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72 Will, A., M. Baldauf, B. Rockel, A. Seifert, “Physics and Dynamics of the COSMO-CLM”, Meteorol. Z., submitted Figure 3. Monthly mean of the diurnal cycle of the 2m temperature in July 2007 as simulated with CCLM for the gridpoint next to Obervellach (808m ASL) in the Hohe Tauern region. Solid: 10 km resolution; dashed: 3 km; dash-dotted: 1 km; dotted: observation. The height difference between the four members is taken into account using a constant lapse rate of 6.5K per km. overestimated also in the 1 km domain. The diurnal cycle of precipitation shows that at the 10 km resolution there are two maxima of precipitation, one in the morning around 8 UTC, one in the late evening around 22 UTC. The two other resolutions have the first maximum two to three hours later while the second maximum occurs one hour earlier. The intensity is captured well at all resolutions. 4. Conclusions so far The preliminary results show that increased model resolution does not necessarily mitigate model biases (after altitude correction) but yield more realistic spatial patterns and partially improve the diurnal cycle. Further, more systematic evaluation results will be presented.. Acknowledgements The authors would like to thank the University Information Service of the University of Graz for the provision of the computing infrastructure, the Central Institute for Meteorology and Geodynamics and the avalanche warning services of Tyrol, Salzburg and Carinthia for providing observation data. This study is part of the project NHCM-1 (P19619-N10) funded by the Austrian Science Fund (FWF). References Kirchengast, G., T. Kabas, S. Binder, A. Leuprecht, C. Bichler, “Pionierexperiment WegenerNet Klimastationsnetz: Ein neuartiges Messnetz in der Region Feldbach (Steiermark/Österreich) zur Beobachtung von Wetter und Klima mit sehr hoher Auflösung”, Wegener Center Verlag Graz, Wiss. Ber. Nr. 23-2008, 2008 Suklitsch, M., A. Gobiet, A. Leuprecht and C. Frei, “High Resolution Sensitivity Studies with the Regional Climate Model CCLM in the Alpine Region”, Meteorol. Z., 17, 4, pp. 467-476, 2008. Uppala, S. M., P. W. Kållberg, A. J. Simmons, U. Andrae, V. da Costa Bechtold, M. Fiorino, J. K. Gibson, J. Haseler et al, “The ERA-40 re-analysis”, Quart. J. Roy. Meteor. Soc., 131, 2961-3012, 2005
73 A general approach for smoothing on a variable grid Dorina Surcel and René Laprise Canadian Network for Regional Climate Modelling and Diagnostics, Centre ESCER, Université du Québec à Montréal, 550 Sherbrooke St. West, 19 th Floor, Montréal (Québec) Canada; colan@sca.uqam.ca 1. Introduction Regional climate modelling using a variable-resolution global approach are an alternative to the widely used nested limited-area models. A variable-resolution GCM do not require lateral boundary conditions and it provides selfconsistent interactions between global and regional scales of motion (Laprise 2006, Fox-Rabinovitz et al. 2006). The concentration of the resolution over a subset of the Earth’s surface increases computational efficiency, but this does not come free of some problems owing to the variation of resolution. The non-uniformity and anisotropy of the computational grid can result in contamination of the solution, as is the case near the poles with latitude-longitude grids. Variable resolution can be achieved in different ways, but the stretched-grid (SG) approach will be in our attention for the purpose of this paper. The technique of grid stretching is one of the most extensively used methods for including the variable resolution in climate models. The resolution of the latitude-longitude grid is smoothly and gradually decreased outside a uniform fine-resolution area that constitutes the region of interest. The implementation of the SG approach for climate simulations requires addressing some problems arising from grid irregularities such as computational dispersion or the pole problem (Fox-Rabinovitz et al. 2008). Even when appropriate stretching constraints are applied for the mesh design, the anisotropy of the grid outside the uniform highresolution area can result in aliasing of short scales into large scales. One way to avoid this problem is to remove the scales that are improperly represented in the stretching areas to obtain a clean smoothly varying representation on the entire mesh. A general convolution filter has been developed and successfully tested in one- and two-dimensional Cartesian geometry. The filter has the property of using a limited spatial stencil for the convolution to reduce computational cost. The filter has also been adapted for polar geometry and tested for different test-functions, first to control the pole problem specific of the latitude-longitude models and second to effectively remove the anisotropy and noise outside the high-resolution area of a polar stretched grid. This filter can be applied for scalars and vectors with appropriate definition constraints. 2. Theoretical approach Variable-resolution stretched grids are designed to have a uniform fine resolution over the area of interest; outside this area the grid intervals are increasing in both horizontal directions, usually as a geometric progression with a constant local stretching factor. To control the undesirable computational problems due to the anisotropy of the grid, a numerical filtering operator is built to remove the unnecessary small-scale signal outside the high-resolution area. The convolution operator was chosen to design the filtering formula. For a signalψ , the filtered value ψ will be: ψ (x)= (ψ ∗ w)(x) = ∞ ∫ −∞ ψ(s)⋅ w(x − s)ds We have demonstrate (Surcel 2005) that the weighting function « w » needed for the convolution is the inverse Fourier transform of the response function. It is necessary to establish the properties of the filter; we here impose that the filter will remove all the scales that are not correct represented outside the high-resolution region. The response is defined as follows: ⎧1 0 ≤ k ≤ a = π ΔX ⎪ 2 π ⎛ k − a ⎞ , R( k ) = ⎨cos ⎜ ⎟ a < k < b ⎪ 2 ⎝ b − a ⎠ ⎪⎩ 0 b ≤ k ≤ π Δx where ΔX is the maximum grid-point distance and Δx is the minimum grid-point distance in the high-resolution area. We find then: π sinax + sinbx 1 w( x ) = ⋅ ⋅ . 2 x π 2 − x 2 ( b − a ) 2 This function is dependent on the wavenumbers a et b. The parameter a is well determined, a = π ΔX ; the parameter b is chosen such as to minimise the Gibbs phenomenon. 3. Adapting the filter for 2D geometry The formal approach developed in 1D was generalized for the two-dimensional domain. The two-dimensional convolution uses a weighting function that is the product of two one-dimensional functions, similar with those used in the first case. The filtered function is: ψ = = ( x, y) ∞ ∞ = ∞ ∞ ∫∫ −∞ −∞ ∫∫ψ ( s,t )w −∞ −∞ ( ψ ∗ wx ) ∗ wy ψ ( s,t x )w( x − s, y − t )dsdt ( x − s )w ( y − t )dsdt y For a polar grid, the filter formulation is obtained by the application of the convolution in radial and azimuthally directions, so: λ ⎛ ψ ( r, λ)= ⎜ () ψ r ⎞ ⎟ r, λ ⎝ ⎠ ( )= ( ψ ∗ w(d r ))∗ w(d λ ) ( ) 12 ⎧ r = x 2 + y 2 where : ⎪ ⎨ , ⎛ λ = tan −1 y⎞ 0 ≤ r
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2 nd International Lund RCM Worksho
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Associated Organisations ENSEMBLES
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Preface This Second Lund Regional-s
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II High-resolution dynamical downsc
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IV Investigation of added value at
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VI Soil organic layer: Implications
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VIII Session 4: Regional Observatio
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X Predicting water resources in Wes
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XII The impact of atmospheric therm
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XIV Observation and simulation with
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XVI Favot F .......................
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XVIII Ruoho-Airola T...............
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2 5. Influence of SN on the Ensembl
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4 (+/- 5 days) were used to increas
