1. Daily runs and real time assimilation during the COPS campaign with AROME Pierre Brousseau, Y. Seity, G. Hello, S. Malardel, C. Fisher, L. Berre, T. Montemerle, E. Wattrelot, C. Faccani, L. Auger, O. Caumont METEO-FRANCE 6 th COPS Workshop 27-29 February 2008 University of Hohnenheim, Stuttgart

2. Outlines  The AROME projet  The AROME data assimilation system  Daily runs during the COPS/MAP-DPHASE campaigns

3. The AROME project  AROME model will complete the french NWP system in 2008 : – ARPEGE : global model (15 km over Europe) – ALADIN-France : regional model (10km) – AROME : mesoscale model (2.5km)  Aim : to improve local meteorological forecasts of potentially dangerous convective events (storms, unexpected floods, wind bursts...) and lower tropospheric phenomena (wind, temperature, turbulence, visibility...). ARPEGE stretched grid and ALADIN-FRANCE domain AROME France domain

4. The AROME project  Means : the AROME software which merges research outcomes and operational progress : – physical package from the Meso-NH research model – Non-Hydrostatic version of the ALADIN software – a complete data assimilation system.  Benefits of the model: high horizontal resolution (2.5km), realistic representation of clouds, turbulence, surface interactions (mountains, cities, coasts,...).  Benefit of the assimilation : use of satellites, radars, regional network...

5. The AROME data assimilation system  Based on the ALADIN-FRANCE 3D-Var scheme (Fisher et al. 2005) : 2 wind components, temperature, specific humidity and surface pressure are analysed at the model resolution (2.5 km). Others model fields ( TKE, Non-hydrostatic and microphysics fields) are cycled from the previous AROME guess background Observations Analysis U, V, T, q and Ps TKE, NH and microphysics fields

6. Rapid Update Cycle  Idea : Forecasts initialized with more recent observations will be more accurate Using high temporal and spatial frequency observations (RADAR measurements for example) to the best possible advantage Use of a Rapid Update Cycle (Benjamin et al. 2003) in order to compensate the lack of temporal dimension in the 3D-Var Analysis data …

7. Background error statistics for AROME  Background-error statistics determine how observations modify the background to produce the analysis  They share the same multivariate formulation as in ALADIN-FRANCE (Berre 2000), This formalism uses errors of vorticity, divergence, temperature, surface pressure and humidity, with scale-dependant statistical regressions to represent cross-covariances.  They have been calculated using an ensemble-based method (Berre et al. 2006) during a convective summer period.

8. Single observation experiment  In AROME case, modification caused by one observation is More localized : shorter background error horizontal correlation lengthscales (increase of the model resolution) more intense : stronger background error standard deviations (explicit representation of small scale structures)  To have an important influence, observation networks must have a good spatial coverage.

9. Observations  Same observations as in ALADIN-France operational suite : conventional observations, 2m temperature and humidity, IR radiances from ATOVS and SEVIRI instruments, winds from AMV and scatterometers among others.  No specific spatial selection (thining) appropriate to AROME resolution. Studies on this topic still are ongoing (plane measurements, IR radiances…)

10. MAP-DPHASE/COPS configuration COPS DPHA  Daily runs since the 1 june 2007 – 36-h forecast at 00 UTC performed on the MAP domain and post-processed on the COPS domain. – Initial and lateral conditions provided by ALADIN-France operational suite (without AROME assimilation).  Real time assimilation since the 7 july : – 3-h continuous assimilation cycle – Observations on GTS – 36-h forecast at 00 UTC : improvement during the first 12-h forecast ranges  Daily run initial conditions provided by the assimilation cycle since the 2 august.

11. COPS IOP 8b, 15 July 2007  Isolated thunderstorm over the Black Forest  Location, temporal evolution and intensity correctly simulated with AROME 30 mm Radar measurement AROME forecast Cumulative rainfalls in 3h, 14-17 TU

12. COPS IOP 8b, 15 July 2007 Rain Snow Graupel ice crystals cloud droplets Vertical cross section

13. CONCLUSION AND OUTLOOK  Daily AROME runs performed during the COPS campaign give encouraging results.  First evaluation of the technical feasability to use operationally the AROME assimilation system, using a 3-hour real time continuous assimilation cycle. Since october 2007, such a system is running daily, using a pre-operational configuration over the AROME-france domain.  Different works are now planned in the framework of COPS : – Evaluation of model performances and test of recent developments (new horizontal diffusion, new shallow convection scheme,…) – Observing system experiments and RE-analyses (groundbased GPS and RADAR observations), cycling frequency

14. OUTLOOK : Radar measurements assimilation 3DVar Arome Observed reflectivities 1D Bayesian inversion Columns of pseudo-observations (only humidity for the moment) analyse OBS Humidity retrievals Innovations After QC and thinning  Radar DOPPLER winds  Radar reflectivities using a 1D+3DVar method (Caumont et al. 2007)

15. REFLECTIVITIESCONTROLDOPPLER WINDS Precipitations are better located on the 3-hour forecast from the analysis with reflectivities… RAINGAUGES OUTLOOK : Radar reflectivities assimilation

16. References  Benjamin S. G., et al., 2004 : An hourly assimilation-forecast cycle : The RUC, Mon. Wea. Rev., 132, 4959-518.  Berre L., 2000 : Estimation of synoptic and mesoscale forecast error cavariances in a limited area model, Mon. Wea. Rev., 128, 644-667.  Berre et al., 2006 : The representative of the analysis effect in three error simulation techniques. Tellus, 58A, 196-209.  Ficher et al., 2005 : An overview of the variational assimilation in the ALADIN/France numerical weather-prediction system, Quart. J. Roy. Meteo. Soc., 131, 3477-3492.

17. Thank you for your attention…