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，它的几个设施已经进入第30个年头。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。自动气象站在名义地表高度约2-3米处测量气温、气压、风速和风向。也可以采用其他参数，如相对湿度和积雪。AWS的观测数据是通过Irdium网络或NOAA或MetOp极轨卫星上的DCSArgos收集的，从而近乎实时地提供给业务和天气预报人员。来自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。来自AAWS网络的地面观测也用于业务和实地工作的规划。AWS网络的地面观测已被用于检查卫星和遥感观测。该项目建议利用AWS网络观测的地面条件来确定气候变化的大尺度模式如何影响南极天气和气候，AWS网络的地面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。具体地说，该项目建议提高我们对导致异常天气事件的过程以及这些事件与气候变化的大规模模式之间的关系的理解。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.