Use of IASI data for tropospheric trace gas retrieval

T. Phulpin (CNES), C. Clerbaux (IPSL/SA), P. Coheur (ULB), S. Turquety (IPSL/SA), C. Camy Peyret (LPMAA), S. Payan (LPMAA) and P. Prunet (Noveltis).

Current studies for new IASI products supportedby CNES

GOALSIT IS ESSENTIAL FOR CNES (French space agency) TO DEMONSTRATE

THE SOCIETAL BENEFITS OF SPACE PROJECTS LIKE IASI.

TRYING TO IDENTIFY THE LIMITATIONS (with IASI) TO BE USED IN DEFINITION OF NEXT GENERATION (PEPS)

Contribution and support to research- Expand IASI sounding to cloudy pixels (Level1)- Improve current level 2 products- Study additional products- Implement and distribute Level 3 or Level 4 products

To foster use of IASI data for meteorology and other areas ofapplication (Climate, air quality monitoring, environment, etc.) by supporting research, distributing high level productsand initiating new services.

METOP [2007-2020]IASI - GOME2

O3, NO2,

SO2, H2CO

Level 1Atmosphericspectra

Level 2Concentrations

Level 4

Inversion algorithms

CTM +

Data assimilation

Tropospheric chemistry

Pollution transport, AQ

Emissions

Level 3

+Declouding

O3, CO, CH4,N2O,CO2,

HNO3, SO2 (volcans),CFCs

Global distributions (multisensors

Global Averaged

distributions

PROCESSING HETEROGENEOUS SCENES(B. Tournier, S. Bijac, O. Lezeaux, P. Prunet)

Use of heterogeneous pixels to increase the coverage

Benefits : Atmospheric profiles on the edges of frontal systemsReducing integration time and spatial averaging domain for trace gases retrieval (CO2)

Issue of spectral shift and pseudo noise due to composition of differentspectra at different location within off axis pixels(see P. Schlüssel Poster)

IASI heterogeneous scene algorithm

Two algorithms are designedScenes decomposition (studied for Eumetsat)• Suitable for radiances direct assimilation.• The selection of the homogeneous scene to be retrieved is allowed.• Retrieved scene type can be chosen, suitable for chosen cloud type and leads

to cloud parameters retrieval.• Amplification of the noise due to scene decomposition not controlled.• Amplification of the noise due to spectral post calibration.

Direct model combination• Suitable for full scene retrieval (all homogeneous components shall be

considered simultaneously). Needs accurate FRTM for Cloudy pixels and knowledge of surface emissivity spectra

• No noise added• System ready for any progress in direct models in the frame of clouds

processing.

These two approaches are currently under development and will be compared. The most accurate and fast will be implemented for test on a routine basis

OLD

NEW

Requirement on the IASI column weighted averaged

Required accuracy of about 1 ppmv on the IASI CO2 column weighted averaged

High spatial and temporal sampling of IASISpatial averaging : ~ 1000 measurement points for (103x103) km2

Temporal averaging : 60 measurements/month

5 % of clear data (20 % with cloud-clearing ?)

Theoretical noise reduction ratio of about 50

Boundary layer (pressure)

Boundary layer (height)

Required accuracy on mean content

(ppmv) IASI/SPECTRE

Required accuracy on mean content

(ppmv) IASI/DFT

1013-900 hPa 1.0 km 0.9 0.7 1013-720 hPa 2.7 km 1.54 1.4

Mission specification : Requirements

Boundary layer (pressure)

Boundary layer (height)

Weight with weighting function IASI/SPECTRE

Weight with weighting function

IASI/DFT 1013-900 hPa 1.0 km 0.45 0.35 1013-720 hPa 2.7 km 0.77 0.7

CO2

Data processing : Discrete Fourier Transform(DFT) methodology

M ea n sig n a l

F u n d a m en ta l

F irst H ar m o nic

From the quasi periodic line structure of the IASI spectrum

re-sampling on a periodic base built from the spectral transitions of the CO2 linesapplication of a Discrete Fourier Transform to specific spectral windows

