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. CaumontMETEO-FRANCE

6th COPS Workshop

27-29 February 2008

University of Hohnenheim, Stuttgart

Outlines

The AROME projet

The AROME data assimilation system

Daily runs during the COPS/MAP-DPHASE campaigns

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

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...

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

Analysis

U, V, T, q and Ps

U, V, T, q and Ps

TKE, NH and microphysics fields

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 data data data data …

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.

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.

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…)

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.

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

COPS IOP 8b, 15 July 2007 RainSnowGraupelice crystalscloud droplets

Vertical cross section

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

OUTLOOK : Radar measurements assimilation

3DVarArome

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)

REFLECTIVITIES CONTROL DOPPLER WINDS

Precipitations are better located on the 3-hour forecast from the analysis with reflectivities…

RAINGAUGES

OUTLOOK : Radar reflectivities assimilation

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.

Thank you for your attention…