1 / 38

GSI developments and plans at NCAR/MMM

GSI developments and plans at NCAR/MMM. Tom Aulign é Aimé Fournier, Hans Huang, Andy Jones, Hui-Chuan Lin, Zhiquan Liu, Yann Michel, Arthur Mizzi, Thomas Nehrkorn, Syed Rizvi, Hongli Wang, Xin Zhang National Center for Atmospheric Research NCAR is supported by the National Science Foundation

dasan
Télécharger la présentation

GSI developments and plans at NCAR/MMM

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. GSI developments and plans at NCAR/MMM Tom Auligné Aimé Fournier, Hans Huang, Andy Jones, Hui-Chuan Lin, Zhiquan Liu, Yann Michel, Arthur Mizzi, Thomas Nehrkorn, Syed Rizvi, Hongli Wang, Xin Zhang National Center for Atmospheric Research NCAR is supported by the National Science Foundation GSI Data Assimilation Workshop - June 28, 2011

  2. Introduction • Focus at NCAR/MMM • Regional GSI • WRF-ARW model (NetCDF files) • Projects funded by AFWA • AFWA Coupled Analysis and Prediction System (ACAPS) • AFWA Data Assimilation • AFWA Aerosols • Collaboration with • GSI developers (EMC, GMAO, GSD, DTC) • JCSDA

  3. Outline • Background and Observation Errors • Variational/Ensemble Hybrid • Displacement Pre-processing • WRF Adjoint: 4DVar and Observation Impact • Aerosol and Cloud Satellite Observations • Verification

  4. Outline • Background and Observation Errors • Variational/Ensemble Hybrid • Displacement Pre-processing • WRF Adjoint: 4DVar and Observation Impact • Aerosol and Cloud Satellite Observations • Verification

  5. Background and Obs Errors: Community tools Observation error tuning with the diagnostic equations (Desroziers 2005) • “Community GEN_BE” utility: https://svn-wrf-var.cgd.ucar.edu/branches/gen_be • Includes all the features of WRFDA V3.2.2 • Multi-variate humidity • Generation of WRF-ARW background errors for GSI • Extension of GEN_BE to include • Aerosol concentrations (univariate) • Cloud parameters (Qcloud, Qrain, Qice, Qsnow) • Expansion of GSI control variable

  6. Background Error Covariances: Masked Statistics

  7. Background Error Covariances: Masked Statistics Michel et al. (MWR, 2011)

  8. Background Error Covariances: Wavelets

  9. Background Error Covariances: Wavelets

  10. Outline • Background and Observation Errors • Variational/Ensemble Hybrid • Displacement Pre-processing • WRF Adjoint: 4DVar and Observation Impact • Aerosol and Cloud Satellite Observations • Verification

  11. Variational/Ensemble Hybrid • WRF/GSI Regional Hybrid • Testing package: https://svn-mmm-hybrid-testbed.cgd.ucar.edu/HYBRID_TRUNK • Cf. presentation by Arthur Mizzi

  12. Outline • Background and Observation Errors • Variational/Ensemble Hybrid • Displacement Pre-processing • WRF Adjoint: 4DVar and Observation Impact • Aerosol and Cloud Satellite Observations • Verification

  13. Displacement Pre-Processing background error => displacements of coherent features + additive (residual) error • Conceptual view of using displacements to characterize errors

  14. Initial time: 08-28-05 06:00:00z Vortex displaced forward along track

  15. 18 Hour forecast time: 08-29-05 00:00:00z 18 hours later vortex maintains forward position

  16. Displacement Pre-Processing: Status and Plans • Collaboration between AER, MIT and NCAR • Integration of displacements • Build on the existing API, with enhancements to add: • Support for multiple displacement algorithms • Algorithmic developments • Constraints formulated and evaluated specifically for cloud-related fields • Candidates: smoothness, non-divergence of displacements • Application in: grid point, spectral, or wavelet space • Time evolution of displacements • Characterize and model the time evolution of displacements • Prepare for integration with 4D-Var • Figures of Merit for cloud-related fields

  17. Outline • Background and Observation Errors • Variational/Ensemble Hybrid • Displacement Pre-processing • WRF Adjoint: 4DVar and Observation Impact • Aerosol and Cloud Satellite Observations • Verification

  18. WRF Adjoint: WRF/GSI 4DVar • New TL/AD code: WRFPLUS • Consistent with latest WRF-ARW (v3.3) • Includes simplified physics (surface drag, large-scale condensation, cumulus scheme, Kessler microphysics) • WRF/GSI 4DVar • Based on GMAO 4DVar framework • New coupling between GSI and WRF/WRFPLUS • Initial testing looks good. • Cf. presentation by Xin Zhang

