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244 Time invariant input parameter processing for applications in the COSMO-CLM Model Gerhard Smiatek 1 , Goran Georgievski 2 and Hermann Asensio 3 1 Institute for Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Forschungszentrum Karlsruhe GmbH (gerhard.smiatek@imk.fzk.de 2 BTU Cottbus, Chair of Environmental Meteorology (goran.georgievski@tu-cottbus.de) 3 Deutscher Wetterdienst, (hermann.asensio@dwd.de) 1. Introduction Topography, soil characteristics, land use and land cover as well as associated vegetation parameters are key information in Soil-vegetation-Atmosphere-Transfer (SVAT) schemes widely applied in atmospheric models in parameterization of surface exchange processes. With exception of seasonal changes in the leaf area index (LAI), vegetation fraction or roughness length, the geodata input is usually kept constant in a simulation and can therefore be considered as a time-invariant parameter.. 2. Data Processing The quality of the data input can have a significant influence on the simulation results. Block (2007) has shown changes of annual mean values in the order of 0.25 K for the 2m-temperature related to the source of the leaf area index data, vegetation cover, soil data, and derived parameters. Box and Rinke (2003) identified systematic model biases of GTOPO30 elevation data set over Greenland. In 50 km grid cells employed by the HIRHAM regional climate model the errors ranged up to −840 meters. Errors result not only from the quality of the globally available data sets on topography, soil characteristics and land use but also from way how these data are processed. Investigations with various compilers and compiler options revealed differences up to 20 meters in the average elevation in some model grid cells covering complex terrain. Slightly reduced precision in the domain coordinates might yield even higher differences (see Figure 1). The paper presents in broad term the time invariant data preprocessor PEP (Smiatek et. al., 2008) used in the COSMO-CLM (COnsortium for Small-scale MOdeling in CLimate Mode). The preprocessor is used to transform various global geospatial data into geometrical resolution and into rotated coordinates system specified by the model user. Especially newly available data, such as the Harmonized World Soil Database at 30 arc-second resolution, or Global Database of Land Surface Parameters (ECOCLIMAP) (Masson et al., 2003) will be presented. Furthermore, issues resulting from the data processing and data aggregation and interpolation into the rotated coordinates system will be discussed. In the third part the paper concentrates upon the question how the errors resulting from the time invariant data preprocessing propagate in a long term COSMO-CLM simulation driven with ECHAM 5 boundary forcings. Figure 1 Differences in elevation in meters in a COSMO-CLM domain resulting from reduced precision in calculation of the domain coordinates References Block, A. Uncertainties of surface and soil parameters and their impacts on regional climate simulations (in german), Ph.D. thesis, BTU Cottbus, Fakultät für Umweltwissenschaften, 2007 Box J.E., and A.Rinke, Evaluation of Greenland ice sheet surface climate in the {HIRHAM} regional climate model using automatic weather station data., J. Climate, Vol. 16,1 302–--1319, 2003 Masson V., J.-L. Champeaux, F. Chauvin, C. Meriguet, and R. Lacaze, A global database of land surface parameters at 1-km resolution in meteorological and climate models, Journal of Climate, Vol. 16, 1261- 1282, 2003 Smiatek, G., Rockel, B. and U. Schättler, Time invariant data preprocessor for the climate version of the COSMO model (COSMO-CLM), Meteorologische Zeitschrift, Vol 17., No. 4, pp. 395-405, 2008
245 Impact of convective parameterization on high ressolution precipitation climatology using WRF for Indian summer monsoon Sourav Taraphdar, P. Mukhopadhyay, B. N. Goswami and K. Krishnakumar Indian Institute of Tropical Meteorology, Pune – 411008, India. sourav@tropmet.res.in 1. Introduction The Indian monsoon rainfall shows significant variability in spatial and temporal scale. The temporal variability causes intraseasonal, interannual and multi-decadal scale oscillations and spatial variability causes the heterogeneous precipitation distribution from north to south and east to west. These variabilities are caused by two types of forcing namely slowly varying large scale surface boundary conditions e. g. sea surface temperature (SST), snow cover etc. and the regional forcing from topography e. g. Himalayas and Western Ghats etc. Thus resolving the regional heterogeneity is one of the key to improve the precipitation distribution over the region (Giorgi and Mearns 1991). It is also reported that higher resolution AGCM helps in improving the monsoon simulation, however very high resolution AGCM requires large computational resources. Thus the computationally less expensive strategy of running regional models embedded in AGCM gain popularity in simulating regional climate at higher resolution since mid 90s. To evaluate the sensitivity of convective parameterization schemes on monsoon simulation, several attempts were made. Ratnam and Kumar (2005) simulated two contrasting years of monsoon 1987 and 1988 by a mesoscale model (MM5) at 45 km resolution with variety of cumulus parameterization schemes. They found Betts-Miller-Janjic (BMJ) and Kain-Fritsch (KF) has certain merits compared to Grell. At this resolution, large scale monsoonal features can be captured but to resolve the physiographical heterogeneity due to topography, ocean etc. of the region, higher resolution regional climate models (RCM) are required (Im et al. 2006). Thus, it remains to be seen whether the mean monsoon rainfall bias improves with a high resolution (Less than 20 km) mesoscale model. Therefore, the first objective of the present paper is to produce robust daily monsoon climatology over Indian region at high resolution by various cumulus parameterization schemes and its validation with available observed rainfall data. Along with this attempt will also be made to identify the models ability to correctly capture the underlying spatio-temporal variability of precipitation. The detail investigation of this kind only can help to decipher the weakness in the formulation of moist convection which can be improved further. To gain sufficient insight for further development that would reduce the uncertainties in the simulations, the third goal of the paper is to investigate and identify the possible sources of deviations in simulation arising from different convective closures. 