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。作为长期观测记录，它的主要重点是测量南极大气的近地表天气和气候学。观测站在地面2-3米的标称高度测量气温、气压、风速和风向。其他参数，如相对湿度和积雪量也可以取。来自AWS的观测数据是通过铱星网络或NOAA或MetOp极地轨道卫星上的DCS Argos收集的，因此可以接近实时地提供给业务和天气预报员。AAWS网络的地面观测资料是近年来气候变化和气象过程的重要记录。来自AAWS网络的地面观测也用于实际操作和野外工作规划。AAWS网络的地面观测资料已用于核对卫星和遥感观测资料。本项目拟利用AWS网络观测到的地表条件，确定气候变率的大尺度模态如何影响南极天气和气候，以及AWS网络的地表观测如何与表层和边界层过程相联系，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的理解，以及这些事件与气候变率的大尺度模式之间的关系。该项目将填补降雪分布知识的空白，并利用麦克默多站附近的一套降水传感器来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.