Optimal deployment of the Antarctic surface weather observing network

南极大陆及其周围的海洋和大气是地球上一个被观测到的区域。环境观测系统历来往往是基于主观的理由而设立的，包括后勤便利和简单的可及性。 由于南极自动气象站（AAWS）等环境观测网络越来越多地被用来进行业务活动，（例如数值天气预报）、天气研究（如中尺度气象学、生态系统变化）和气候（例如冰质量平衡;气候变异和变化）研究，需要更加客观地考虑如何经济有效地保留或扩大有限的观测资产。网络设计中的新兴理论为这一领域的一些客观指导提供了基础。华盛顿大学和NCAR的研究人员将使用来自（存档的）南极中尺度预报系统（AMPS）的网格化数据，通过集合灵敏度理论优化现有AWS网络的空间相关长度尺度。 这些预测的最佳站的位置将反过来受到观测系统实验（OSE）和观测系统模拟实验（OSSE），使用数据拒绝和重新配置的方法。这项研究的结果可以为气象学（例如NWP）和气候相关研究提供AWS台站最具空间代表性的位置。一名研究生和一名博士后将参与这个跨学科的问题。

The Antarctic continent, its surrounding ocean and atmosphere are a vastly under observed region of the planet. Environmental observing systems have often historically been sited on largely subjective grounds, including logistic convenience and simple accessibility. As environmental observing networks such as those Antarctic Automated Weather Stations (AAWS) are increasingly being relied upon for operational (e.g. numerical weather prediction), weather research (e.g. mesoscale meteorology, ecosystem change) and climate (e.g. ice mass balance; climate variability and change) studies, more objective considerations are needed for the economical and effective retention or expansion of limited observing assets. Emerging theory from network design offers the basis for some objective guidance in this area. Gridded data from (archived) Antarctic Mesoscale Prediction Systems (AMPS) will be used by University of Washington and NCAR researchers to optimize the spatial correlation length scales for the existing AWS network via ensemble sensitivity theory. These predictions of optimal station placement will in turn be subject to observing systems experiments (OSE) and observing system simulation experiments (OSSE), using both data denial and reconfiguration approaches. Results of this research may inform the placement of the most spatially representative locations of AWS stations for both meteorology (e.g. NWP) and climate related studies. A graduate student and a post-doctoral associate will be involved in this interdisciplinary problem.