1. Assimilation of Satellite Radiances into LM with 1D-Var and Nudging Reinhold, Christoph, Francesca, Blazej, Piotr, Iulia, Michael, Vadim DWD, ARPA-SIM, IMGW, NMA, RHM COSMO General Meeting, Cracow 15-19 September 2008 - plenary session -

2. COSMO-Project : Assimilation of satellite radiances with 1D-Var and Nudging 1DVAR + Nudging = Nudgevar i.e. RETRIEVE temperature and humidity profiles and then nudge them as “pseudo”-observations Goals of Project: Assimilate radiances (SEVIRI, ATOVS, AIRS/IASI) in COSMO-EU Explore the use of nonlinear observation operators with Nudging Explore the use of retrievals for regional models

3. Variational use of Satellite Radiances Principle: use model first guess (temperature and humidity profiles)‏ simulate radiances from first guess (radiative transfer computation)‏ adjust profiles until observed and simulated radiances match - inversion by minimisation - optimal merge of information defined by observation and background errors - keep vertical structure of model Observation of NOAA 17, HIRS 8 (window channel)‏ Simulation based on 3-hour GME forecast Example: ATOVS of NOAA 15-18, METOP-A: 40 Channels (15 microwave, 19 infrared, 1 visible) AMSU-A Temperature Weighting Functions

4. Reinhold Hess, 4 Assimilation of satellite radiances with 1D-Var and Nudging mean sea level pressure & max. 10-m wind gustsvalid for 20 March 2007, 0 UTC m/s + 48 h, REF (no 1DVAR)‏analysis + 48 h, 1DVAR-THIN3+ 48 h, 1DVAR-THIN2 AMSU-A: Status: Slightly positive impact both for AMSU-A and SEVIRI...

5. Reinhold Hess, 5 Assimilation of satellite radiances with 1D-Var and Nudging Athens, 2007...but more tuning and long term trials are required for operational application Still to be done:... Activities during last COSMO-year: Preparation of AMSU-Data from IMGW Centre, Processing from Database Tuning of bias correction Use of IFS forecast above model top instead of climate first guess Tuning of observation error covariance matrix R Tuning of background error covariance matrix B Developments for IASI (cloud detection, bias correction, monitoring, tests)‏

6. Reinhold Hess, 6 Assimilation of satellite radiances with 1D-Var and Nudging...but more tuning and long term trials are required for operational application Still to be done: Thorough validation of Profiles Further tuning of Nudging Parallel Experiments, long term studies Activities during last COSMO-year: Preparation of AMSU-Data from IMGW Centre, Processing from Database Tuning of bias correction Use of IFS forecast above model top instead of climate first guess Tuning of observation error covariance matrix R Tuning of background error covariance matrix B Developments for IASI (cloud detection, bias correction, monitoring, tests)‏

7. Reinhold Hess, 7 Cost Funktion Bias Correction for limited area model COSMO-EU bias correction in two steps: remove scan line dependent bias considered in H, however residual errors remove air mass dependent bias systematic errors related to air mass temperature air mass humidity surface conditions modeled with predictors observed AMSU-4(5) and -9 simulated AMSU-4 and 9 model values, e.g. geop. thick, IWV, SST Variational Assimilation requires bias free observation increments H(x)-y bias from observation y, first guess x and radiative transfer H (RTTOV)‏ theoretical study (Gaussian error analysis): two weeks of data is long enough for significant statistics sample size predictors are highly correlated – chose representative synoptical and seasonal conditions

8. Reinhold Hess, 8 GME lat 30 to 60 deg, lon:-30 to 0 degCOSMO-EU: approx 1200-1500 fovs approx 1200 obs/fovapprox 1000-1500 obs/fov scanline biases AMSU/NOAA 18 (15 to 25 June 2007)‏

9. Reinhold Hess, 9 timeserie of bias corrected observations minus first guess AMSU-A channels 4-11, NOAA-16, ERA 40 stratosphere stable in the troposphere, however large variations for high sounding channels => use of channels AMSU-A 5-7 only

10. Reinhold Hess, 10 timeserie of bias corrected observations minus first guess AMSU-A channels 4-11, NOAA-16, IFS stratosphere stable in the troposphere, small variations for high sounding channels => use of channels AMSU-A 5-9

11. Tuning of observation error covariance matrix R Estimation of satellite observation-error statistics in radiance space with simulations based on radiosondes intra-channel (vertical) correlations horizontal correlations

