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11 th JCSDA Science Workshop on Satellite Data Assimilation

11 th JCSDA Science Workshop on Satellite Data Assimilation. Latest Developments on the Assimilation of Cloud-Affected Satellite Observations Tom Auligné National Center for Atmospheric Research

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11 th JCSDA Science Workshop on Satellite Data Assimilation

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  1. 11th JCSDA Science Workshop on Satellite Data Assimilation • Latest Developments on the Assimilation of Cloud-Affected Satellite Observations • Tom Auligné • National Center for Atmospheric Research • Acknowledgments: Gael Descombes, Greg Thompson, Francois Vandenberghe,DongmeiXu (NCAR)Thomas Nehrkorn,Brian Woods (AER) • Funding provided by the Air Force Weather Agency

  2. Introduction • Approaches for the use of cloud-affected radiances • Nowcasting • COP + advection • 3D cloud fraction + WRF dynamical transport • Oklahoma U. / GSI-RR cloud analysis • Expansion of analysis control variable • Total water + linearized physics • Microphysical variables

  3. Introduction • Approaches for the use of cloud-affected radiances in NWP • Nowcasting • COP + advection • 3D cloud fraction + WRF dynamical transport • Oklahoma U. / GSI-RR cloud analysis • Expansion of analysis control variable • Total water + linearized physics • Microphysical variables (WRF Data Assimilation)

  4. Outline • Control Variable Transform • Multivariate Background Error Covariances • Hybrid Ensemble/Variational • Displacement Analysis • Processing of Cloudy Satellite Observations • Experimental Demonstration

  5. Outline • Control Variable Transform • Multivariate Background Error Covariances • Hybrid Ensemble/Variational • Displacement Analysis • Processing of Cloudy Satellite Observations • Experimental Demonstration

  6. Pseudo Single Observation Test (PSOT) • Multivariate covariancesfor qc, qr, qi, qsn • Binning option: dynamical cloud mask • Vertical and Horizontal autocorrelations via Recursive Filters • 3D Variance

  7. Outline • Control Variable Transform • Multivariate Background Error Covariances • Hybrid Ensemble/Variational • Displacement Analysis • Processing of Cloudy Satellite Observations • Experimental Demonstration

  8. Ensemble/Variational Integrated Localized (EVIL) Ensemble covariance included in 3D/4DVar through state augmentation(Lorenc 2003, Wang et al. 2008, Fairbairn et al., 2012) with NEW ALGORITHM: update of the ensemble perturbations inside the Variational analysis through Lanczos minimization (without external EnKF)

  9. Outline • Control Variable Transform • Multivariate Background Error Covariances • Hybrid Ensemble/Variational • Displacement Analysis • Processing of Cloudy Satellite Observations • Experimental Demonstration

  10. ForecastCalibration &Alignment Hurricane Katrina OSSE Synthetic observations (TCPW) Balanced displacement Nehrkorn et al., MWR 2013 (submitted)

  11. Outline • Control Variable Transform • Multivariate Background Error Covariances • Hybrid Ensemble/Variational • Displacement Analysis • Processing of Cloudy Satellite Observations • Experimental Demonstration

  12. Unified Processing of Cloud-affected Satellite data • Unified processing for Radiances and COP Retrievals • VarBC: Variational Bias Correction(unchanged predictors) • Revisited QC: extended Gross and First-Guess check(to conserve cloudy data) • Huber Norm: robust definition of observation error • Land Surface: Tskin introduced as a sink variable • Field of View: advanced interpolation scheme • CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) • Middle Loop: Multiple re-linearizations of obs. operator

  13. Cloud-Affected Radiances • AIRS, IASI, AMSU-A, AMSU-B, MHS, HIRS, SSMI/S BUFR files from NCEP • MODIS HDF files (Terra & Aqua) from NASA • GOES SndrASCII files (E & W) from Uwisc. • GOES ImgrNetCDF files from NCAR/RAL Observation Radiance(GOES imgrchannel 4) Background

  14. Cloud Optical Properties • GOES ImgrNetCDFfiles from Uwisc. • MODIS NetCDF files from AFWA • with(Benedetti and Janiskova,MWR 2008) Observation Log10(COD) (GOES imgr) Background

  15. Unified Processing of Cloud-affected Satellite data • Unified processing for Radiances and COP Retrievals • VarBC: Variational Bias Correction(unchanged predictors) • Revisited QC: extended Gross and First-Guess check(to conserve cloudy data) • Huber Norm: robust definition of observation error • Land Surface: Tskin introduced as a sink variable • Field of View: advanced interpolation scheme • CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) • Middle Loop: Multiple re-linearizations of obs. operator

