Preprints
https://doi.org/10.5194/essd-2021-267
https://doi.org/10.5194/essd-2021-267

  23 Aug 2021

23 Aug 2021

Review status: this discussion paper is a preprint. It has been under review for the journal Earth System Science Data (ESSD). The manuscript was not accepted for further review after discussion.

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

Qian He1,2, Ming Wang1,3, Kai Liu1,3, Kaiwen Li1,2, and Ziyu Jiang1,2 Qian He et al.
  • 1Academy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, China
  • 2Faculty of Geographical Science, Beijing Normal University, 100875 Beijing, China
  • 3The School of National Safety and Emergency Management, Beijing Normal University, 100875 Beijing, China

Abstract. An accurate spatially continuous air temperature dataset is crucial for multiple applications in environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. Comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that Gaussian process regression had high accuracy and clearly outperformed the other two models regarding interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN. Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. Comparison with the TerraClimate, FLDAS, and ERA5 datasets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature dataset with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The dataset consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterized by a significant trend of increase in each month, whereas monthly maximum air temperature showed a more spatially heterogeneous pattern with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km dataset is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

Qian He et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-267', Anonymous Referee #1, 21 Sep 2021
  • RC2: 'Comment on essd-2021-267', Anonymous Referee #2, 04 Oct 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-267', Anonymous Referee #1, 21 Sep 2021
  • RC2: 'Comment on essd-2021-267', Anonymous Referee #2, 04 Oct 2021

Qian He et al.

Data sets

GPRChinaTemp1km: 1 km monthly maximum air temperature for China from January 1951 to December 2020 He, Qian; Wang, Ming; Liu, Kai; Li, Kaiwen; Jiang, Ziyu https://doi.org/10.5281/zenodo.5112122

GPRChinaTemp1km: 1 km monthly minimum air temperature for China from January 1951 to December 2020 He, Qian; Wang, Ming; Liu, Kai; Li, Kaiwen; Jiang, Ziyu https://doi.org/10.5281/zenodo.5112232

GPRChinaTemp1km: 1 km monthly mean air temperature for China from January 1951 to December 2020 He, Qian Beijing Normal University ; Wang, Ming; Liu, Kai; Li, Kaiwen; Jiang, Ziyu https://doi.org/10.5281/zenodo.5111989

Qian He et al.

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Short summary
We used three machine learning models and determined that Gaussian process regression (GPR) is best suited to interpolation of air temperature data for China. The GPR-derived results were compared with that of traditional interpolation techniques and existing datasets and it was found that the accuracy of the GPR-derived data was better. Finally, we generated a gridded monthly air temperature dataset with 1 km resolution and high accuracy for China (1951–2020) using the GPR model.