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NSFGEO-NERC: Multiscale Stochastic Modeling and Analysis of the Ocean Circulation

NSFGEO-NERC: Multiscale Stochastic Modeling and Analysis of the Ocean Circulation
NSFGEO-NERC:海洋环流的多尺度随机建模与分析
批准号:
1658357
负责人:
Dmitri Kondrashov
金额:
$60.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-15 至 2020-10-31

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中文摘要
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英文摘要
Turbulent oceanic flows consist of complex motions - jets, vortices and waves that co-exist on very different spatio-temporal scales but also without clear scale separation. Along with computational challenges to simulate multiscale oceanic circulation in high numerical resolution, as well as resulting difficulties in dynamically and kinematical understanding of multiscale flows, naturally goes practical need to develop prognostic models of reduced complexity that reproduce the whole complexity of turbulent oceanic motions across scales. This project aims to develop such reduced-order stochastic models by dynamical, i.e., equations-based as well as statistical data-driven reduction methods, describing the evolution of relatively few (from tens to hundreds) spatio-temporal modes and capturing essential statistical properties of the underlying multiscale oceanic flow and stratification. It will combine development and applications of state-of-the-art data-adaptive methods and rigorous mathematical theory for dynamical and empirical reduction in the hierarchy of oceanic models. The methods to be developed in this project are very general and can be easily extended to other problems in fluid mechanics and geophysical flows. The reduced-order stochastic models that emulate the turbulent flows in a coarse-grained sense can be adopted as efficient and low-cost oceanic components of general circulation models that could improve quantitative prediction of climate change. The Project will foster a USA-UK research collaboration and provide opportunities for cross-training in an international setting. The UK collaborator will recruit a PhD student via the EPSRC Centre for Doctoral Training "Mathematics of Planet Earth" at the Imperial College to work on development and applications of stochastic reduced-order ocean models.This Project aims to develop versatile and novel methods for constructing stochastic oceanic emulators of reduced complexity, based either on high-end model simulations or on underlying dynamical equations, or both, and for capturing oceanic variability across scales, i.e., from large-scale decadal variability to mesoscale eddies, and resulting dynamical and kinematical understanding of multiscale flows. The goals of this Project are (i) to extend recent theoretical results and to emulate the full spectrum of dynamically important scales including mesoscale eddies; (ii) to demonstrate that the stochastic flow emulators can provide fundamental novel insights into dynamical and kinematical properties of the multiscale transient flow patterns and their interactions, and to search for dynamical interpretations of mode interactions; (iii) to extend empirical and dynamical reduction methods to spatially inhomogeneous and turbulent flows; (iv) to consider several types of dynamically simulated eddying multiscale flows of the ocean circulation in the hierarchy of oceanic models of different complexity and geography, such as anisotropic turbulence on zonal currents and wind-driven gyres with western boundary currents, (v) to embed the stochastic flow emulators into non-eddy-resolving dynamical oceanic models as effective parameterizations of the eddy effects.
期刊论文(8)
专著(0)
科研奖励(0)
会议论文
Data-adaptive harmonic decomposition and prediction of Arctic sea ice extent
北极海冰范围的数据自适应调和分解与预测
DOI: 10.1093/climsys/dzy001
发表时间: 2018
期刊: Dynamics and Statistics of the Climate System
影响因子: --
作者: [Kondrashov, Dmitri, Chekroun, Mickaël D, Ghil, Michael]
通讯作者: Ghil, Michael
Data-adaptive harmonic analysis and modeling of solar wind-magnetosphere coupling
太阳风-磁层耦合的数据自适应谐波分析和建模
DOI: 10.1016/j.jastp.2017.12.021
发表时间: 2018
期刊: Journal of Atmospheric and Solar-Terrestrial Physics
影响因子: 1.9
作者: [Kondrashov, Dmitri, Chekroun, Mickaël D.]
通讯作者: Chekroun, Mickaël D.
On data-driven augmentation of low-resolution ocean model dynamics
低分辨率海洋模型动力学的数据驱动增强
DOI: 10.1016/j.ocemod.2019.101464
发表时间: 2019
期刊: Ocean Modelling
影响因子: 3.2
作者: [Ryzhov, E.A., Kondrashov, D., Agarwal, N., Berloff, P.S.]
通讯作者: Berloff, P.S.
Topological instabilities in families of semilinear parabolic problems subject to nonlinear perturbations
受非线性扰动的半线性抛物型问题族中的拓扑不稳定性
DOI: 10.3934/dcdsb.2018075
发表时间: 2017
期刊: Discrete & Continuous Dynamical Systems - B
影响因子: --
作者: [D. Chekroun, Mickaël]
通讯作者: D. Chekroun, Mickaël
8
    Collaborative Research: GEM--Towards Developing Physics-informed Subgrid Models for Geospace MagnetoHydroDynamics (MHD) Simulations
    • 批准号:
      2247677
    • 项目类别:
      Standard Grant
    • 资助金额:
      $34.81万
    • 财政年份:
      2023
    • 负责人:
      Dmitri Kondrashov
    • 依托单位:
    EAGER: Machine Learning and Data Assimilation for Discovery of Generalized Fokker-Planck Equation for Radiation Belt Modeling
    • 批准号:
      2211345
    • 项目类别:
      Standard Grant
    • 资助金额:
      $25.01万
    • 财政年份:
      2022
    • 负责人:
      Dmitri Kondrashov
    • 依托单位:
    Collaborative Research: EaSM 2: Stochastic Simulation and Decadal Prediction of Large-Scale Climate
    • 批准号:
      1243175
    • 项目类别:
      Standard Grant
    • 资助金额:
      $39.86万
    • 财政年份:
      2013
    • 负责人:
      Dmitri Kondrashov
    • 依托单位:
    Gap Filling of Solar Wind Data by Singular Spectrum Analysis
    • 批准号:
      1102009
    • 项目类别:
      Standard Grant
    • 资助金额:
      $9.76万
    • 财政年份:
      2011
    • 负责人:
      Dmitri Kondrashov
    • 依托单位:
    海外基金