Slide 1 Bilateral meeting 2011Slide 1, ©ECMWF Status and plans for the ECMWF forecasting System

Slide 2 Bilateral meeting 2011Slide 2, ©ECMWF 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

Slide 3 Bilateral meeting 2011Slide 3, ©ECMWF 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

Slide 4 Deterministic forecast headline score Bilateral meeting 2011Slide 4, ©ECMWF

Slide 5 Comparison with other centres: autumn, NH Bilateral meeting 2011Slide 5, ©ECMWF

Slide 6 Bilateral meeting 2011 Precipitation skill Europe D+2 D+4 Slide 6, ©ECMWF

Slide 7 Comparison of TC forecasts from HKO, , western North Pacific Bilateral meeting 2011Slide 7, ©ECMWF

Slide 8 Russian heat wave Bilateral meeting 2011Slide 8, ©ECMWF

Slide 9 Bilateral meeting 2011Slide 9, ©ECMWF 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

November 2010 IFS cycle 36r4  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

Slide 11 New prognostic cloud microphysics scheme WATER VAPOUR CLOUD Liquid/Ice PRECIP Rain/Snow Evaporation Autoconversion Evaporation Condensation CLOUD FRACTION Current Cloud Scheme New Cloud Scheme 2 prognostic cloud variables + w.v. Ice/water diagnostic Fn(T) Diagnostic precipitation 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!

Slide 12 New prognostic cloud microphysics Representation 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)

Slide 13 A new snow analysis (I) For snow SYNOP reports an satellite based snow cover are assimilated A new 4 km IMS snow cover is assimilated into a new OI analysis replacing Cressman interpolation Here shown is the analysed snow cover Cressman and IMS_24km OI and IMS 4km

Slide 14 Impact of Cycle 36r4 Bilateral meeting 2011Slide 14, ©ECMWF

Slide 15 SEEPS: impact of 36r4 Bilateral meeting 2011Slide 15, ©ECMWF

Slide 16 Bilateral meeting 2011Slide 16, ©ECMWF 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

In the pipeline: IFS cycle 37R2  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

Slide 18 Ensemble of Data Assimilations (EDA)  Perturbed observations  Perturbed SSTs  Stochastic physics X0X0 X +12h 4DVAR Δx2Δx2 Δx1Δx1 Δx3Δx3 Δx4Δx4 Ensemble initial perturbations →

Slide 19 EDA – flow dependent variances Standard deviation of zonal wind component at ~850hPa and p msl ms -1 9h forecasts 23/ UTC 24/ UTC

Slide 20 Impact of Cycle 37R2 Bilateral meeting 2011Slide 20, ©ECMWF NH SH ZVW

Slide 21 Bilateral meeting 2011Slide 21, ©ECMWF 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)

Slide 22 Research Department Annual Plan 2011Slide 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

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

Slide 24 Ensemble Kalman filter development Bilateral meeting 2011Slide 24, ©ECMWF