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Status and plans for the ECMWF forecasting System

Status and plans for the ECMWF forecasting System. Overview. Performance of the forecasting system Research highlights: CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS) CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis,…

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Status and plans for the ECMWF forecasting System

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  1. Status and plans for the ECMWF forecasting System Bilateral meeting 2011

  2. Overview • Performance of the forecasting system • Research highlights: • CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS) • CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis,… • CY37R2 (in the pipeline): Ensemble Data Assimilation to provide flow-dependent variances to 4D-Var, reduction of observation error for AMSU-A, GRIB-2 for model level fields Bilateral meeting 2011

  3. Overview • Performance of the forecasting system • Research highlights: • CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS) • CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis,… • CY37R2 (in the pipeline): Ensemble Data Assimilation to provide flow-dependent variances to 4D-Var, reduction of observation error for AMSU-A, GRIB-2 for model level fields Bilateral meeting 2011

  4. Deterministic forecast headline score Bilateral meeting 2011

  5. Comparison with other centres:autumn, NH Bilateral meeting 2011

  6. Precipitation skill Europe D+2 D+4 Bilateral meeting 2011

  7. Comparison of TC forecastsfrom HKO, 2008-2009, western North Pacific Bilateral meeting 2011

  8. Russian heat wave Bilateral meeting 2011

  9. Overview • Performance of the forecasting system • Research highlights: • CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS) • CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis… • CY37R2 (in the pipeline): Ensemble Data Assimilation to provide flow-dependent variances to 4D-Var, reduction of observation error for AMSU-A, GRIB-2 for model level fields • others Bilateral meeting 2011

  10. Selected contents Prognostic rain and snow with more comprehensive cloud microphysics EKF for soil moisture analysis New snow analysis (O-I) Enhancement of all-sky radiance assimilation November 2010 IFS cycle 36r4

  11. New prognostic cloud microphysics scheme WATER VAPOUR Evaporation Condensation CLOUD FRACTION CLOUD Liquid/Ice Evaporation CLOUD FRACTION Autoconversion PRECIP Rain/Snow Current Cloud Scheme New Cloud Scheme • 5 prognostic cloud variables + water vapour • Ice and water now independent • More physically based, greater realism • Significant change to degrees of freedom • Change to water cycle balances in the model • More than double the lines of “cloud” code! • 2 prognostic cloud variables + w.v. • Ice/water diagnostic Fn(T) • Diagnostic precipitation

  12. New prognostic cloud microphysicsRepresentation of mixed phase • The most significant change in the new scheme is the improved physical representation of the mixed phase. • Current scheme: diagnostic fn(T) split between ice and liquid cloud(a crude approximation of the wide range of values observed in reality). • New scheme: wide range of supercooled liquid water for a given T. PDF of liquid water fraction of cloud for the diagnostic mixed phase scheme (dashed line) and the prognostic ice/liquid scheme (shading)

  13. For snow SYNOP reports an satellite based snow cover are assimilated A new snow analysis (I) A new 4 km IMS snow cover is assimilated into a new OI analysis replacing Cressmaninterpolation Here shown isthe analysedsnow cover Cressman and IMS_24km OI and IMS 4km

  14. Impact of Cycle 36r4 Bilateral meeting 2011

  15. SEEPS: impact of 36r4 Bilateral meeting 2011

  16. Overview • Performance of the forecasting system • Research highlights: • CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS) • CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis… • CY37R2 (in the pipeline): Ensemble Data Assimilation to provide flow-dependent variances to 4D-Var, reduction of observation error for AMSU-A, GRIB-2 for model level fields • others Bilateral meeting 2011

  17. Selected contents Increased weight to AMSU-A data Direct use of EDA in 4D-Var Retuning of new physics GRIB-2 for model level fields In the pipeline: IFS cycle 37R2

  18. Ensemble of Data Assimilations (EDA) • Perturbed observations • Perturbed SSTs • Stochastic physics Δx2 Δx1 Ensemble initial perturbations → Δx3 Δx4 X0 4DVAR X+12h

  19. EDA – flow dependent variances 9h forecasts 23/1 2009 21 UTC Standard deviation of zonal wind component at ~850hPa and pmsl ms-1 24/1 2009 21 UTC

  20. Impact of Cycle 37R2 NH SH Z VW Bilateral meeting 2011

  21. Overview • Performance of the forecasting system • Research highlights: • CY36R2 (22 June 2010): GRIB API and Ensemble Data Assimilation (to initiate the EPS) • CY36R4 (9 November 2010): New physics package, surface EKF, snow analysis… • CY37R2 (in the pipeline): Ensemble Data Assimilation to provide flow-dependent variances to 4D-Var, reduction of observation error for AMSU-A, GRIB-2 for model level fields • Others (small selection) Bilateral meeting 2011

  22. Main research/development topics 2011: • Ensemble data assimilation methods (EDA, EKF) • Weak constraint, long window 4D-Var • Vertical resolution increase • Numerical experimentation into the “grey zone” • Improved physical parameterizations • Implement NEMO ocean model and NEMOVAR in EPS • Seasonal forecasting system 4 • ERA-CLIM • MACC in Near-Real-Time • IFS maintenance and optimisation (cycles, code, scripts) • Object Oriented Prediction System Research Department Annual Plan 2011

  23. One concern: Speed-up of 4D-Var Nodes

  24. Ensemble Kalman filter development Bilateral meeting 2011

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