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

• Published in: Earth System Science Data
• Main Authors: M. Ding, X. Zou, Q. Sun, D. Yang, W. Zhang, L. Bian, C. Lu, I. Allison, P. Heil, C. Xiao
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: geo; envir; Amery; Amery Ice Shelf; Antarctic; Austral; East Antarctic Ice Sheet; East Antarctica; Prydz Bay; Zhongshan; Antarc*; Antarctica; Ice Sheet; Ice Shelf
• Online Access: https://doi.org/10.5194/essd-14-5019-2022; https://essd.copernicus.org/articles/14/5019/2022/essd-14-5019-2022.pdf; https://doaj.org/article/4ab57ae9878d4a4db3cc92bcb8c2d239

This paper introduces a unique multiyear dataset and the monitoring capability of the PANDA automatic weather station network, which includes 11 automatic weather stations (AWSs) across the Prydz Bay–Amery Ice Shelf–Dome A area from the coast to the summit of the East Antarctic Ice Sheet. The ∼ 1460 km transect from Zhongshan to Panda S follows roughly along ∼ 77∘ E longitude and covers all geographic units of East Antarctica. Initial inland observations, near the coast, started in the 1996/97 austral summer. All AWSs in this network measure air temperature, relative humidity, air pressure, wind speed and wind direction at 1 h 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. The 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 PANDA AWS network. This dataset, which is publicly available, is planned to be updated on a near-real-time basis 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., 2022b).