基于支持向量机的云量精细化预报研究

A Study on Refined Forecast of Cloud Cover Based on Support Vector Machine

基于T639数值预报产品与地面气象观测资料,以环渤海地区兴城站为例,选取与云的形成密切相关的4类预报因子——水汽类、大气不稳定度类、大气上升运动类和天气系统强度类,以总云量、低云量为预报对象,运用支持向量机,选取最佳参数,建立兴城站云量的逐月、逐时次精细化预报模型。试预报结果表明:平均预报准确率总云量为71%,低云量为69%,预报准确率较逐步回归模型有所提高;在大部分月份、时次,试预报值的变化趋势与观测值一致,可以较好地反映实际阴晴变换和云量变化;基于支持向量机的回归模型对云量有较好的预报能力。

The refined forecast of cloud cover based on Support Vector Machine regression method was studied by using the products of T639 model and the data of surface meteorological observation station at Xingcheng. Physical quantities about water vapor, atmospheric instability, ascending motion of atmosphere and intensity of weather system, are closely related to cloud formation, so they were selected as forecast factors of cloud cover. Then the refined forecast models of total cloud cover and low cloud cover were built by using Support Vector Machine with best parameters. The forecast results of Support Vector Machine regression models showed that mean forecast accuracy of total cloud cover was about 71%, while that was about 69% for low cloud cover, which were higher than those of stepwise regression models. And the trend of forecasted cloud cover was close to observation data at most times and in most months. So the model based on Support Vector Machine could forecast cloud cover well.