![]() Although fog could be caused by mixing two slightly subsaturated air parcels and causing saturation due to curvature of the saturated mixing ratio versus temperature line, most fog formation is initialized by cooling the lower parts of a column of moist, but unsaturated, air. They discuss fog water deposition to vegetation extensively but not turbulent deposition to water surfaces, and it is missing from their Fig. 1 (and Fig. 9.1 in Koračin, 2017) which shows “the main processes governing the formation, evolution, and dissipation of marine fog”. (2014) review marine fog processes and studies up to 2014, noting the importance of air–sea interactions. Modelling of fog, in idealized one dimensional or single column models up to operational 3-D weather prediction and climate models, is a challenge which many have addressed over the years, as noted by Koračin (2017), Gultepe et al. On land and over the sea, the formation and decay of fog in the atmospheric boundary layer is a complex issue involving many processes including cloud microphysics, longwave and solar radiation, turbulent boundary layer mixing, advection and surface interactions. (2014) had noted that, “From the many modeling studies of sea fog, essentially numerical experiments/simulations/forecasting that started in the immediate post WWII period, it becomes clear that deterministic forecasting of sea fog onset and its duration has generally been unsuccessful”. Other studies of marine fog had encountered similar problems (e.g. This study was initiated when it was found that predicting fog in areas offshore from Atlantic Canada using the National Center for Atmospheric Research and University Corporation for Atmospheric Research (NCAR/UCAR) Weather Research and Forecasting model (WRF-ARW) was generally satisfactory in terms of fog occurrence but gave high values of cloud water mixing ratio, leading to visibilities that were too low compared to observations. 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. ![]() This process can be parameterized as an additional deposition velocity with a model that could be based on a roughness length for water droplets, z 0c, that may be significantly larger than the roughness length for water vapour, z 0q. A water surface, or any wet surface, can then be an effective sink for fog water droplets. ![]() ![]() However, there should be an additional deposition as turbulence causes fog droplets to collide and coalesce with the 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. One possible cause of this overestimation could be the treatment of a surface deposition rate of fog droplets at the underlying water surface. Several model studies report overpredictions of near-surface liquid water content ( Q c), leading to visibility estimates that are too low. Specifically, in regard to predicting sea fog with a visibility of less than or equal to 1 km with a lead time of 60 hours, our methodology achieves superior results by increasing the probability of detection (POD) while simultaneously reducing the false alarm ratio (FAR).There have been many studies of marine fog, some using Weather Research and Forecasting (WRF) and other models. Remarkably, our machine learning-based approach surpasses the predictive performance of two conventional methods, the weather research and forecasting nonhydrostatic mesoscale model (WRF-NMM) and the algorithm developed by the National Oceanic and Atmospheric Administration (NOAA) Forecast Systems Laboratory (FSL). To verify the accuracy of our method, we evaluate its performance using a comprehensive dataset spanning one year, which encompasses both weather station observations and historical forecasts. In addition, we implement ensemble learning and a focal loss function to address the issue of imbalanced data, thereby enhancing the predictive ability of our model. Prior to training our machine learning model, we employ a time-lagged correlation analysis technique to identify key predictors and decipher the underlying mechanisms driving sea fog occurrence. This study aims to develop an advanced sea fog forecasting method embedded in a numerical weather prediction model using the Yangtze River Estuary (YRE) coastal area as a case study. Given the intricate nature and inherent variability of sea fog, traditional numerical and statistical forecasting methods are often proven inadequate. ![]() Accurate and timely prediction of sea fog is very important for effectively managing maritime and coastal economic activities. ![]()
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