  16. Unified Processing of Cloud-affected Satellite data • Unified processing for Radiances and COP Retrievals • VarBC: Variational Bias Correction(unchanged predictors) • Revisited QC: extended Gross and First-Guess check(to conserve cloudy data) • Huber Norm: robust definition of observation error • Land Surface: Tskin introduced as a sink variable • Field of View: advanced interpolation scheme • CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) • Middle Loop: Multiple re-linearizations of obs. operator

  17. Unified Processing of Cloud-affected Satellite data • Unified processing for Radiances and COP Retrievals • VarBC: Variational Bias Correction(unchanged predictors) • Revisited QC: extended Gross and First-Guess check(to conserve cloudy data) • Huber Norm: robust definition of observation error • Land Surface: Tskin introduced as a sink variable • Field of View: advanced interpolation scheme • CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) • Middle Loop: Multiple re-linearizations of obs. operator

  18. Huber Norm: robust observation errors Normalized Obs Weight Innovations Figure from Fisher (2008) Distribution of innovations

  19. Unified Processing of Cloud-affected Satellite data • Unified processing for Radiances and COP Retrievals • VarBC: Variational Bias Correction(unchanged predictors) • Revisited QC: extended Gross and First-Guess check(to conserve cloudy data) • Huber Norm: robust definition of observation error • Land Surface: Tskin introduced as a sink variable • Field of View: advanced interpolation scheme • CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) • Middle Loop: Multiple re-linearizations of obs. operator

  20. Unified Processing of Cloud-affected Satellite data • Unified processing for Radiances and COP Retrievals • VarBC: Variational Bias Correction(unchanged predictors) • Revisited QC: extended Gross and First-Guess check(to conserve cloudy data) • Huber Norm: robust definition of observation error • Land Surface: Tskin introduced as a sink variable • Field of View: advanced interpolation scheme • CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) • Middle Loop: Multiple re-linearizations of obs. operator

  21. Satellite Field of View (FoV) interpolation • Calculate polygons (ellipses) • List model grid points inside ellipse • Use average input for CRTM • Currently testing for AIRS and IASI

  22. Unified Processing of Cloud-affected Satellite data • Unified processing for Radiances and COP Retrievals • VarBC: Variational Bias Correction(unchanged predictors) • Revisited QC: extended Gross and First-Guess check(to conserve cloudy data) • Huber Norm: robust definition of observation error • Land Surface: Tskin introduced as a sink variable • Field of View: advanced interpolation scheme • CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) • Middle Loop: Multiple re-linearizations of obs. operator

  23. Unified Processing of Cloud-affected Satellite data • Unified processing for Radiances and COP Retrievals • VarBC: Variational Bias Correction(unchanged predictors) • Revisited QC: extended Gross and First-Guess check(to conserve cloudy data) • Huber Norm: robust definition of observation error • Land Surface: Tskin introduced as a sink variable • Field of View: advanced interpolation scheme • CRTM Jacobians: rescaled base state (floor and ceiling values for cloud parameters) • Middle Loop: Multiple re-linearizations of obs. operator

  24. 3DVar Middle loop Cost Function First Guess Second Guess Third Guess First Analysis Second Analysis AIRS Window Channel #787

  25. Second Guess First Guess Third Guess Observation Update ofqcloud, qicein WRF Observations AIRS Window Channel #787

  26. Outline • Control Variable Transform • Multivariate Background Error Covariances • Hybrid Ensemble/Variational • Displacement Analysis • Processing of Cloudy Satellite Observations • Experimental Demonstration

  27. Experimental Framework • WRF-ARW model, CONUS 15km, Thompson microphysics • First Guess = Meanof 50-memb ens. from EnKFexpt(Romine) • Single analysis at 20012/06/03 (12UTC), 5 middle-loops • GOES-Imager: Radiances (Thompson) & COD (Uwisc.) • CTRL no DA • COD Cloud Optical Depth (3DVar) • FCA Cloud Optical Depth (Displacement) • 3DVAR Radiances (3DVar) • EVIL Radiances (Hybrid)

  28. Incremental fit to obs 3DVar Obs EVIL Cloud Optical Depth Radiances

  29. Analysis increments COD FCA qcloudLevel 10 3DVAR EVIL

  30. Analysis increments COD FCA qcloudLevel 10 3DVAR EVIL

  31. Multi-scale verification 0.5° 0.12° 0.25° 1° 2° 3°

  32. Multi-scale verification Analysis Forecast CTRL CTRL 3DVAR 3DVAR EVIL EVIL • Impact of all-sky radiances on forecast up to 3-4h • Best forecast with EVIL system

  33. Conclusion • Expansion of analysis vector to clouds • Multivariate, flow-dependent background errors • Displacement pre-processing • Updated processing of cloud-affected satellite data (bias correction, QC, interpolation, RTM, middle-loop) • Sustained impact in short-term forecast • More work required…

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