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Explore the development of a multi-model superensemble for convective-scale weather forecasting in Western Europe as part of the SESAR project. Learn about the collaborative effort to create a unified airspace management system in the EU states and the creation of a superensemble combining national models for improved aviation hazard forecasting. Discover the model specifics, data collection methods, and merging techniques used in this innovative approach. Gain insight into the goals, future work, and impact of the superensemble on aviation safety and forecasting accuracy in the region.
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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
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) http://www.sesarju.eu
Regional Model Domains MOGREPS + AROME = 24 members COSMO + AROME = 32 members
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)
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
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)
Example: Zmaxfor Superensemble 5/8/2012 at 15 hr 15/8/2012 at 21 hr
Ensemble Spread and Probability of Zmax > 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, 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
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
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
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
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
Thank You • Questions, comments, or suggestions welcome!