Collaborative Research: Antarctic Automatic Weather Station Program 2016-2019

南极自动气象站（AWS）网络是南极最广泛的地面气象网络，其几个设施已接近30年。它作为长期观测记录的主要重点是测量南极大气的近地表天气和气候学。自动气象站测量大气温度、气压、风速和风向，标称表面高度约为2- 3米。其他参数，如相对湿度和积雪也可以采取。来自自动气象站的观测数据是通过铱网络或NOAA或MetOp极轨道卫星上的DCS Argos收集的，因此可以在接近真实的时间内提供给业务和天气预报员。AAWS网络的地面观测是近期气候变化和气象过程的重要记录。AAWS网络的地面观测也用于实际操作和实地工作规划。该项目提议利用美国南极气象局网络观测到的地面状况来确定大规模气候变化模式如何影响南极天气和气候，美国南极气象局网络的地面观测如何与表层和边界层过程联系起来，并量化降雪和吹雪事件的影响。具体而言，该项目旨在提高我们对导致异常天气事件的过程以及这些事件如何与大尺度气候变率模式相关的理解。该项目将填补降雪分布知识的空白，并使用麦默多站附近的一套降水传感器区分降雪和吹雪事件。

The Antarctic Automatic Weather Station (AWS) network is the most extensive ground meteorological network in the Antarctic, approaching its 30th year at several of its installations. Its prime focus as a long term observational record is to measure the near surface weather and climatology of the Antarctic atmosphere. AWS stations measure air-temperature, pressure, wind speed and direction at a nominal surface height of ~ 2-3m. Other parameters such as relative humidity and snow accumulation may also be taken. Observational data from the AWS are collected via Iridium network, or DCS Argos aboard either NOAA or MetOp polar orbiting satellites and thus made available in near real time to operational and synoptic weather forecasters. The surface observations from the AAWS network are important records for recent climate change and meteorological processes. The surface observations from the AAWS network are also used operationally, and in the planning of field work. The surface observations made from the AAWS network have been used to check on satellite and remote sensing observations.This project proposes to use the surface conditions observed by the AWS network to determine how large-scale modes of climate variability impact Antarctic weather and climate, how the surface observations from the AWS network are linked to surface layer and boundary layer processes, and to quantify the impact of snowfall and blowing snow events. Specifically, this project proposes to improve our understanding of the processes that lead to unusual weather events and how these events are related to large-scale modes of climate variability. This project will fill a gap in knowledge of snowfall distribution, and distinguishing between snowfall and blowing snow events using a suite of precipitation sensors near McMurdo Station.