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X. Liang, S. Miao, J. Li, R. Bornstein, X. Zhang, Y. Gao, F. Chen, X. Cao, Z. Cheng, C. Clements, W. Dabberdt, A. Ding, D. Ding, J. J. Dou, J. X. Dou, Y. Dou, C. S. B. Grimmond, J. E. González-Cruz, J. He, M. Huang, X. Huang, S. Ju, Q. Li, D. Niyogi, J. Quan, J. Sun, J. Z. Sun, M. Yu, J. Zhang, Y. Zhang, X. Zhao, Z. Zheng, and M. Zhou

Abstract

Urbanization modifies atmospheric energy and moisture balances, forming distinct features [e.g., urban heat islands (UHIs) and enhanced or decreased precipitation]. These produce significant challenges to science and society, including rapid and intense flooding, heat waves strengthened by UHIs, and air pollutant haze. The Study of Urban Impacts on Rainfall and Fog/Haze (SURF) has brought together international expertise on observations and modeling, meteorology and atmospheric chemistry, and research and operational forecasting. The SURF overall science objective is a better understanding of urban, terrain, convection, and aerosol interactions for improved forecast accuracy. Specific objectives include a) promoting cooperative international research to improve understanding of urban summer convective precipitation and winter particulate episodes via extensive field studies, b) improving high-resolution urban weather and air quality forecast models, and c) enhancing urban weather forecasts for societal applications (e.g., health, energy, hydrologic, climate change, air quality, planning, and emergency response management). Preliminary SURF observational and modeling results are shown (i.e., turbulent PBL structure, bifurcating thunderstorms, haze events, urban canopy model development, and model forecast evaluation).

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Xin-Zhong Liang, Min Xu, Xing Yuan, Tiejun Ling, Hyun I. Choi, Feng Zhang, Ligang Chen, Shuyan Liu, Shenjian Su, Fengxue Qiao, Yuxiang He, Julian X. L. Wang, Kenneth E. Kunkel, Wei Gao, Everette Joseph, Vernon Morris, Tsann-Wang Yu, Jimy Dudhia, and John Michalakes

The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model.

This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.

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