东南极沿岸至冰穹A的PANDA自动气象站网观测数据（1989-2021）

自动气象站已经被证明是监测冰川/盖近地表气象要素来确定表面能量收支从而量化冰川/盖消融及其对气候变化响应的强有力的工具。本数据集介绍了PANDA断面自动气象站网络，该网络共包括11个自动气象站（AWS），中山站，Panda 100，Panda 200，Panda 300，Panda 400，泰山站，Eagle，Panda 1100，Dome A，昆仑站和Panda S，该断面网络横跨Prydz湾- amery冰架-Dome A地区，从海岸到东南极洲冰盖的顶部。该网络断面大致沿经度~77°E，纬度范围为69.37°S-82.33°S，覆盖了东南极地区所有的地理和气候单元。该网络中所有自动气象台每隔1小时测量一次气温、相对湿度、气压、风速和风向，部分自动气象站还能测量地表温度和短波/长波辐射。所有的自动站数据是通过ARGOS系统实时传输的。数据质量是非常可靠的，其中Dome A及Eagle站等数据已经被广泛使用。目前该数据集已经被我们更新到2021年，除中山站和Panda S外，其余各站都是多层观测，主要为有1/2/4/6m四种高度，数据已经经过严格的质量控制。我们将计划每年更新一次，该数据集对南极地区气候变化估计、极端天气事件诊断、数据同化、天气预报等都有非常重要的价值。

Automatic weather stations (AWS) have proven to be powerful tools for monitoring near-surface meteorological elements over glaciers and ice sheets to determine surface energy budgets, thereby quantifying glacier and ice sheet ablation and their responses to climate change. This dataset introduces the AWS network along the PANDA transect, which comprises 11 AWS sites: Zhongshan Station, Panda 100, Panda 200, Panda 300, Panda 400, Taishan Station, Eagle, Panda 1100, Dome A, Kunlun Station, and Panda S. This transect spans the Prydz Bay-Amery Ice Shelf-Dome A region, extending from the coast to the summit of the East Antarctic Ice Sheet. The transect runs roughly along ~77°E, with a latitudinal range of 69.37°S–82.33°S, covering all geographic and climatic units in the East Antarctic region. All AWS in this network measure air temperature, relative humidity, atmospheric pressure, wind speed, and wind direction at 1-hour intervals. Some stations additionally measure surface temperature and shortwave/longwave radiation. All AWS data are transmitted in real time via the ARGOS system. The data quality is highly reliable, and datasets from Dome A, Eagle, and other sites have been widely utilized. As of now, this dataset has been updated to 2021. With the exception of Zhongshan Station and Panda S, all other sites conduct multi-layer observations, primarily at four heights: 1 m, 2 m, 4 m, and 6 m. All data have undergone strict quality control. We plan to conduct annual updates. This dataset holds significant scientific value for Antarctic climate change estimation, extreme weather event diagnosis, data assimilation, weather forecasting, and related research.