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Melanie A. Wetzel, Randolph D. Borys, and Ling E. Xu


Digital data from the National Oceanic and Atmospheric Administration Advanced very High Resolution Radiometer (AVHRR) satellite instrument provides multispectral images in visible near-infrared and thermal infrared wave bands, which have been utilized to develop retrieval techniques for estimating the droplet effective radius and optical depth of land-based fog. The retrieval methods are based on multiple scattering calculations that simulate the increased near-infrared absorption by fog layers with increasing droplet size and liquid water path. The AVHRR thermal window channels are utilized to remove the effects of thermal emission in the near-infrared band.

New instrumentation and field sampling methods have been developed for obtaining detailed vertical profiles of fog droplet size distributions and thermodynamic conditions in fog decks. The in situ measurements derived from the field observations were employed to test the satellite retrieval techniques. Intercomparison shows a close correspondence between field observations and retrieved values of the fog droplet effective radius as well as fog optical depth. Simulated 4-km near-infrared and visible pixel data are also used to test retrievals from GOES-8. The AVHRR and GOES-8 retrievals provide a mapped database of the fog microphysical and depth parameters over the entire region of fog, which may be applied to numerical simulation of fog evolution and pollutant deposition, newcasting of fog visibility hazards, and global monitoring of fog influences on the atmosphere-surface radiation budget.

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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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