European Convective-Scale EPS

1. Production of a multi-model, convective-scale superensemble over western Europe as part of the SESAR project EMS AnnualConference, Sept. 13th, 2013 Jeffrey Beck, F. Bouttier, O. Nuissier, and L. Raynaud* CNRM-GAME *GMAP/RECYF Météo-France/CNRS

2. European Convective-Scale EPS • Transition toward convection-resolving ensembles (e.g.): • France: PEArome (2.5 km, 12 members, 24-hour forecasts) – Pre-Op • UK: MOGREPS-UK (2.2 km, 12 members, 24-hour forecasts) – Pre-Op • Germany: COSMO-DE (2.8 km, 20 members, 21-hour forecasts) – Op • Computational resources focused toward high-resolution representation of small-scale features (e.g., extreme events, fog), but creates limitations: • Number of members and therefore ensemble sampling/performance is restricted • Size of domain and forecast duration also constraints • Potential solution is to combine multiple national models in a “superensemble”

3. Single European Sky ATM Research (SESAR) • Collaborative project to overhaul European airspace and Air Traffic Management (ATM) • Goal is to unify ATM over EU states • Key necessity: Continent-wide convective-scale modeling for aviation hazards with ensemble (probabilistic) forecasts • Within the context of the SESAR project, an experimental version of a superensemble is being created (operational in several years) http://www.sesarju.eu

4. Regional Model Domains MOGREPS + AROME = 24 members COSMO + AROME = 32 members

5. Model Specifics for Superensemble • Uniform resolution, grid, and forecasts required in order to merge individual models from Met Office, Météo-France, and DWD: • 0.022° lat x 0.027° lon grid, ~2.2 km resolution • Slightly adjusted (interpolated) domains allowing for collocated grid points • Hourly forecasts out to 21 hours (00Z or 03Z initialization) • Model parameters collected: • 2- and 10-m variables, pressure level temperature, wind, and hydrometeor content, plus total surface accumulated precipsince initialization • Derived variables: simulated reflectivity, echotop, and vertically integrated liquid (VIL) for hazardous weather forecasting • Preliminary dataset collected during convective events between July and August 2012 (42 days)

6. Model Domain Merging weight At all model points, PDF = { wi • Xi } for all members “i” w = weight for member “i” X = variable for member “I” Model 1 (black) Model 2 (red) w=1 w=0 x/y • Exponential decrease in member weight < 100 km from boundary in overlap zones • Used for mean, median, quantile and probability plots; not used during model inter-comparison

7. Smoothing Example: 2-m Relative Humidity

8. 2-m Relative Humidity and 250 mb Temperature

9. Derived, Convection-Related Variables • Calculate simulated reflectivity at each grid point using rain, snow, and hail/graupel hydrometeor mixing ratios • Find upper-most pressure level with 18 dBZ (echotop) and maximum dBZ in column (Zmax) • Integrate reflectivity factor for column above grid point to derive vertically integrated liquid (VIL) for hail detection (Z ∝ D6) z Echotop (18 dBZ) 850 mb Simulated Reflectivity (dBZ) x/y VIL (kg m-2) Zmax (dBZ)

10. Example: Zmaxfor Superensemble 5/8/2012 at 15 hr 15/8/2012 at 21 hr

11. Zmax animation for 15/8/2012

12. Ensemble Spread and Probability of Zmax > 30 dBZ 15/8/2012 at 20 hours

13. Superensemble Goals and Future Work • Initial focus is to meet SESAR deliverables with regard to aviation • Show ability of superensemble to seamlessly forecast strong convection and hail threat (e.g., simulated reflectivity, echotop, VIL, Zmax) • Point data versus different types of objective analysis smoothing for optimal end-user probabilistic forecasts • Identify potential inconsistencies and biases between models when merging ensembles (quantiles, spread, probabilities) • Model verification using surface observations in overlap regions to illustrate added value of superensemble • Convection-oriented model verification using 3D radar data from the ARAMIS French national radar network

14. Impact of Smoothing on Mean Point Circle • High-resolution ensemble predicts very small-scale convection • May be advantageous to adopt smoothing for probability forecasts used for regional purposes; to be seen

15. Superensemble Goals and Future Work • Initial focus is to meet SESAR deliverables with regard to aviation • Show ability of superensemble to seamlessly forecast strong convection and hail threat (e.g., simulated reflectivity, echotop, VIL, Zmax) • Point data versus different types of objective analysis smoothing for optimal end-user probabilistic forecasts • Identify potential inconsistencies and biases between models when merging ensembles (quantiles, spread, probabilities) • Model verification using observations in overlap regions to illustrate added value of superensemble • Convection-oriented model verification using 3D radar data from the ARAMIS French national radar network

16. Precipitation Scores (AROME/COSMO/Super-Ens)

17. Superensemble Goals and Future Work • Initial focus is to meet SESAR deliverables with regard to aviation • Show ability of superensemble to seamlessly forecast strong convection and hail threat (e.g., simulated reflectivity, echotop, VIL, Zmax) • Point data versus different types of objective analysis smoothing for optimal end-user probabilistic forecasts • Identify potential inconsistencies and biases between models when merging ensembles (quantiles, spread, probabilities) • Model verification using surface observations in overlap regions to illustrate added value of superensemble • Convection-oriented model verification using 3D radar data from the ARAMIS French national radar network

18. ARAMIS 3-D Radar Dataset • 512 x 512 x 500 m resolution dataset for all of metropolitan France up to 12 km • Echotop, VIL, and Zmax have been calculated as was done with model data • Verification/scores of reflectivity and derived quantities will be carried out with superensemble

19. Thank You • Questions, comments, or suggestions welcome!