DFT pseudo data (mean, fundamental and first harmonic)

CO2

Selection of 16 specific spectral windows (1282 spectral samples): regions with strong atmospheric CO2 absorption or emission

DFT approach• Permits an efficient extraction of the CO2 information

– Filtering the impact of other variables (to be confirmed for surface parameters)– No significant loss of information (to be confirmed near the surface)– Data compression

• Gives reliable results on measured noisy data, consistent with independent estimates, showingthe robustness of the algorithm

Validation on representative sets of data

Retrieval accuracy from a single spectrum• IASI Balloon

– 4 ppmv (1 %) on the column averaged mixing ratio– no reliable estimate in low troposphere

• IASI Metop– 1.2 ppmv (0.3 %) on the column averaged mixing ratio– 20 ppmv (5 %) in low troposphere: information correlated with free troposphere

TEMPORARY CONCLUSIONS

CO2 information from IASI is at the level of mission specifications for a single spectrumRetrievals from simulated data indicate that IASI CO2 product on 3 layers would be more efficient than a column average, in order to exploit the low level information present in the data

Remaining to be done :Explore the potential of spatial and temporal averagingExplore the CO2 processing in partially cloudy scenes, in order to separate upper and lower troposphere information

THIS GOING TO BE STUDIED IN THE NEXT MONTHS

CO2

AerosolsInfrared spectra are sensitive to aerosols specially absorbing aerosols like sanddust, volcanic ash and biomass burning

Important parameters which can be retrieved from spectra are optical depth andaltitude (Pierangelo, 2005)

Algorithm developed at LMD and applied to AIRS has been expanded to IASI. A selection of IASI channels dedicated to aerosol has been proposed.

TRACE GAS RETRIEVAL FROM IASI

Level 2 Processing based on Neural network at stage 1.

Those networks have been trained using pseudo IASI spectrasimulated from IMG data

Alternative inversion method is 1d Var. This method has been used to analyze sensibility of retrievals to various parameters. It will beimplemented in a research mode and tested to compare its results with operational retrievals

Ts

COCH4

Clerbaux et al., IEEE 1999; JGR 2001Hadji-Lazaro et al., JGR 1999

O3

IMG distributions using IASI processing tools (operational mode)

Turquety et al. GRL 2002

Clerbaux et al., ACP 2003

+ Cloud filteringHadji-Lazaro et al., GRL 2001

SA-Neural network [Turquety et al, JGR 2004] implemented in the Eumetsat ground segment for IASI real-time data processing

0

10

20

30

40

0 25 50 75 100 125 150

error (%)

altit

ude

(km

)

a priori variability total error

error sources: smoothing measurement upper-air temperature profile ILS

25 50 75 100

25

50

75

100

25 50 75 100

25

50

75

100

100 200 300

100

200

300

R = 0.93bias = -1 %σ = 18 %

R = 0.98bias = -3 %σ = 10 %

R = 0.75bias = 21 %σ = 36 %

Ny Alesund Uccle Hilo Wallops Easter Island Lauder Neumayer

[O3] re

triev

ed (D

U)

Ground-12 km

24-30 km

[O3]sondes (DU)

12-24 km

Vertical profiles Partial columns

OZONE

[Coheur et al. JGR 2005 ]

Errors

Accuracy and precision:► High for the lower and midlle stratospheric columns► Good for the tropospheric columns in most cases (bias decreases to 4% partial columns < 25 DU are neglected)

Boundary layer CO (1 km) Upper tropospheric CO (10 km)

CARBON MONOXIDE

[Barret et al. ACPD 2005 ]

Convective transportBiomass burning

Transport

Pollution

Good agreement with surface measurements (CMDL network) andmodel distributions (GEOS-CHEM)

