Optimal deployment of the Antarctic surface weather observing network

南极地面天气观测网优化部署

基本信息

批准号：
1043090
负责人：
Gregory Hakim
金额：
$ 58.62万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2011
资助国家：
美国
起止时间：
2011-03-01 至 2017-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1043090&HistoricalAwards=false
关键词：
Optimal deployment Antarctic surface weather

项目摘要

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.

南极大陆，其周围的海洋和大气在地球的观察区域下。历史上，环境观察系统通常是基于主观的理由，包括物流便利性和简单的可访问性。随着环境观察网络（例如南极自动化天气站（AAW））的越来越多地被依靠运营（例如数值天气预测），天气研究（例如，中尺度的气象学，生态系统变化）和气候变化）和气候（例如，冰质量质量衡量；气候变化；气候变异和变化）的经济因素有限或有限的估算。网络设计的新兴理论为该领域的一些客观指导提供了基础。华盛顿大学和NCAR研究人员将使用来自（存档的）南极中尺度预测系统（AMP）的网格数据，以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。最佳站点放置的这些预测将依次受到系统实验（OSE）和观察系统仿真实验（OSSE）的约束，同时使用数据否定和重新配置方法。这项研究的结果可能会为气象学（例如NWP）和气候相关研究的AWS站的最空间代表性位置的位置提供信息。研究生和博士后助理将参与这个跨学科问题。