Data assimilation for nowcasting potential and limits of

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

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 4 DVar assimilation

Impact of NWP model resolution: 10 km vs 2. 5 km, fields of low-level wind (blue) and T (red) on the model grids (different wind scaling in each figure)

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

Arome-2. 5 km 9 h-range fog dissipation forecast Meteosat visible image

The AROME data assimilation ● ● ● ● derived from ECMWF 4 D-Var, plus mesoscale features 3 DVar 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) 1 D cloud bogussing, starting with nowcasts of convective clouds (ISIS/RDT software) (planned) Direct multivariate assimilation of radial Doppler winds from radars, and 1 D radar precipitation bogussing

Impact study : Dyn. Adapt. Precipitation forecast Raingauges 2004/07/18 12 UTC RR P 12 – P 6 3 DVar with SEVIRI

Objective score impact of 10 km assimilation vs. range (rmse and bias) (pink=ARPEGE 4 DVar dynamical adpation, blue=ALADIN LAM 3 DVar) RR RH 2 m

AROME real-time forecast on 21 June 2005

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

AROME status & plans ● ● ● 2. 5 km forecast model runs daily since May 2005 on 500 km 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 10 km resolution so far with very positive impact on 0 -12 h forecast ranges wrt. dynamical adaptation main target: 6 -hourly 36 -h NWP forecasts over France (1000 kmx 1000 km) 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

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

Usable observations for convective systems assimilation

Concepts of balance & control ● 3 DVar 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, 3 D convective & frontal structures) Two competing strategies at mesoscale: – « automatic » balance estimation: 4 DVar and (ensemble)KF – « ad hoc » spatialization: object bogussing from image processing

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 ?