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.

湍流的海洋流由复杂的运动组成 - 喷气机，涡流和波浪，它们在非常不同的时空尺度上并存，但也没有明确的尺度分离。除了在高数值分辨率中模拟多尺度海洋循环的计算挑战外，以及对多尺度流动的动态和运动学理解的困难，自然而然地需要实用，以发展降低复杂性的预后模型，从而重现跨尺度湍流海洋运动的全部复杂性。该项目旨在通过动力学，即基于方程式的以及统计数据驱动的还原方法来开发此类减少的随机模型，描述了相对较少（从数十个到数百个）时空模式的演变，并捕获了基础多盘流和分层的基本统计特性。它将结合最先进的数据自适应方法的开发和应用，以及在海洋模型层次结构中的动力学和经验降低的严格数学理论。该项目中要开发的方法非常笼统，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。在粗粒感中模仿湍流的减少顺序随机模型可以用作一般循环模型的有效和低成本的海洋成分，可以改善对气候变化的定量预测。该项目将促进美国-UK研究的合作，并在国际环境中为交叉培训提供机会。 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从大规模衰老变异到中尺度的涡流，以及对多尺度流的动力学和运动学理解，尺度。该项目的目标是（i）扩展最新的理论结果，并模仿包括中尺度涡流在内的全部动态重要尺度；（ii）证明随机流仿真器可以为多尺度瞬态流动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态解释；（iii）将经验和动态还原方法扩展到空间不均匀和湍流；（iv）在海洋循环中考虑几种动态模拟的涡流多尺度流动，以不同的复杂性和地理位置的海洋模型层次结构，例如在区域电流上的各向异性湍流和与西部边界电流的风向驱动的陀螺仪（V）（V），（V）将仿真的仿真型模型嵌入了无效的洋流效应，可有效地分配到有效的洋流效应中。