A methodological approach for filling the gap in extreme daily temperature data: an application in the Calabria region (Southern Italy)

IF 2.8 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Emanuele Barca, Ilaria Guagliardi, Tommaso Caloiero
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Abstract

Regional studies are crucial for monitoring and managing the impacts of extreme climatic events. This phenomenon is particularly important in some areas, such as the Mediterranean region, which has been identified as one of the most responsive regions to climate change. In this regard, the analysis of large space-time sets of climatic data can provide potentially valuable information, although the datasets are commonly affected by the issue of missing data. This approach can significantly reduce the reliability of inferences derived from space-time data analysis. Consequently, the selection of an effective missing data recovery method is crucial since a poor dataset reconstruction could lead to misleading the decision makers’ judgments. In the present paper, a methodology that can enhance the confidence of the statistical analysis performed on the reconstructed data is presented. The basic assumption of the proposed methodology is that missing data within certain percentages cannot significantly change the shape or parameters of the complete data distribution. Therefore, by applying several missing data recovery methods whose reconstructed dataset better overlaps the original dataset, larger confidence is needed. After the gap filling procedure, the temporal tendencies of the annual daily minimum temperature (T < 0 °C) were analysed in the Calabria region (southern Italy) by applying a test for trend detection to 8 temperature series over a 30-year period (1990–2019). The results showed that there was a constant reduction in the duration of frosty days, indicating the reliability of the effect of climate change.

Abstract Image

填补极端日气温数据缺口的方法论:在卡拉布里亚地区(意大利南部)的应用
区域研究对于监测和管理极端气候事件的影响至关重要。这一现象在某些地区尤为重要,如地中海地区,该地区已被确定为对气候变化反应最灵敏的地区之一。在这方面,对大量时空气候数据集的分析可以提供潜在的宝贵信息,尽管这些数据集通常受到缺失数据问题的影响。这种方法会大大降低通过时空数据分析得出的推论的可靠性。因此,选择有效的缺失数据恢复方法至关重要,因为糟糕的数据集重建可能会误导决策者的判断。本文提出了一种可以提高对重建数据进行统计分析的可信度的方法。所提方法的基本假设是,一定百分比内的缺失数据不会显著改变完整数据分布的形状或参数。因此,通过应用几种缺失数据恢复方法,其重建的数据集与原始数据集有更好的重叠,就需要更大的置信度。在缺口填补程序之后,对卡拉布里亚地区(意大利南部)30 年间(1990-2019 年)的 8 个气温序列进行了趋势检测测试,分析了年日最低气温(T < 0 °C)的时间趋势。结果表明,霜冻日的持续时间不断缩短,表明气候变化影响的可靠性。
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来源期刊
Theoretical and Applied Climatology
Theoretical and Applied Climatology 地学-气象与大气科学
CiteScore
6.00
自引率
11.80%
发文量
376
审稿时长
4.3 months
期刊介绍: Theoretical and Applied Climatology covers the following topics: - climate modeling, climatic changes and climate forecasting, micro- to mesoclimate, applied meteorology as in agro- and forestmeteorology, biometeorology, building meteorology and atmospheric radiation problems as they relate to the biosphere - effects of anthropogenic and natural aerosols or gaseous trace constituents - hardware and software elements of meteorological measurements, including techniques of remote sensing
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