HRLT: a high-resolution (1 d, 1 km) and long-term (1961–2019) gridded dataset for surface temperature and precipitation across China
<p>Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for Chin...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-11-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/14/4793/2022/essd-14-4793-2022.pdf |
| Summary: | <p>Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China with a high spatial resolution of 1 km that covers a long-term period (1961 to 2019). It has been named the HRLT, and the dataset is publicly available at <a href="https://doi.org/10.1594/PANGAEA.941329">https://doi.org/10.1594/PANGAEA.941329</a> (Qin and Zhang, 2022). In this study, the daily gridded data were interpolated using comprehensive statistical analyses, which included machine learning methods, the generalized additive model, and thin plate splines. It was based on the 0.5<span class="inline-formula"><sup>∘</sup></span> <span class="inline-formula">×</span> 0.5<span class="inline-formula"><sup>∘</sup></span> gridded dataset from the China Meteorological Administration, together with covariates for elevation,
aspect, slope, topographic wetness index, latitude, and longitude. The
accuracy of the HRLT daily dataset was assessed using observation data from
meteorological stations across China. The maximum and minimum temperature
estimates were more accurate than the precipitation estimates. For maximum
temperature, the mean absolute error (MAE), root mean square error (RMSE),
Pearson's correlation coefficient (Cor), coefficient of determination after
adjustment (<span class="inline-formula"><i>R</i><sup>2</sup></span>), and Nash–Sutcliffe modeling efficiency (NSE) were 1.07 <span class="inline-formula"><sup>∘</sup></span>C, 1.62 <span class="inline-formula"><sup>∘</sup></span>C, 0.99, 0.98, and 0.98, respectively. For minimum temperature, the MAE, RMSE, Cor, <span class="inline-formula"><i>R</i><sup>2</sup></span>, and NSE were 1.08 <span class="inline-formula"><sup>∘</sup></span>C, 1.53 <span class="inline-formula"><sup>∘</sup></span>C, 0.99, 0.99, and 0.99, respectively. For precipitation, the MAE, RMSE, Cor, <span class="inline-formula"><i>R</i><sup>2</sup></span>, and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70, respectively. The accuracy of the HRLT was compared to those of three other existing datasets, and its accuracy was either greater than the others, especially for precipitation, or comparable in accuracy, but with higher spatial resolution or over a longer time period. In summary, the HRLT dataset, which has a high spatial resolution, covers a longer period of time and has reliable accuracy.</p> |
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| ISSN: | 1866-3508 1866-3516 |