other minor species, as well as SO2.

However: - we're not interested in most of these potential state variables- SO2 is very rarely present in significant amounts in the IASI spectra (except during volcanic eruptions and degassing regions)- H2O,T are well predicted by Met data- Other gases have features which are spectrally uncorrelated with SO2

OPTIMAL ESTIMATION APPROACH

physics, radiative transfer

Sy(i,j) = [(ymi-ysi)-(ymi-ysi)][(ymj-ysj)-(ymj-ysj)]

we can greatly simplify the retrieval problem by including in the Sy covariance matrix all the differences between the IASI observed radiances and radiances predicted by a forward model, based on ECMWF met data. and...

4 day - 14 orbit a day-> 5x106 pixels

GlobalAprilMay

We compute the error covariance Sy using similar conditions but when we know there is no SO2 in the atmosphere.This will give a measurement covariance which allows deviations due to cloud and trace-gases but still enable the SO2 signature to be detected.

For Eyja eruption we consider the data, in the same geographic region, of the same month the year before (eg. April or May 2009)

We also use a global covariance matrix, considering all the data of 4 days (1 every season) of 2009

Fast forward model

State vector:- Total column amount of So2 - Altitude H - Thickness s- Surface temperature Ts

H

s

+ ECMWF profile (temperature, h2o, p, z)

fast radiative transfer (RTTOV + SO2 RAL coefficients)

FM

IASI simulated spectra

Ts

IASI measurements

OE retrieval

best estimate of stare vector: SO2 amount, plume altitude, Ts

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

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

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

IASI forward model can include aerosol and cloud.

Obtained with refractive index measured by Daniel Peters.

Ash and cloud effects?

To test if the retrieval is sensitive to ash/cloud etc...

Sensitivity of retrieval to presence of ash and cloud

Retrieval using simulations with ASH at different optical depth and altitude

SO2 underestimate for thick ash high cost !!

SO2 signal ‘covered’ from cloud within or above the plume

OK for cloud below the plume!!!grrr

IASI - SO2 [DU] Eyjafjallajökull eruption14 April - 17 May 2010

From (Thomas and Prata, ACP 2011)SO

2 pl

ume

mas

s [T

g]

Total mass of So2 in the atmospheric plume obtained assuming an average area of 25 x 25 km for any IASI pixel

The error bar is in the worse scenario of correlated errors: obtained as sum of all the pixels errors (overestimate in case of independent error))

IASI total mass of SO2

IASI

IASI

IASI - CALIPSO ~ 3h difference

Grimsvötn eruption

21 - 26 May 2011

Puyehue-Cordón Caulle eruption

5 - 13 June 2011

Minimum error estimate

surface contrast = skin temperature - temperature of the first atm. layer

std. atm. profiles assumption: we know the altitude of the plume.

considering 60 overpass a month => error reduced of 1/sqrt(60)

11 9 10-4

8.3 13 10-4

6.4 17 10-4

3.5 24 10-4

1.5 73 10-4

0 388 10-4

[g/m2][km]

SO2 monthly errors

1 DU = 0.0285 g/m2

Ecuador monthly mean - degassing

The monthly mean SO2 column amount for January 2010. The black triangle indicates the location of the Tungurahua volcano.

Error on monthly mean

Summary and further work

New scheme for IASI developed to retrieve height and amount of SO2

Uses our new detection scheme applied to pixels for the full retrieval

Initial results seem to compare reasonably to other observations.

Though sometimes see much larger amounts then uv methods

Thick ash can affect the retrieval, recognizable from cost >2 Cloud at the same altitude or above the plume mask the So2 signal

Retrieving the ash and cloud (optical depth, altitude and effective radius) properties is possible and subject of parallel work at AOPP & RAL.

Next steps:

- SO2comparison with ground and others satellite measurements

- Altitude compare with Models and more comparison with CALIPSO

- Combined retrieval IASI + G0ME-2

THANK YOU!

