Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-331-2023
https://doi.org/10.5194/essd-15-331-2023
Data description paper
 | 
19 Jan 2023
Data description paper |  | 19 Jan 2023

A long-term 1 km monthly near-surface air temperature dataset over the Tibetan glaciers by fusion of station and satellite observations

Jun Qin, Weihao Pan, Min He, Ning Lu, Ling Yao, Hou Jiang, and Chenghu Zhou

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Latest update: 29 Jun 2024
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Short summary
To enrich a glacial surface air temperature (SAT) product of a long time series, an ensemble learning model is constructed to estimate monthly SATs from satellite land surface temperatures at a spatial resolution of 1 km, and long-term glacial SATs from 1961 to 2020 are reconstructed using a Bayesian linear regression. This product reveals the overall warming trend and the spatial heterogeneity of warming on TP glaciers and helps to monitor glacier warming, analyze glacier evolution, etc.
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