Zhipeng XIE, Weiqiang Ma, Binbin Wang, Cunbo Han, Bin Ma, Xuelong Chen, Yongjie Wang, Maoshan Li, Lei Zhong, Yunshuai Zhang, Weiyao Ma, Xingdong Shi, Weimo Li, Zhengling Cai, Wei HU, Lian Liu, Nan Yao, Xin Xu, Hanyin Xu, Lijun Sun, Jianan He, Qiang Zhang, yue xu, Longtengfei Ma, Wenqing Zhao, Xuan Li, Yangkun Lyu, Yaoming Ma. 2026: A comprehensive hourly land-atmosphere interaction dataset from a coordinated 15-station network spanning the environmental gradients of the Tibetan Plateau (2021-2024). Adv. Atmos. Sci., https://doi.org/10.1007/s00376-026-6282-3
Citation: Zhipeng XIE, Weiqiang Ma, Binbin Wang, Cunbo Han, Bin Ma, Xuelong Chen, Yongjie Wang, Maoshan Li, Lei Zhong, Yunshuai Zhang, Weiyao Ma, Xingdong Shi, Weimo Li, Zhengling Cai, Wei HU, Lian Liu, Nan Yao, Xin Xu, Hanyin Xu, Lijun Sun, Jianan He, Qiang Zhang, yue xu, Longtengfei Ma, Wenqing Zhao, Xuan Li, Yangkun Lyu, Yaoming Ma. 2026: A comprehensive hourly land-atmosphere interaction dataset from a coordinated 15-station network spanning the environmental gradients of the Tibetan Plateau (2021-2024). Adv. Atmos. Sci., https://doi.org/10.1007/s00376-026-6282-3

A comprehensive hourly land-atmosphere interaction dataset from a coordinated 15-station network spanning the environmental gradients of the Tibetan Plateau (2021-2024)

  • Accurate characterization of land–atmosphere interactions over the Tibetan Plateau (TP) is hindered by fragmented measurements, inconsistent processing standards, and a scarcity of integrated multi-component observations. To address this, we present an integrated dataset from 15 stations spanning monsoon-dominated, westerlies-controlled, and transitional regions of the TP. The dataset features hourly gradient meteorological variables, radiation components, soil hydrothermal measurements, and turbulent fluxes, providing a vertically resolved description of land surface processes. Processed under standardized protocols with harmonized resolutions, consistent variable definitions, and rigorous quality control, the data ensures high reliability and internal consistency. This dataset provides a robust observational foundation for advancing process-based understanding and model representation of land–atmosphere coupling over the TP. It is expected to support land surface and Earth system model evaluation, satellite product validation, and data assimilation. Furthermore, it enables hybrid physics-machine learning approaches for improved in-situ data quality control and gap-filling in complex high-altitude environments. The dataset is available at Tibetan Plateau Data Center (https://data.tpdc.ac.cn/zh-hans/data/fdc75381-7e58-4cef-9f93-dcedb71e8ad6) and Science Data Bank (https://www.scidb.cn/s/BnyE3a).
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