Intelligent model for offshore China sea fog forecasting

TL;DR

This paper presents a machine learning-based sea fog forecasting model for China's offshore regions, outperforming traditional methods by effectively predicting fog with high accuracy and reduced false alarms.

Contribution

The study develops an advanced sea fog prediction approach using ensemble learning and focal loss, incorporating key predictors identified through correlation analysis, tailored for the YRE coastal area.

Findings

Outperforms WRF-NMM and NOAA FSL models in predicting sea fog.

Achieves higher detection probability and lower false alarm ratio for fog with ≤1 km visibility.

Demonstrates effectiveness of machine learning in complex maritime weather forecasting.

Abstract

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).