The PANDA automatic weather station network between the coast and Dome A, East Antarctica
<p>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. T...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-11-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/14/5019/2022/essd-14-5019-2022.pdf |
| Summary: | <p>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 <span class="inline-formula">∼</span> 1460 km transect from Zhongshan to Panda S
follows roughly along <span class="inline-formula">∼</span> 77<span class="inline-formula"><sup>∘</sup></span> 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 <a href="https://doi.org/10.11888/Atmos.tpdc.272721">https://doi.org/10.11888/Atmos.tpdc.272721</a> (Ding
et al., 2022b).</p> |
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| ISSN: | 1866-3508 1866-3516 |