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

基本信息

批准号：
1658357
负责人：
Dmitri Kondrashov
金额：
$ 60万
依托单位：
University of California-Los Angeles
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
起止时间：
2017-09-15 至 2020-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1658357&HistoricalAwards=false
关键词：
NSFGEO NERC Multiscale Stochastic Modeling

项目摘要

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.

海洋湍流由复杂的运动组成——喷流、涡流和波浪，它们在非常不同的时空尺度上共存，但也没有明显的尺度分离。除了以高数值分辨率模拟多尺度海洋环流的计算挑战，以及对多尺度流的动态和运动学理解造成的困难之外，自然地需要开发降低复杂性的预测模型，以重现跨尺度的湍流海洋运动的整体复杂性。。该项目旨在通过动态（即基于方程以及统计数据驱动的简化方法）开发此类降阶随机模型，描述相对较少（从数十到数百）时空模式的演化并捕获基本的统计特性底层的多尺度海洋流动和分层。它将结合最先进的数据自适应方法和严格的数学理论的开发和应用，以动态和经验地减少海洋模型的层次结构。该项目要开发的方法非常通用，可以很容易地扩展到流体力学和地球物理流中的其他问题。在粗粒度意义上模拟湍流的降阶随机模型可以作为大气环流模型的高效且低成本的海洋组成部分，从而改善气候变化的定量预测。该项目将促进美国与英国的研究合作，并提供在国际环境中进行交叉培训的机会。英国合作者将通过帝国理工学院 EPSRC“行星地球数学”博士培训中心招募一名博士生，从事随机降阶海洋模型的开发和应用。该项目旨在开发通用且新颖的构建方法降低复杂性的随机海洋模拟器，基于高端模型模拟或基础动力学方程，或两者兼而有之，用于捕获跨尺度的海洋变化，即从大规模年代际变化到中尺度涡流，以及由此产生的对多尺度流动的动力学和运动学理解。该项目的目标是（i）扩展最近的理论结果并模拟包括中尺度涡流在内的动态重要尺度的全谱； (ii) 证明随机流模拟器可以为多尺度瞬态流模式及其相互作用的动力学和运动学特性提供基本的新颖见解，并寻找模式相互作用的动力学解释； (iii) 将经验和动态还原方法扩展到空间不均匀和湍流； (iv) 考虑不同复杂性和地理的海洋模型层次中几种类型的动态模拟海洋环流涡流多尺度流，例如纬向流的各向异性湍流和西边界流的风驱动环流， (v)将随机流模拟器嵌入到非涡旋解析动力海洋模型中，作为涡流效应的有效参数化。