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

作　　者：陈诗祺 张吉 岳彩军 沙莎 黄筱灿 CHEN Shiqi;ZHANG Ji;YUE Caijun;SHA Sha;HUANG Xiaocan(Shanghai Marine Observatory,Shanghai 201306,China;Joint Laboratory for Phased Array Weather Radar,Shanghai 201300,China)

机构地区：[1]上海海洋中心气象台,上海201306 [2]华东区域相控阵天气雷达应用联合实验室,上海200030

出　　处：《热带气象学报》2024年第4期659-668,共10页Journal of Tropical Meteorology

基　　金：国家自然科学基金项目(41875059、41875071);中国气象局青年创新团队(CMA2024QN02);上海台风研究基金(TFFJJ202319);中国气象局气象能力提升联合研究专项(22NLTSQ005)共同资助。

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-Class Focal 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。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.

关 键 词：强对流天气 机器学习 CatBoost 强风 潜势预报 

分 类 号：P456.7[天文地球—大气科学及气象学]