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

This dataset is developed to support the comprehensive observation and quantitative characterization of land–atmosphere interactions over the Tibetan Plateau (TP), aiming to address key limitations in existing observations, including fragmented spatial coverage, inconsistent processing standards, and insufficient multi-component integrated measurements. It integrates observations from 15 stations distributed across monsoon-dominated regions, westerlies-controlled regions, and their transitional zones over the TP (Baingoin, Burang, Coqen, Gyirong, Mangai, Mangkam, MAWORS, Medog, NAMORS, NASED, Nyima, Qamdo, QOMS, SETORS, and Shuanghu), thereby providing systematic regional coverage of land–atmosphere interaction processes under different climatic regimes.In terms of data content, the dataset provides multi-component observations at an hourly temporal resolution, including gradient meteorological variables, radiation components, soil hydrothermal parameters, and turbulent fluxes. These measurements enable a vertically resolved characterization of land surface processes and their coupling with the atmosphere. All data have been processed using standardized protocols with harmonized temporal resolutions and consistent variable definitions, and have undergone rigorous quality control procedures (0 for correct, 1 for suspicious, 2 for erroneous, 4 for gap filled, 8 for missing, and 9 for not quality controlled), ensuring high reliability and strong internal consistency across stations and variables.This dataset provides a robust observational foundation for advancing the understanding of land–atmosphere coupling mechanisms over the Tibetan Plateau, particularly under complex terrain conditions. It is well suited for applications such as the evaluation and improvement of land surface models and Earth system models, validation of satellite-derived products, and multi-source data assimilation. In addition, the dataset supports the development of hybrid physics–machine learning approaches for data quality control and gap-filling, thereby enhancing the usability and scientific value of observations in complex high-altitude environments.