Journal
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
Volume 90, Issue 3, Pages 341-+Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/2008BAMS2354.1
Keywords
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Categories
Funding
- Canadian National Search and Rescue Secretariat and Environment Canada
- ISDAC
- U. S. Department of Energy [DE-FG02-08ER64554]
- ARM Aerial Vehicle Program
- DOE Atmospheric Sciences Program (ASP)
- Environment Canada and the National Research Council of Canada
- ARM program archive
- U. S. DOE
- Office of Science
- Office of Biological and Environmental Research
- Environmental Sciences Division
- European [COST-722]
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The main purpose of this work is to describe a major field project on fog and summarize the preliminary results. Three field phases of the Fog Remote Sensing and Modeling (FRAM) project were conducted over the following two regions of Canada: 1) the Center for Atmospheric Research Experiments (CARE), in Toronto, Ontario (FRAM-C), during the winter of 2005/06, and 2) Lunenburg, Nova Scotia (FRAM-L), during June 2006 and June 2007. Fog conditions observed during FRAM-C were continental in nature, while those conditions observed during FRAM-L were of marine origin. The main objectives of the project were to attain 1) a better description of fog environments, 2) the development of microphysical parameterizations for model applications, 3) the development of remote sensing methods for fog nowcasting/forecasting, 4) an understanding of issues related to instrument capabilities and improvement of the analysis, and 5) an integration of model data with observations to predict and detect fog areas and particle phase. During the project phases, various measurements at the surface, including droplet and aerosol spectra, ice crystal number concentration, visibility, 3D turbulent wind components, radiative fluxes, precipitation, liquid water content profiles, and cloud ceiling, were collected together with satellite measurements. These observations will be studied to better forecast/nowcast fog events in association with results obtained from numerical forecast models. It is suggested that improved scientific understanding of fog will lead to better forecasting/nowcasting skills, benefiting the aviation, land transportation, and shipping communities.
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