Midtropospheric CO Concentration derived from infrared and...
Transcript of Midtropospheric CO Concentration derived from infrared and...
![Page 1: Midtropospheric CO Concentration derived from infrared and ...cimss.ssec.wisc.edu/itwg/itsc/itsc16/presentations/... · Angra dos Reis, 7-13 May 2008 Cyril Crevoisier, Alain Chédin,](https://reader033.fdocuments.in/reader033/viewer/2022050311/5f73ac9c387352687823efc8/html5/thumbnails/1.jpg)
ITSC XVIAngra dos Reis, 7-13 May 2008
Cyril Crevoisier, Alain Chédin, Noëlle A. Scott, Gaelle Dufour, Raymond Armante and Virginie Capelle
Midtropospheric CO2 Concentration derived from infrared and microwave sounders.
Application to the TOVS, AIRS/AMSU, and IASI/AMSU instruments.
Laboratoire de Météorologie Dynamique, CNRS, IPSL, Palaiseau
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CO2 from infrared/microwave sounders
Since 2006
IASI/AMSUESA/MetOp
AIRS/AMSUNASA/AquaSince 2002
NOAA10TOVS
NOAAkATOVS
1987-1991 1999-2005
19/15
7.30
19/4
7.30
AquaAIRS/AMSU
MetOpIASI/AMSU
Time coverage May 2002-… Oct. 2006-…
Spectral resolution
0.5 - 2 cm-1 0.5 cm-1
(apodized)# IR/MWchannels
2378/15(324/15)
8461/15(421/15)
Local time 1.30 9.30
NOAA10/HIRS/MSU1987-1991
NOAAk/HIRS/AMSU1999-2005
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CO2 seasonal cycle - Northern tropics [0-20°N]
NOAA10TOVS
NOAA15ATOVS*
AquaAIRS/AMSU
MetOpIASI/AMSU
87 91 01 03 0793 95 97 99 0589340350360370380
•NOAA10: Chédin et al., JGR, 2003, 2008.
•AIRS: Crevoisier et al., GRL, 2004.
•NOAA15: Very preliminary results…
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CO2 seasonal cycle - Northern tropics [0-20°N]
NOAA10TOVS
NOAA15ATOVS*
AquaAIRS/AMSU
MetOpIASI/AMSU
87 91 01 03 0793 95 97 99 0589340350360370380
Difference between7.30 am/pm observations
Diurnal Tropospheric Excess of CO2due to biomass burning emissions
See Poster B12 [Chédin et al.]
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Spectrum and sensitivity to atmospheric components
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
CO2 (1%)H2O (20%)O3 (20%)CH4 (20%)CO (40%)N2O (2%)Surface
Sensitivity of IASI TB to variations of atmospheric and surface variables (simulations with the 4A RT model)
T Bpe
rt -T B
ref(K
)
10 μm 6 μm 4 μm15 μm
wavenumber (cm-1)
CO2
CO
CH4
surface
H2O
O3
N2O
1 % of CO2 variation → 0.04% of TB variation3ppmv → 0.3 K
The full information contained in the channels is needed to extract the CO2 signal!
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Spectrum and sensitivity to atmospheric components
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
CO2 (1%)H2O (20%)O3 (20%)CH4 (20%)CO (40%)N2O (2%)Surface
Sensitivity of IASI TB to variations of atmospheric and surface variables (simulations with the 4A RT model)
T Bpe
rt -T B
ref(K
)
10 μm 6 μm 4 μm15 μm
wavenumber (cm-1)
CO2
CO
CH4
surface
H2O
O3
N2O
1 % of CO2 variation → 0.04% of TB variation3ppmv → 0.3 K
The full information contained in the channels is needed to extract the CO2 signal!
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Spectrum and sensitivity to atmospheric components
CO2 (1%)H2O (20%)O3 (20%) Surf. emissivity (5%)
CO (40%)Surface T (1%)
Sensitivity of AIRS and IASI channels in the two CO2 bands(simulations with the 4A RT model)
AIRS
wavenumber (cm-1)
CO2
CO noiseH2OO3CO2noise
1.51
0.50
-0.5
1.51
0.50
T Bpe
rt -T B
ref(K
)-0.5
670 690 710 730 750 2220 2260 2300 2340 2380
IASI
4 μm15 μm
650
surface T
At 4 μm: Non-LTE
Only nightime retrieval
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T JacobianCO2 Jacobian
O3 Jacobian
Jacobians of two “CO2” AIRS channels
Channel 80 - 15μm Channel 261 - 4μm
Spectrum and sensitivity to atmospheric components
10
100
1000
1
0.1
•Channels at 4μm peak lower in the atmosphere.
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Jacobians of two “CO2” AIRS channelsand AMSU weighting functions
Channel 80 - 15μm Channel 264 - 4μm
AMSU 7AMSU 6
AMSU 8
AMSU 10
Spectrum and sensitivity to atmospheric components
T JacobianCO2 Jacobian
O3 Jacobian
10
100
1000
1
0.1
AMSU 9
•Channels at 4μm peak lower in the atmosphere.
•AMSU channels bring the information on temperature.
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CO2 channel selection•Aqua AIRS/AMSU:
-15 AIRS channels (8 in the 15 μm band - 7 in the 4 μm band).-2 AMSU channels (6 and 8).
