Identifying Time Patterns of Highland and Lowland Air Temperature Trends in Italy and UK across monthly and annual scales
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| Format: | Preprint |
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2024
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| _version_ | 1866929516173066240 |
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| author | Liyew, Chalachew Muluken Di Nardo, Elvira Meo, Rosa Ferraris, Stefano |
| author_facet | Liyew, Chalachew Muluken Di Nardo, Elvira Meo, Rosa Ferraris, Stefano |
| contents | This paper presents a statistical analysis of air temperature data from 32 stations in Italy and the UK up to 2000 m above sea level, from 2002 to 2021. The data came from both highland and lowland areas, in order to evaluate both the differences due to location, and elevation. The analysis focused on detecting trends at annual and monthly time scales, employing both ordinary least squares, robust S-estimator regression, and Mann-Kendall and Sen's slope methods. Then hierarchical clustering using Dynamic Time Warping (DTW) was applied to the monthly data to analyze the intra-annual pattern similarity of trends within and across the groups. Two different regions of Europe were chosen because of the different climate and temperature trends, namely the Northern UK (smaller trends) and the North-West Italian Alps (larger trends). The main novelty of the work is to show that stations having similar locations and altitudes have similar monthly slopes by quantifying them using DTW and clustering. These results reveal the nonrandomness of different trends along the year and among different parts of Europe, with a modest influence of altitude in wintertime. The findings revealed that group average trends were close to the NOAA values for the areas in Italy and the UK, confirming the validity of analyzing a small number of stations. More interestingly, intra-annual patterns were detected commonly at the stations of each of the groups, and clearly different between them Hierarchical clustering in combination with DTW showed consistent similarity between monthly patterns of means and trends within the group of stations and inconsistent similarity between patterns across groups. Distance correlation matrices also contributes to what is the main result of the paper, which is to clearly show the different temporal patterns in relation to location and (in some months) altitude. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17732 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identifying Time Patterns of Highland and Lowland Air Temperature Trends in Italy and UK across monthly and annual scales Liyew, Chalachew Muluken Di Nardo, Elvira Meo, Rosa Ferraris, Stefano Applications This paper presents a statistical analysis of air temperature data from 32 stations in Italy and the UK up to 2000 m above sea level, from 2002 to 2021. The data came from both highland and lowland areas, in order to evaluate both the differences due to location, and elevation. The analysis focused on detecting trends at annual and monthly time scales, employing both ordinary least squares, robust S-estimator regression, and Mann-Kendall and Sen's slope methods. Then hierarchical clustering using Dynamic Time Warping (DTW) was applied to the monthly data to analyze the intra-annual pattern similarity of trends within and across the groups. Two different regions of Europe were chosen because of the different climate and temperature trends, namely the Northern UK (smaller trends) and the North-West Italian Alps (larger trends). The main novelty of the work is to show that stations having similar locations and altitudes have similar monthly slopes by quantifying them using DTW and clustering. These results reveal the nonrandomness of different trends along the year and among different parts of Europe, with a modest influence of altitude in wintertime. The findings revealed that group average trends were close to the NOAA values for the areas in Italy and the UK, confirming the validity of analyzing a small number of stations. More interestingly, intra-annual patterns were detected commonly at the stations of each of the groups, and clearly different between them Hierarchical clustering in combination with DTW showed consistent similarity between monthly patterns of means and trends within the group of stations and inconsistent similarity between patterns across groups. Distance correlation matrices also contributes to what is the main result of the paper, which is to clearly show the different temporal patterns in relation to location and (in some months) altitude. |
| title | Identifying Time Patterns of Highland and Lowland Air Temperature Trends in Italy and UK across monthly and annual scales |
| topic | Applications |
| url | https://arxiv.org/abs/2409.17732 |