european convective-scale eps
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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. - PowerPoint PPT PresentationTRANSCRIPT
PowerPoint Presentation
Production of a multi-model, convective-scale superensemble over western Europe as part of the SESAR project
EMS Annual Conference, Sept. 13th, 2013
Jeffrey Beck, F. Bouttier, O. Nuissier, and L. Raynaud*CNRM-GAME*GMAP/RECYFMto-France/CNRS
1
European Convective-Scale EPSTransition toward convection-resolving ensembles (e.g.):France: PEArome (2.5 km, 12 members, 24-hour forecasts) Pre-OpUK: MOGREPS-UK (2.2 km, 12 members, 24-hour forecasts) Pre-OpGermany: 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 restrictedSize 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 membersCOSMO + AROME = 32 members
Uniform resolution, grid, and forecasts required in order to merge individual models from Met Office, Mto-France, and DWD:0.022 lat x 0.027 lon grid, ~2.2 km resolutionSlightly adjusted (interpolated) domains allowing for collocated grid pointsHourly 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 initializationDerived 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
weightx/yModel 2 (red)Model 1 (black)w=1w=0At all model points, PDF = { wi Xi } for all members iw = weight for member iX = variable for member IExponential decrease in member weight < 100 km from boundary in overlap zones
Used for mean, median, quantile and probability plots; not used during model inter-comparisonSmoothing 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 (Zmax)
Integrate reflectivity factor for column above grid point to derive vertically integrated liquid (VIL) for hail detection (Z D6)
Derived, Convection-Related Variables
zx/yEchotop (18 dBZ)VIL (kg m-2) Zmax (dBZ)850 mb Simulated Reflectivity (dBZ)
Example: Zmax for Superensemble
15/8/2012 at 21 hr5/8/2012 at 15 hr
Zmax animation for 15/8/2012
Ensemble Spread and Probability of Zmax > 30 dBZ
15/8/2012 at 20 hours
Superensemble Goals and Future WorkInitial focus is to meet SESAR deliverables with regard to aviationShow 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
PointCircleHigh-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 WorkInitial focus is to meet SESAR deliverables with regard to aviationShow 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
Precipitation Scores (AROME/COSMO/Super-Ens)
Superensemble Goals and Future WorkInitial focus is to meet SESAR deliverables with regard to aviationShow 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 YouQuestions, comments, or suggestions welcome!