Articles | Volume 17, issue 12
https://doi.org/10.5194/essd-17-7251-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-7251-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A 1 km daily high-accuracy meteorological dataset of air temperature, atmospheric pressure, relative humidity, and sunshine duration across China (1961–2021)
Keke Zhao
State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Denghua Yan
CORRESPONDING AUTHOR
State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Tianling Qin
State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Chenhao Li
State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Dingzhi Peng
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Yifan Song
State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W14-2025, 91–96, https://doi.org/10.5194/isprs-archives-XLVIII-4-W14-2025-91-2025, https://doi.org/10.5194/isprs-archives-XLVIII-4-W14-2025-91-2025, 2025
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Ying Li, Chenghao Wang, Ru Huang, Denghua Yan, Hui Peng, and Shangbin Xiao
Hydrol. Earth Syst. Sci., 26, 6413–6426, https://doi.org/10.5194/hess-26-6413-2022, https://doi.org/10.5194/hess-26-6413-2022, 2022
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Spatial quantification of oceanic moisture contribution to the precipitation over the Tibetan Plateau (TP) contributes to the reliable assessments of regional water resources and the interpretation of paleo archives in the region. Based on atmospheric reanalysis datasets and numerical moisture tracking, this work reveals the previously underestimated oceanic moisture contributions brought by the westerlies in winter and the overestimated moisture contributions from the Indian Ocean in summer.
Baisha Weng, Zhaoyu Dong, Yuheng Yang, Denghua Yan, Mengyu Li, and Yuhang Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2022-1290, https://doi.org/10.5194/egusphere-2022-1290, 2022
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The study selected a structural equation model to construct the turnover rate of amino sugars with soil physicochemical properties and extracellular enzymes under the warming and increased precipitation scenarios. The results of this study answer the mechanism of action of warming and precipitation on the effect of soil amino sugars which will play an important scientific and technical support role in the development of plateau agriculture and carbon and nitrogen cycles.
Tongtiegang Zhao, Haoling Chen, Yu Tian, Denghua Yan, Weixin Xu, Huayang Cai, Jiabiao Wang, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 26, 4233–4249, https://doi.org/10.5194/hess-26-4233-2022, https://doi.org/10.5194/hess-26-4233-2022, 2022
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This paper develops a novel set operations of coefficients of determination (SOCD) method to explicitly quantify the overlapping and differing information for GCM forecasts and ENSO teleconnection. Specifically, the intersection operation of the coefficient of determination derives the overlapping information for GCM forecasts and the Niño3.4 index, and then the difference operation determines the differing information in GCM forecasts (Niño3.4 index) from the Niño3.4 index (GCM forecasts).
Ying Li, Chenghao Wang, Hui Peng, Shangbin Xiao, and Denghua Yan
Hydrol. Earth Syst. Sci., 25, 4759–4772, https://doi.org/10.5194/hess-25-4759-2021, https://doi.org/10.5194/hess-25-4759-2021, 2021
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Precipitation change in the Three Gorges Reservoir Region (TGRR) plays a critical role in the operation and regulation of the Three Gorges Dam and the protection of residents and properties. We investigated the long-term contribution of moisture sources to precipitation changes in this region with an atmospheric moisture tracking model. We found that southwestern source regions (especially the southeastern tip of the Tibetan Plateau) are the key regions that control TGRR precipitation changes.
Cited articles
Bisong, E.: The Multilayer Perceptron (MLP), in: Building Machine Learning and Deep Learning Models on Google Cloud Platform, Apress, Berkeley, CA, 401–405, https://doi.org/10.1007/978-1-4842-4470-8_31, 2019.
Brocca, L., Crow, W. T., Ciabatta, L., Massari, C., De Rosnay, P., Enenkel, M., Hahn, S., Amarnath, G., Camici, S., Tarpanelli, A., and Wagner, W.: A Review of the Applications of ASCAT Soil Moisture Products, IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 10, 2285–2306, https://doi.org/10.1109/JSTARS.2017.2651140, 2017.
