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Case A radar 0 100 km compare 10 km Case A forecast 1 2 4 8 16 32 1 2 4 8 16 32 Figure 3 Case A. Rainfall accumulations (mm) over the period 14 UTC to 20 UTC 3rd August 2004 from radar (left) and a 1-km-model forecast (right). The dashed blue lines enclose the highest 5% of radar accumulations (>~20mm). Case B radar compare Case B forecast 0 100 km 0 188 km 1 2 4 8 16 32 1 2 4 8 16 32 Figure 4 Case B. Rainfall accumulations (mm) over the period 14 UTC to 20 UTC 27th April 2004 from radar (left) and a 1-km-model forecast (right). The dashed blue lines enclose the highest 5% of radar accumulations (>~7mm). 228
1.0 95th percentile threshold Fractions skill score (FSS) 0.8 0.6 0.4 0.2 10 km Case A 86 km Case B uniform 188 km random 0.0 0 50 100 150 200 250 Horizontal scale (length of square) (km) Figure 5 Graph of Fractions Skill Score (FSS) against spatial scale for Case A (good forecast) and Case B (poor forecast) for the highest 5% of rainfall accumulations within the domain (95th percentile value). The FSS can have values in the range 0 (zero skill) to 1 (perfect skill). The two dashed lines show (1) the skill of a completely random forecast covering 5% of the domain (‘random’), and (2) the skill of a forecast with a value of 0.05 (5% chance) everywhere (‘uniform’). The ‘uniform’ forecast provides a target level of satisfactory skill. Figure 6 Area-averaged rainfall rate plotted as a function of time with respect to analysis time for all forecasts run from the summer 2005 cases. In order to make a fair comparison, all cases the area averaging for all models and radar was carried out over the area covered by the 1km model domain. The black line shows the averaged radar rainfall rate and the colours show the models. Solid lines show the runs including assimilation (these lines extend back to T-2 where each run starts) and the dotted lines are the forecasts run from a T+1 12km analysis. Figure 7 Fraction Skill scores for hourly accumulations plotted as a function of forecast length for a fixed sampling radius of 52km and a relative threshold of the 90th percentile. The plotted points are shown on the time axis half way at the midpoint of the hour. The solid lines show the scores for the three models aggregated over cases from the summers of 2004 and 2005. The solid lines show the scores for the three models including assimilation. The dotted lines show equivalent curves from 4km and 1km models spinning up each forecast from a 12km analysis 229
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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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2. LM performance known, critically
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expected to result in an improved r
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Schrodin, R. and H. Heise, 2002: Th
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HIRLAM-A activities in 2006 Jeanett
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addition, research on the construct
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HIRLAM Reference System development
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Fig. 2: Case study for 10 June 2006
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Unified Model Developments 2006 Met
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Figure 4: Noise in increments from
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Cycle Date Model change and impact
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1.5km On-demand model Figure 8: 1.5
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3.1 Belgium 59
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• A prognostic mass-flux scheme f
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Perspectives The integrated package
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3.2 Croatia 65
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New computer Core of the new comput
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3.3 Czech Republic 69
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LAM efforts at Estonian Meteorologi
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grid is 186x170 points in horizonta
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McDonald, A., and J. E. Haugen, 199
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Operational NWP Activities at the F
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Model Forecast model Limited area g
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3.7 France 87
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Fig.1. The ALADIN-France domain, wi
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Towards AROME, A first try of a Rap
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Status Report of the Deutscher Wett
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In August 2006 production with the
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3.9 Hungary 97
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• LBC coupling at every 3 hours O
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IFS as driving model for ALADIN. Th
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3.10 Ireland 103
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Filtering: Fourth order implicit ho
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The next chart shows wind arrows at
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Italian Meteorological Service Stat
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Fig.3 Integration domain for LM- EU
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3.12 Netherlands 113
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esults with fixed entrainment and d
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simulation. In order to allow a goo
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layer for three contrasting nights
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21 UTC). The severe thunderstorm fo
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Limited Area Modelling Activities a
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(a) Figure 2 ALADIN/Portugal geogra
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een locally implemented as an optim
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LAM ACTIVITIES IN ROMANIA Cornel SO
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Fig.3 HRM domain, 78 cumulated prec
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- Experiments : variation of the fr
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NWP activities at Slovak Hydrometeo
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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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4.1 F. Vana: Dynamics & Coupling, A
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Page 181 and 182: 5 Scientific presentations on data
Page 183 and 184: COSMO: Overview and Strategy on Dat
Page 185 and 186: Meteorology in Hamburg (MPI-M). The
Page 187 and 188: temperature forecasts, this had a s
Page 189 and 190: 5.2 D. Leuenberger et al.: The Late
Page 191 and 192: scaling factor α and of the latent
Page 193 and 194: a) b) RAD 1 2 3 4 5 6 7 8 9 10 det
Page 195 and 196: 5.3 C. Fischer and A. Horanyi: Alad
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Page 201 and 202: In parallel, a direct radar observa
Page 203 and 204: ►Algorithms: • FGAT (with Hunga
Page 205 and 206: Data assimilation activities in LAC
Page 207 and 208: Fig.3 RMSE difference (WDEF-NAM2).
Page 209 and 210: 6 Scientific presentations on physi
Page 211 and 212: Physics improvements in the NAE mod
Page 213 and 214: The top panel in figure 3 shows tha
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Page 217 and 218: Relaxation term Due to these proble
Page 219 and 220: Figure 12: SOx emissions in 1990 an
Page 221 and 222: Buncefield oil depot fire Figure 15
Page 223 and 224: 6.2 P. Clark et al.: The Convective
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Page 227 and 228: the Met Office Large Eddy Model (LE
Page 229: Figure 4 Domains used in high resol
Page 233 and 234: Figure 10 Surface precipitation rat
Page 235 and 236: HIRLAM Physics developments Sander
Page 237 and 238: strong winds. This causes a too lar
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Page 255 and 256: 2.1 Pseudo prognostic turbulent kin
Page 257 and 258: TKE and the integral buoyancy of th
Page 259 and 260: 7 Scientific presentations on predi
Page 261 and 262: LAMEPS Development of ALADIN-LACE Y
Page 263 and 264: The more iterations performed, the
Page 265 and 266: uncertainty in the model physics, t
Page 267 and 268: In Budapest, dynamical downscaling
Page 269 and 270: 7.2 J. García-Moya et al.: Recent
Page 271 and 272: Currently the size of the super ens
Page 273 and 274: Palmer, T.N., Alessandri, A., Ander
Page 275 and 276: Figure 4 Example of probability map
Page 277 and 278: More than 15 mm/6 hours Figure 6 Re
Page 279 and 280: 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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