{"title":"Using Quantile Regression to Estimate Spatial Patterns of Surface Temperature Trends over the Territory of Russia","authors":"A. M. Sterin, A. S. Lavrov","doi":"10.1134/s0001433823140128","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This work involves calculations of climatic trends of anomalies in daily minimum, maximum, and average air temperatures based on the quantile regression method (QRM), which allows one to estimate trends in detail for any quantile in the range of quantile values from 0 to 1. Based on the QRM climate trend calculations detailed for different quantiles of trends in daily air temperature anomalies, clustering of more than 1400 meteorological stations of Russia is performed. Clustering is carried out in the multidimensional space, the formation of which takes into account seasonal peculiarities of the QRM trends of anomalies for three types of daily temperatures (daily minimum, maximum, and average temperatures) and features of the QRM trends in different parts of the quantile range. Twelve clusters of weather stations have been distinguished in the created multidimensional space using the k-means method. The stations that are included in each of the distinguished clusters are similar in terms of manifestation of the QRM trends of temperature. Despite the absence of characteristics of the geographical location of the observation stations among the variables of the multidimensional space, the stations within each of the twelve distinguished clusters are situated geographically quite compactly. The geographical distribution of stations assigned to different clusters is demonstrated and discussed. Based on the results of clustering, some features of quantile trends of temperature anomalies of specific seasons within the groups of stations assigned to individual clusters are described. Differences in manifestation of quantile trends between 12 clusters of Russian stations distinguished based on QRM quantile trends are obvious. At the same time, however, significant similarities can be observed between some individual pairs of clusters. The approaches and results of this work can be used to improve the climatic zoning of the Russian territory, which seems to be very relevant for the preparation and implementation of regional plans of adaptation to climate changes. The results can also be used for solving various applied climatology problems based on calculations of quantiles of different meteorological parameters.</p>","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1134/s0001433823140128","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This work involves calculations of climatic trends of anomalies in daily minimum, maximum, and average air temperatures based on the quantile regression method (QRM), which allows one to estimate trends in detail for any quantile in the range of quantile values from 0 to 1. Based on the QRM climate trend calculations detailed for different quantiles of trends in daily air temperature anomalies, clustering of more than 1400 meteorological stations of Russia is performed. Clustering is carried out in the multidimensional space, the formation of which takes into account seasonal peculiarities of the QRM trends of anomalies for three types of daily temperatures (daily minimum, maximum, and average temperatures) and features of the QRM trends in different parts of the quantile range. Twelve clusters of weather stations have been distinguished in the created multidimensional space using the k-means method. The stations that are included in each of the distinguished clusters are similar in terms of manifestation of the QRM trends of temperature. Despite the absence of characteristics of the geographical location of the observation stations among the variables of the multidimensional space, the stations within each of the twelve distinguished clusters are situated geographically quite compactly. The geographical distribution of stations assigned to different clusters is demonstrated and discussed. Based on the results of clustering, some features of quantile trends of temperature anomalies of specific seasons within the groups of stations assigned to individual clusters are described. Differences in manifestation of quantile trends between 12 clusters of Russian stations distinguished based on QRM quantile trends are obvious. At the same time, however, significant similarities can be observed between some individual pairs of clusters. The approaches and results of this work can be used to improve the climatic zoning of the Russian territory, which seems to be very relevant for the preparation and implementation of regional plans of adaptation to climate changes. The results can also be used for solving various applied climatology problems based on calculations of quantiles of different meteorological parameters.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