12. Tuning of background error covariance matrix B covariances with 500hPacorrelations with 500hPa vertical error structures derived from IFS blue: westerly winds red: stable high pressure B defines the scales that are to be corrected Idea: define B according to cloud classification SAF-NWC software for MSG1 and MSG2 situation dependent scale dependent flow dependent

13. Developments for IASI: 8641 IR-channels (started in July 2007)‏ cloud detection NWP-SAF McNally bias correction (generalisation of bias correction predictors)‏ upgrade to RTTOV-9 monitoring (tartan/dns-plots)‏ tests studies started‏ Analysis difference 500 hPa temperature [K] after 24 hours of assimilation Time series (dna, tartan) of bias corrected o-b differences

14. Reinhold Hess, 14w COSMO Priority Project: Assimilation of Satellite Radiances with 1DVAR and Nudging Status of Developments September 2008  technical implementation ready (ATOVS/SEVIRI/AIRS/IASI)‏  basic monitoring of radiances (day by day basis)‏  basic set up, case studies available  neutral to slightly positive results  stratospheric background with IFS forecasts  tuning of bias correction, R, B Use of 1D-Var developments available for other activities: GPS tomography Radar reflectivities To be done:  more nudging coefficients/thinning of observations required  long term evaluation  positive results

15. Reinhold Hess, 15 Assimilation of satellite radiances with 1D-Var and Nudging Lessons learned: ->Boundary values have a paramount impact on forecast quality, better use of observations in the centre of the models, quality of parameterisations ->Large scales hardly to be improved with radiances small scales and humidity to be improved ->Number of observations sufficient for bias correction, but representativity is issue ->Climate first guess above model top has (negative) impact also for trophospheric channels ->Assimilation of clouds/humidity required

16. Reinhold Hess, 16 Thank You for attention

17. Reinhold Hess, 17 Reading, 2007 GME lat 30 to 60 deg, lon:-30 to 0 degCOSMO-EU: approx 1200-1500 fovs approx 1200 obs/fovapprox 1000-1500 obs/fov scanline biases AMSU/NOAA 18 (15 to 25 June 2007)‏ lapse rate?

18. Reinhold Hess, 18 timeserie of bias corrected observations minus first guess AMSU-A channels 4-11, NOAA-18, ERA 40 stratosphere stable in the troposphere, however large variations for high sounding channels => use of channels AMSU-A 5-7 only Athens, 2007

19. Reinhold Hess, 19 levels: 0.10, 0.29, 0.69, 1.42, 2.611, 4.407, 6.95, 10.37, 14.81 hPa ECMWF profiles versus estimated profiles, top GME levels accuracy about 5K for lower levels, but ECMWF may have errors in stratosphere too  linear regression of top RTTOV levels from stratospheric channels (other choice: use IFS forecasts as stratospheric first guess)‏  use of climatological values (ERA40) seems not sufficient provide first guess values above model top (COSMO-EU: 30hpa)‏ Athens, 2007 Cooperation with Vietnam: Application of 1D-Var and 3D-Var with HRM

20. Reinhold Hess, 20 Athens, 2007

21. no thinning of 298 ATOVS30 ATOVS by old thinning (3)‏30 ATOVS, correl. scale 70% 40 ATOVS by thinning (3)‏82 ATOVS by thinning (2)‏82 ATOVS, correl. scale 70%  T-‘analysis increments’ from ATOVS, after 1 timestep (sat only), k = 20 Reinhold Hess, 21 Athens, 2007

22. 1D-Var for LME – Cloud and Rain detection Validation with radar data Microwave surface emissivity model: rain and cloud detection (Kelly & Bauer)‏ Validation with MSG imaging Darmstadt, 2007 Reinhold Hess, 22

23. Reinhold Hess, 23 Reading, 2007 courtesy: HIRLAM-DMI

24. Reinhold Hess, 24 Reading, 2007 courtesy: HIRLAM-DMI (Bjarne Amstrup)‏ Jan - 2003 - Feb

25. Reinhold Hess, 25 1D-Var (compute each vertical profile individually): minimise cost functional temperature and humidity profile first guess and error covariance matrix observations (several channels) and error covariance matrix radiation transfer operator The condition gives: analysed profile and analysis error covariance matrix,,, The analysis is the mathematically optimal combination of first guess and observation given the respective errors Satellite Radiances – Developments at DWD for GME

26. Reinhold Hess, 26 Athens, 2007 1D-Var for LME – Assimilation of AMSU-A: Cloud and Rain detection