Che, T., Li, X., Liu, S., Li, H., Xu, Z., Tan, J., Zhang, Y., Ren, Z., Xiao, L., Deng, J., Jin, R., Ma, M., Wang, J., and Yang, X.: Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China, Earth Syst. Sci. Data, 11, 1483–1499, https://doi.org/10.5194/essd-11-1483-2019, 2019.
Choubin, B., Khalighi-Sigaroodi, S., Malekian, A., and Kişi, Ö.: Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals, Hydrological Sciences Journal, 61, 1001–1009, https://doi.org/10.1080/02626667.2014.966721, 2016.
Desai, M. and Shah, M.: An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN), Clinical eHealth, 4, 1–11, https://doi.org/10.1016/j.ceh.2020.11.002, 2021.
Duan, J.: Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction, Journal of the Franklin Institute, 356, 4716–4731, https://doi.org/10.1016/j.jfranklin.2019.01.046, 2019.
Fu, Y., Ma, Y., Zhong, L., Yang, Y., Guo, X., Wang, C., Xu, X., Yang, K., Xu, X., Liu, L., Fan, G., Li, Y., and Wang, D.: Land-surface processes and summer-cloud-precipitation characteristics in the Tibetan Plateau and their effects on downstream weather: a review and perspective, National Science Review, 7, 500–515, https://doi.org/10.1093/nsr/nwz226, 2020.
Gao, H.: Meteorological observation data of the Xiying River on the east section of the Qilian Mountains (2006–2010), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/AtmosphericPhysics.tpe.5.db, 2018.
Gao, Y. C. and Liu, M. F.: Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau, Hydrol. Earth Syst. Sci., 17, 837–849, https://doi.org/10.5194/hess-17-837-2013, 2013.
Hartmann, D. L.: Global physical climatology, 2nd edn., Elsevier, Amsterdam; Boston, 485 pp., ISBN 978-0-12-328531-7, 2016.
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first high-resolution meteorological forcing dataset for land process studies over China, Sci Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y, 2020.
He, J., Yang, K., Li, X., Tang, W., Shao, C., Jiang, Y., and Ding, B.: China meteorological forcing dataset v2.0 (1951–2020), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Atmos.tpdc.302088, 2024.
He, Q., Wang, M., Liu, K., Li, K., and Jiang, Z.: GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning, Earth Syst. Sci. Data, 14, 3273–3292, https://doi.org/10.5194/essd-14-3273-2022, 2022.
He, Y.: Homogenized daily sunshine duration at 2° × 2° over China from 1961 to 2022, National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Atmos.tpdc.301478, 2024.
He, Y., Wang, K., Yang, K., Zhou, C., Shao, C., and Yin, C.: Homogenized daily sunshine duration over China from 1961 to 2022, Earth Syst. Sci. Data, 17, 1595–1611, https://doi.org/10.5194/essd-17-1595-2025, 2025.
Hong, Z., Han, Z., Li, X., Long, D., Tang, G., and Wang, J.: Generation of an improved precipitation data set from multisource information over the Tibetan Plateau, Journal of Hydrometeorology, https://doi.org/10.1175/JHM-D-20-0252.1, 2021.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp., https://doi.org/10.1017/9781009157896, 2021.
Jarvis, A., Reuter, H., Nelson, A., and Guevara, E.: Hole-filled seamless SRTM data v4, International Centre for Tropical Agriculture (CIAT), http://srtm.csi.cgiar.org (last access: 25 November 2025), 2008.
Jing, Y., Lin, L., Li, X., Li, T., and Shen, H.: An attention mechanism based convolutional network for satellite precipitation downscaling over China, Journal of Hydrology, 613, 128388, https://doi.org/10.1016/j.jhydrol.2022.128388, 2022.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution, J. Hydrometeorol., 5, 487–503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004.
