Component decomposition of IASI measurements 23/11/2018 NOV-3046-SL-730

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. 23/11/2018 NOV-3046-SL-730

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 23/11/2018 NOV-3046-SL-730

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) 23/11/2018 NOV-3046-SL-730

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 23/11/2018 NOV-3046-SL-730

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. 23/11/2018 NOV-3046-SL-730

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 23/11/2018 NOV-3046-SL-730

3. Database genration (3/3): Simulation tools The radiative transfer modelling 23/11/2018 NOV-3046-SL-730

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. 23/11/2018 NOV-3046-SL-730

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). 23/11/2018 NOV-3046-SL-730

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. 23/11/2018 NOV-3046-SL-730

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. 23/11/2018 NOV-3046-SL-730

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. 23/11/2018 NOV-3046-SL-730

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. 23/11/2018 NOV-3046-SL-730

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. 23/11/2018 NOV-3046-SL-730

IASI cloud decontamination with AVHRR information: (3/3) Histogram of the spectral mean values of differences for the 5% perturbation cases. . 23/11/2018 NOV-3046-SL-730

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 . 23/11/2018 NOV-3046-SL-730

Spectral pseudo noise: (2/4) without spectral correction Statistics over the spectral differences between decontaminated and clear-sky spectra. 23/11/2018 NOV-3046-SL-730

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. . 23/11/2018 NOV-3046-SL-730

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. . 23/11/2018 NOV-3046-SL-730

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 NOV-3046-SL-730