Parisfog

Parisfog Fog is a weather condition with significant socioeconomic impacts associated with increased hazards and constraints in road, maritime, and air traffic. While current numerical weather prediction models are able to forecast situations that are favorable to fog events, it is very difficult to determine the exact location and time of formation or dissipation. One-dimensional assimilation-forecast models have been implemented at a few airports and provide improved local predictions of fog events, but this approach is limited to specific locations. The occurrence, development, and dissipation of fog result from multiple processes (thermodynamical, radiative, dynamical, microphysical) that occur simultaneously, through a wide range of conditions, and that interact nonlinearly with each other. Hence, to advance our ability to forecast fog processes, we must gain a better understanding of how critical physical processes feed back on each other and improve their parametric representations in models. To provide a dataset suitable to study these processes simultaneously in continental fog, a suite of active and passive remote sensing instruments and in situ sensors was deployed at the Site Instrumental de Recherche en Tldtection Atmosphrique (SIRTA; instrumented site for atmospheric remote sensing research) observatory, near Paris, France, for 6 months (winter 2006/07) to monitor profiles of wind, turbulence, microphysical, and radiative properties as well as temperature, humidity, aerosol, and fog droplet microphysics and chemical composition in the surface layer. This field experiment, called ParisFog, provides a comprehensive characterization of over 100 fog and near-fog events. The ParisFog dataset contains contrasted events of stratus-lowering fog and radiative cooling fog as well as a large number of situations considered as favorable to fog formation but where fog droplets did not materialize. The effect of hydrated aerosols on visibility, the role of aerosols' microphysical and chemical properties on supersaturation and droplet activation, and the role of turbulence and sedimentation on fog life cycles have been investigated using the ParisFog dataset. The interactions between these processes, however, remain to be explored. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bulletin of the American Meteorological Society American Meteorological Society

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0477
D.O.I.
10.1175/2009BAMS2671.1
Publisher site
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Abstract

Fog is a weather condition with significant socioeconomic impacts associated with increased hazards and constraints in road, maritime, and air traffic. While current numerical weather prediction models are able to forecast situations that are favorable to fog events, it is very difficult to determine the exact location and time of formation or dissipation. One-dimensional assimilation-forecast models have been implemented at a few airports and provide improved local predictions of fog events, but this approach is limited to specific locations. The occurrence, development, and dissipation of fog result from multiple processes (thermodynamical, radiative, dynamical, microphysical) that occur simultaneously, through a wide range of conditions, and that interact nonlinearly with each other. Hence, to advance our ability to forecast fog processes, we must gain a better understanding of how critical physical processes feed back on each other and improve their parametric representations in models. To provide a dataset suitable to study these processes simultaneously in continental fog, a suite of active and passive remote sensing instruments and in situ sensors was deployed at the Site Instrumental de Recherche en Tldtection Atmosphrique (SIRTA; instrumented site for atmospheric remote sensing research) observatory, near Paris, France, for 6 months (winter 2006/07) to monitor profiles of wind, turbulence, microphysical, and radiative properties as well as temperature, humidity, aerosol, and fog droplet microphysics and chemical composition in the surface layer. This field experiment, called ParisFog, provides a comprehensive characterization of over 100 fog and near-fog events. The ParisFog dataset contains contrasted events of stratus-lowering fog and radiative cooling fog as well as a large number of situations considered as favorable to fog formation but where fog droplets did not materialize. The effect of hydrated aerosols on visibility, the role of aerosols' microphysical and chemical properties on supersaturation and droplet activation, and the role of turbulence and sedimentation on fog life cycles have been investigated using the ParisFog dataset. The interactions between these processes, however, remain to be explored.

Journal

Bulletin of the American Meteorological SocietyAmerican Meteorological Society

Published: Jun 1, 2010

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