基于集成学习的沿海低能见度天气分类预报方法.

A classification forecast method based on Light Gradient Boosting Machine (LightGBM) was utilized in this study to predict low visibility weather, using the coastal fusion observations and EC-thin model products of Zhangzhou from March 2020 to July 2021. The experiment was divided into four groups, including the new feature construction and model fusion schemes. The Bootstrap Aggregating (Bagging) technology and Area Under Curve (AUC) score were used to diminish the negative effect of extreme imbalance of samples, and the benchmark experiment employed the Logistics Regression (LR) method. The results showed that: (1) The most significant feature for estimating the possibility of low visibility weather was the 2 m dew point, followed by the temperature difference between 2m and 1000 hPa. (2) All model schemes exhibited improvement in comparison to the original forecast from the numerical model to varying degrees. In terms of metrics, the LightGBM model performed better than the LR model, largely due to its lower false alarm rate. (3) The skills of reasonable feature construction and model fusion contributed to optimizing the prediction performance and achieving higher scores on the test set. The impact of reasonable feature construction was particularly noteworthy. [ABSTRACT FROM AUTHOR]