Karayilan, T. and Kilic, O.: Prediction of heart disease using neural network, in: 2017 International Conference on Computer Science and Engineering (UBMK), 2017 International Conference on Computer Science and Engineering (UBMK), Antalya, 719–723, https://doi.org/10.1109/UBMK.2017.8093512, 2017.
Khaki, M., Hendricks Franssen, H.-J., and Han, S. C.: Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation, Sci. Rep., 10, 18791, https://doi.org/10.1038/s41598-020-75710-5, 2020.
King, M. D., Menzel, W. P., Kaufman, Y. J., Tanre, D., Bo-Cai Gao, Platnick, S., Ackerman, S. A., Remer, L. A., Pincus, R., and Hubanks, P. A.: Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS, IEEE Trans. Geosci. Remote Sensing, 41, 442–458, https://doi.org/10.1109/TGRS.2002.808226, 2003.
Laiolo, P., Gabellani, S., Campo, L., Cenci, L., Silvestro, F., Delogu, F., Boni, G., Rudari, R., Puca, S., and Pisani, A. R.: Assimilation of remote sensing observations into a continuous distributed hydrological model: Impacts on the hydrologic cycle, in: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IGARSS 2015 – 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy, 1308–1311, https://doi.org/10.1109/IGARSS.2015.7326015, 2015.
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and Wood, E. F.: Inroads of remote sensing into hydrologic science during the WRR era, Water Resources Research, 51, 7309–7342, https://doi.org/10.1002/2015WR017616, 2015.
Li, J.: A review of spatial interpolation methods for environmental scientists, Geoscience Australia, Canberra, https://d28rz98at9flks.cloudfront.net/68229/Rec2008_023.pdf (last access: 25 November 2025), 2008.
Li, J. and Heap, A. D.: A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors, Ecological Informatics, 6, 228–241, https://doi.org/10.1016/j.ecoinf.2010.12.003, 2011.
Li, L. and Zha, Y.: Mapping relative humidity, average and extreme temperature in hot summer over China, Science of The Total Environment, 615, 875–881, https://doi.org/10.1016/j.scitotenv.2017.10.022, 2018.
Li, T., Zheng, X., Dai, Y., Yang, C., Chen, Z., Zhang, S., Wu, G., Wang, Z., Huang, C., Shen, Y., and Liao, R.: Mapping near-surface air temperature, pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution, Adv. Atmos. Sci., 31, 1127–1135, https://doi.org/10.1007/s00376-014-3190-8, 2014.
Liu, J., Shi, C., Sun, S., Liang, J., and Yang, Z.-L.: Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data, J. Meteorol. Res., 33, 1194–1206, https://doi.org/10.1007/s13351-019-9067-0, 2019.
Liu, N., Yan, Z., Tong, X., Jiang, J., Li, H., Xia, J., Lou, X., Ren, R., and Fang, Y.: Meshless Surface Wind Speed Field Reconstruction Based on Machine Learning, Adv. Atmos. Sci., 39, 1721–1733, https://doi.org/10.1007/s00376-022-1343-8, 2022.
Liu, S. M., Li, X., Xu, Z. W., Che, T., Xiao, Q., Ma, M. G., Liu, Q. H., Jin, R., Guo, J. W., Wang, L. X., Wang, W. Z., Qi, Y., Li, H. Y., Xu, T. R., Ran, Y. H., Hu, X. L., Shi, S. J., Zhu, Z. L., Tan, J. L., Zhang, Y., and Ren, Z. G.: The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China, Vadose Zone J., 17, 180072, https://doi.org/10.2136/vzj2018.04.0072, 2018.
Luo, L.: Shergyla Mountain meteorological data (2005–2017), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/AtmosphericPhysics.tpe.249395.db, 2019.
Luo, L. and Zhu, L.: Meteorological observation data of the comprehensive observation and research station of alpine environment in Southeast Tibet (2017–2018), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Meteoro.tpdc.270313, 2020.
