Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3273-2022
https://doi.org/10.5194/essd-14-3273-2022
Data description paper
 | 
15 Jul 2022
Data description paper |  | 15 Jul 2022

GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning

Qian He, Ming Wang, Kai Liu, Kaiwen Li, and Ziyu Jiang

Related authors

GPRChinaTemp1km: a high-resolution monthly air temperature dataset for China (1951–2020) based on machine learning
Qian He, Ming Wang, Kai Liu, Kaiwen Li, and Ziyu Jiang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-267,https://doi.org/10.5194/essd-2021-267, 2021
Manuscript not accepted for further review
Short summary

Related subject area

Meteorology
Global projections of heat stress at high temporal resolution using machine learning
Pantelis Georgiades, Theo Economou, Yiannis Proestos, Jose Araya, Jos Lelieveld, and Marco Neira
Earth Syst. Sci. Data, 17, 1153–1171, https://doi.org/10.5194/essd-17-1153-2025,https://doi.org/10.5194/essd-17-1153-2025, 2025
Short summary
A new high-resolution multi-drought-index dataset for mainland China
Qi Zhang, Chiyuan Miao, Jiajia Su, Jiaojiao Gou, Jinlong Hu, Xi Zhao, and Ye Xu
Earth Syst. Sci. Data, 17, 837–853, https://doi.org/10.5194/essd-17-837-2025,https://doi.org/10.5194/essd-17-837-2025, 2025
Short summary
Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen
Earth Syst. Sci. Data, 16, 5753–5766, https://doi.org/10.5194/essd-16-5753-2024,https://doi.org/10.5194/essd-16-5753-2024, 2024
Short summary
HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada)
Matilde García-Valdecasas Ojeda, Feliciano Solano-Farias, David Donaire-Montaño, Emilio Romero-Jiménez, Juan José Rosa-Cánovas, Yolanda Castro-Díez, Sonia Raquel Gámiz-Fortis, and María Jesús Esteban-Parra
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-522,https://doi.org/10.5194/essd-2024-522, 2024
Revised manuscript accepted for ESSD
Short summary
The PAZ polarimetric radio occultation research dataset for scientific applications
Ramon Padullés, Estel Cardellach, Antía Paz, Santi Oliveras, Douglas C. Hunt, Sergey Sokolovskiy, Jan-Peter Weiss, Kuo-Nung Wang, F. Joe Turk, Chi O. Ao, and Manuel de la Torre Juárez
Earth Syst. Sci. Data, 16, 5643–5663, https://doi.org/10.5194/essd-16-5643-2024,https://doi.org/10.5194/essd-16-5643-2024, 2024
Short summary

Cited articles

Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.: TerraClimate, a high-resolution global data set of monthly climate and climatic water balance from 1958–2015, Scientific Data, 5, 170191, https://doi.org/10.1038/sdata.2017.191, 2018. 
Alizamir, M., Kisi, O., Ahmed, A. N., Mert, C., Fai, C. M., Kim, S., Kim, N. W., and El-Shafie, A.: Advanced machine learning model for better prediction accuracy of soil temperature at different depths, PLOS ONE, 15, e0231055, https://doi.org/10.1371/journal.pone.0231055, 2020. 
Alvarez, O., Guo, Q., Klinger, R. C., Li, W., and Doherty, P.: Comparison of elevation and remote sensing derived products as auxiliary data for climate surface interpolation, Int. J. Climatol., 34, 2258–2268, https://doi.org/10.1002/joc.3835, 2014. 
Amini, M. A., Torkan, G., Eslamian, S., Zareian, M. J., and Adamowski, J. F.: Analysis of deterministic and geostatistical interpolation techniques for mapping meteorological variables at large watershed scales, Acta Geophys., 67, 191–203, https://doi.org/10.1007/s11600-018-0226-y, 2019. 
Appelhans, T., Mwangomo, E., Hardy, D. R., Hemp, A., and Nauss, T.: Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania, Spatial Statistics, 14, 91–113, https://doi.org/10.1016/j.spasta.2015.05.008, 2015. 
Download
Short summary
We used three machine learning models and determined that Gaussian process regression (GPR) is best suited to the interpolation of air temperature data for China. The GPR-derived results were compared with that of traditional interpolation techniques and existing data sets and it was found that the accuracy of the GPR-derived data was better. Finally, we generated a gridded monthly air temperature data set with 1 km resolution and high accuracy for China (1951–2020) using the GPR model.
Share
Altmetrics
Final-revised paper
Preprint