Sampling Peculiarity of Sea Surface Temperature Data Sets from Drifting Buoys due to the Lagrangian Nature of Observing Platforms

GSTDTAP

项目编号	1853717
	Sampling Peculiarity of Sea Surface Temperature Data Sets from Drifting Buoys due to the Lagrangian Nature of Observing Platforms
	Alexey Kaplan (Principal Investigator)
主持机构	Columbia University
项目开始年	2019
	2019-09-01
项目结束日期	2022-08-31
资助机构	US-NSF
项目类别	Standard Grant
项目经费	392069(USD)
国家	美国
语种	英语
英文摘要	Drifting buoys are a common platform for observing surface ocean currents and sea surface temperature (SST). Their sampling is Lagrangian, meaning that they move with the ocean currents, as opposed to other observational platforms which are fixed in space (Eulerian) or move in some other manner (like ships). This project will investigate the effects that moving with the currents has on the differences between SST measured from drifters with SST observed from satellite. The hypothesis is that there is a bias completely due to the Lagrangian sampling which can be quantified and corrected for. The project has important implications for improving the SST data sets that are used for initialization or assimilation in climate models. The project will involve undergraduate students in the research. The results will be made publicly available and will be presented at the LDEO Open House and at an Earth2Class workshop for K-12 Earth Science teachers. The project focuses on the sampling interdependency of SST observations from drifting buoys that occurs due to their Lagrangian nature, combined with the approximately conservative property of SST. It is hypothesized that root mean square differences between SST measured from drifting buoys and satellite can be quantified as a sampling bias attributable to the Lagrangian nature of the drifters. The research involves quantifying this bias and correcting for it. The project will use SST from drifters and satellite and will use the Mercator ocean model as well. The results are anticipated to be useful for properly initializing climate models with accurate SST fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/213341
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Alexey Kaplan .Sampling Peculiarity of Sea Surface Temperature Data Sets from Drifting Buoys due to the Lagrangian Nature of Observing Platforms.2019.