Marine Fog: A Review on Microphysics and Visibility Prediction

Gültepe, Ismail; Milbrandt, Jason A.; Zhou, Binbin

doi:10.1007/978-3-319-45229-6_7

Cited by 56 publications

(47 citation statements)

References 147 publications

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“…Gultepe and Isaac (, ) found that fog droplet number concentration can be obtained as a function of aerosol number concentration for air masses over the ocean based on the observation. These studies showed that aerosol particles could affect fog microphysics by serving as fog condensation nuclei, like the aerosol effect on cloud microphysical properties via cloud condensation nuclei (Albrecht, ; Gultepe et al, ; Twomey & Warner, ).…”

Section: Introductionmentioning

confidence: 99%

Impacts of Anthropogenic Aerosols on Fog in North China Plain

et al. 2019

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Fog poses a severe environmental problem in the North China Plain, China, which has been witnessing increases in anthropogenic emission since the early 1980s. This work first uses the WRF/Chem model coupled with the local anthropogenic emissions to simulate and evaluate a severe fog event occurring in North China Plain. Comparison of the simulations against observations shows that WRF/Chem well reproduces the general features of temporal evolution of PM2.5 mass concentration, fog spatial distribution, visibility, and vertical profiles of temperature, water vapor content, and relative humidity in the planetary boundary layer throughout the whole period of the fog event. Sensitivity studies are then performed with five different levels of anthropogenic emission as model inputs to systematically examine the comprehensive impacts of aerosols on fog microphysical, macrophysical, radiative, and dynamical properties. The results show that as aerosol concentration increases, fog droplet number concentration and liquid water content all increase nonlinearly; but effective radius decreases. Macrophysical properties (fog fraction, fog duration, fog height, and liquid water path) also increase nonlinearly with increasing aerosol concentration, with rates of changes smaller than microphysical properties. Further analysis reveals distinct aerosol effects on thermodynamic and dynamical conditions during different stages of fog evolution: increasing aerosols invigorate fog formation and development by enhancing longwave‐induced instability, fog droplet condensation accompanying latent heat release, and thus turbulence, but delay fog dissipation by reducing surface solar radiation, surface sensible, and latent heat fluxes, and thus suppressing turbulence during the dissipation stage.

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Section: Introductionmentioning

confidence: 99%

Impacts of Anthropogenic Aerosols on Fog in North China Plain

et al. 2019

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“…This type of fog formation is often encountered in sea regions at high latitudes, e.g., Norway, or around lakes, rivers and swamps [4].…”

Section: Fog Types By Dynamical Originmentioning

confidence: 43%

Fogs: Physical Basis, Characteristic Properties, and Impacts on the Environment and Human Health

Pérez-Díaz

Ivanov

Peshev

et al. 2017

Water

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This work presents a selective overview of natural fogs in terms of fog types, forms and states of occurrence, physical, micro-physical, chemical and dynamic properties, basic characterizing parameters, etc. In focus are related achievements and contributions reported mainly during the last decade and a half, as a result of both laboratory studies and field observations. Processes of homogeneous and heterogeneous nucleation are analyzed in the aspects of condensation, nuclei diversity and specifics, as related to the activation, growth and deposition of fog droplets. The effect is highlighted of the water vapor's partial pressure on the surface tension of the liquid water-air interface and the freezing point of the water droplets. Some problems and aspects of fog modeling, parameterization, and forecasting are outlined and discussed on the examples of newly developed relevant 1D/3D theoretical models. Important issues of fog impacts on the air quality, ecosystems, water basins, societal life, and human health are also addressed and discussed, particularly in cases of anthropogenically modified (chemical, radioactive, etc.) fogs. In view of reducing the possible negative effects of fogs, conclusions are drawn concerning the new demands and challenges to fog characterization imposed by the changing natural and social environment and the needs for new data on and approaches to more adequate observations of fog-related events.

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“…Reducing the uncertainty in the analysis requires improvements in the quality of collected data and microphysical algorithms in the NWP model simulations. The microphysical model of atmospheric Vis needs to be significantly improved, especially for sea fog [4]. At the same time, with the development of Big Data technology, a deeper network could be used to infer Vis to reduce the uncertainty of the inferred Vis in the future.…”

Section: Discussionmentioning

confidence: 99%

“…The main factors that affect the navigation risk are sea wave, sea ice, visibility (Vis), icing, wind, and turbulence, as well as precipitation [2,3]. Information on Vis and freezing precipitation over marine environments are usually limited and not easy to be predicted or monitored over various scales [3,4]. For this reason, having gridded data of Vis with high quality is a precondition to assess the navigation risk in marine environments.…”

Section: Introductionmentioning

confidence: 99%

“…The fog prediction of Vis in marine environments using NWP models includes large uncertainties on small space scales over the short time periods, because the microphysical schemes used in numerical models [4] have limited boundary layer processes such as turbulence. To achieve more stable and reliable historical gridded Vis data around the world's marine environments, remote sensing retrievals of Appl.…”

Section: Introductionmentioning

confidence: 99%

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Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis

et al. 2019

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The reconstruction and monitoring of visibility over marine environments is critically important because of a lack of observations. To travel safely in marine environments, a high quality of visibility data is needed to evaluate navigation risk. Currently, although visibility is available through numerical weather prediction models as well as ground and spaceborne remote sensing platforms and ship measurements, issues still exist over the remote marine environments and northern latitudes. To improve visibility prediction and reduce navigational risks, gridded visibility data based on artificial neural network analysis can be used over marine environments, and the problem can be regarded as an air quality prediction problem based on machine learning algorithms. This new method based on artificial intelligence techniques developed here is tested over the Indian Ocean. The mean error of the inferred visibility from the artificial neural network analysis is found to be less than 8.0%. The results suggested that satellite-based optical thickness and numerical model-based reanalysis data can be used to infer gridded visibility values based on artificial neural network analysis, and that could help us reconstruct and monitor surface gridded visibility values over marine and remote environments.

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Marine Fog: A Review on Microphysics and Visibility Prediction

Cited by 56 publications

References 147 publications

Impacts of Anthropogenic Aerosols on Fog in North China Plain

Impacts of Anthropogenic Aerosols on Fog in North China Plain

Fogs: Physical Basis, Characteristic Properties, and Impacts on the Environment and Human Health

Gridded Visibility Products over Marine Environments Based on Artificial Neural Network Analysis

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