LANFEX: A Field and Modeling Study to Improve Our Understanding and Forecasting of Radiation Fog

Autor: Christine Lac, A. Kerr-Munslow, L. Duconge, Thierry Bergot, James McGregor, D. K. E. Smith, Ian A. Boutle, S. Lane, R. Clark, Jeremy D. Price, M. Pickering
Rok vydání: 2018
Předmět:
Zdroj: Bulletin of the American Meteorological Society. 99:2061-2077
ISSN: 1520-0477
0003-0007
DOI: 10.1175/bams-d-16-0299.1
Popis: Fog is a high-impact weather phenomenon affecting human activity, including aviation, transport, and health. Its prediction is a longstanding issue for weather forecast models. The success of a forecast depends on complex interactions among various meteorological and topographical parameters; even very small changes in some of these can determine the difference between thick fog and good visibility. This makes prediction of fog one of the most challenging goals for numerical weather prediction. The Local and Nonlocal Fog Experiment (LANFEX) is an attempt to improve our understanding of radiation fog formation through a combined field and numerical study. The 18-month field trial was deployed in the United Kingdom with an extensive range of equipment, including some novel measurements (e.g., dew measurement and thermal imaging). In a hilly area we instrumented flux towers in four adjacent valleys to observe the evolution of similar, but crucially different, meteorological conditions at the different sites. We correlated these with the formation and evolution of fog. The results indicate new quantitative insight into the subtle turbulent conditions required for the formation of radiation fog within a stable boundary layer. Modeling studies have also been conducted, concentrating on high-resolution forecast models and research models from 1.5-km to 100-m resolution. Early results show that models with a resolution of around 100 m are capable of reproducing the local-scale variability that can lead to the onset and development of radiation fog, and also have identified deficiencies in aerosol activation, turbulence, and cloud micro- and macrophysics, in model parameterizations.
Databáze: OpenAIRE
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