基于机器学习的中国冬季气温影响因子分析及模型估算

.Machine learning-based regression analysis of the winter temperature anomalies in China and associated influencing factors

使用1951—2021年160个中国国家级气象观测站冬季平均气温及多项大气环流及海温等指数,用机器学习方法研究影响中国冬季气温异常的大气环流及海温等外强迫因子,并建立估算拟合模型,评价筛选出的影响因子组合对中国冬季气温异常分布的贡献。使用最小绝对收缩和选择算子(LASSO)算法提取与冬季气温异常相关的影响因子。为体现特征因子之间非线性关系,使用泰勒公式对筛选后的特征进行多项式增广。使用最小二乘梯度提升决策树(LS-GBDT)算法对筛选出的特征因子与冬季气温异常之间的非线性关系进行估算拟合。结果表明,机器学习方法能够对影响冬季气温异常的特征因子进行合理筛选与重要性分析,建立的估算模型在一定程度上体现了气候系统特征因子与冬季气温距平的非线性联系。本研究为了解中国冬季气温异常分布的影响因素及其模拟与估算提供了新方法和途径。

In this study,the mean winter temperature collected at 160 stations in China from 1951 to 2021 and a number of atmospheric circulation and sea temperature indices are used to investigate the relationship between the distribution of winter temperature anomalies and the atmospheric circulation and external forcing factors.A model of fitting is also established by using machine learning methods.In this way,we can understand to what extent the screened combination of influencing factors can explain the distribution of winter temperature anomalies in China.The Least Absolute Shrinkage and Selection Operator(LASSO)algorithm is used to extract the influencing factors related to winter temperature anomalies.In addition,to reflect the nonlinear relationship between these factors,the original features are augmented to polynomial features using Taylor’s formula.To further study the nonlinear relationship between the selected factors,the Least Squares Gradient Boosting Decision Tree(LS-GBDT)algorithm is used to estimate and fit winter temperature.Experiments are conducted on the training samples and test samples respectively and have achieved good results.The result verifies that machine learning can be used to screen and analyze the importance of factors affecting winter temperature anomalies more reasonably,and the estimation model established can to a certain extent reflects the nonlinear relationship between the factors influencing the climate system and winter temperature.This work provides a new way to simulate and estimate distribution of the winter tmperature anomalies in China.