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The assimilation of satellite radiances in LM

The assimilation of satellite radiances in LM. F. Di Giuseppe, B. Krzeminski ,R. Hess, C. Shraff. (1) ARPA-SIM Italy (2) IMGW,Poland (3)DWD, Germany. AIMs. To develop a software package for the assimilation of: temperature and humidity profiles Surface param e te r s (T_2m, Qv_2M, T_g)

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The assimilation of satellite radiances in LM

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  1. The assimilation of satellite radiances in LM F. Di Giuseppe, B. Krzeminski ,R. Hess, C. Shraff (1) ARPA-SIM Italy (2) IMGW,Poland (3)DWD, Germany

  2. AIMs To develop a software package for the assimilation of: • temperature and humidity profiles • Surface parameters (T_2m, Qv_2M, T_g) from satellite radiances • MSG (SEVIRI) • NOAA-15-16-17 (AMSU-A, AMSU-B) In the future .. • HIRS, AIRS, IASI

  3. REQUIREMENTS • Flexibleto easy the addition of new observing systems • Fastto process large amount of data • Exportableto be interfaced with the nudging scheme now but also partable in case of 3Dvar devolopments.

  4. Motivation I-Increase data coverage MSG single observations with repeating time of 15min!!! Conventional observations are integrated over 12 hrs,

  5. Motivation II Open the way to the assimilation of any kind of non conventional observations throught the variational approach. Envisaging also: • Cloud and rain -contaminated radiances • Radar observations

  6. Approach“1D-VAR + Nudging” SAT OBS MSG:T1 = -7 minT2= 0 NOAA:T1 = -1.5 hrT2 = -1.0 hrT3 = -0.5 hrT4 = 0.0 hr T_1 T_2 Analysis integration First Minimization Second Minimization

  7. 1DVAR - CORE • The goal of the 1D-Var retrieval system is to find the optimal model state Xa, that simultaneously minimises the distance to the observations, Y0, and the background model state, Xb.. • B and R are the background and observation error covariance matrices • H is the observation operator which projects the model state into the observation space. Here H consists of a fast radiative transfer code with its adjoint and tangent linear version (RTTOV, Rogers 1998).

  8. J= Dx Dy B + R Dy Dx Jb Jo H Y_b X = 1DVAR – CORE II X=(T(1:nlev), Qv(1:nlev),T_2m,Qv_2m,T_G)-> control vector X_b  background state X_a  analysis state Y_0=(BT_1,BT_2, BT_3,BT_4,…..,BT_n)  observation vector Y_b=H X_b

  9. PRE Processing I –Bias Corrections • MSG:Multilinear regression using four predictors: • Thickness 1000-300 • Thickness 200-50 • Column integrated liquid water content • Surface temperature • NOAA-15-16-17:2 steps: • 1. Correction of bias corrrelated with viewing angle • 2. Correction of bias correlated with „air-mass” represented by measurements in selected channels: • Linear regression using AMSU 5,9 as predictors (choose of predictors - subject for investigation)

  10. PRE Processing I I–Cloud clearing MSG: Cloud clearing using SAF-NWC software or variational cloud mask Cloud Type from SAF-NWC-PGE02

  11. PRE Processing III–Rain clearing NOAA: Rain contaminated pixels (in microwave channels) cannot be reliably simulated with RTTOV7 - should be excluded from retrieval Scene identification algorthm (Grody 89)used to identify clouds and rain. Codeadapted from GME 1DVar. INPUT: - AMSU-A „window” channels 1,2,3,15 - surface temperature OUTPUT: - can recognize one of 9 surface types, clouds or rain Currently used over water only.

  12. Temperature Humidity Anticorrelation PRE ProcessingIV – ERROR COVARIANCE MATRIX

  13. Implementation in LM Nudging Interface Pre-processing 1Dvar core Structure definition

  14. RESULTS • Statistics of model departures from observations • Comparisons with independent observations

  15. Statistics of model departures from observations1. Bi-dimensional PDF of observed vs modelled BT Reduction of the spread In the first guess compared To the analysis Reduction of the bias in The window channels

  16. Statistics of model departures from observations2. background increments / analysis increments Bias non negligible over land expecially in the window channels

  17. Statistics of model departures from observations3. analysis increments in the temperature and humidity profiles SEA Analysis warmer then background Analysis drier then background LAND Analysis cooler then background Analysis wetter then background

  18. 0hrs 24hrs 48hrs 72hrs Comparisons with independent observationsI -radiosound comparison LM systematic cooler then radiosounds LM systematically drier 408 observations from 1 of July 2005

  19. F <0 1DVAR analysis correlates worst then background to RDS obs F=0 1DVAR analysiscorrelates as the background to RDS obs F=11DVAR analysis correlates perfectly with radiosounds obs F=Var (XRDS-XFG)-Var(XRDS-XA) Var(XRDS-XA) Comparisons with independent observationsII -1Dvar vs radiosounds

  20. CONCLUSIONS • We have developed and implemented a 1DVAR • Package to include satellite radiances into the nudging scheme • Of LM • Off-line tests have shown that 1DVAR is able to • remove the model systematic biases if the observations are unbiased • Potentially it is possible to assimilate MSG radiances over land BUT • this requires a Lot of effort in the bias-correction algorithm.

  21. What next? We are in the process to start the analysis of selected case studies We aim to have a full pre-operational configuration for the end of 2006

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