The PANDA automatic weather station network between the coast and Dome A, East Antarctica

This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network which includes eleven automatic weather stations (AWS) across Prydz Bay-Amery Ice Shelf-dome area from the coast to the summit of the East Antarctica ice sheet. The ~1460 km...

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Bibliographic Details
Main Authors: Ding, Minghu, Zou, Xiaowei, Sun, Qizhen, Yang, Diyi, Zhang, Wenqian, Bian, Lingen, Lu, Changgui, Allison, Ian, Heil, Petra, Xiao, Cunde
Format: Text
Language:English
Published: 2022
Subjects:
Amery
Amery Ice Shelf
Austral
East Antarctica
Prydz Bay
Zhongshan
Antarc*
Antarctica
Ice Sheet
Ice Shelf
Online Access:https://doi.org/10.5194/essd-2022-188
https://essd.copernicus.org/preprints/essd-2022-188/
Description
Summary:This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network which includes eleven automatic weather stations (AWS) across Prydz Bay-Amery Ice Shelf-dome area from the coast to the summit of the East Antarctica ice sheet. The ~1460 km transect from Zhongshan to Panda S station follows roughly along ~77° E longitude and covers all geographic and climatic units of East Antarctica. Initial inland observation, near the coast, started in the 1996/1997 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1-hour intervals, and some of them can also measure firn temperature and shortwave/longwave radiation. Data are relayed in near real-time via the ARGOS system. Data quality is generally very reliable and the data have been used widely. In this paper, we firstly present a detailed overview of the AWSs, including the sensor characteristics, installation procedure, data quality control protocol, and the basic analysis of each variable. We then give an example of a short-term atmospheric event that shows the monitoring capacity of the network. This dataset, which is publicly available, is planned to be updated on a near-real time and should be valuable for climate change estimation, extreme weather events diagnosis, data assimilation, weather forecasting, etc. The dataset is available at https://doi.org/10.11888/Atmos.tpdc.272721 (Ding et al., 2022).

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