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. The assimilation of satellite radiances in LM 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. The assimilation of satellite radiances in LM REQUIREMENTS FlexibleFlexible to easy the addition of new observing systems FastFast to process large amount of data ExportableExportable to be interfaced with the nudging scheme now but also partable in case of 3Dvar devolopments.

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

5. The assimilation of satellite radiances in LM 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. The assimilation of satellite radiances in LM Approach “1D-VAR + Nudging” First MinimizationSecond Minimization SAT OBS Analysis integration T_1 T_2 MSG: T 1 = -7 min T 2 = 0 NOAA: T 1 = -1.5 hr T 2 = -1.0 hr T 3 = -0.5 hr T 4 = 0.0 hr

7. The assimilation of satellite radiances in LM 1DVAR - CORE The goal of the 1D-Var retrieval system is to find the optimal model state X a, that simultaneously minimises the distance to the observations, Y 0, and the background model state, X b.. 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. The assimilation of satellite radiances in LM 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 B R J= + JbJo xx xx yy yy  X Y_b =

9. The assimilation of satellite radiances in LM PRE Processing I –Bias Corrections MSG: Multilinear regression using four predictors: 1.Thickness 1000-300 2.Thickness 200-50 3.Column integrated liquid water content 4.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. The assimilation of satellite radiances in LM PRE Processing I I–Cloud clearing MSG: Cloud clearing using SAF-NWC software or variational cloud mask Cloud Type from SAF-NWC-PGE02

11. The assimilation of satellite radiances in LM 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. Code adapted 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. The assimilation of satellite radiances in LM PRE Processing IV – ERROR COVARIANCE MATRIX TemperatureHumidity Anticorrelation

13. The assimilation of satellite radiances in LM Implementation in LM Nudging Interface Pre-processing 1Dvar core Structure definition

14. The assimilation of satellite radiances in LM RESULTS 1.Statistics of model departures from observations 2.Comparisons with independent observations

15. The assimilation of satellite radiances in LM Statistics of model departures from observations 1. 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. The assimilation of satellite radiances in LM Statistics of model departures from observations 2. background increments / analysis increments Bias non negligible over land expecially in the window channels

17. The assimilation of satellite radiances in LM Statistics of model departures from observations 3. 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. The assimilation of satellite radiances in LM Comparisons with independent observations I -radiosound comparison LM systematic cooler then radiosounds LM systematically drier 0hrs 24hrs48hrs72hrs 408 observations from 1 of July 2005

19. The assimilation of satellite radiances in LM Comparisons with independent observations II -1Dvar vs radiosounds F=Var (X RDS -X FG )-Var(X RDS -X A ) Var(X RDS -X A ) F <0 1DVAR analysis correlates worst then background to RDS obs F=0 1DVAR analysis correlates as the background to RDS obs F=1 1DVAR analysis correlates perfectly with radiosounds obs

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

21. The assimilation of satellite radiances in LM 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