Collaborative Research:  Optimized Deployment of Antarctic Surface Weather Observations

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项目编号	1542794
	Collaborative Research: Optimized Deployment of Antarctic Surface Weather Observations
	Jordan Powers
主持机构	University Corporation For Atmospheric Res
项目开始年	2016
	2016-06-01
项目结束日期	2018-05-31
资助机构	US-NSF
项目类别	Standard Grant
项目经费	95258(USD)
国家	美国
语种	英语
英文摘要	Numerical experiments are to be used to test probabilistic predictions as to the optimal and sub-optimal siting of surface Automated Weather Station (AWS) observations in Antarctica. Sensor placement is a generic problem across the goesciences, and this project may have far-reaching impact as to fidelity and cost of observational networks. In particular, problems where measurements are difficult to obtain and/or expensive may be prioritized objectively with the approach tested here. If successful, this research provides the basis for an objectively optimized augmentation or re-configuration of the AWS network to meet climate monitoring (such as locating new or paleoclimate proxy records) and weather forecasting goals. 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, using both data denial and reconfiguration approaches. A related line of enquiry is to be an evaluation of the current locations of the active Antarctic radiosonde network, as judged by the network?s ability to account for tropospheric variance in archived AMPS forecast temperature data. Sonde measurements also may collocate with validation observations from infrared soundings from satellites, or GPS radio occultation.
URL	查看原文
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/69557
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Jordan Powers.Collaborative Research: Optimized Deployment of Antarctic Surface Weather Observations.2016.