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

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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!