Ma, Y.: Meteorological observation data from Qomolangma Station for atmospheric and environmental research (2005–2016), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/AtmosEnviron.tpe.0000014.file, 2018.
Ma, Y., Xie, Z., Chen, Y., Liu, S., Che, T., Xu, Z., Shang, L., He, X., Meng, X., Ma, W., Xu, B., Zhao, H., Wang, J., Wu, G., and Li, X.: Dataset of spatially extensive long-term quality-assured land–atmosphere interactions over the Tibetan Plateau, Earth Syst. Sci. Data, 16, 3017–3043, https://doi.org/10.5194/essd-16-3017-2024, 2024.
Martin, R. V.: Satellite remote sensing of surface air quality, Atmospheric Environment, 42, 7823–7843, https://doi.org/10.1016/j.atmosenv.2008.07.018, 2008.
Mason, J. A., Muller, P. O., Burt, J. E., and De Blij, H. J.: Physical geography: the global environment, Oxford University Press, ISBN 9780190246860, 2016.
Meng, X. and Li, Z.: Zoige Plateau Wetland Ecosystem Research Station meteorological dataset (2019–2022), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Atmos.tpdc.300548, 2023.
Pang, B., Yue, J., Zhao, G., and Xu, Z.: Statistical Downscaling of Temperature with the Random Forest Model, Advances in Meteorology, 2017, 1–11, https://doi.org/10.1155/2017/7265178, 2017.
Peixoto, J. P. and Oort, A. H.: Physics of climate, New York, NY (United States); American Institute of Physics, United States, ISBN 9780883187128, 1992.
Peng, S., Ding, Y., Liu, W., and Li, Z.: 1 km monthly temperature and precipitation dataset for China from 1901 to 2017, Earth Syst. Sci. Data, 11, 1931–1946, https://doi.org/10.5194/essd-11-1931-2019, 2019.
Qi, W., Liu, J., and Chen, D.: Evaluations and Improvements of GLDAS2.0 and GLDAS2.1 Forcing Data's Applicability for Basin Scale Hydrological Simulations in the Tibetan Plateau, J. Geophys. Res.-Atmos., 123, https://doi.org/10.1029/2018JD029116, 2018.
Ren, T., Liu, X., Niu, J., Lei, X., and Zhang, Z.: Real-time water level prediction of cascaded channels based on multilayer perception and recurrent neural network, Journal of Hydrology, 585, 124783, https://doi.org/10.1016/j.jhydrol.2020.124783, 2020.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004.
Sadeghi, M., Asanjan, A. A., Faridzad, M., Nguyen, P., Hsu, K., Sorooshian, S., and Braithwaite, D.: PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks, Journal of Hydrometeorology, 20, 2273–2289, https://doi.org/10.1175/JHM-D-19-0110.1, 2019.
Sheffield, J., Wood, E. F., Pan, M., Beck, H., Coccia, G., Serrat-Capdevila, A., and Verbist, K.: Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data-Poor Regions, Water Resources Research, 54, 9724–9758, https://doi.org/10.1029/2017WR022437, 2018.
Singh, A., Imtiyaz, M., Isaac, R. K., and Denis, D. M.: Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India, Agricultural Water Management, 104, 113–120, https://doi.org/10.1016/j.agwat.2011.12.005, 2012.
Singh, V. P.: Hydrologic modeling: progress and future directions, Geosci. Lett., 5, 15, https://doi.org/10.1186/s40562-018-0113-z, 2018.
Sorooshian, S., Hsu, K., Braithwaite, D., Ashouri, H., and NOAA CDR Program: NOAA Climate Data Record (CDR) of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Version 1 Revision 1, https://doi.org/10.7289/V51V5BWQ, 2014.