•MetOp IASI/AMSU:-14 IASI channels (all in the 15 μm band).-3 AMSU channels (6, 7, and 8).
IASI - CO2 channel selection
-0.05
0
0.05
0.1
0.15
0.2
0.25
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Sen
siti
vit
y (
K) CH4 4%
CO 20%
N2O 1%
O3 20%
CO2 1%
H2O 20%
AMSU 6
AMSU 8
AMSU 7
IASI CO2 JacobiansIASI sensitivity (K)
CO2 (1%) H2O (20%) O3 (20%) 10
100
1000
0.05
0
-0.05
0.1
0.15
0.2
0.25
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The level1b validation suite at LMD
ECMWF
FTP « Clean »Radio-
soundings
L1B Satellite
Data(1979-2006)
RT model(Stransac, 4A)
Fixed CO2=372 ppmv
See Poster B10 [Armante et al.]
Radiative biasesbetween calculated and observed BT
Colocation of radiosoundings/re-analyses ERA40 with IR/MW observations
Space and Time
Colocation(100 Km, 3h)
Quality control of the Radiosoundings
Inter/extrapolationUGAMP climatology
Radio-soundings
/Reanalysis(ERA-40)
- Radiosoundings «ERA40 »(23 Go from 1979 to 2007 )-Re-analyses ERA-40 (79 Mo / day, 2 days /month)
Example IASI/AMSU (MetOp)Satellite data :14 orbits/day 900 Mo/day; 421 IR, 15 MW
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AIRS Radiative biases: Monthly evolution (5 years over the tropics)
~60 situations/month over sea~30 situations/month over land
03 04 05 06 07 08-0.2
0
0.2
0.4
0.6
0.8
03 04 05 06 07 08
03 04 05 06 07 08
0.6
0.8
1
1.2
1.4
1.6
0.3
0.5
0.7
0.9
1.1
1.3
Cal
c.-O
bs. (
K) AIRS 80 (15 μm) AIRS 264 (4 μm)
AMSU 6
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AIRS Radiative biases: Monthly evolution (5 years over the tropics)
“CO2” slopes (mean over 5 years):AIRS 80: 2.16 ppmv.yr-1
AIRS 264: 2.39 ppmv.yr-1
Mauna Loa: 2.05 ppmv.yr-1
~60 situations/month over sea~30 situations/month over land
03 04 05 06 07 08-0.2
0
0.2
0.4
0.6
0.8
03 04 05 06 07 08
03 04 05 06 07 08
0.6
0.8
1
1.2
1.4
1.6
0.3
0.5
0.7
0.9
1.1
1.3
Cal
c.-O
bs. (
K) AIRS 80 (15 μm) AIRS 264 (4 μm)
AMSU 6
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A stand-alone approach
General features of the CO2 retrieval scheme :non-linear regressions
qCO2
Training of Neural Networks
Non-linear inference scheme
calc-obsbias removal
« clear sky »detection
Off-line
•Simultaneous use of IR and MW channels to decorrelate T/CO2.
IASI AMSU
•Retrieval limited to the tropical region.
Training data set (TIGR)
Selection of a set of CO2channels
[Chédin et al., JGR, 2003; Crevoisier et al., GRL, 2004]
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Evaluation of the inference scheme characteristics
Pre
ssur
e (h
Pa)
We retrieve a mid-to-upper tropospheric integrated content of CO2.
5-15 km
10
100
1000
Mean CO2 averaging kernel over TIGR atmospheric dataset for nadir observation
AIRS/AMSUNOAA15IASI/AMSU
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CO2 distribution from AIRS and IASI - Monthly average
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Evaluation of IASI CO2
•Aircraft [Matsueda et al.]-8-10 km
•IASI CO2-integrated content 5-15 km
-1-2 points/month-until March. 2007
-period: July 2007-March 2008-monthly mean
JAL commercial airliners between Australia and Japan
10
100
1000
IASI CO2 weighting function
altitude of the aircraft
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Seasonal cycle
Detrended CO2 seasonal cycle as observed in situ by JAL aircraft for 2003-2006
CO
2(p
pmv)
Apr Jun Aug Oct Dec Feb
-2
-1
0
1
2
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Seasonal cycle
Apr Jun Aug Dec Feb
-2
-1
0
1
2
Detrended CO2 seasonal cycle as observed in situ by JAL aircraft for 2003-2006
CO
2(p
pmv)
Oct
IASI(07/07-08/03)
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Latitudinal variationCO2 latitudinal variation in July
2006
2003
20052004
1 ppmv
2007
JAL aircraft (10 km)
IASI (5-15 km)
30N
-25N
25N
-20N
20N
-15N
15N
-10N
10N
-05N
05N
-EQ
EQ
-05S
05S
-10S
10S
-15S
15S
-20S
20S
-25S
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Conclusions
The full information contained in the channels is needed (that excludes using PCA-like data).
•We retrieve a mid-to-upper tropospheric integrated content of CO2 from simultaneous IR/MW observations (TOVS, ATOVS, AIRS/AMSU, IASI/AMSU).
•The CO2 signal is very low:
-Reducing radiometric noise is as important as improving spectral resolution.
-A “good” AMSU instrument is important.
•Good agreement of CO2 distribution between IASI and AIRS but lower variability/uncertainty with IASI:
•General good agreement with in-situ observation in terms of seasonal cycle and latitudinal gradients.