TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations
<p>Reliable precipitation data are highly necessary for geoscience research in the Third Pole (TP) region but still lacking, due to the complex terrain and high spatial variability of precipitation here. Accordingly, this study produces a long-term (1979–2020) high-resolution (<span class=&...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-02-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/15/621/2023/essd-15-621-2023.pdf |
| Summary: | <p>Reliable precipitation data are highly necessary for geoscience
research in the Third Pole (TP) region but still lacking, due to the complex
terrain and high spatial variability of precipitation here. Accordingly,
this study produces a long-term (1979–2020) high-resolution (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">1</mn><mo>/</mo><mn mathvariant="normal">30</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="27pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="2f267da3b3e9353ce2f79fcf05d280d6"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-15-621-2023-ie00003.svg" width="27pt" height="14pt" src="essd-15-621-2023-ie00003.png"/></svg:svg></span></span><span class="inline-formula"><sup>∘</sup></span>, daily) precipitation dataset (TPHiPr) for the TP by merging the
atmospheric simulation-based ERA5_CNN with gauge observations
from more than 9000 rain gauges, using the climatologically aided interpolation
and random forest methods. Validation shows that TPHiPr is generally
unbiased and has a root mean square error of 5.0 mm d<span class="inline-formula"><sup>−1</sup></span>, a
correlation of 0.76 and a critical success index of 0.61 with respect to 197
independent rain gauges in the TP, demonstrating that this dataset is
remarkably better than the widely used datasets, including the latest generation of reanalysis (ERA5-Land), the state-of-the-art
satellite-based dataset (IMERG) and the multi-source merging datasets
(MSWEP v2 and AERA5-Asia). Moreover, TPHiPr can better detect
precipitation extremes compared with these widely used datasets. Overall,
this study provides a new precipitation dataset with high accuracy for the
TP, which may have broad applications in meteorological, hydrological and
ecological studies. The produced dataset can be accessed via
<span class="uri">https://doi.org/10.11888/Atmos.tpdc.272763</span> (Yang and Jiang, 2022).</p> |
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| ISSN: | 1866-3508 1866-3516 |