SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018

Tang, Guoqiang; Clark, Martyn P.; Newman, Andrew J.; Wood, Andrew W.; Papalexiou, Simon Michael; Vionnet, Vincent; Whitfield, Paul H.

doi:https://doi.org/10.5194/essd-12-2381-2020

Articles | Volume 12, issue 4

https://doi.org/10.5194/essd-12-2381-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-12-2381-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 12, issue 4

Data description paper

|

02 Oct 2020

Data description paper |

| 02 Oct 2020

SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018

Guoqiang Tang, Martyn P. Clark, Andrew J. Newman, Andrew W. Wood, Simon Michael Papalexiou, Vincent Vionnet, and Paul H. Whitfield

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Final revised paper (published on 02 Oct 2020)
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Preprint (discussion started on 13 May 2020)
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Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Review ESSD 2020 92', Anonymous Referee #1, 06 Jul 2020
RC2: 'SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018', Anonymous Referee #2, 15 Jul 2020
AC1: 'Response to comments', Guoqiang Tang, 31 Jul 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Guoqiang Tang on behalf of the Authors (31 Jul 2020)

ED: Publish as is (20 Aug 2020) by Scott Stevens

AR by Guoqiang Tang on behalf of the Authors (20 Aug 2020) Manuscript

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Short summary

Station observations are critical for hydrological and meteorological studies, but they often contain missing values and have short measurement periods. This study developed a serially complete dataset for North America (SCDNA) from 1979 to 2018 for 27 276 precipitation and temperature stations. SCDNA is built on multiple data sources and infilling/reconstruction strategies to achieve high-quality estimates which can be used for a variety of applications.