Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3273-2022
© Author(s) 2022. 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-14-3273-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Jul 2022
Data description paper |
| 15 Jul 2022
| 15 Jul 2022
GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning
Qian He
Academy of Disaster Reduction and Emergency Management, Beijing
Normal University, 100875 Beijing, China
Faculty of Geographical Science, Beijing Normal University, 100875
Beijing, China
Ming Wang
CORRESPONDING AUTHOR
Academy of Disaster Reduction and Emergency Management, Beijing
Normal University, 100875 Beijing, China
School of National Safety and Emergency Management, Beijing Normal
University, 100875 Beijing, China
Kai Liu
Academy of Disaster Reduction and Emergency Management, Beijing
Normal University, 100875 Beijing, China
School of National Safety and Emergency Management, Beijing Normal
University, 100875 Beijing, China
Kaiwen Li
Academy of Disaster Reduction and Emergency Management, Beijing
Normal University, 100875 Beijing, China
Faculty of Geographical Science, Beijing Normal University, 100875
Beijing, China
Ziyu Jiang
Academy of Disaster Reduction and Emergency Management, Beijing
Normal University, 100875 Beijing, China
Faculty of Geographical Science, Beijing Normal University, 100875
Beijing, China
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Latest update: 20 Nov 2024
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.
We used three machine learning models and determined that Gaussian process regression (GPR) is...
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