基于机器学习的非线性局部Lyapunov向量集合预报订正

Machine learning based method of correcting nonlinear local Lyapunov vectors ensemble forecasting

基于Lorenz96模型初步探讨了机器学习算法提高非线性局部Lyapunov向量(NLLV)集合预报效果的可行性和有效性.结果表明:基于岭回归算法和NLLV集合预报结果建立的机器学习模型(Ens-ML)能够有效提高整体预报技巧,而且优于集合平均预报(Ens Ave)、控制预报(Ctrl)以及基于Ctrl结果建立的机器学习模型(Ctrl-ML).同时,还发现Ens-ML的预报技巧改进程度依赖于集合成员的数量,即增加集合成员数有助于提高Ens-ML模型的整体预报准确率.通过对比个例预报表现得到,随着预报时间延长,Ens-ML,Ctrl-ML和Ens Ave的个例预报误差逐渐小于Ctrl.进一步分析Ens-ML,Ctrl-ML和Ens Ave预报的吸引子,发现它们的概率分布的值域收缩、峰度增大并向平均值靠拢,尤其Ens-ML的表现更为明显.

In this study,the feasibility and effectiveness of machine learning algorithm to improve ensemble forecasts using nonlinear local Lyapunov vectors(NLLVs)are explored preliminarily based on the Lorenz96 model.The results show that the machine learning model(Ens-ML)based on the ridge regression algorithm and the results of NLLV ensemble forecasting can effectively improve the overall forecasting skill.The Ens-ML outperforms the ensemble-averaged forecasting(EnsAve)and control forecasts(Ctrl)as well as the machine learning model based on Ctrl results(Ctrl-ML).It is also found that the improvement of forecasting skill depends on the total number of ensemble members used in the Ens-ML model,i.e.the increase of the number of ensemble members is conducive to the improvement of forecasting skill and to the decrease of overfitting in the early stage.By comparing the performances among different experimental cases,we find that the experimental forecasting errors of Ens-ML,Ctrl-ML and EnsAve are gradually smaller than that of Ctrl as the forecasting time increases.The attractors forecasted by Ens-ML,Ctrl-ML and EnsAve are also analyzed.Their attractor probability distributions show a contraction of the value domain,an increase in kurtosis and a convergence to the mean,especially for Ens-ML.