Nitric Acid (HNO3)

220

225

230

235

240

245

250

255

260

265

270

275

280

285

290

295

300

305

310

315

320

1000

1020

1040

1060

1080

1100

1120

1140

1160

1180

1200

1220

1240

1260

1280

1300

1320

1340

1360

1380

1400

1420

1440

1460

1480

1500

-3,5

-3

-2,5

-2

-1,5

-1

-0,5

0

0,5

SO2 *1000SO2 500 8 et 9SO2 *1000 9SO2 *250 500 250

Ecarts avec profil 6

220

230

240

250

260

270

280

290

300

310

320

1000

1020

1040

1060

1080

1100

1120

1140

1160

1180

1200

1220

1240

1260

1280

1300

1320

1340

1360

1380

1400

1420

1440

1460

1480

1500

Wavenumber (cm-1)

Tb (K

)

-17

-15

-12

-10

-7,5

-5

-2,5

0

2,5

5

SO2 *E04Delta 8-9 5000 5000delta 9 10000delta 10 2500 5000 2500

Tb =f (colonne SO2)

200

210

220

230

240

250

260

270

280

290

300

0,00E+00 5,00E+02 1,00E+03 1,50E+03 2,00E+03 2,50E+03 3,00E+03

1166,5116813671370HIRS ch 121166,5 T1168 T1367 T1370 THIRS 12 T

Sensitivity to level anddistribution of SO2

Much stronger at 1350 cm-1

Volcanic plumes (SO2)

If strong amounts a combination ofthe two bands could be used to retrieve Integrated amount andplume altitude

0

10

20

30

-0.1 0.0 0.1 0.2 0.3

averaging kernel A

ltitu

de (k

m)

GOME AVK

20 km18 km16 km14 km12km

10 km

1 km

IMG AVK

30 km

OZONE FROM IR-UVVertical sensitivity

Taking advantage of multiple instruments Synergetic retrievals

10 20 30 40

10

20

30

40

Altitude (km)

10 20 30 40

GOME IMG→AGOMEA

-0.20 -0.12 -0.04 0.04 0.12 0.20 0.28

O3 profile retrieval from IR and UV measurements

Application to GOME-2 / IASISolène Turquety, Roeland Van Oss

ds

UV 3.4

IR 5.4

UV-IR 7.7

OZONE PROFILE FROM IR AND UVS SPECTROMETERS(P. Coheur et al.)

1/3

ASSIMILATION OF CO DERIVED FROM IASI IN A CTM(C. Clerbaux and A. Klonecki)

OBSERVATION OPERATOR BASED ON MOPITT DATA

CARBON MONOXIDE

STRATEGYDEVELOPEMENT AND TESTS IN LABS BASED FIRST ON SIMULATED DATA AND THEN ON DATA SAMPLES FROM UMARFIMPLEMENTATION IN ROUTINE PSEUDO OPERATIONAL PROCESSING

AT CMS FOR LOCAL AT METEO France TOULOUSE FOR GLOBALOR ON ETHER (National Data Center for Atmospheric chemistry) FACILITIES

COMPARISON WITH OTHER GROUPS IN THE FRAMEWORK OF THE ISSWGEITHER IMPLEMENTATION IN THE CGS, THE SAFs OR DISTRIBUTION BY ETHER

ConclusionsBesides its capacity for atmospheric profiles through assimilation

in NWP models,

POTENTIAL APPLICATIONS OF IASI ARE NUMEROUS AND MANY ADDITIONAL PRODUCTS ARE TO BE DEVELOPED

According to simulations, IASI looks then very promising for greenhouse gases monitoring.

Studies are continuing and new topics like cloud properties retrieval, surface parameters are being considered

About one year after MetOp launch CNES and Eumetsat organize a Joint Workshop (study conference?) on the first IASI results which will initiateISSWG phase 2. We hope to see you there!!!