The AntAWS dataset: a compilation of Antarctic automatic weather station observations

Wang, Yetang; Zhang, Xueying; Ning, Wentao; Lazzara, Matthew A.; Ding, Minghu; Reijmer, Carleen H.; Smeets, Paul C.J.P.; Grigioni, Paolo; Heil, Petra; Thomas, Elizabeth R. ORCID: https://orcid.org/0000-0002-3010-6493; Mikolajczyk, David; Welhouse, Lee J.; Keller, Linda M.; Zhai, Zhaosheng; Sun, Yuqi; Hou, Shugui. 2023 The AntAWS dataset: a compilation of Antarctic automatic weather station observations. Earth System Science Data, 15 (1). 411-429. https://doi.org/10.5194/essd-15-411-2023

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Official URL: https://essd.copernicus.org/articles/15/411/2023/

Abstract/Summary

A new meteorological dataset derived from records of Antarctic automatic weather stations (here called the AntAWS dataset) at 3 h, daily and monthly resolutions including quality control information is presented here. This dataset integrates the measurements of air temperature, air pressure, relative humidity, and wind speed and direction from 267 Antarctic AWSs obtained from 1980 to 2021. The AWS spatial distribution remains heterogeneous, with the majority of instruments located in near-coastal areas and only a few inland on the East Antarctic Plateau. Among these 267 AWSs, 63 have been operating for more than 20 years and 27 of them in excess of more than 30 years. Of the five meteorological parameters, the measurements of air temperature have the best continuity and the highest data integrity. The overarching aim of this comprehensive compilation of AWS observations is to make these data easily and widely accessible for efficient use in local, regional and continental studies; it may be accessed at https://doi.org/10.48567/key7-ch19 (Wang et al., 2022). This dataset is invaluable for improved characterization of the surface climatology across the Antarctic continent, to improve our understanding of Antarctic surface snow–atmosphere interactions including precipitation events associated with atmospheric rivers and to evaluate regional climate models or meteorological reanalysis products.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.5194/essd-15-411-2023
ISSN:	18663508
Date made live:	25 Jan 2023 13:01 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533058

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.5194/essd-15-411-2023

ISSN:

18663508

Date made live:

25 Jan 2023 13:01 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/533058