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<p>The Qinghai-Tibet Plateau, often referred to as the “Roof of the World”, has temperature variations that significantly affect both the local and surrounding ecosystems. Qinghai Province, located at the northeastern gateway to the plateau, serves as a microcosm of the plateau’s broader temperature changes. Therefore, it is crucial to study the temperature changes in Qinghai Province. However, existing meteorological data, including ground observations, atmospheric reanalysis data, and satellite remote sensing data, suffer from low accuracy due to limitations in time and space. In this research, we introduce a deep learning approach to integrate multi-source temperature data and reconstruct a 10-year near-surface temperature dataset with a temporal resolution of 1 hour and a spatial resolution of 0.01°. The reconstruction of near-surface temperature was performed by inputting two atmospheric reanalysis products-European Centre for Medium-Range Weather Forecasts Regional Reanalysis Version 5 (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)-along with auxiliary variables that influence temperature, into the newly developed Temporal Gated Positional Model (TGPM) deep learning model. The reconstructed dataset from the TGPM model was then compared to ground observation data and evaluated against datasets generated by five other machine learning methods. The results show that the fusion dataset generated by the TGPM model closely fits the target data, outperforming the products from the other five methods across all evaluation metrics. The correlation coefficient (CC), coefficient of determination (R<sup>2</sup>), mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and relative bias (RB) for the TGPM near-surface temperature dataset were 0.9949, 0.9896, 0.7713°C, 1.0901°C, 1.1888 °C<sup>2</sup>, and 4.02%, respectively. Overall, the deep learning method proposed in this study is more suitable for near-surface temperature reconstruction in Qinghai Province, providing a robust foundation for further research on temperature variations and ecological conservation in both Qinghai and the broader Qinghai-Tibet Plateau. The reconstructed dataset also offers actionable insights for climate policy formulation, disaster risk reduction, and ecosystem management in high-altitude regions.</p>

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A deep learning approach for reconstructing hourly surface air temperature in Qinghai for the period 2006-2015

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月6日

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  • Qiyuan Zhang,
  • Xiaodan Zhang,
  • Yongkun Zhang,
  • Tong Zhao,
  • Shaojie You,
  • Wei Huo,
  • Naihao Hu

摘要

The Qinghai-Tibet Plateau, often referred to as the “Roof of the World”, has temperature variations that significantly affect both the local and surrounding ecosystems. Qinghai Province, located at the northeastern gateway to the plateau, serves as a microcosm of the plateau’s broader temperature changes. Therefore, it is crucial to study the temperature changes in Qinghai Province. However, existing meteorological data, including ground observations, atmospheric reanalysis data, and satellite remote sensing data, suffer from low accuracy due to limitations in time and space. In this research, we introduce a deep learning approach to integrate multi-source temperature data and reconstruct a 10-year near-surface temperature dataset with a temporal resolution of 1 hour and a spatial resolution of 0.01°. The reconstruction of near-surface temperature was performed by inputting two atmospheric reanalysis products-European Centre for Medium-Range Weather Forecasts Regional Reanalysis Version 5 (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)-along with auxiliary variables that influence temperature, into the newly developed Temporal Gated Positional Model (TGPM) deep learning model. The reconstructed dataset from the TGPM model was then compared to ground observation data and evaluated against datasets generated by five other machine learning methods. The results show that the fusion dataset generated by the TGPM model closely fits the target data, outperforming the products from the other five methods across all evaluation metrics. The correlation coefficient (CC), coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and relative bias (RB) for the TGPM near-surface temperature dataset were 0.9949, 0.9896, 0.7713°C, 1.0901°C, 1.1888 °C2, and 4.02%, respectively. Overall, the deep learning method proposed in this study is more suitable for near-surface temperature reconstruction in Qinghai Province, providing a robust foundation for further research on temperature variations and ecological conservation in both Qinghai and the broader Qinghai-Tibet Plateau. The reconstructed dataset also offers actionable insights for climate policy formulation, disaster risk reduction, and ecosystem management in high-altitude regions.