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Vinodkumar, A. Chandrasekar, K. Alapaty, and Dev Niyogi

intercomparison. Bull. Amer. Meteor. Soc. , 79 , 265 – 283 . Das Gupta , M. , S. Basu , R. K. Paliwal , U. C. Mohanty , and N. V. Sam , 2003 : Assimilation of special observations taken during the INDOEX and its impact on the global analysis-forecast system. Atmósfera , 16 , 103 – 118 . Doyle , J. D. , and T. T. Warner , 1988 : Verification of mesoscale objective analysis of VAS and rawinsonde data using the March 1982 AVE/VAS special network data. Mon. Wea. Rev. , 116 , 358

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M. Baldi, G. A. Dalu, and R. A. Pielke Sr.

1. Introduction It is well known that mesoscale processes can act to provide adequate moisture and instability for convection to initiate, and surface processes such as those driven by surface heterogeneity or soil moisture gradients can play a fundamental role in the development of convection. Once initiated, the interaction of convection with shear can enhance storm evolution and lead to severe weather ( Chang and Wetzel 1991 ). In this perspective, the surface conditions (dry versus wet soil

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K. W. Oleson, G. B. Bonan, J. Feddema, M. Vertenstein, and C. S. B. Grimmond

( Grimmond et al. 1991 )]. Furthermore, such approaches may not fully describe the fundamental processes that determine urban effects on climate ( Piringer et al. 2002 ). On the other hand, the level of complexity in a model is limited by the availability of the data that the model requires, the computational burden imposed, and difficulty in understanding complex behavior of the model. Here, following recent developments in detailed urban parameterizations designed for mesoscale models ( Masson 2000

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