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

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”

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)

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

 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 precip since 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) Model Specifics for Superensemble

Model Domain Merging weight x/y Model 2 (red) Model 1 (black) w=1 w=0 At all model points, PDF = { w i X i } for all members “i” w = weight for member “i” X = variable for member “I”  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

Smoothing Example: 2-m Relative Humidity

2-m Relative Humidity and 250 mb Temperature

 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 (Z max )  Integrate reflectivity factor for column above grid point to derive vertically integrated liquid (VIL) for hail detection (Z ∝ D 6 ) Derived, Convection-Related Variables z x/y Echotop (18 dBZ) VIL (kg m -2 ) Z max (dBZ) 850 mb Simulated Reflectivity (dBZ)

Example: Z max for Superensemble 15/8/2012 at 21 hr5/8/2012 at 15 hr

Z max animation for 15/8/2012

Ensemble Spread and Probability of Z max > 30 dBZ 15/8/2012 at 20 hours

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, Z max )  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

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

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, Z max )  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

Precipitation Scores (AROME/COSMO/Super-Ens)

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, Z max )  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

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 Z max have been calculated as was done with model data  Verification/scores of reflectivity and derived quantities will be carried out with superensemble

Thank You Questions, comments, or suggestions welcome!