An analysis and modeling study of a sea fog event over the Yellow and Bohai Seas

Fu, Gang; Guo, Jingtian; Pendergrass, Angeline G.; Li, Pengyuan

doi:10.1007/s11802-008-0027-z

Cited by 22 publications

(9 citation statements)

References 10 publications

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“…Differences in Richardson Number also exist between fog and stratus days (Fig.8a). On fog days, turbulence (Ri<0.5) seems readily to occur at two levels: one being below 200 m in the mixing layer, and the other between 500-600 m. The latter is very likely associated with the existence of the dry layer, which allows heat to escape from the top of the fog by longwave radiation, thus leading to cooling and turbulence, and this physical process, being very important for the formation of sea fogs, has been proved by numerical simulations (Gao et al, 2007;Fu et al, 2008;Hu et al, 2006). The lapse rates on fog days are greater than on stratus days at 500-800 m altitude, which is consistent with the stronger turbulence between 500-600 m (Fig.8b), confirming further the contribution made by the dry layer.…”

Section: Contrast Between Fog and Stratusmentioning

confidence: 90%

Variations in the lower level of the PBL associated with the Yellow Sea fog-new observations by L-band radar

Zhang

Ren

et al. 2008

J. Ocean Univ. China

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The Chinese east coastal areas and marginal seas are foggy regions. The development of effective forecasting methods rests upon a comprehensive knowledge of the fog phenomena. This study provides new observations associated with the sea fogs over the northwestern Yellow Sea by means of L-band radar soundings with a high vertical resolution of 30 m. The monthly temperature lapse rate, the Richardson Numbers, and the humidity show obvious seasonal variations in the lower level of the planetary boundary layer (PBL) that are related to the onset, peak and end of the Yellow Sea fog season. The typical pattern of stratification for the sea fog season in the northwestern Yellow Sea is that a stable layer of about 400 m thick caps a 150 m conditionally unstable layer. Besides, the differences between fogs and stratus clouds in terms of humidity, turbulence and temperature are analyzed, which is of significance for sea fog forecast and detection by satellites. The thickness of the sea fogs varies in different stages of the fog season, and is associated with the temperature inversion. The numerical simulation proves that the seasonal variations obtained by the radar well represent the situations over the Yellow Sea.

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Section: Contrast Between Fog and Stratusmentioning

confidence: 90%

Variations in the lower level of the PBL associated with the Yellow Sea fog-new observations by L-band radar

Zhang

Ren

et al. 2008

J. Ocean Univ. China

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“…Previous studies show that springtime fog in Qingdao is often associated with sea fog over the Yellow Sea (Fu et al, 2008;Gao et al, 2007;Zhang et al, 2012). The Yellow sea fog is typical advection fog, occurring when prevailing southerlies transport moist and warm air from low latitude northward to the sea surface (Sugimoto et al, 2013;Gao et al, 2016).…”

Section: Introductionmentioning

confidence: 96%

Springtime sea fog penetration in Qingdao: Anomalous moistening and diurnal cooling

et al. 2022

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Sea fog often penetrates adjacent coastal areas, a process called sea fog penetration (SFP). SFP can cause traffic accidents and other economic losses. Qingdao, an international port city with a dense population, suffers from SFP originating over the Yellow Sea in the boreal spring (March–May); the process, however, is not well-studied. Based on hourly observations from buoys and automatic weather stations distributed in Qingdao and its adjacent islands, we composite SFP events to reveal their spatiotemporal features and to investigate the mechanisms involved. Results show that these SFP events often penetrate inland areas from southeast to northwest and last 5–8 h at night. We further use reanalysis data to reveal that during the daytime before SFP, strong moisture advection at 925–975 hPa brings sufficient water vapor from the Yellow Sea to Qingdao; the water vapor then transfers downward to the surface via background descending motion and turbulent mixing. The daytime anomalous moistening, together with the following diurnal cooling at night, saturates the surface atmosphere and, hence, facilitates SFP. The strength of SFP depends on the strength of daytime anomalous moistening. Considering that moistening leads SFP by about a day, we use this relationship to predict the intensity of SFP. The accuracy of predicting SFP events could reach 50–80%, which highlights the predictability of intensity of SFP in Qingdao.

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“…In some early attempts to numerically simulate sea fog, many scholars employed mesoscale forecasting models (Ballard et al, 1991;Golding, 1993;Nakanishi, 2000;Pagowski et al, 2004;Koračin et al, 2005). Mesoscale models have been used to simulate the formation of sea fog in China since the turn of the century, and the corresponding mechanisms have been investigated (Fu et al, 2004;Fu et al, 2006;Fu et al, 2008;Wang et al, 2012;Cheng et al, 2013;Huang et al, 2015;Huang et al, 2019). Focusing on ten cases of sea fog over the Yellow Sea (YS) in spring, Lu et al (2014) carried out a sensitivity study on the parameterization schemes of the Weather Research and Forecasting (WRF) model and found that the best combination of the boundary layer scheme and the microphysical scheme comprises the Yonsei University (YSU) scheme and the Purdue Lin scheme, respectively.…”

Section: Introductionmentioning

confidence: 99%

Improvements of Sea Fog Forecasting Based on CMA-TYM

et al. 2022

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Based on the operational version of the China Meteorological Administration Typhoon Model (CMA-TYM, formerly known as GRAPES_TYM), a series of numerical tests are conducted by optimizing the boundary layer parameterization scheme, vertical resolution, and boundary conditions. Instead of the sea surface temperature (SST) from the Global Forecast System (GFS) model, more accurate daily SST data reflecting the daily SST variation are used as the boundary condition. The new SST dataset is capable of representing the key points in the area, including the low coastal SST related to upwelling, the intrusion of the Yellow Sea (YS) Warm Current, and the ocean front between the YS and the East China Sea. An analysis of the performances of two boundary layer parameterization schemes (the Yonsei University scheme and the Medium-Range Forecast scheme) in characterizing turbulent heat exchange reveals that the former can more accurately reflect offshore turbulence and forecast the fog area. By increasing the number of vertical layers of the model to 68 and reducing the height of the bottom layer to approximately 10 m, the model presents a better performance in simulating the rapid formation and dissipation of sea fog. With the above improvements, the equitable threat score (ETS) for the hindcasting of eleven sea fog cases in the spring of 2018 increases by 61%, mainly due to the increase in the correctly forecasted fog area.

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An analysis and modeling study of a sea fog event over the Yellow and Bohai Seas

Cited by 22 publications

References 10 publications

Variations in the lower level of the PBL associated with the Yellow Sea fog-new observations by L-band radar

Variations in the lower level of the PBL associated with the Yellow Sea fog-new observations by L-band radar

Springtime sea fog penetration in Qingdao: Anomalous moistening and diurnal cooling

Improvements of Sea Fog Forecasting Based on CMA-TYM

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