Sun, H. and Su, F.: Precipitation correction and reconstruction for streamflow simulation based on 262 rain gauges in the upper Brahmaputra of southern Tibetan Plateau, Journal of Hydrology, 590, 125484, https://doi.org/10.1016/j.jhydrol.2020.125484, 2020.
Tang, G., Ma, Y., Long, D., Zhong, L., and Hong, Y.: Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales, Journal of Hydrology, 533, 152–167, https://doi.org/10.1016/j.jhydrol.2015.12.008, 2016.
Wallace, J. M. and Hobbs, P. V.: Atmospheric science: an introductory survey, Elsevier, ISBN 9780127329512, 2006.
Wang, J. and Wu, G.: Meteorological observation data of Namuco multi-circle comprehensive observation and research station (2017–2018), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/AtmosphericPhysics.tpe.5.db, 2019.
Wang, W., Cui, W., Wang, X., and Chen, X.: Evaluation of GLDAS-1 and GLDAS-2 Forcing Data and Noah Model Simulations over China at the Monthly Scale, Journal of Hydrometeorology, 17, 2815–2833, https://doi.org/10.1175/JHM-D-15-0191.1, 2016.
Wang, Y., Yang, H., Yang, D., Qin, Y., Gao, B., and Cong, Z.: Spatial Interpolation of Daily Precipitation in a High Mountainous Watershed Based on Gauge Observations and a Regional Climate Model Simulation, Journal of Hydrometeorology, 18, 845–862, https://doi.org/10.1175/JHM-D-16-0089.1, 2017.
Weytjens, H., Lohmann, E., and Kleinsteuber, M.: Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet, Electron. Commer. Res., 21, 371–391, https://doi.org/10.1007/s10660-019-09362-7, 2021.
World Meteorological Organization (WMO): Guide to Meteorological Instruments and Methods of Observation, World Meteorological Organization, Geneva, https://www.weather.gov/media/epz/mesonet/CWOP-WMO8.pdf (last access: 25/11/2025), 2023.
Wu, H., Yang, Q., Liu, J., and Wang, G.: A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China, Journal of Hydrology, 584, 124664, https://doi.org/10.1016/j.jhydrol.2020.124664, 2020.
Wunch, D., Wennberg, P. O., Osterman, G., Fisher, B., Naylor, B., Roehl, C. M., O'Dell, C., Mandrake, L., Viatte, C., Kiel, M., Griffith, D. W. T., Deutscher, N. M., Velazco, V. A., Notholt, J., Warneke, T., Petri, C., De Maziere, M., Sha, M. K., Sussmann, R., Rettinger, M., Pollard, D., Robinson, J., Morino, I., Uchino, O., Hase, F., Blumenstock, T., Feist, D. G., Arnold, S. G., Strong, K., Mendonca, J., Kivi, R., Heikkinen, P., Iraci, L., Podolske, J., Hillyard, P. W., Kawakami, S., Dubey, M. K., Parker, H. A., Sepulveda, E., García, O. E., Te, Y., Jeseck, P., Gunson, M. R., Crisp, D., and Eldering, A.: Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON, Atmos. Meas. Tech., 10, 2209–2238, https://doi.org/10.5194/amt-10-2209-2017, 2017.
Xie, P., Joyce, R., Wu, S., Yoo, S.-H., Yarosh, Y., Sun, F., and Lin, R.: Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998, Journal of Hydrometeorology, 18, 1617–1641, https://doi.org/10.1175/JHM-D-16-0168.1, 2017.
Yang, J., Gong, P., Fu, R., Zhang, M., Chen, J., Liang, S., Xu, B., Shi, J., and Dickinson, R.: The role of satellite remote sensing in climate change studies, Nature Clim. Change, 3, 875–883, https://doi.org/10.1038/nclimate1908, 2013.
Yang, R. and Xing, B.: A Comparison of the Performance of Different Interpolation Methods in Replicating Rainfall Magnitudes under Different Climatic Conditions in Chongqing Province (China), Atmosphere, 12, 1318, https://doi.org/10.3390/atmos12101318, 2021.