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6 Dynamical downscaling: Assessment
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8 Improvement of long-term integrat
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10 air temperature annual cycle (Fi
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12 Sensitivity study of CRCM-simula
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14 Regional precipitation anomalies
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16 Assessment of precipitation as s
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18 The Asian summer monsoon in ERA4
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20 Added value of limited area mode
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123 Temperature and precipitation s
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125 4. Preliminary results for down
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127 a) Knutson, T.R., J.J. Sirutis,
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129 Effects of variations in climat
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131 3.2. Precipitation The ensemble
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133 knowledge is essential to asses
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135 pronounced biases in the simula
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137 variability was concentrated at
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139 Investigation of ‘Hurricane K
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141 Evaluation of seasonal forecast
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143 NASH J. E., SUTCLIFFE J. V., 19
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145 Climate change assessment over
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147 For relative operating characte
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149 responsible for the excessive s
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151 and 30km). Domains D1 (90 and 6
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153 can be seen in Fig. 2, where th
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155 High-resolution simulation of a
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157 MesoClim - A mesoscale alpine c
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159 Observed and modeled extremes i
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161 analyzed the surface wind behav
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163 summer particularly in the Paci
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165 since 01.01.2004. For the Meteo
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167 Wind, m/s 12 10 8 6 4 2 Vilsand
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169 heterogeneity. For example, lar
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171 Analysis of surface air tempera
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173 Environmental database and the
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Climate change and water pollution
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177 During summer, winds over the M
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179 followed nearly the correct pat
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181 Verification of simulated near
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183 The North American Regional Cli
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185 An atmosphere-ocean regional cl
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187 different rainfall characterist
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189 with increasing temperature. Mo
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191 Inter-comparison of Asian monso
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193 On the validation of RCMs in te
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195 AMMA-Model Intercomparison Proj
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197 parameterization are used in th
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199 Evaluation of European snow cov
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201 Projections of eastern Mediterr
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203 Atmospheric river induced heavy
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205 CLARIS project: Towards climate
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207 Influence of soil moisture init
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209 Projection of the Changes in th
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211 Regional climate model studies
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213 ENSEMBLES regional climate mode
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215 Assessing the performance of se
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217 Applying the Rossby Centre Regi
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219 Interactions between European s
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221 ALADIN-Climate/CZ simulation of
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223 experiment of recreating the pr
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225 Figure 2. the 10-year averaged
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227 Use of regional climate models
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229 Wave estimations using winds fr
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231 Figure 1. Model domain for the
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233 A coupled regional climate mode
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235 Arctic regional coupled downsca
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237 Convection-resolving regional c
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239 4. Outlook Currently a coupled
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241 distribution within the Netherl
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243 Very high-resolution regional c
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245 Impact of convective parameteri
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247 Development of a regional ocean
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249 Regional climate change impact
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251 River runoff projection of futu
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253 Application of regional-scale c
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255 Local air mass dependence of ex
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257 Impact of vegetation on the sim
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259 Climate change impacts on the w
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261 Influence of soil moisture-near
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263 Forest damage in a changing cli
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265 Future challenges for regional
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267 Linking climate factors and ada
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269 GCMs. In the end of the 21 st c
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271 Climate change impacts on extre
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273 Winter storms with high loss po
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275 The impact of land use change a
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277 6. Analysis of Results (Rain) T
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279 (a) (b) Figure 3. Impact of veg
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281 that cold (Fig. 5). Winter temp
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283 Streamflow in the upper Mississ
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285 Dynamical downscaling of urban
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287 Souma, K., K. Tanaka, E. Nakaki
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289 In April 2000, the fire emissio
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291 Impacts of vegetation on global
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International BALTEX Secretariat Pu
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No. 34: BALTEX Phase II 2003 - 2012
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