Recent developments at ECMWF Working Group in Diagnostic: structure and role

1. 1. Over-sight of collaborative projects • • Diagnosis of existing assimilation or forecasting problems • • Diagnosis of major new model cycles • 2. Strategic coordination of diagnostic developments • • Highlighting opportunities for new diagnostic tools of common interest • • Making existing diagnostic tools more widely usable • • Ensuring sufficient computing and storage resources for diagnostics • 3. Across-section communication of information and results • • Using a central diagnostics web page • • By coordination with the special topics of the OD/RD meeting • • With seminars on tools and collaborative projects Recent developments at ECMWFWorking Group in Diagnostic: structure and role Carla Cardinali DAOS –TORPEX meeting Exeter June 2011

2. Trouble shooting: Spring scores investigation • Diagnostics and investigation are underway to address the issue of a number “of” busts in Spring.

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5. The largest degradation follow the amplification of the ridge

6. Land surface data assimilation evolution Patricia de Rosnay et al 1999 2004 2009 2010/2011 Optimum Interpolation (OI) Screen Level Analysis Douville et al. (2000)‏ Mahfouf et al. (2000)‏ Soil moisture analysis based on Temperature and relative humidity analysis Revised snow analysis Drusch et al. (2004)‏ Cressman snow depth analysis using SYNOP data improved by using NOAA / NSEDIS Snow cover extend data Structure Surface Analysis: • OI snow analysis and high resolution NESDIS data (4km) • SEKF Soil Moisture analysis • Simplified Extended Kalman Filter • METOP-ASCAT SMOS Recent developments/implementations: • SEKF surface analysis • Use of active microwave data: ASCAT soil moisture product • Use of passive microwave SMOS Brightness Temperature product • New snow analysis and use of NOAA/NESDIS 4km snow cover product De Rosnay et al., ECMWF NewsLetter, 2011 Sabateret al., ECMWF NewsLetter, 2011

7. EKF soil moisture analysis • For each grid point, Analysed soil moisture state vector θa: θa(t) = θb(t) + K (y(t)-H θb(t)) θb Background soil moisture state vector Dimension Nx (=3, for the top three layers analysed), y Observation vector , Dimension Ny (=2 when T2m and Rh2m are used) • HJacobian matrix of the observation operator • Estimated in finite differences (perturbed simulations) • Dimension Nxraws, Ny columns KKalman gain matrix, fn of H and covariance matrix of background Bg (Nx . Nx) and observation R (Ny.Ny) errors. Relevant Observations: • Used in operations: Conventional observations (T2m, RH2m) • Used in research: ASCAT Soil Moisture • Under development: SMOS Brightness temperature EKF corrects the trajectory of the Land Surface Model

8. ECMWF Soil Moisture Analysis verification Albergel Clement • Validated for several sites across Europe (Italy, France, Spain, Belgium) • Validation results in FranceDec 2008- Dec 2009 Verification of ECMWF SM over the SMOSMANIA Network

9. Snow analysis • Snow analysis uses SYNOP snow depth data and NOAA/NESDIS IMS snow cover • 2010 implementation: • New Snow analysis based on the Optimum Interpolation with Brasnett 1999 structure functions • A new IMS 4km snow cover product to replace the 24km product • Improved QC (monitoring, Blacklisting) 2011: - Assimilate additional snow data From Sweden (New Report Type) cm New Surface data SYNOPdata

10. Direct 4D-Var assimilation of NCEP Stage IV rain data (Lopez 2011, MWR, in press) • Ingredients: • Data:NCEP Stage IV radar + gauge precipitation product (4-km resol.). • Data are averaged to model resolution prior to assimilation. • Domain: eastern USA. • 6-hourly accumulations are assimilated  smoother & more linear. • Ln(RR6h[mm/h]+1) transform (background departures closer to Gaussian). • Screening: • Obs rejected in regions with steep orography, surface snowfall or ducting. • Only points that are rainy in both background and obs are assimilated. • Fixed observation error: o=0.2 (in log-space). • Variational bias correction implemented. ECMWF 2011

11. Direct 4D-Var assimilation of NCEP Stage IV rain data Short-range precipitation forecast is significantly improved.. 12h-accumulated precipitation FC 00Z+12h (T511 L91) Sept-Oct 2009 April-May 2009 Equitable Threat Score Equitable Threat Score False Alarm Rate False Alarm Rate ECMWF 2011

12. Impact of NCEP Stage IV assimilation on 12h forecasts of precipitation. Sept-Oct 2009 average (CY35R2; T511 L91) NCEP Stage IV obs (mm/day) CTRL – NCEP Stage IV  Mean bias and RMS error are reduced NEW – NCEP Stage IV ECMWF 2011

13. Direct 4D-Var assimilation of NCEP Stage IV rain data ECMWF 2011 Impact on forecast scores for other parameters (Z, T, wind, RH): - neutral or slightly positive impact on the global scale. - some hint of positive impact over Europe (days 4-5) and Asia (days 8-10). RMSE North. Hemis. 500hPa wind RMSE Europe 500hPa temperature Forecast Root Mean Square Error changes due to direct 4D-Var assimilation of NCEP Stage IV rain data 1 April – 6 June 2010, T1279 (~15 km global) L91 good RMSE South. Hemis. 500hPa wind RMSE Asia 850hPa Temperature

14. EDA • The EDA system is designed to provide estimates of analysis and background uncertainty • This has intrinsic value as an estimate of the quality of the deterministic analysis (i.e., Re-analysis applications, synoptic evaluation,…) • It improves the representation of initial uncertainties in the Ensemble Prediction System • It is used to estimate state-dependent background error variancesin the deterministic 4D-Var