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David McLaughlin, David Pepyne, V. Chandrasekar, Brenda Philips, James Kurose, Michael Zink, Kelvin Droegemeier, Sandra Cruz-Pol, Francesc Junyent, Jerald Brotzge, David Westbrook, Nitin Bharadwaj, Yanting Wang, Eric Lyons, Kurt Hondl, Yuxiang Liu, Eric Knapp, Ming Xue, Anthony Hopf, Kevin Kloesel, Alfred DeFonzo, Pavlos Kollias, Keith Brewster, Robert Contreras, Brenda Dolan, Theodore Djaferis, Edin Insanic, Stephen Frasier, and Frederick Carr

Dense networks of short-range radars capable of mapping storms and detecting atmospheric hazards are described. Composed of small X-band (9.4 GHz) radars spaced tens of kilometers apart, these networks defeat the Earth curvature blockage that limits today s long-range weather radars and enables observing capabilities fundamentally beyond the operational state-of-the-art radars. These capabilities include multiple Doppler observations for mapping horizontal wind vectors, subkilometer spatial resolution, and rapid-update (tens of seconds) observations extending from the boundary layer up to the tops of storms. The small physical size and low-power design of these radars permits the consideration of commercial electronic manufacturing approaches and radar installation on rooftops, communications towers, and other infrastructure elements, leading to cost-effective network deployments. The networks can be architected in such a way that the sampling strategy dynamically responds to changing weather to simultaneously accommodate the data needs of multiple types of end users. Such networks have the potential to supplement, or replace, the physically large long-range civil infrastructure radars in use today.

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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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