Accurate and timely prediction of sea fog is very important for effectively
managing maritime and coastal economic activities. Given the intricate nature
and inherent variability of sea fog, traditional numerical and statistical
forecasting methods are often proven inadequate. 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. 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. 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. 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. 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). 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).

基于机器学习的方法，通过数值天气预报模型，对扬子江河口（YRE）沿海地区进行了先进的海雾预测方法开发，验证了其在海雾的准确性方面的优势。