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    <titleInfo>
        <title>SC-Earth: A Station-Based Serially Complete Earth Dataset from 1950 to 2019</title>
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    <name type="personal">
        <namePart type="given">Guoqiang</namePart>
        <namePart type="family">Tang</namePart>
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    <name type="personal">
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        <namePart type="given">Michael</namePart>
        <namePart type="family">Papalexiou</namePart>
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    <abstract>Abstract Meteorological data from ground stations suffer from temporal discontinuities caused by missing values and short measurement periods. Gap-filling and reconstruction techniques have proven to be effective in producing serially complete station datasets (SCDs) that are used for a myriad of meteorological applications (e.g., developing gridded meteorological datasets and validating models). To our knowledge, all SCDs are developed at regional scales. In this study, we developed the serially complete Earth (SC-Earth) dataset, which provides daily precipitation, mean temperature, temperature range, dewpoint temperature, and wind speed data from 1950 to 2019. SC-Earth utilizes raw station data from the Global Historical Climatology Network–Daily (GHCN-D) and the Global Surface Summary of the Day (GSOD). A unified station repository is generated based on GHCN-D and GSOD after station merging and strict quality control. ERA5 is optimally matched with station data considering the time shift issue and then used to assist the global gap filling. SC-Earth is generated by merging estimates from 15 strategies based on quantile mapping, spatial interpolation, machine learning, and multistrategy merging. The final estimates are bias corrected using a combination of quantile mapping and quantile delta mapping. Comprehensive validation demonstrates that SC-Earth has high accuracy around the globe, with degraded quality in the tropics and oceanic islands due to sparse station networks, strong spatial precipitation gradients, and degraded ERA5 estimates. Meanwhile, SC-Earth inherits potential limitations such as inhomogeneity and precipitation undercatch from raw station data, which may affect its application in some cases. Overall, the high-quality and high-density SC-Earth dataset will benefit research in fields of hydrology, ecology, meteorology, and climate. The dataset is available at https://zenodo.org/record/4762586 .</abstract>
    <identifier type="citekey">Tang-2021-SC-Earth:</identifier>
    <identifier type="doi">10.1175/jcli-d-21-0067.1</identifier>
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        <url>https://gwf-uwaterloo.github.io/gwf-publications/G21-70001</url>
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        <date>2021</date>
        <detail type="volume"><number>34</number></detail>
        <detail type="issue"><number>16</number></detail>
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            <start>6493</start>
            <end>6511</end>
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%0 Journal Article
%T SC-Earth: A Station-Based Serially Complete Earth Dataset from 1950 to 2019
%A Tang, Guoqiang
%A Clark, Martyn
%A Papalexiou, Simon Michael
%J Journal of Climate, Volume 34, Issue 16
%D 2021
%V 34
%N 16
%I American Meteorological Society
%F Tang-2021-SC-Earth:
%X Abstract Meteorological data from ground stations suffer from temporal discontinuities caused by missing values and short measurement periods. Gap-filling and reconstruction techniques have proven to be effective in producing serially complete station datasets (SCDs) that are used for a myriad of meteorological applications (e.g., developing gridded meteorological datasets and validating models). To our knowledge, all SCDs are developed at regional scales. In this study, we developed the serially complete Earth (SC-Earth) dataset, which provides daily precipitation, mean temperature, temperature range, dewpoint temperature, and wind speed data from 1950 to 2019. SC-Earth utilizes raw station data from the Global Historical Climatology Network–Daily (GHCN-D) and the Global Surface Summary of the Day (GSOD). A unified station repository is generated based on GHCN-D and GSOD after station merging and strict quality control. ERA5 is optimally matched with station data considering the time shift issue and then used to assist the global gap filling. SC-Earth is generated by merging estimates from 15 strategies based on quantile mapping, spatial interpolation, machine learning, and multistrategy merging. The final estimates are bias corrected using a combination of quantile mapping and quantile delta mapping. Comprehensive validation demonstrates that SC-Earth has high accuracy around the globe, with degraded quality in the tropics and oceanic islands due to sparse station networks, strong spatial precipitation gradients, and degraded ERA5 estimates. Meanwhile, SC-Earth inherits potential limitations such as inhomogeneity and precipitation undercatch from raw station data, which may affect its application in some cases. Overall, the high-quality and high-density SC-Earth dataset will benefit research in fields of hydrology, ecology, meteorology, and climate. The dataset is available at https://zenodo.org/record/4762586 .
%R 10.1175/jcli-d-21-0067.1
%U https://gwf-uwaterloo.github.io/gwf-publications/G21-70001
%U https://doi.org/10.1175/jcli-d-21-0067.1
%P 6493-6511

Markdown (Informal)

[SC-Earth: A Station-Based Serially Complete Earth Dataset from 1950 to 2019](https://gwf-uwaterloo.github.io/gwf-publications/G21-70001) (Tang et al., GWF 2021)

• SC-Earth: A Station-Based Serially Complete Earth Dataset from 1950 to 2019 (Tang et al., GWF 2021)

ACL

• Guoqiang Tang, Martyn Clark, Simon Michael Papalexiou, Guoqiang Tang, Martyn Clark, and Simon Michael Papalexiou. 2021. SC-Earth: A Station-Based Serially Complete Earth Dataset from 1950 to 2019. Journal of Climate, Volume 34, Issue 16, 34(16):6493–6511.