Detailing Climatic Trends of Temperature and Precipitation in the Territory of the Russian Federation Using Quantile Regression and Clustering

Using quantile regression, which gives a detailed picture of climate trends for any of the quantiles in the range from 0 to 1, calculations of climate trends of maximum daily temperature and daily precipitation for the territory of the Russian Federation are made. For calculations, observational data from more than 1450 meteorological stations for the period from 1987 to 2021 inclusive are used. The clustering of the calculated climate trends is carried out and the spatial features of the clusters of meteorological stations and their correspondence to the quasi-homogeneous climatic regions used for assessments in the annual “Roshydromet Reports on the Peculiarities of the Climate on the Territory of the Russian Federation” have been analyzed. Fifteen clusters of meteorological stations on the territory of the Russian Federation have been identified, such that the stations included in the clusters demonstrate similar features (“patterns”) of trends in maximum daily temperature and daily precipitation amounts. At the same time, the values that determine the geographical location of weather stations are not involved in the clustering process. The results of the work can be used to refine the climatic zoning of the territory of the Russian Federation, taking into account the features of trends in long-term changes in surface climate.

Authors

Institutions

Publication Details

Journal
Izvestiya Atmospheric and Oceanic Physics
Published
2024-12-01
DOI
https://doi.org/10.1134/s0001433824700543
Citations
1
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
0.28
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detailing Climatic Trends of Temperature and Precipitation in the Territory of the Russian Federation Using Quantile Regression and Clustering

A. S. Lavrov, Alexander Sterin
1 citations
Izvestiya Atmospheric and Oceanic Physics
Climate variability and models
0.28
article

Detailing Climatic Trends of Temperature and Precipitation in the Territory of the Russian Federation Using Quantile Regression and Clustering

A. S. Lavrov, Alexander Sterin
article en
1 citations

Abstract

Using quantile regression, which gives a detailed picture of climate trends for any of the quantiles in the range from 0 to 1, calculations of climate trends of maximum daily temperature and daily precipitation for the territory of the Russian Federation are made. For calculations, observational data from more than 1450 meteorological stations for the period from 1987 to 2021 inclusive are used. The clustering of the calculated climate trends is carried out and the spatial features of the clusters of meteorological stations and their correspondence to the quasi-homogeneous climatic regions used for assessments in the annual “Roshydromet Reports on the Peculiarities of the Climate on the Territory of the Russian Federation” have been analyzed. Fifteen clusters of meteorological stations on the territory of the Russian Federation have been identified, such that the stations included in the clusters demonstrate similar features (“patterns”) of trends in maximum daily temperature and daily precipitation amounts. At the same time, the values that determine the geographical location of weather stations are not involved in the clustering process. The results of the work can be used to refine the climatic zoning of the territory of the Russian Federation, taking into account the features of trends in long-term changes in surface climate.

Izvestiya Atmospheric and Oceanic PhysicsVol. 60(S1)
All Russian Research Institute of Hydrometeorological information World Data Center (RU)
Climate action
Openalex Percentile: Top 39%
Climate variability and models
0.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.