梯度提升决策树在雷达定量降水估测中的应用研究

Application of Gradient Boost Decision Tree in Radar Quantitative Precipitation Estimation

利用广东省新一代多普勒气象雷达(CINRAD/SA)三维拼图和地面自动气象站雨量观测资料,基于梯度提升决策树(GBDT)建立了雷达定量估测降水模型,将其用于地面降水估测。通过与固定Z-I关系法和动态Z-I关系法定量估测降水进行误差分析,结果表明,新建立的GBDT雷达定量估测降水模型的估测精度和效果优于Z-I关系法和动态Z-I关系法,能较好地反映降水真实情况,特别是改进了对30mm/h以上的强降水估测偏小的现象。

Based on gradient boost decision tree (GBDT),a radar quantitative precipitation estimation (QPE) model is established through using the three-dimensional CINRAD (China New Generation of Weather Radar) radar echo data and the precipitation data from the regional automatic weather stations (AWSs) in Guangdong Province.In order to evaluate the GBDT scheme,both the Z-I relations and dynamic Z-I relations are used to estimate precipitation.The comparison experiment results show that the established radar quantitative precipitation estimation of GBDT scheme is better than Z-I relations and dynamic Z-I relations both in accuracy and stability,and can reflect the real situation of rainfall better.According to analysis indices such as mean error, relative error and root mean square error,the GBDT scheme can estimate precipitation fairly accurately.It improves the estimation of short-term precipitation for torrential rain,especially the precipitation greater than 30mm/h.Rainfall estimation of GBDT scheme is in good consistency with the observation by rain-gauge and can truly reflect the precipitation on ground surface.