Forecasting Convective Gust Potential in the Shanghai Yangtze River Estuary Based on FLBO-CatBoost
Based on ERA5 reanalysis data and observational data from five automatic meteorological stations in the Yangtze River Estuary from 2015 to 2021,the FLBO-CatBoost is used to classify and predict the probability of convective gusts at level 7 and above in the Yangtze River Estuary.A total of six strong convection indexes are used as input factors and the Shapley additive explanation method is used for factor analysis.The results show that thanks to the incorporation of Multi-Class Focal Loss and Bayesian optimization,the FLBO-CatBoost has performed significantly well.At the same time,the physical meaning of the factors selected by the model is relatively clear.For level 7 convective gusts,the probability of detechion,critical success index,and false alarm ratio values are 0.70,0.67,and 0.12 respectively.For convective gusts at level 8 and above,they become 0.97,0.91,and 0.07 respectively.The model outperforms the other five ensemble learning models used in this study.Furthermore,by using the SHAP method for importance ranking,the model demonstrates excellent capacity in diagnosing and selecting influential factors related to moisture,energy,dynamics,and other conditions.In addition,the optimal probability threshold for predicting convective gusts at level 7 and above that are influenced by convective cloud clusters is determined as 0.5.Subsequently,individual cases are examined to further demonstrate the predictability of the model for convective gusts in the region.Overall,the proposed convective gust forecast model proves to be practically useful in the Yangtze River Estuary.
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