AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, 2003 1 Data assimilation and variational algorithms.

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Presentation transcript:

AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, Data assimilation and variational algorithms in ALADIN Claude Fischer and Gergely Bölöni

AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, Science, publications, people 2. Assimilation cycles 3. Observations 4. Pre-operations 5. Projections into the future

AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, Science and publications Berre, Monthly Weather Review, 2000 Sadiki et al., Monthly Weather Review, 2000 Široka et al., Meteorology and Atmospheric Physics, 2003 Soci et al., Idöjàràs, 2003 Deckmyn, submitted to Applied and Computational Harmonic Analysis Other projected papers: Soci, Sadiki, Guidard, Bölöni, Stefanescu

AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, People (manpower) Algorithms (TL/AD models, minimisation): 11 / 1.5 Error statistics (« B » matrix): 9 / 1.5 Observations, « Jo »: 8 / 3 Surface analysis: 10 / 2 Total: 30 / 7 (French not counted) – Users / Experts Important addendum: these figures are in no comparison with the full requirements for building a data assimilating and forecasting system from scratch (eg: ARPEGE/IFS estimated 150 men.year for the French side only …) Consequence: just as ALADIN, the AROME development will remain bound to the algorithmic compatibility with the ARPEGE/IFS backbone => regular phasing and mutual benefits

AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, BlendVar: a mixed “empirical/statistical” assimilation cycle Blending: digital filter blend for 3D fields + linear combination of Surface fields, in order to combine Arpège analyzed large scales with Aladin forecast small scales Aladin 3D-VAR analysis: J = Jb + Jo Several Jb formulations exist: Standard Jb (large scale) Lagged Jb (mesoscale) LBC0LBC1 Arp Analysis Ald 6h fct

AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, MAP/IOP14 case study using BlendVar assimilation cycles (Vincent Guidard) 24h BlendVar cycle RR between 12h and 18h forecast rangeSSMI Microwave sounder

AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, Use of new sources of observations denser use of aircraft data analysis of screen-level relative humidity 10 m wind use of pseudo-profiles of relative humidity (MeteoSat imagery) raw radiances radar data

AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, ALADIN 3d-Var in Hungary Implementation (2000) Experiments (2000-): single or double nested assimilation cycles and impact of coupling (Alatnet), error statistics Quasi-operational application (Dec 2002): 6 hour assimilation cycle (4 analyses/day) background: 6 hour forecast (LBC: ARPEGE analysis) observations: TEMP and SYNOP production at 0 and 12 UTC Future plans: Use of new observations (satellite, aircraft data) Blending 3d-Fgat Operational application

AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, ALADIN 3d-var in Morocco 360 points 180 points BlendVar assimilation cycle plus raw radiances

AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, Research versions Toulouse: up-to-date reference version for 3d-var, already used by an increasing local community (PhD, AROME prototype) Prague: local experimentations (impact of surface observations, first successful implementation of « lagged Jb »), operational Blending cycle (« assimilation without data »)

AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, Projections into the future 1.Further encourage work on data at high resolution or high frequency 2.The three pillars of variational assimilation: scientific goals, local 3D-VAR consolidation, maintenance of the TL/AD/Obs code 3.Any new home installation requires: ODB, local feeding and monitoring of an obs data base, installation of screening and minimization, Jb computation, scripts and first experimentations -> much more demanding than the installation of the forecast model alone 4.3D-FGAT, 4D-VAR in a nutshell, more sophisticated « Jb » 5.interactions with the AROME project