2. Model, data used and experimental design The non-hydrostatic, fully compressible with a terrain following sigma mass co-ordinate mesoscale model WRF- ARW version 2.2 developed by National Center for Atmospheric Research (NCAR) has been used for the present study. The model is used with two nested domains with horizontal resolutions of 45 and 15 km and 31 sigma levels with model top at 10 hPa. The model mother domain covers the large scale Indian monsoon region where the nested domain focuses mainly on the Indian land masses. The physical parameterizations schemes used in the model is with Lin microphysics, Monin-Obukhov similarity scheme for surface layer, Yonsei university scheme for PBL, RRTM scheme for long wave and Dudhia scheme for short wave in all the numerical experiments. The experiments are differing only by three convective parameterization schemes namely KF (Kain and Fritsch 1993), BMJ (Betts-Miller (1986); Janjic 1994) and Grell- Devenyi (GD; Grell and Devenyi 2002). In this study, the mother domain simulations are driven by the National Center for Environmental Prediction (NCEP)/ NCAR reanalysis data at a resolution of 2.5 o . The 6-hourly SST was obtained by linearly interpolating the daily SSTs of RTG and used as the slowly varying lower boundary condition for the model. The model simulation spans from 1 May to 31 October for the year 2001 to 2007. The daily precipitation simulated by the model is compared with the daily gridded rainfall data of India Meteorological Department (IMD) at 1 o x1 o resolutions for the land areas and for the land-ocean area the GPCP and TRMM rainfall are used. 3. Results and Discussion a. The monsoon circulation pattern It is important to examine whether the driving field in the mother domain are adequate for the nested domain particularly in connection with synoptic scale climatic features of Indian summer monsoon. So the JJAS mean wind (2001-2007) at 850 hPa for each of the three schemes and from NCEP/NCAR (NNRP) reanalysis are showed in Fig. 1. The large scale southwesterly flow over Arabian Sea and Bay of Bengal (BOB) and a cyclonic vorticity in the north of BOB are reasonably captured by all the three cumulus schemes (Figs. 1b-d) as compared to the NNRP (Fig. 1a). Figure 1. JJAS averaged mean 850 hPa wind (m s -1 ) from (a) NNRP, (b) BMJ, (c) KF and (d) GD for the year 2001-2007. However the wind field by KF (Fig. 1c) over the Arabian Sea and BOB appears to be stronger than the observation (Fig. 1a). The BMJ is found to have (Fig. 1b) simulated the most realistic wind field in both the oceanic basin where as the GD (Fig. 1d) has produced a weaker wind field over BOB although the Arabian Sea branch is reasonably reproduced. The upper air (200 hpa) easterly and the Tibetan anticyclone are captured by all the three experiments with BMJ, KF and GD (Figures not shown) but with varied intensity. The center of the anticyclone is
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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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2 5. Influence of SN on the Ensembl
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6 Dynamical downscaling: Assessment
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8 Improvement of long-term integrat
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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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28 technique, as it is based on the
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30 4. Heating mechanism In order to
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50 References Biau. G., E. Zorita,
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72 Will, A., M. Baldauf, B. Rockel,
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91 Examining the relative roles con
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95 A parameterization of aircraft i
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107 Simulation of the precipitation
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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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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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165 since 01.01.2004. For the Meteo
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169 heterogeneity. For example, lar
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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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181 Verification of simulated near
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183 The North American Regional Cli
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191 Inter-comparison of Asian monso
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Page 233 and 234: 207 Influence of soil moisture init
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Page 241 and 242: 215 Assessing the performance of se
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Page 279 and 280: 253 Application of regional-scale c
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Page 291 and 292: 265 Future challenges for regional
Page 293 and 294: 267 Linking climate factors and ada
Page 295 and 296: 269 GCMs. In the end of the 21 st c
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Page 299 and 300: 273 Winter storms with high loss po
Page 301 and 302: 275 The impact of land use change a
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Page 307 and 308: 281 that cold (Fig. 5). Winter temp
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Page 311 and 312: 285 Dynamical downscaling of urban
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International BALTEX Secretariat Pu
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No. 34: BALTEX Phase II 2003 - 2012
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