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Zenodo
2024
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| Online-Zugang: | https://doi.org/10.5281/zenodo.10966064 |
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| _version_ | 1866902137076711424 |
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| author | Cheng, Minghan |
| author_facet | Cheng, Minghan |
| contents | <div> <p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_10966064 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2013-2014) Cheng, Minghan <div> <p>The dataset presents the daily near surface air temperature of China with 1km spatial resolution, including daily average air temperature, maximum temperature and minimum air temperature. The dataset was generated using machine learning and multiple variables, the accuracy was:T<sub>ave</sub>, R<sup>2</sup> = 0.97, RMSE = 1.61℃ and rRMSE = 13.24%; T<sub>max</sub>, R<sup>2</sup> = 0.94, RMSE = 2.35℃ and rRMSE = 13.02%; T<sub>min</sub>, R<sup>2</sup> = 0.95, RMSE = 2.04℃ and rRMSE = 27.09%).</p> </div> |
| title | Near Surface Air Temperature Dataset for China with high temporal and spatial resolution generated using random forest and multi-source data (2013-2014) |
| url | https://doi.org/10.5281/zenodo.10966064 |