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广泛观测不到的区域。历史上，环境观测系统的选址往往主要基于主观原因，包括物流便利和简单的可达性。在业务（如数值天气预报）、天气研究（如中尺度气象学、生态系统变化）和气候研究（如冰质量平衡、气候变率和变化）日益依赖诸如南极自动气象站（aws）这样的环境观测网络的情况下，需要更客观地考虑如何经济有效地保留或扩大有限的观测资产。网络设计的新兴理论为这一领域的客观指导提供了依据。来自存档的南极中尺度预测系统（AMPS）的网格数据将被华盛顿大学和NCAR的研究人员使用，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。这些最优站点布局的预测将依次受到观测系统实验（OSE）和观测系统模拟实验（OSSE）的影响，使用数据否认和重新配置方法。这项研究的结果可能会为气象学（如NWP）和气候相关研究提供最具空间代表性的AWS站位置。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.