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
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error (%)
altit
ude
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)
a priori variability total error
error sources: smoothing measurement upper-air temperature profile ILS
25 50 75 100
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25 50 75 100
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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)
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-3,5
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-0,5
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SO2 *1000SO2 500 8 et 9SO2 *1000 9SO2 *250 500 250
Ecarts avec profil 6
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Wavenumber (cm-1)
Tb (K
)
-17
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-12
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-7,5
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SO2 *E04Delta 8-9 5000 5000delta 9 10000delta 10 2500 5000 2500
Tb =f (colonne SO2)
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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
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-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
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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!!!