  19. WRF Adjoint: Observation Impact Analysis (xa) Forecast (xf) Observation (y) WRFDA/GSI Data Assimilation WRF-ARW Forecast Model Define Forecast Accuracy Background (xb) Forecast Accuracy (F) Observation Impact <y-H(xb)> (F/ y) Gradient of F (F/ xf) Analysis Sensitivity (F/ xa) Observation Sensitivity (F/ y) Adjoint of WRF-ARW Forecast TL Model (WRF+) Adjoint of WRFDA/GSI Data Assimilation Derive Forecast Accuracy Background Sensitivity (F/ xb) Figure adapted from Liang Xu (NRL) Obs Error Sensitivity (F/ eob) Bias Correction Sensitivity (F/ k)

  20. Outline • Background and Observation Errors • Variational/Ensemble Hybrid • Displacement Pre-processing • WRF Adjoint: 4DVar and Observation Impact • Aerosol and Cloud Satellite Observations • Verification

  21. Aerosol Satellite Observations Assimilation of MODIS Aerosol Optical Depth in GSI Process MODIS AOD data (HDF to BUFR converter) Use CRTM-AOD (Quanhua Liu) Couple with WRF-Chem GOCART (14 aerosol species) Status and plans Assimilate surface PM2.5 (ongoing) Assimilate MODIS Visible/NIR radiances (planned, pending) Cf. presentation by Zhiquan Liu

  22. Cloud Satellite Observations: Retrievals • MODIS cloud retrieval products • Cloud liquid/ice water path, cloud optical depth, particle effective radius (1km resolution observations) • Cloud top properties: pressure, temperature, fraction/emissivity (5km resolution observations) • Assimilation of MODIS Cloud Water Path • Process MODIS CWP data (HDF to BUFR converter) • Observation Operator (+ TL & AD) • Status and plans • Assimilate MODIS CWP at convective scale (ongoing) • Assimilate MODIS Cloud Optical Depth (planned)

  23. Cloud Satellite Observations: Radiance Assimilation Very first shot at cloudy radiances, still needs a lot more work… Cloud parameters from WRF-ARW first-guess CRTM forward model and Jacobian Inclusion of cloud (microphysical) parameters in control variable (implemented in both WRFDA and GSI)

  24. Observation AIRS (12micron) Background (WRF-DART) Observation – Background

  25. Cloud Satellite Observations: Radiance Assimilation Simple B Matrix for cloud parameters copied from humidity Ensemble assimilation usingthe alpha control variable (no tuning)

  26. Cloud Satellite Observations: Radiance Assimilation Simple B Matrix for cloud parameters copied from humidity Clear observations only

  27. Remaining issues include: - Bias Correction - Quality Control - Non-linearities in the observation operator - Representativeness Error Cloud Satellite Observations: Radiances

  28. with Cloud Satellite Observations: Radiances Cloud fractions Nk are ajusted variationally to fit observations: Cloud Top Pressure (hPa) Nk3 Nk2 Nk1 No AIRS MMR MODIS Level2 Pixel

  29. Cloud Satellite Observations: Radiances AIRS MMR Effective Cloud Fraction CloudSat Reflectivity

  30. Towards Cloudy Radiance Assimilation Cloud Satellite Observations: Radiances Nk3 Nk2 Nk1 No Pixel

  31. Towards Cloudy Radiance Assimilation Cloud Satellite Observations: Representativeness Simulated mismatch in resolution: - Perfect observations (high resolution) - Perfect Background (lower resolution) Background Innovations 31

  32. Towards Cloudy Radiance Assimilation Cloud Satellite Observations: Representativeness New interpolation scheme: 1. Automatic detection of sharp gradients 2. New “proximity” for interpolation Background New Innovations Innovations 32

  33. Cloud Satellite Observations: Representativeness

  34. The raw yo− yb(left) includes errors due to yo and yb coming from completely different representations, that (hypothetically) have been reconciled by the foregoing wavelet-coefficient selection procedure. Cloud Satellite Observations: Representativeness

  35. Outline • Background and Observation Errors • Variational/Ensemble Hybrid • Displacement Pre-processing • WRF Adjoint: 4DVar and Observation Impact • Aerosol and Cloud Satellite Observations • Verification

  36. Verification: Test Case (courtesy Glen Romine) Period: 4-17 June 2009 Analyses and 6 hr forecasts from 50-member ensembles using Data Assimilation Research Testbed (DART) system 15 km mesoscale, 3 km storm-scale

  37. Verification: Validation Data Example of GOES 8 background image World-Wide Merged Cloud Analysis (WWMCA) • Main Archive: • Quality-controlled, GOES East and GOES West over CONUS • Covers January 1998 – December 2009 • Resolution – 4x4 km for all channels except #3 which is 4x6 km • Monthly/hourly cloud cleared background for all visible hours • Monthly/hourly Cloud % using visible threshold • Monthly/every other hour Cloud % using IR threshold since 2003 • Addition hours of QC’d GOES West for May-Sept 1999-2009

  38. Conclusion • New WRF Adjoint for GSI 4DVar and Observation Impact • Community tool for Background Error calculation (GEN_BE) • Specific developments for Cloud and Aerosol assimilation • Opportunity for inter-comparison

More Related