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Assimilation of Satellite Radiances into LM with 1D-Var and Nudging

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 -. 1DVAR + Nudging = Nudgevar.

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Assimilation of Satellite Radiances into LM with 1D-Var and Nudging

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  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. 1DVAR + Nudging = Nudgevar COSMO-Project:Assimilation of satellite radiances with 1D-Var and Nudging • 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 i.e. RETRIEVE temperature and humidity profiles and then nudge them as “pseudo”-observations

  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 AMSU-A Temperature Weighting Functions Example: ATOVS of NOAA 15-18, METOP-A: 40 Channels (15 microwave, 19 infrared, 1 visible) Simulation based on 3-hour GME forecast Observation of NOAA 17, HIRS 8 (window channel)‏

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

  5. Assimilation of satellite radiances with 1D-Var and Nudging ...but more tuning and long term trials are required for operational application • 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)‏ • Still to be done: • ... Reinhold Hess, 5 Athens, 2007

  6. Assimilation of satellite radiances with 1D-Var and Nudging ...but more tuning and long term trials are required for operational application • 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)‏ • Still to be done: • Thorough validation of Profiles • Further tuning of Nudging • Parallel Experiments, long term studies Reinhold Hess, 6

  7. Bias Correction for limited area model COSMO-EU • 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 Reinhold Hess, 7 Cost Funktion Variational Assimilation requires bias free observation increments H(x)-y bias from observation y, first guess x and radiative transfer H (RTTOV)‏ • 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

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

  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 Reinhold Hess, 9

  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 Reinhold Hess, 10

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

  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 Time series (dna, tartan) of bias corrected o-b differences Analysis difference 500 hPa temperature [K] after 24 hours of assimilation

  14. Reinhold Hess, 14 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 ww • To be done: • more nudging coefficients/thinning of observations required • long term evaluation • positive results • Use of 1D-Var developments available for other activities: • GPS tomography • Radar reflectivities

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

  16. Thank You for attention Reinhold Hess, 16

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

  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 Reinhold Hess, 18 Athens, 2007

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

  20. Reinhold Hess, 20 Athens, 2007

  21. T-‘analysis increments’ from ATOVS, after 1 timestep (sat only), k = 20 no thinning of 298 ATOVS 30 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% Reinhold Hess, 21 Athens, 2007

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

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

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

  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 gives: The condition , analysed profile and analysis error covariance matrix The analysis is the mathematically optimal combination of first guess and observation given the respective errors Reinhold Hess, 25 Satellite Radiances – Developments at DWD for GME

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

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