The Impact of Turbulent Diffusion Driven by Fog‐Top Cooling on Sea Fog Development

Yang, Yue; Gao, Shanhong

doi:10.1029/2019jd031562

Cited by 21 publications

(46 citation statements)

References 62 publications

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“…Note that in generating these results we have not included radiation (short wave or long wave) effects in order to focus on the impacts of turbulent deposition at the water surface. Radiation can play a significant role once fog has formed, and in particular long wave radiational cooling at the fog top (Yang and Gao, 2020) ln((z+z0)/z0) while the eddy diffusivities used to compute fluxes at the top of the first level and levels above are based on length scales proportional to kz without the z0 addition. This will not be significant for z >> z0 but with the lowest computational levels close to the surface this could be modified.…”

Section: Wrf Scm Set-up and Testsmentioning

confidence: 99%

Surface deposition of marine fog and its treatment in the WRF model

Taylor

Chen

et al. 2021

Preprint

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Abstract. There have been many studies of marine fog, some using WRF and other models. Several model studies report over-predictions of near surface liquid water content (Qc) leading to visibility estimates that are too low. This study has found the same. One possible cause of this overestimation could be the treatment of a surface deposition rate of fog droplets at the underlying water surface. Most models, including the Advanced Research Weather Research and Forecasting (WRF-ARW) Model, available from the National Center for Atmospheric Research (NCAR), take account of gravitational settling of cloud droplets throughout the domain and at the surface. However, there should be an additional deposition as turbulence causes fog droplets to collide and coalesce with the water surface. A water surface, or any wet surface, can then be an effective sink for fog water droplets. This process can be parameterized as an additional deposition velocity with a model that could be based on a roughness length for water droplets, z0c, that may be significantly larger than the roughness length for water vapour, z0q. This can be implemented in WRF either as a variant of the Katata scheme for deposition to vegetation, or via direct modifications in boundary-layer modules.

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Section: Wrf Scm Set-up and Testsmentioning

confidence: 99%

Surface deposition of marine fog and its treatment in the WRF model

Taylor

Chen

et al. 2021

Preprint

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“…e Advanced Research core of the WRF (ARW, version 3.8.1) and its corresponding WRF-3DVAR were employed in the present study for numerical simulations and data assimilations. Based on the previous work related to sea fog simulation over the Yellow Sea fog [22][23][24]42], the Yonsei University (YSU) planetary boundary layer (PBL) scheme and the Lin microphysics scheme (LIN) were selected for the present study. Appropriate, vertical levels were specified as well.…”

Section: Model and Its Configurationmentioning

confidence: 99%

“…However, the EnKF requires much more computing resources compared to the 3DVAR, which is an issue that has to be considered for real-time forecast of sea fog. e WRF-3DVAR [18] has been widely used for sea fog modeling over the Yellow Sea due to its low computational cost [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Dhanya and Chandrasekar [34,35] found that the forecasts of rainfall caused by monsoon depressions and tropical cyclones can be improved by applying data assimilation with CV6, which is better than CV5. However, among existing research work on sea fog over the Yellow Sea involving numerical modeling [24,[36][37][38][39][40], quite a few studies, especially those works with focus on the mechanism of sea fog evolution, have little or no mention of data assimilation. Even if data assimilation is employed in the numerical simulations for sea fog, CV5 is generally used [22,23,[41][42][43].…”

Section: Introductionmentioning

confidence: 99%

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Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling

Gao

2020

Advances in Meteorology

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Numerical modeling of sea fog is highly sensitive to initial conditions, especially to moisture in the marine atmospheric boundary layer (MABL). Data assimilation plays a vital role in the improvement of initial MABL moisture for sea fog modeling over the Yellow Sea. In this study, the weather research and forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation module are employed for sea fog simulations. Two kinds of background error (BE) covariances with different control variables (CV) used in WRF-3DVAR, that is, CV5 and multivariate BE (CV6), are compared in detail by explorative case studies and a series of application experiments. Statistical verification metrics including probability of detection (POD) and equitable threat scores (ETS) of forecasted sea fog area are computed and compared for simulations with the implementations of CV5 and CV6 in the WRF-3DVAR system. The following is found: (1) there exists a dominant negative correlation between temperature and moisture in CV6 near the sea surface, which makes it possible to improve the initial moisture condition in the MABL by assimilation of observed temperature; (2) in general, the performance of the WRF-3DVAR assimilation with CV6 is distinctly better, and the results of 10 additional sea fog cases clearly suggest that CV6 is more suitable than CV5 for sea fog modeling. Compared to those with CV5, the average POD and ETS of forecasted sea fog area using 3DVAR with CV6 can be improved by 27.6% and 21.0%, respectively.

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“…Sea fog is a hazardous phenomenon wherein myriad saturated microscopic water droplets lowers visibility within the marine atmospheric boundary layer (Fu et al., 2006; Gao et al., 2007; Gultepe et al., 2007; Yang & Gao, 2019). The presence of sea fog also affects the chemical composition of surrounding gaseous and aerosol pollutants since dissolved soluble trace gases participate in subsequent chemical and physical processes such as wet deposition, in‐cloud scavenging, and secondary aerosol formation via aqueous‐phase chemistry, which causes more rapid formation of the aerosol pollutants (e.g., sulfate and secondary organic aerosols [SOAs]) than gas‐phase chemistry (Carlton et al., 2008; Ervens, 2015; Faust et al., 1993; Wang et al., 2016; Yuan et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Role of Sea Fog Over the Yellow Sea on Air Quality With the Direct Effect of Aerosols

et al. 2021

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In this study, we investigate the impact of sea fog over the Yellow Sea on air quality with the direct effect of aerosols for the entire year of 2016. Using the WRF‐CMAQ two‐way coupled model, we perform four model simulations with the up‐to‐date emission inventory over East Asia and dynamic chemical boundary conditions provided by hemispheric model simulations. During the spring of 2016, prevailing west‐southwesterly winds and anticyclones caused the formation of a temperature inversion over the Yellow Sea, providing favorable conditions for the formation of fog. The inclusion of the direct effect of aerosols enhanced its strength. On foggy days, we find dominant changes of aerosols at an altitude of 150–200 m over the Yellow Sea resulted by the production through aqueous chemistry (∼12.36% and ∼3.08% increases in sulfate and ammonium) and loss via the wet deposition process (∼‐3.79% decrease in nitrate); we also find stronger wet deposition of all species occurring in PBL. Stagnant conditions associated with reduced air temperature caused by the direct effect of aerosols enhanced aerosol chemistry, especially in coastal regions, and it exceeded the loss of nitrate. The transport of air pollutants affected by sea fog extended to a much broader region. Our findings show that the Yellow Sea acts as not only a path of long‐range transport but also as a sink and source of air pollutants. Further study should investigate changes in the impact of sea fog on air quality in conjunction with changes in the concentrations of aerosols and the climate.

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The Impact of Turbulent Diffusion Driven by Fog‐Top Cooling on Sea Fog Development

Cited by 21 publications

References 62 publications

Surface deposition of marine fog and its treatment in the WRF model

Surface deposition of marine fog and its treatment in the WRF model

Impact of Multivariate Background Error Covariance on the WRF-3DVAR Assimilation for the Yellow Sea Fog Modeling

Role of Sea Fog Over the Yellow Sea on Air Quality With the Direct Effect of Aerosols

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