European Convective-Scale EPS

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

6. Initial Superensemble Derived Variables • 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 ∝ D6) z Echotop ~ 18 dBZ 850 mb Simulated Reflectivity x VIL = kg m-2

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

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 Model 1 (black) Model 2 (red) w=1 PDF = { wi xi } for all members “I” w=0 x or y

14. Impact of spatialization method on reflectivity quantiles Methods to derivea PDF ata givenpointx: 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 ● ● ● ● distancetoxincreases. Notes: sizeofsquareor circleis uniform(here:circleradius =12km, ● squarewithsamearea) 12-memberAromeensemble ●

15. ImpactofSpatializationonMean 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!