SGER: Quantification of Uncertainty in Argo Observation of Ocean Response to Hurricanes

SGER：Argo 飓风海洋响应观测不确定性的量化

• 批准号: 0847160
• 负责人: Young-Oh Kwon
• 金额: $ 9.02万
• 依托单位: Woods Hole Oceanographic Institution
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-11-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0847160&HistoricalAwards=false
• 关键词: SGER; Quantification; Uncertainty; Argo; Observation

The objective of this project is to assess quantitatively the capability and limitations of the Argo float array for the investigation of basin-to-global scale upper ocean response to hurricanes based on the comparison between the Argo observation and numerical model results. The Argo array measures subsurface temperature and salinity profiles before and after storms, and so provides a unique dataset to study the impact of the storms in a basin-to-global scale and for the subsurface as well as the sea surface.To properly understand the basin-to-global scale impact of tropical storms, a sustained global observational network with a resolution sufficient to observe the impact of tropical storms is needed for surface as well as the subsurface ocean. On the other hand, our understanding on the upper ocean response to the storms has progressed mostly based on the studies focused on individual storms.Successful use of the global observational network of Argo array in conjunction with satellite observations would allow the investigators to extrapolate our understanding to basin-to-global scale consequences.However, despite the unique opportunity provided by the near-global and subsurface coverage of the Argo array, the uncontrolled sampling in space with a relatively sparse 10 day interval produces substantial uncertainty primarily due to the inability to filter out near-inertial pumping and randomposition uncertainties with respect to the storm and the background flow. Therefore, a method to quantify uncertainty in the Argo sampling of ocean response to the hurricane is needed as a prerequisite to addressthe scientific questions using the dataset. The main strategy is to use a realistic numerical ocean simulation under a given initial background condition and hurricane forcing to perform an observation sampling experiment. During the experiments, virtual floats will be deployed in the model to mimic Argo floats in the real ocean. The data sampled by virtual float profiles will be used to estimate the uncertainty against the simulated full response. The 3-dimensional Price-Weller-Pinkel model will be used to perform observation sampling experiment.Intellectual Merit:Assessing uncertainty of the uncontrolled Argo observation in a statistically robust fashion with respect to the response to hurricane is unprecedented (as far as we are aware of). The proposed activity will not only advance the community's capability to apply statistical methods to scattered observations, but also enable it to use the Argo observations to address the fundamental scientific questions associated with the basin-scale ocean response to the hurricane, such as what is the contribution of tropical storms to the vertical mixing and the meridional transport of heat in the upper ocean.Broader ImpactsOur proposed work will provide the broader oceanography and atmospheric science community with a statistically robust tool to use the Argo observations to study the upper ocean response to an extreme event such as the hurricane and associated air-sea interaction, an application of Argo array that is well beyond its original vision. Argo has been a long-term international community effort that benefited many areas from the large-scale oceanography to the interannual-to-decadal predictability efforts. Broadeningthe use of Argo array will be very desirable and beneficial to justify the extension and expansion of this community asset. The project will also contirubte to the careeer development of two promising new NSF investigators.

该项目的目的是根据ARGO观察结果与数值模型结果之间的比较来定量评估Argo Float阵列的能力和局限性，以研究盆地到全球尺度上海对飓风的反应。 ARGO阵列量度衡量暴风雨前后的地下温度和盐度概况，因此提供了一个独特的数据集，以研究风暴在盆地到全球范围内以及地下以及海面的影响以及海面的影响。适当地了解盆地至全球范围的影响，并在热带风暴中进行层面上的层面，并在层面上进行层面的影响，并在层面上逐渐地构成一部分，以至于稳定地构成了一项稳定的效果。 海洋。另一方面，我们对上海对风暴反应的理解主要是基于关注各个风暴的研究进行了进展。核对全球观察性argo阵列网络结合卫星观察与卫星观察的结合以及研究人员可以使我们能够超越我们的理解，以弥补我们的理解。相对较少的10天间隔的空间中的不受控制的采样主要是由于无法过滤掉在暴风雨和背景流动方面的近乎惯性的泵送和rand剂不确定性。因此，需要一种量化海洋对飓风反应反应的不确定性的方法，作为使用数据集解决科学问题的先决条件。主要策略是在给定的初始背景条件下使用现实的数值海洋模拟，并强迫执行观察采样实验。在实验过程中，虚拟浮子将部署在模型中，以模仿Argo浮子在真实的海洋中。虚拟浮点轮廓采样的数据将用于估计模拟完整响应的不确定性。三维价格 - 价格 - 粉红色模型将用于执行观察抽样实验。智能优点：在对飓风的反应方面，以统计强劲的方式评估未受控制的ARGO观察的不确定性是没有预言的（据我们了解）。拟议的活动不仅将推动社区将统计方法应用于分散观察的能力，而且还使其能够使用Argo观察来解决与盆地规模海洋对飓风的反应相关的基本科学问题，例如，热带风暴对垂直混合和范围内的高海洋上的范围的贡献是什么是什么是垂直的综合和海洋的范围。一种使用ARGO观测值来研究对极端事件的上海反应的统计坚固工具，例如飓风和相关的空气相互作用，这是Argo阵列的应用，远远超出了其原始视野。 Argo一直是一项长期的国际社会努力，从大规模海洋学到年间到十年的可预测性努力，使许多领域受益。扩大对Argo阵列的使用将是非常理想的和有益的，可以证明该社区资产的扩展和扩展是合理的。该项目还将引发两个有前途的新NSF调查人员的护理人发展。