1. Production of a multi-model, convective- scale superensemble over western Europe as part of the SESAR project PHY-EPS Workshop, June 19 th, 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 Others? 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 super-ensemble

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, similar to NextGen ATM program in the USA 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. 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) Parameters collected: 10-m variables, pressure level temperature, wind, and hydrometeor content, plus total surface accumulated precip since initialization Derived variables: simulated reflectivity, echotop, and VIL for hazardous weather forecasting Preliminary dataset collected during convective events between July and August 2012 (42 days) Model Specifics for Superensemble

6. Calculate simulated reflectivity at each grid point using rain, snow, and hail hydrometeor mixing ratios Find upper-most pressure level with 18 dBZ = Echotop Integrate reflectivity factor for column above grid point to derive vertically integrated liquid (VIL) for hail detection (Z D 6 ) Initial Superensemble Derived Variables z x Echotop ~ 18 dBZ VIL = kg m -2 850 mb Simulated Reflectivity

7. Sim. Ref. Example (AROME and COSMO) MOGREPS + AROME = 24 members Model overlap region 15/8/2012 – 18 UTC 850 mb simulated reflectivity ensemble mean (dBZ)

8. Sim. Ref. Example (AROME and COSMO) MOGREPS + AROME = 24 members 15/8/2012 – 18 UTC 850 mb simulated reflectivity ensemble spread is qualitatively similar in single domain and overlap regions

9. Sim. Ref. Animation (AROME and COSMO) 850 mb simulated reflectivity 21-hr simulation from 03Z 5/8/2012 to 00Z 5/9/2012

10. Echotop Example (AROME and COSMO) MOGREPS + AROME = 24 members Echotop (in mb) defined using 18 dBZ Warm colors indicate lower cloud tops

11. Echotop Example (AROME and COSMO) MOGREPS + AROME = 24 members Echotop ensemble spread (mb) Warmer colors indicate more spread Similar spread in overlap regions

12. Echotop Animation (AROME and COSMO) Echotop (mb) 21-hr simulation from 03Z 5/8/2012 to 00Z 5/9/2012

13. Superensemble Challenges How to interpret output: Initial focus is to meet SESAR deliverables with regard to aviation hazards: Strong convection, echotop, hail threat (VIL), turbulence, upper-level variables Use of quantiles, ensemble spread, and probability in both overlap and single model regions Identify potential inconsistencies and biases when merging ensembles Currently employ a linear decrease in member weight < 50 km from boundary Point data versus different types of objective analysis smoothing weight x or y Model 2 (red) Model 1 (black) w=1 w=0 PDF = { w i x i } for all members I

14. Impact ofspatialization method on reflectivity quantiles Methods to derive a PDF at a given point x: m1 'point': use member values at point x m2 'square': equiprobable values in square around x m3 'circle': like m2, in circle centered around x m4 'cone': use values in disk, with decreasing weight as distance to x increases. Notes: size of square or circle is uniform (here: circle radius = 12 km, square with same area) 12-member Arome ensemble

15. Impact of Spatialization on Mean point circle square cone

16. Future Work Incorporation of other data: Add MOGREPS-UK (the data are in the mail) Produce and derive other variables of interest, both for SESAR and for ensemble modeling research purposes Other countries interested in participation? Analyse and verify data: Inter-comparison between models in overlap zones (score analyses) Rain gauge and surface based observations for precipitation total and 10-m forecast variables Validation of probabilistic products using 3D archived radar data from the French ARAMIS radar network Verification to show impact/benefit of increased ensemble sampling

17. Thank You Questions, comments, or suggestions welcome!