1. UKmet February 2011 1 Hybrid Ensemble-Variational Data Assimilation Development A partnership to develop and implement a hybrid 3D-VAR system –Joint venture between ESRL, NASA/GMAO, Univ of Oklahoma and NCEP –Hybrid uses ensemble-based information to improve representation of flow-dependent background errors –Must serve global ensemble system as well as analysis –Global, regional and hurricane applications

2. UKmet February 2011 2 What is a Var/EnKF hybrid? Gets flow dependent background error covariance into 3D-Var by using an ensemble estimate. Ensemble comes from an EnKF cycling concurrently with same obs (but at lower resolution). Ensemble perturbations (with localization) incorporated directly into cost function using extended control variable approach (see e.g. Wang 2010 – MWR, p. 2990)

3. UKmet February 2011 3 Relationship Between ENKF and Variational Components

4. UKmet February 2011 4 Development of Hybrid Var-EnKF with GFS on Tjet in Boulder  Test Period: 01 Aug to 22 September 2010  Deterministic Forecasts: Operational GFS @ T574L64  Ensemble Configuration:  80 ensemble members  GSI for observation operators  T254L64 operational GFS  Initialized 00 UTC 15 July 2010 from interpolated GEFS members  allowed over 2 weeks spin-up  Assimilate all operational observations  Includes early (GFS) and late (GDAS/cycled) cycles  Operational prepbufr files (no prep/additional qc)  Dual-resolution/Coupled  High resolution control/deterministic component  Includes TC Relocation on guess  Ensemble is recentered every cycle about hybrid analysis  Throw out EnKF analyis mean  Bias correction (satellite) coefficients come from GSI/VAR  Minimal tuning done for hybrid  1/3 static B, 2/3 ensemble

5. UKmet February 2011 5 500 hPa SH AC Time Series 6 Aug to 21 Sept 2011 Day 5 Day 6 +0.026 AC +0.035 AC Black – Control Red – Hybrid Green – Operational

6. 6 Issues/Risks Issues: Risks: Mitigation: Finances Scheduling Project Information and Highlights Lead: John Derber, EMC and Chris Magee, NCO Scope: 1. Improved OMI QC 2. Removal of redundant SBUV/2 total ozone 3. Retune SBUV/2 ozone ob errors 4. Relax AMSU-A Channel 5 QC 5. New version of CRTM 2.0.2 6. Inclusion of Field of View Size/Shape/Power for Radiative transfer 7. Remove down weighting of collocated radiances 8. Limit moisture to be >= 1.e-10 in each outer iteration and at end of analysis 9. Inclusion of uniform (higher resolution) thinning for satellite radiances 10. Improve location of Buoys in vertical (move from 20 to 10m) 11. Improved GSI code with optimization and additional options 12. Recomputed background errors 13. Inclusion of SBUV from NOAA-19 14. Ambiguous vector quality control for ASCAT (type 290) data 15. Thermal Roughness Length 16. Increase minimum moisture threshold in stratosphere 17. Reduce background diffusion in Stratosphere Expected Benefits: Small incremental improvement is the initial conditions for the Global Forecast System, resulting in small improvements in model performance. Associated Costs: Funding Sources: EMC Base: T2O 9 Man-months. NCO Base: 3 man-months for implementation, 1 man-month annually for maintenance Management Attention Required Potential Management Attention Needed On Target G v1.0 09/14//07 G R Hybrid EnKF-3DVar Assimilation Project Status as of 4/14/2011 Milestone (NCEP)DateStatus EMC testing complete10/28/2011 Initial Code Delivery to NCO11/1/2011 Technical Information Notice Issued12/15/2012 Initial Test Complete1/3/2012 CCB approve parallel data feed1/5/2012 IT testing begins IT testing ends Parallel testing begun in NCO1/13/2012 Real-Time Evaluation Ends2/14/2012 Management Briefing2/21/2012 Implementation2/28/2012 Y GG G