the AROME system: status and plans 3DVar vs other techniques The concepts of balance & control

1. Data assimilation for nowcastingpotential and limits of a 3D variational approachHow to use the newer, better NWP models to help nowcasting applications • the AROME system: status and plans • 3DVar vs other techniques • The concepts of balance & control • Redefining the NWC/NWP boundary

2. Example of kilometric-scale NWP model: AROME • a new mesoscale convection-permitting NWP system built from ECMWF's IFS, Europe's ALADIN, and France's Meso-NH models • Efficient spectral, semi-Lagrangian, semi-implicit NH compressible numerics to allow fast real-time production • Reasonably sophisticated physics: prognostic TKE turbulence, 5-species prognostic cloud microphysics, RRTM/FM radiation, tiled surface scheme with soil, vegetation, lakes, sea, snow, town energy balance, high-quality physiographies • With own data assimilation using radar, satellite, in situ operational observations • 1-way nesting in 10-km ALADIN data assimilation, itself nested in 20-km ARPEGE global 4DVar assimilation

3. (different wind scaling in each figure) Impact of NWP model resolution: 10km vs 2.5km, fields of low-level wind (blue) and T (red) on the model grids

4. Arome MCS simulation (04-08-94 15 to 18 UTC)2,5 km / dt=15s / domain 144 * 144 / analysis Diagpack + Humidity bogus

5. Arome-2.5km 9h-range fog dissipation forecast Meteosat visible image

6. The AROME data assimilation • derived from ECMWF 4D-Var, plus mesoscale features • 3DVar algorithm with FGAT (first guess at appropriate time) allowing 1-min time resolution, with 1-hour cycling • Multivariate non-separable Jb structure functions derived from ensemble statistics • Variational relaxation of large scales to coupling model • Use of automated screen-level obs network (T, Td, wind) with variational control of PBL stability • Direct multivariate assimilation of geostationary IR radiances in clear air (control of tropospheric humidity) • (planned) 1D cloud bogussing, starting with nowcasts of convective clouds (ISIS/RDT software) • (planned) Direct multivariate assimilation of radial Doppler winds from radars, and 1D radar precipitation bogussing

7. Dyn. Adapt. Raingauges 3DVar 3DVar with SEVIRI Impact study : Precipitation forecast 2004/07/18 12UTC RR P12 – P6

8. Objective score impact of 10km assimilation vs. range (rmse and bias)(pink=ARPEGE 4DVar dynamical adpation, blue=ALADIN LAM 3DVar) RH2m RR

9. AROME real-time forecast on 21 June 2005

10. radar composite 15 TU AROME fc started from mesoscale assimilation AROME fc dynamical adaptation

11. AROME status & plans • 2.5km forecast model runs daily since May 2005 on 500km domain with 1-minute timestep • Excellent performance on wind, low-level temperature and convective weather • Quality is situation-dependent: long routine verification is needed • Assimilation runs at lower 10km resolution so far with very positive impact on 0-12h forecast ranges wrt. dynamical adaptation • main target: 6-hourly 36-h NWP forecasts over France (1000kmx1000km) in less than 30 minutes, in 2008 + hourly very short-range forecasts • priority on relocatable nowcasting applications in 2009-2010 • see presentations by G Jaubert, V Ducrocq, O Caumont, T Bergot

12. 3DVar vs other techniques • 3DVar is complex software, but numerically cheap i.e. quick (unpreconditioned ALADIN 3DVar converges in 50 iterations i.e. about 5 minutes) • 4DVar would take at least 10 times more computing, delaying forecasts by tens of minutes: serious handicap for short-range NWP • short-window 4DVar works well for Doppler wind processing • Kalman filter can beat 3DVar in theory without the timeliness penalty (heavy computations are done out of the critical path) but not as mature yet for operations • 3DVar physical foundation makes it nicely extensible to new observation types (e.g. the ever-changing satellites) • future algorithm: probably a 3DVar basis mixed with short-window 4DVar + an ensemble KF focused on sensitive phenomena

13. Usable observations for convective systems assimilation

14. Concepts of balance & control • 3DVar smoothing functions & multivariate relationships must be specified a priori by a « background Jb term » forecast error model: • either you have observations of the phenomena that drive the prediction: e.g. PBL humidity and convergence lines for convection initiation --- the choice of DA algorithm will not matter • or, you have indirect observations and you need to spatialize them using likely Jb multivariate structures: local, weather-dependent balance properties, to retrieve the driving phenomenon • It is often better to observe causes than effects (e.g.: ground precip) • Automatic model feedbacks & static Jbs work better for large scales (geostrophism, Ekman pumping...) than mesoscales (PBL tops, orography, 3D convective & frontal structures) • Two competing strategies at mesoscale: • « automatic » balance estimation: 4DVar and (ensemble)KF • « ad hoc » spatialization: object bogussing from image processing

15. Perspective:From NWP to Nowcasting • challenge 1: refresh NWP forecasts faster than forecast error growth • will require ad hoc structuring of NWP production systems (Rapid Update Cycle, decentralized computing or superfast telecoms) • challenge 2: produce short-term direct forecasts of observables and end user products • simulation of satellite, radar etc. output at high resolution & quality • human monitoring tool to intercept/correct poor model output • challenge 3: intelligent probabilistic post-processing of hard-to-model weather elements e.g. storm risk areas vs. exact Cb cell location How can we help humans to cope with increasing data volumes of irregular quality ?