1. Component decomposition of IASI measurements
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2. 1. Introduction (1/2) Needs
Improvement of the treatment of cloud-contaminated radiances is essential for NWP, as they represent 75 % of the infrared sounder data
Operational use of satellite measurements for NWP considers cloud-free or cloud-cleared radiances
Development of a cloud decontamination procedure:
extraction the part of the radiance emitted from the cloud-free portion of the instrument footprint.
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3. 1. Introduction (2/2)
Generalisation to a process of extracting the part of the radiance emitted from each homogeneous physical surface inside the instrument footprint.
Required for calibration of measurement coming from heterogeneous footprint
Usefull for study of radiative properties of clouds, for study of geographycal surface emissivity variability at fine scales
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4. Scene radiance decomposition
2. Principle (1/3)
Scene radiance decomposition
Rk = ajk Rcj with Rc1: clear sky component
(1) Rk = Rp ajk .( Rp - Rcj) (2) Rcj = Rp hjk .( Rp - Rk)
a13
a21
a11
Rp : pivot point
ajk : Rcj proportion in FOV k
Rcj : jth homogeneous component
Rk : radiance measure in FOV k
hk : cloud clearing parameter k
a23
a12
a24
a22
a14
Exemple with 2 components (J=2, clear sky and 1 cloud)) and 4 FOVs (K=4)
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5. 2. Principle (2/3) Variational approach
Goal: Estimate the decomposition parameters for a given component j, from:
The measurements of adjacent FOVs
The knowledge of the ajk
provide a guess estimate of the radiance component
Use this guess estimate to solve Equation (2), to obtain an estimate of the hjk (from data and guess estimate of hjk )
reconstruct the radiance component using Equation (2)
retrieve the atmospheric state above the homogeneous surface through 1DVAR inversion of the radiance component
Iterate
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6. 2. Principle (3/3)
Steps 1 and 4 requires a Radiative transfer model and the associated Jacobians. RTM fails to simulate cloudy radiance components
The variational approach allows to considers prior information on the hjk . An accurate prior information is available from AVHRR radiance analysis inside the IASI FOVs, which provides the ajk (correspondance between Equations (1) and (2))
If this information is available, the scheme process without steps 1, 4 and 5, and gives accurate results for any component j
If not, the scheme can be processed, and gives useful results for cloud decontamination.
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7. 3. Database genration (1/3): criteria for scene selection
comprehensive analysis of cloud parameters
cloud type
cloud optical thickness
cloud top height
 POLDER/Meteosat composite images
availability of atmospheric profiles
 ECMWF reanalysis fields
global representativity
8 (9) cloud types with different scattering parameters
St, ScI, ScII, Cu, Ns, As, Cb, Ci(I, CiII)
atmospheric conditions
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8. 3. Database genration (3/3): Simulation tools
The radiative transfer modelling
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9. IASI cloud decontamination, no AVHRR information: (1/5) No iteration
Statistics over the spectral difference between decontaminated and clear-sky spectra, before iteration of the algorithm, and without AVHRR analysis information.
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10. IASI cloud decontamination, no AVHRR information: (2/5) Iteration behaviour
The cloud-cleared spectra at different iterations (CC) are compared with the forecast guess spectrum (Guess), the clear-sky spectrum (Clear) and the analysed one from the retrieved profiles after 10 iterations (R10).
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11. IASI cloud decontamination, no AVHRR information: (3/5) Iteration behaviour
Temperature profile retrievals (red). Comparison with the guess profiles (green) and the true profiles (blue) is made. Surface temperatures are also shown.
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12. IASI cloud decontamination, no AVHRR information: (4/5) Iteration behaviour
Humidity profile retrievals (red). Comparison with the guess profiles (green) and the true profiles (blue) is made.
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13. IASI cloud decontamination, no AVHRR information: (5/5) Perfect guess
Statistics over the spectral difference between decontaminated radiance and clear-sky spectra, without AVHRR analysis information, at convergence.
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14. IASI cloud decontamination with AVHRR information: (1/3)
Statistics over the spectral difference between decontaminated and clear-sky spectra, when coefficients of relative coverage of each component in the FOV are perturbed.
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15. IASI cloud decontamination with AVHRR information: (2/3)
Statistics over the spectral difference between decontaminated and clear-sky spectra, when coefficients of relative coverage of each component in the FOV are perturbed.
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16. IASI cloud decontamination with AVHRR information: (3/3)
Histogram of the spectral mean values of differences for the 5% perturbation cases. .
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17. Spectral pseudo noise: (1/4) problem
The presence of heterogeneous scenes in the IASI FOV introduce a spectral distortion due to their effect on the Instrument Spectral Response Function (ISRF)
A correction algorithm was developed at IASI level 2 processing.
The computation of (0) requires the coupling of the correction algorithm and of the decomposition scheme .
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18. Spectral pseudo noise: (2/4) without spectral correction
Statistics over the spectral differences between decontaminated and clear-sky spectra.
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19. Spectral pseudo noise: (3/4) with spectral correction
Statistics over the spectral difference between decontaminated and clear-sky spectra, for the correction algorithm, after 5 iterations. .
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20. Spectral pseudo noise: (4/4) with spectral correction
Statistics over the spectral difference between retrieved and true cloudy component spectra, for the algorithm, after 5 iterations. .
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21. Concluding remarks
A variational radiance decomposition scheme is developped for IASI, which
extract radiance coming from each homogeneous surface of the FOV, from adjacent FOV measurements and information on component relative coverage
Retrieve atmospheric and surface parameters associated to the homogeneous components
It allows to accurately process the cloud decontamination of IASI data by exploitation of AVHRR analysis information
It allows the consistent treatment of heterogeneous measurements
It provides the correct approach to derive information on radiative properties of sea, land and cloud surfaces.
23/11/2018
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