| 项目编号: | 1552304
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| 项目名称: | CAREER: Departure from Monin-Obukhov Similarity Theory (MOST) using high-resolution turbulence models |
| 作者: | Pierre Gentine
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| 承担单位: | Columbia University
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| 批准年: | 2016
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| 开始日期: | 2016-03-01
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| 结束日期: | 2021-02-28
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| 资助金额: | 188626
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| 资助来源: | US-NSF
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| 项目类别: | Continuing grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Geosciences - Earth Sciences
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| 英文关键词: | proposal
; turbulence
; model
; land-surface model
; surface
; monin-obukhov similarity theory
; land-surface
; scientific career
; high-resolution turbulence model
; surface turbulence
; accurate model representation
; current model representation
; weather model
; profile similarity accounting
|
| 英文摘要: | Turbulence controls the rate at which heat, moisture, air, and passive chemical tracers such as CO2 flow between the land and the atmosphere. Accurate model representation of such turbulent fluxes from the surface is essential for precise hydrologic, weather, and climate predictions. Our current model representation of turbulent fluxes assumes that most eddies transport can be explained by local observations and parameters in models. Nonetheless variability in the horizontal (e.g. due to variability in the surface characteristics) and in the vertical (due to eddies that span an unusually large vertical extend) directions can invalidate these assumptions. In this proposal we will test the latter effect using a combination of high-resolution turbulence models and observations. Our main objective is to better account for the largest ? most efficient ? eddies in our representation of turbulent exchange at the surface. This should ultimately improve the way we measure surface fluxes and model them. One flux of special interest is evaporation (the flux of moisture), which impacts hydrological forecasts (such as streamflow) along with weather and climate predictions. Along with this research activity, one of the main educational objectives of this proposal is to provide science exposure and encourage under-represented groups in Harlem, NY to choose scientific careers and education through science demonstrations and high-school internships.
Most current formulations of the surface turbulent transport laws are based on Monin-Obukhov Similarity Theory (MOST), which is based on local surface layer scaling. This theory has been shown to be deficient in recent years. One of the main causes of this deficiency is due to the presence of coherent turbulent structures, which transport turbulent properties over large distances from the top of the boundary layer down to the surface. These structures cannot readily be observed by time-averaging eddy-covariance technique and may be one of the main reasons of non-closure of the in situ surface energy budget, which are used to validate our land-surface models. To address these issues, the research objectives of this proposal are to:
i) Investigate the role of non-local transport related to the entrainment at the boundary layer top and its interaction with surface turbulence using Direct Numerical Simulations (DNS) and Large-Eddy Simulations (LES),
ii) Derive new surface turbulent laws and profile similarity accounting for the effect of non-local transport,
iii) Define large-eddy corrections for eddy-covariance observations of surface turbulent fluxes.
iv) Evaluate the impact of these new formulations in a coupled land-surface and weather model.
Consistent with this research activity, the educational objectives of the proposal are to: a) develop international student exchange programs, b) encourage and advise under-represented groups to participate in STEM research and c) develop classes (e.g. land-atmosphere interactions and turbulence) with a broad vision of the problem geared toward multiple scientific communities to facilitate cross-disciplinary collaborations and work. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/92746
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Pierre Gentine. CAREER: Departure from Monin-Obukhov Similarity Theory (MOST) using high-resolution turbulence models. 2016-01-01.
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