IASI 11:30

SO

2 [

DU

]

H [

hPa

]

From (Thomas and Prata, ACP 2011)

Error analysis

‘true state’

DXa=[100, 1000, 1, 20]

Xa=[ 0.5, 400, 100, 290]

NO thermal contrast

+10o thermal contrast

SO2, H, s, Tslinear error on the ‘true’ state obtained as:

‘true state’

NO thermal contrast

+10o thermal contrast

Error analysis

retrieved state

retrieved state

y is the measurement vector, x the state vector, F forward model, Sy measurement error vector

Usually: y would contain the IASI measured radiancesSy would represent noise on those measurementsx would contain all atmospheric + surface parameters that affect these radiances and

are imperfectly known - in this case including H2O,T, cloud and many other minor species, as well as SO2.

However: - we're not interested in most of these potential state variables- SO2 is very rarely present in significant amounts in the IASI spectra (except during volcanic eruptions and degassing regions)- H2O,T are well predicted by Met data- Other gases have features which are spectrally uncorrelated with SO2

OPTIMAL ESTIMATION APPROACH

physics, radiative transfer

Sy(i,j) = [(ymi-ysi)-(ymi-ysi)][(ymj-ysj)-(ymj-ysj)]

we can greatly simplify the retrieval problem by including in the Sy covariance matrix all the differences between the IASI observed radiances and radiances predicted by a forward model, based on ECMWF met data. and...

Sensitivity of retrieval to presence of ash

Sensitivity of retrieval to presence of clouds

Global Ozone Monitoring Experiment (GOME-2) ESA/EUMETSATScanning spectrometer, 250-790 nm, resolution 0.2-0.4 nm, 960 km or 1920 km swath, resolution 80 x 40 km. Global coverage can be achieved within one day.

High-resolution Infra-Red Sounder (HIRS/4) NOAA20-channel optical/IR filter-wheel radiometer, 2000 km swath, IFOV 17.4 km (nadir). 35 kg, 2.9 kbit/s, 21 W. Replaced on MetOp-C by IASI.

Infrared Atmospheric Sounding Interferometer (IASI) CNES/EUMETSATFourier-transform spectrometer, 3.62-15.5 µm in three bands. Four IFOVs of 20 km at nadir in a square 50 x 50 km, step-scanned across track (30 steps), synchronised with AMSU-A. 2000 km swath. Resolution 0.35 cm-1. Radiometric accuracy 0.25-0.58K. Global coverage will be achieved in 12 hours.

http://www.esa.int/

METeorological OPerational satellite programme (MetOp)

European polar-orbiting meteorological satellite. Operational in May 2007First of tree polar satellite system (EPS) that will cover 14 yearsEquator crossing time 9:30 local time

Advanced Very High Resolution Radiometer (AVHRR/3) NOAA6-channel visible/IR (0.6-12 µm) imager, 2000 km swath, 1 x 1 km resolution. Global imagery of clouds, ocean and land. 35 kg, 622/39.9 kbit/s (high/low rate), 27 W.

Eyjafjallajökull eruption - MIRS stereo height vs IASI

7 May

16 May

0

10

0

10

5

5

Sensitivity of retrieval to presence of ash

Sensitivity of retrieval to presence of clouds

Eyjafjallajökull eruption - SO2 amount IASI vs OMI

Night time AIRS SO2 retrieval at ~03:00 UT

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 ]

Brig

htn

ess

Te

mp

era

ture

[k]

Brig

htn

ess

Te

mp

era

ture

[k]

Reference spectra - IASI spectra

Reference spectra - IASI spectra

SO2 detection

Morning of 15th April 2010 as observed using OMI, GOME-2 and SCIAMACHY near-real-time total column SO2 retrievals as implemented in the Support to Aviation Control Service (SACS Web, 2010).

15 April - morningv1 SO2 fractional enhancement v3 SO2 fractional enhancement

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

Total mass of SO2 S

O2

plum

e m

ass

[Tg]

20 30 10 20

April May

Julian day

Total mass of So2 in the atmospheric plume obtained assuming an average area of 25 x 25 km for any IASI pixel

The error bar is in the worse scenario of correlated errors: obtained as sum of all the pixels errors (overestimate in case of independent error)