1. C-FOG: Life of Coastal Fog
- Author
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Raghavendra Krishnamurthy, B. Nagare, Ashish Sharma, Ismail Gultepe, Iossif Lozovatsky, Charlotte E. Wainwright, A. Olson, E. D. Creegan, Rachel Y.-W. Chang, R. S. Coppersmith, H. J. S. Fernando, Sebastian W. Hoch, Stef L. Bardoel, M. Wroblewski, D. D. Flagg, William Perrie, Christopher M. Hocut, Dhiraj Singh, O. Hyde, T. Morrison, R. Dimitrova, E. Gonzalez, Clive E. Dorman, S. Wang, Saša Gaberšek, N. Gunawardena, Eric R. Pardyjak, Ryan Yamaguchi, Qing Wang, Denny P. Alappattu, David Richter, N. Chisholm, Terry Bullock, Sandeep Wagh, Benjamin J. Wauer, A. O. Perelet, and Andrey A. Grachev
- Subjects
Atmospheric Science ,Meteorology ,Visibility (geometry) ,Environmental science - Abstract
C-FOG is a comprehensive bi-national project dealing with the formation, persistence, and dissipation (life cycle) of fog in coastal areas (coastal fog) controlled by land, marine, and atmospheric processes. Given its inherent complexity, coastal-fog literature has mainly focused on case studies, and there is a continuing need for research that integrates across processes (e.g., air–sea–land interactions, environmental flow, aerosol transport, and chemistry), dynamics (two-phase flow and turbulence), microphysics (nucleation, droplet characterization), and thermodynamics (heat transfer and phase changes) through field observations and modeling. Central to C-FOG was a field campaign in eastern Canada from 1 September to 8 October 2018, covering four land sites in Newfoundland and Nova Scotia and an adjacent coastal strip transected by the Research Vessel Hugh R. Sharp. An array of in situ, path-integrating, and remote sensing instruments gathered data across a swath of space–time scales relevant to fog life cycle. Satellite and reanalysis products, routine meteorological observations, numerical weather prediction model (WRF and COAMPS) outputs, large-eddy simulations, and phenomenological modeling underpin the interpretation of field observations in a multiscale and multiplatform framework that helps identify and remedy numerical model deficiencies. An overview of the C-FOG field campaign and some preliminary analysis/findings are presented in this paper.
- Published
- 2021
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