GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning
<p>An accurate spatially continuous air temperature data set is crucial for multiple applications in the 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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-07-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/14/3273/2022/essd-14-3273-2022.pdf |
| _version_ | 1828461935613444096 |
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| author | Q. He Q. He M. Wang M. Wang K. Liu K. Liu K. Li K. Li Z. Jiang Z. Jiang |
| author_facet | Q. He Q. He M. Wang M. Wang K. Liu K. Liu K. Li K. Li Z. Jiang Z. Jiang |
| author_sort | Q. He |
| collection | DOAJ |
| description | <p>An accurate spatially continuous air temperature data set is crucial for
multiple applications in the 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. A comparison of the mean absolute error, root mean square error,
coefficient of determination, and residuals revealed that a Gaussian process
regression had high accuracy and clearly outperformed the other two models
regarding the 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 (Australian
National University Spline). 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. A comparison with the TerraClimate
(Monthly Climate and Climatic Water Balance for Global Terrestrial
Surfaces), FLDAS (Famine Early Warning Systems Network (FEWS NET) Land Data
Assimilation System), and ERA5 (ECMWF, European Centre for Medium-Range Weather Forecasts, Climate Reanalysis) data sets 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 data set, with 1 km resolution and high
accuracy for China, which we named GPRChinaTemp1km. The data set 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 characterised by a significant trend of
increase in each month, whereas monthly maximum air temperatures showed a
more spatially heterogeneous pattern, with significant increase,
non-significant increase, and non-significant decrease. The GPRChinaTemp1km
data set is publicly available at <a href="https://doi.org/10.5281/zenodo.5112122">https://doi.org/10.5281/zenodo.5112122</a> (He
et al., 2021a) for monthly maximum air temperature, at
<a href="https://doi.org/10.5281/zenodo.5111989">https://doi.org/10.5281/zenodo.5111989</a> (He et al., 2021b) for monthly mean
air temperature, and at <a href="https://doi.org/10.5281/zenodo.5112232">https://doi.org/10.5281/zenodo.5112232</a> (He et al.,
2021c) for monthly minimum air temperature.</p> |
| first_indexed | 2024-12-11T02:27:29Z |
| format | Article |
| id | doaj.art-d3d13c585866437795075efd42586bf4 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-12-11T02:27:29Z |
| publishDate | 2022-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-d3d13c585866437795075efd42586bf42022-12-22T01:23:54ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162022-07-01143273329210.5194/essd-14-3273-2022GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learningQ. He0Q. He1M. Wang2M. Wang3K. Liu4K. Liu5K. Li6K. Li7Z. Jiang8Z. Jiang9Academy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, ChinaFaculty of Geographical Science, Beijing Normal University, 100875 Beijing, ChinaAcademy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, ChinaSchool of National Safety and Emergency Management, Beijing Normal University, 100875 Beijing, ChinaAcademy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, ChinaSchool of National Safety and Emergency Management, Beijing Normal University, 100875 Beijing, ChinaAcademy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, ChinaFaculty of Geographical Science, Beijing Normal University, 100875 Beijing, ChinaAcademy of Disaster Reduction and Emergency Management, Beijing Normal University, 100875 Beijing, ChinaFaculty of Geographical Science, Beijing Normal University, 100875 Beijing, China<p>An accurate spatially continuous air temperature data set is crucial for multiple applications in the 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. A comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that a Gaussian process regression had high accuracy and clearly outperformed the other two models regarding the 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 (Australian National University Spline). 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. A comparison with the TerraClimate (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces), FLDAS (Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System), and ERA5 (ECMWF, European Centre for Medium-Range Weather Forecasts, Climate Reanalysis) data sets 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 data set, with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The data set 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 characterised by a significant trend of increase in each month, whereas monthly maximum air temperatures showed a more spatially heterogeneous pattern, with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km data set is publicly available at <a href="https://doi.org/10.5281/zenodo.5112122">https://doi.org/10.5281/zenodo.5112122</a> (He et al., 2021a) for monthly maximum air temperature, at <a href="https://doi.org/10.5281/zenodo.5111989">https://doi.org/10.5281/zenodo.5111989</a> (He et al., 2021b) for monthly mean air temperature, and at <a href="https://doi.org/10.5281/zenodo.5112232">https://doi.org/10.5281/zenodo.5112232</a> (He et al., 2021c) for monthly minimum air temperature.</p>https://essd.copernicus.org/articles/14/3273/2022/essd-14-3273-2022.pdf |
| spellingShingle | Q. He Q. He M. Wang M. Wang K. Liu K. Liu K. Li K. Li Z. Jiang Z. Jiang GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning Earth System Science Data |
| title | GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning |
| title_full | GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning |
| title_fullStr | GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning |
| title_full_unstemmed | GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning |
| title_short | GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning |
| title_sort | gprchinatemp1km a high resolution monthly air temperature data set for china 1951 2020 based on machine learning |
| url | https://essd.copernicus.org/articles/14/3273/2022/essd-14-3273-2022.pdf |
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