Articles | Volume 18, issue 1
https://doi.org/10.5194/essd-18-371-2026
https://doi.org/10.5194/essd-18-371-2026
Data description article
 | 
14 Jan 2026
Data description article |  | 14 Jan 2026

An hourly 0.02° total precipitable water dataset for all-weather conditions over the Tibetan Plateau through the fusion of observations of geostationary and multi-source microwave satellites

Qixiang Sun, Husi Letu, Yongqian Wang, Peng Zhang, Hong Liang, Chong Shi, Shuai Yin, Jiancheng Shi, and Dabin Ji

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Cited articles

Abbasi, B., Qin, Z., Du, W., Fan, J., Zhao, C., Hang, Q., Zhao, S., and Li, S.: An algorithm to retrieve total precipitable water vapor in the atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, Remote Sensing, 12, 3469, https://doi.org/10.3390/rs12213469, 2020. 
Alshawaf, F., Fersch, B., Hinz, S., Kunstmann, H., Mayer, M., and Meyer, F. J.: Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations, Hydrol. Earth Syst. Sci., 19, 4747–4764, https://doi.org/10.5194/hess-19-4747-2015, 2015. 
Bao, F., Letu, H., Shang, H., Ri, X., Chen, D., Yao, T., Wei, L., Tang, C., Yin, S., Ji, D., Lei, Y., Shi, C., Peng, Y., and Shi, J.: Advancing cloud classification over the Tibetan Plateau: A new algorithm reveals seasonal and diurnal variations, Geophysical Research Letters, 51, e2024GL109590, https://doi.org/10.1029/2024GL109590, 2024. 
Bao, S., Letu, H., Zhao, J., Shang, H., Lei, Y., Duan, A., Chen, B., Bao, Y., He, J., Wang, T., Ji, D., Tana, G., and Shi, J.: Spatiotemporal distributions of cloud parameters and their response to meteorological factors over the Tibetan Plateau during 2003–2015 based on MODIS data, International Journal of Climatology, 39, 532–543, https://doi.org/10.1002/joc.5826, 2019. 
Bonafoni, S., Mattioli, V., Basili, P., Ciotti, P., and Pierdicca, N.: Satellite-based retrieval of precipitable water vapor over land by using a neural network approach, IEEE Transactions on Geoscience and Remote Sensing, 49, 3236–3248, https://doi.org/10.1109/TGRS.2011.2114870, 2011. 
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Short summary
The Tibetan Plateau plays a vital role in Asia’s water cycle, but tracking water vapor in this mountainous region is difficult, especially under cloudy conditions. We developed a new satellite-based method to generate hourly water vapor data at 0.02-degree resolution from 2016 to 2022, now available at https://doi.org/10.11888/Atmos.tpdc.301518, which improves accuracy and reveals fine-scale moisture transport critical for understanding rainfall and extreme weather.
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