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Status of operational and research activities in France: Aladin-France and Arome C. Fischer About ALADIN-FRANCE The French domain can be seen in Figure 1. The centre of the domain is located at 46.47°N; 2.58°E. Computations are performed in spectral bi-Fourier space with elliptic truncation at wave number 149. The equivalent grid has 9.51 Km gridmesh. The vertical dimension is discretized in 46 levels (+ a surface) During a forecast, ALADIN-FRANCE is coupled to its coupling model (ARPEGE) every 3 hours. The timestep is 415.385 s Operationally, 4 runs are performed each day at 00, 06 12 ant 18 UTC. Forecasts terms are 54H for the 00H forecast, 48H at 06H, at 42H at 12 and 36H at 18H. The operational Data assimilation The assimilation scheme is 3D-Var with a 6H window. A continuous “long cut-off” cycle provides the guess for a “Short cut-off” production which provides the operationally used analysis. Assimilated observations are –Surface pressure and SHIP winds –2m temperature and RH –Aircraft data –SATOB motion winds –Drifting buoys –Soundings (TEMP, PILOT) –Satellite radiances: AMSU-A, AMSU-B, HIRS, Meteosat-8 SEVIRI –QuikSCAT winds –Ground-based GPS zenithal delays A refinement of the analysis scheme: The Jk term The variational scheme used by ALADIN 3D-Var allows for the introduction of supplementary penalisation terms in the cost function. In order to keep advantage from the ARPEGE analysis of large atmospheric scales, a new term is introduced: the Jk term. It penalises departures of the ALADIN analysis from ARPEGE for wave numbers ranging from 1 to 12. The idea is close to the « blending » procedure, which has been shown to improve the structure of precipitation fields. So far, slightly positive improvements in term of forecast error for temperature, brought by this new term, have been demonstrated. The effect is located in the upper stratosphere, showing that this analysis takes profit of ARPEGE’s better representation of this region of the atmosphere. The extra term has however not been included in our operations for the time being. 88
Fig.1. The ALADIN-France domain, with the orography. An ALADIN based nowcasting tool : VARPACK Varpack diagnostic analysis is a diagnostic tool that provides to forecasters every hour an analysis of atmospheric fields as precise as possible for immediate forecasting purposes. Most currently used output fields are 2m humidity and temperature, 10m winds, CAPE and MOCON (Moisture Convergence). Two more levels are used close to the ground: 10m and 2m levels in order to have an analysis independent of surface fields. A new B matrix was designed to fit the new geometry, statistics for the two new levels are deduced from statistics of the last level from the usual B matrices. All the observations available for ALADIN-3dvar plus 10m wind observations are currently used. When tested on some heavy precipitation events over France, the Varpack outputs have allowed to foresee by about two hours in advance the areas for major rain outbreaks. Most noticeably, fields of predicted CAPE can be compared with radar echoes. The September/October/December E-suite What is in the E-suite ? The most remarkable items: ● Observations: independent extractions for Aladin; de-biasing of MSLP; ● Assimilation methods: Sigma_b maps of the day; W and NL balance in Aladin Jb ● Physics: envelope orography halved, 6 bands in the SW Morcrette radiation; and in the APCS: ■ pseudo-statistical sedimentation 89
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NEWSLETTER of the 28 th EWGLAM and
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Newsletter of the 28th EWGLAM and 1
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6.2 P. Clark et al.: The Convective
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1.1 Foreword In 2006, MeteoSwiss ha
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Cornel Soci National Meteorological
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16:15 - 16:45 Filip Vana general Dy
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2 Consortia and ECMWF reports 12
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Research Progress at ECMWF 2005-200
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ocean data assimilation system for
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Physical Aspects 1) The introductio
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either with a 1-dimensional observa
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2.2 ALADIN 22
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Another important event happened, i
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• However inspection of T2m forec
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Figure 4: One case of intercomparis
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The COSMO Consortium in 2005-2006 T
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2. Model system overview The key fe
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Data Assimilation for LM Method: Nu
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Page 51 and 52: Fig. 2: Case study for 10 June 2006
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Page 63 and 64: • A prognostic mass-flux scheme f
Page 65 and 66: Perspectives The integrated package
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Page 69 and 70: New computer Core of the new comput
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Page 79 and 80: LAM efforts at Estonian Meteorologi
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Page 93 and 94: Towards AROME, A first try of a Rap
Page 95 and 96: Status Report of the Deutscher Wett
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Page 107 and 108: Filtering: Fourth order implicit ho
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Page 131 and 132: LAM ACTIVITIES IN ROMANIA Cornel SO
Page 133 and 134: Fig.3 HRM domain, 78 cumulated prec
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Page 137 and 138: NWP activities at Slovak Hydrometeo
Page 139 and 140: Operational suite upgrades • 23/0
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3.16 Slovenia 139
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First experiments with new physical
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• resolution of meteorological fi
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NWP activities in INM Bartolomé Or
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1 System definition • Creation of
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MSLP June-August 2006 Geopotential
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NATIONAL STATUS REPORT on Operation
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Model input The observations used f
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Numerical Weather Prediction at Met
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The setup is based on a new numeric
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weight of importance in the voting
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COSMO: Overview and Strategy on Dat
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Meteorology in Hamburg (MPI-M). The
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temperature forecasts, this had a s
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5.2 D. Leuenberger et al.: The Late
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scaling factor α and of the latent
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a) b) RAD 1 2 3 4 5 6 7 8 9 10 det
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5.3 C. Fischer and A. Horanyi: Alad
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Figure 1: analysis increments of wi
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3) Averaged emissivities + dynamica
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In parallel, a direct radar observa
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►Algorithms: • FGAT (with Hunga
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Data assimilation activities in LAC
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Fig.3 RMSE difference (WDEF-NAM2).
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6 Scientific presentations on physi
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Physics improvements in the NAE mod
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The top panel in figure 3 shows tha
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The Murk aerosol is based on the ur
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Relaxation term Due to these proble
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Figure 12: SOx emissions in 1990 an
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Buncefield oil depot fire Figure 15
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6.2 P. Clark et al.: The Convective
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y the model (e.g. streaks of gradua
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the Met Office Large Eddy Model (LE
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Figure 4 Domains used in high resol
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1.0 95th percentile threshold Fract
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Figure 10 Surface precipitation rat
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HIRLAM Physics developments Sander
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strong winds. This causes a too lar
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2.1 Pseudo prognostic turbulent kin
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TKE and the integral buoyancy of th
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7 Scientific presentations on predi
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LAMEPS Development of ALADIN-LACE Y
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The more iterations performed, the
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uncertainty in the model physics, t
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In Budapest, dynamical downscaling
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7.2 J. García-Moya et al.: Recent
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Currently the size of the super ens
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Palmer, T.N., Alessandri, A., Ander
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Figure 4 Example of probability map
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More than 15 mm/6 hours Figure 6 Re
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7.3 C. Marsigli et al.: The COSMO S
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Overview COSMO ensemble systems Pie
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COSMO-LEPS 16-MEMBER EPS An objecti
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The used models are LAMI (Italy), a
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28th EWGLAM Meeting 9-11 October 20
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9 SRNWP business meeting report 293
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Minutes of the Meeting Participatio
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Training and Education Activities i
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These reports were very divers, fro
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Contacts with the Academia In his d
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