基于FLBO-CatBoost算法的上海长江口区强风潜势预报技术研究

Forecasting Convective Gust Potential in the Shanghai Yangtze River Estuary Based on FLBO-CatBoost

关键词:  强对流天气  机器学习  CatBoost  强风  潜势预报

Key words:  convective weather  machine learning  CatBoost  convective gusts  potential forecast

摘要:

选取 2015—2021年逐小时 0.25 ° ×0.25 ° ERA5再分析资料和上海长江口区 5个沿江地面自动气象站观测资料，运用 FLBO-CatBoost集成学习算法，将 6种强对流指数作为输入因子，实现对强对流影响下的长江口区强风的潜势分类及概率预报，并利用 SHAP 方法进行输入因子分析。结果表明：通过加入 Multi-ClassFocal Loss 与贝叶斯优化模块，提高了 FLBO-CatBoost 综合性能，模型筛选的输入因子物理意义较明确，判断 7级强风时 POD、CSI、FAR均达到 0.70，0.67、0.12，判断 8级以上强风时分别达到 0.97、0.91、0.07，优于其他五种集成学习模型。运用 SHAP方法进行重要性排序可知，在水汽、能量、动力等条件方面，模型能起到优秀的影响要素诊断与筛选功能。同时，将对流云团影响下，长江口区 7 级以上强风概率预报的最优阈值选定为 0.5，之后进一步结合个例验证模型对长江口区的强风的可预报性。整体而言，建立的强风预报模型具有一定的业务应用前景。

Abstract:

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.