基于PCA-ARMA和PCC的气候变化预测与异常分析

Shudong Guo, Weisong Qiao, Binbin Chen, Bo Wang
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引用次数: 2

摘要

气候变化作为一个重要的环境问题,近几十年来得到了广泛的研究。一方面,气候预测是政策制定者应对气候变化的重要组成部分,受到了广泛关注。另一方面,全球范围内出现的一些异常天气似乎与气候变化有关,例如全球温室效应,但目前对这种关系的研究很少。因此,本文提出了两种基于统计数据的气候和气象模型:1)基于主成分分析(PCA)的自回归移动平均(ARMA)预测模型和2)基于Pearson相关系数(PCC)的异常分析模型。首先,我们提出了未来25年气候变化的PCA-ARMA预测模型,包括两个步骤:1)利用过去75年的数据生成新的PCA数据约简分量;2)在第一步的基础上进行未来25年的ARMA预测。在此基础上,以PCC为主要模型,建立了气候变化与极端寒冷天气等异常天气的关系模型。本文收集了相关数据,通过这两个模型得到了相应的结果,表明我们的预测拟合良好,异常天气与气候变化密切相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction and Abnormality Analysis of Climate Change Based on PCA-ARMA and PCC
Climate change, as an important environmental issue, has been widely investigated in recent decades. On the one hand, the climate prediction is an essential part for policy makers to response to the change of climate, which has received many attentions. On the other hand, there is another challenging problem facing us today that some abnormal weathers occur globally, which seems to have relation to climate change, e.g., the global greenhouse effect, but with little existing researches on this relation. Therefore, in this paper, we propose two kinds of climatic and meteorological models based on statistical data: 1) an autoregressive-moving-average (ARMA) prediction model with principal component analysis (PCA) and 2) abnormal analysis model based on Pearson correlation coefficient (PCC). In detail, firstly, we propose the PCA-ARMA prediction model to predict climate change in the next 25 years, including two steps: 1) generation of new components for data reduction by PCA using the past 75 years' data, and 2) prediction based on step 1 by ARMA for next 25 years. Then, we establish another model to find out the relation between climate change and abnormal weathers, e.g., the extreme cold weather, mainly by PCC. The relevant data are collected, and by these two models, we get the corresponding results, which show that our prediction fits well and the abnormal weather is strongly connected with the climate change.
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