FE 1 Ensemble Predictions Based on the Convection-Resolving Model COSMO-DE Susanne Theis Christoph Gebhardt Tanja Winterrath Volker Renner Deutscher Wetterdienst

FE 1 Project COSMO-DE-EPS Part of the „Innovationsprogamm 2007“ at DWD Duration: Scientific Staff: - Susanne Theis, N.N., Michael Denhard, Volker Renner - Tanja Winterrath, Marcus Paulat (Nov 07) - Christoph Gebhardt (Project EELMK) - Roland Ohl (EPS visualization in NinJo)

FE 1 Aims of COSMO-DE-EPS long-term (2011): operational ensemble prediction system based on COSMO-DE short-term (2008): experimental ensembles based on COSMO-DE identify relevant sources of forecast uncertainty forecast lead time

FE 1 Model COSMO-DE COSMO-EU GME COSMO-DE very short range: < 24 h grid box size: 2.8 km convection-resolving operational since 04/2007

FE 1 Current Work  identify relevant sources of forecast uncertainty  find ways how to represent them  estimate their impact  start with: variation of model physics variation of lateral boundary conditions forecast uncertainty lateral boundary conditions initial conditions model physics

FE 1 Current Work  identify relevant sources of forecast uncertainty  find ways how to represent them  estimate their impact  start with: variation of model physics variation of lateral boundary conditions forecast uncertainty lateral boundary conditions initial conditions model physics

FE 1  perturb fixed parameters within: - cloud microphysics - turbulence - boundary layer processes - vegetation  each ensemble member one perturbed parameter  perturbation is constant during forecast  23 members in total (1 default and 22 perturbed)  test period: August 2006 (31 forecasts 0-24 hours; start 00 UTC) Variation of Model Physics: Method entrscv zclc0 rlam_heat crsmin rat_lam etc.

FE 1 Hydrological catchment: 7430 km 2 ca. 330 km Focus on Small-Scale Predictions

FE 1 Variation of Model Physics: Example Precipitation accumulation 12-24UTC, Start: 17 th August 00UTC Radar ca. 330 km mm zclc0crs_min murlai

FE 1 1. Are individual simulations still realistic?  look at individual members: eye-ball inspection & deterministic verification 2. Do the perturbations have impact on the forecasts?  look at individual members and ensemble spread: ensemble diagnostics & probabilistic verification Evaluation of Results Test period: August 2006

FE 1 Quality of Individual Members Frequency BiasEquitable Threat Score Forecast Lead Time (hr) Individual Members seem realistic No obviously „wrong“ forecasts RR > 0.1 mm/h

FE 1 Impact on Individual Members Which percentage of grid point forecasts is in accordance with the control? (RR yes/no) ensemble member forecast lead time (hr)

FE 1 Impact on Individual Members Which percentage of grid point forecasts is in accordance with the control? (RR yes/no) ensemble member forecast lead time (hr)  criterion to reduce number of perturbations

FE 1 Ensemble Verification Talagrand DiagramROC Curve False Alarm Rate Hit Rate 90% 10% Rank of Observation area = 0.76 underdispersive Forecast lead time: 24 hours Precipitation accumulations: 18-24hrs

FE 1 Ensemble Verification Model perturbations have impact on forecasts But model perturbations do not represent overall uncertainty Talagrand DiagramROC Curve False Alarm Rate Hit Rate 90% 10% Rank of Observation area = 0.76 underdispersive

FE 1 Sources of Uncertainty forecast uncertainty lateral boundary conditions initial conditions model physics

FE 1 forecast uncertainty lateral boundary conditions initial conditions model physics Sources of Uncertainty

FE 1  boundary conditions from COSMO-SREPS (grid box size: 10 km)  16 ensemble members Variation of Boundary Conditions forecast uncertainty lateral boundary conditions initial conditions model physics

FE 1 Variation of Boundary Conditions: Method COSMO-SREPS (10 km) INM-Ensemble (25 km) COSMO-DE EPS (2.8 km) Current Status: 1 case study (September 17th 2006) ARPA-SIM DWD

FE 1 Variation of Boundary Conditions: Example Precipitation accumulation 12-24UTC, Start: 17th September 00UTC ca. 330 km mm „IFS“„GME“ „NCEP“„UKMO“ Radar

FE 1 Further Plans  produce a number of case studies - for model perturbations - for boundary conditions (MAP D-Phase)  systematic evaluation of impacts - model perturbations - boundary conditions  compare impacts to each other  compare them to overall forecast error

FE 1 Further Plans  produce a number of case studies - for model perturbations - for boundary conditions (MAP D-Phase)  systematic evaluation of impacts - model perturbations - boundary conditions  compare impacts to each other  compare them to overall forecast error  indications for next steps in ensemble development