Yu, W., Nan, Z., Wang, Z., Chen, H., Wu, T., and Zhao, L.: An Effective Interpolation Method for MODIS Land Surface Temperature on the Qinghai–Tibet Plateau, IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 8, 4539–4550, https://doi.org/10.1109/JSTARS.2015.2464094, 2015.
Zeng, Y., Hao, D., Huete, A., Dechant, B., Berry, J., Chen, J. M., Joiner, J., Frankenberg, C., Bond-Lamberty, B., Ryu, Y., Xiao, J., Asrar, G. R., and Chen, M.: Optical vegetation indices for monitoring terrestrial ecosystems globally, Nat. Rev. Earth Environ., 3, 477–493, https://doi.org/10.1038/s43017-022-00298-5, 2022.
Zhang, H., Immerzeel, W. W., Zhang, F., De Kok, R. J., Gorrie, S. J., and Ye, M.: Creating 1-km long-term (1980–2014) daily average air temperatures over the Tibetan Plateau by integrating eight types of reanalysis and land data assimilation products downscaled with MODIS-estimated temperature lapse rates based on machine learning, Int. J. Appl. Earth Obs., 97, 102295, https://doi.org/10.1016/j.jag.2021.102295, 2021.
Zhang, X., Yang, Y., Gao, H., Xu, S., Feng, J., and Qin, T.: Land Cover Changes and Driving Factors in the Source Regions of the Yangtze and Yellow Rivers over the Past 40 Years, Land, 13, 259, https://doi.org/10.3390/land13020259, 2024.
Zhao, K., Peng, D., Gu, Y., Luo, X., Pang, B., and Zhu, Z.: Temperature lapse rate estimation and snowmelt runoff simulation in a high-altitude basin, Sci. Rep., 12, 13638, https://doi.org/10.1038/s41598-022-18047-5, 2022a.
Zhao, K., Peng, D., Gu, Y., Pang, B., and Zhu, Z.: Daily precipitation dataset at 0.1° for the Yarlung Zangbo River basin from 2001 to 2015, Sci. Data, 9, 349, https://doi.org/10.1038/s41597-022-01471-7, 2022b.
Zhao, K., Yan, D., Qin, T., Li, C., Peng, D., and Song, Y.: China's 1km Daily Reconstructed Product of Six Meteorological Elements (1961–2021). National Tibetan Plateau/Third Pole Environment Data Center, https://doi.org/10.11888/Atmos.tpdc.301341, https://cstr.cn/18406.11.Atmos.tpdc.301341, 2024.
Zhang, Y.: Meteorological observation data of Kunsha Glacier (2015–2017), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Meteoro.tpdc.270086, 2018a.
Zhang, Y.: Meteorological observation dataset of Shiquan River Source (2012–2015), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Meteoro.tpdc.270548, 2018b.
Zhang, Z., Fang, S., and Han, J.: A daily sunshine duration (SD) dataset in China from Himawari AHI imagery (2016–2023), Earth Syst. Sci. Data, 17, 1427–1439, https://doi.org/10.5194/essd-17-1427-2025, 2025.
Zhou, P., Tang, J., Ma, M., Ji, D., and Shi, J.: High resolution Tibetan Plateau regional reanalysis 1961–present, Sci Data, 11, 444, https://doi.org/10.1038/s41597-024-03282-4, 2024.
Short summary
This study presents a high-quality daily weather dataset for all of China from 1961 to 2021, including air temperature, atmospheric pressure, relative humidity, and sunshine duration. It was produced using a reconstruction framework that combines thousands of ground observations with landform and elevation data. The dataset provides consistent weather information even in mountainous regions and supports studies on land surface and water processes, climate change, and environmental impacts.
This study presents a high-quality daily weather dataset for all of China from 1961 to 2021,...
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