ANOVA (Benova) correction in relative homogenization: Why it is indispensable

IF 3.5 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES International Journal of Climatology Pub Date : 2024-08-14 DOI:10.1002/joc.8594
Peter Domonkos, Lars Magnus Torvald Joelsson
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Abstract

This paper reviews the role of ANOVA correction model in the homogenization of climatic time series. In the present context ANOVA has only weak connection to its original meaning (analysis of variance), so we propose the new name “Benova” to replace the confusing old name. In the linear model of Benova corrections (hereafter Benova) the information of statistically detected inhomogeneities and metadata are jointly considered for all time series of a given climatic region. Benova has indisputable advantages on the accuracy of homogenization results, and this has both theoretical and practical evidence. The study presents two principal versions of Benova: in simple Benova the climate signal is presumed to be spatially invariant, while in weighted Benova the spatial variation of climate is considered. In Benova models usually only breaks (i.e., sudden shifts of section mean) are considered, but this restriction has practical reasons, rather than theoretical limits, and the study shows an extended version of the method with which trend-like inhomogeneity biases can also be removed. Benova can be used for a group of time series covering varied time periods, the operations can be performed in any time resolution, and statistical characteristics others than climatic means can also be homogenized by the method. Benova can be used together with any break detection method. The study discusses the likely reasons of the relatively slow spread in practical application. PRODIGE was the first homogenization method which used Benova corrections. Until now, all homogenization methods including Benova corrections include also the same kind break detection method, i.e., penalized maximum likelihood method with step function fitting. The brief descriptions of two modern methods of this method family, that is, ACMANT and Bart methods, are also provided.

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相对同质化中的方差分析(Benova)校正:为何不可或缺
本文回顾了方差分析修正模型在气候时间序列同质化中的作用。在目前情况下,方差分析与其原始含义(方差分析)只有微弱的联系,因此我们提出了 "Benova "这个新名称,以取代令人困惑的旧名称。在贝诺瓦修正线性模型(以下简称 "贝诺瓦")中,对给定气候区域的所有时间序列而言,统计检测到的非均质性信息和元数据信息被共同考虑。贝诺瓦在均质化结果的准确性方面具有无可争议的优势,这一点在理论和实践上都得到了证明。研究提出了两种主要的贝诺瓦版本:简单贝诺瓦假定气候信号在空间上不变,而加权贝诺瓦则考虑气候的空间变化。在贝诺瓦模型中,通常只考虑断裂(即断面平均值的突然移动),但这种限制有其实际原因,而不是理论限制。Benova 可用于一组涵盖不同时间段的时间序列,操作可在任何时间分辨率下进行,气候平均值以外的统计特征也可通过该方法进行同质化。Benova 可以与任何断点检测方法一起使用。本研究讨论了在实际应用中推广相对缓慢的可能原因。PRODIGE 是第一种使用贝诺瓦修正的均质化方法。到目前为止,所有包含贝诺瓦修正的均质化方法都包含同一种断裂检测方法,即带有阶跃函数拟合的受惩罚最大似然法。本文还简要介绍了该方法系列中的两种现代方法,即 ACMANT 和 Bart 方法。
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来源期刊
International Journal of Climatology
International Journal of Climatology 地学-气象与大气科学
CiteScore
7.50
自引率
7.70%
发文量
417
审稿时长
4 months
期刊介绍: The International Journal of Climatology aims to span the well established but rapidly growing field of climatology, through the publication of research papers, short communications, major reviews of progress and reviews of new books and reports in the area of climate science. The Journal’s main role is to stimulate and report research in climatology, from the expansive fields of the atmospheric, biophysical, engineering and social sciences. Coverage includes: Climate system science; Local to global scale climate observations and modelling; Seasonal to interannual climate prediction; Climatic variability and climate change; Synoptic, dynamic and urban climatology, hydroclimatology, human bioclimatology, ecoclimatology, dendroclimatology, palaeoclimatology, marine climatology and atmosphere-ocean interactions; Application of climatological knowledge to environmental assessment and management and economic production; Climate and society interactions
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