CMG COLLABORATIVE RESEARCH: Development of New Statistical Learning Theory and Techniques for Improvement of Convection Parameterization in Climate Models

CMG 合作研究：开发新的统计学习理论和技术以改进气候模型中的对流参数化

• 批准号: 0721585
• 负责人: Michael Fox-Rabinovitz
• 金额: $ 35.19万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0721585&HistoricalAwards=false
• 关键词: CMG; COLLABORATIVE; RESEARCH; Development; New

This proposal focuses upon two interconnected and equally important problems. The first of them is developing a new Statistical Learning Theory (SLT) dedicated to modeling specific complex systems. The second one is to develop a new convection representation for numerical climate models. Understanding climate and weather is important to science, society and the economy. The processes we focus upon (clouds, and particularly convection) are critical to climate and weather. The proposal involves a novel approach to improving the representation of those processes. Our goal is to combine a team of mathematical scientists with expertise in SLT, and atmospheric scientists with expertise in cloud modeling and climate system modeling to produce an innovative representation for convection in the atmospheric models used for numerical weather prediction and climate change studies. The project will develop an SLT system that emulates the statistical behavior of a more realistic but very expensive high resolution Cloud System Resolving Model (CSRM) in a variety of cloud regimes. Employing even the simplest of these CSRM frameworks in a large scale model increases the cost of today?s atmospheric models by factors of thousands, which make their use impractical for many studies. By emulating the behavior of these more realistic frameworks in a large scale model we develop a new SLT parameterization, dramatically reducing the cost of the more realistic representations of model convection, and providing an opportunity to address problems currently viewed as critical within the scientific community. By developing the application-oriented SLTs we hope to make the more realistic cloud and convective formulations currently being explored, computationally feasible and use them in climate models. This proposal combines research used in the computational statistics scientific community with climate science. One of the most important components of the climate system is the representation of clouds. They control many aspects of the energy and heat that enter and leave the climate system, and they interact with many components of the earth system (agriculture, weather, society, and the economy). But clouds are so complex that they can not be treated very precisely in models that are used for understanding climate and weather. The equations required to represent clouds are so complex that a precise treatment would slow down current models by factors of thousands or millions. Current computational climate and weather models cannot afford a precise representation of clouds so faster approximate treatments of clouds are needed. Traditional representations for clouds in climate and weather models are not sufficiently accurate, and progress has been slow in improving these model components. This proposal employs advanced statistical-mathematical methods to try and improve the situation. These methods (called Statistical Learning Theory or SLT) allow one to represent very complex systems with accurate, and very fast approximations. We are going to try to approximate very detailed, complex and expensive models of convective clouds using SLT to produce an accurate approximation for clouds with the goal of using this approximation (these approximations are frequently called a parameterization in climate and weather models). This research will push forward the knowledge base used in both the SLT community, and the climate community.

该提案着重于两个相互联系且同样重要的问题。他们中的第一个是开发一种新的统计学习理论（SLT），该理论致力于建模特定的复杂系统。第二个是为数值气候模型开发新的对流表示。了解气候和天气对科学，社会和经济很重要。我们关注的过程（云，特别是对流）对于气候和天气至关重要。该提案涉及一种新的方法来改善这些过程的表示。我们的目标是将数学科学家团队与SLT方面的专业知识相结合，并在云建模和气候系统建模方面具有专业知识，以在用于对流的数字天气预测和气候变化研究的大气模型中产生创新的代表。该项目将开发一个SLT系统，该系统模仿更现实但非常昂贵的高分辨率云系统解析模型（CSRM）的统计行为。在大规模模型中使用这些CSRM框架中最简单的框架甚至通过数千个因素增加了当今大气模型的成本，这使得它们在许多研究中的使用不切实际。通过在大规模模型中模仿这些更现实的框架的行为，我们开发了新的SLT参数化，从而大大降低了模型对流模型对流的更现实表示的成本，并提供了解决当前认为科学界至关重要的问题的机会。通过开发面向应用程序的SLT，我们希望使当前正在探索，计算可行的云和对流配方更现实，并在气候模型中使用它们。该提案将计算统计学科学界使用的研究与气候科学结合在一起。气候系统最重要的组成部分之一是云的表示。他们控制进入和离开气候系统的能量和热量的许多方面，并与地球系统（农业，天气，社会和经济）的许多组成部分相互作用。但是云是如此复杂，以至于无法在用于理解气候和天气的模型中得到非常精确的处理。代表云所需的方程式是如此复杂，以至于精确的处理将以数千或数百万的因素减慢当前模型。当前的计算环境和天气模型无法提供云的精确表示，因此需要更快的近似云处理。气候和天气模型中云的传统表示不够准确，并且在改善这些模型组件方面的进展也很慢。该建议采用先进的统计数学方法来尝试改善情况。这些方法（称为统计学习理论或SLT）允许一种具有准确且非常快速近似的非常复杂的系统。我们将尝试使用SLT近似非常详细，复杂且昂贵的对流云模型，以产生云的准确近似值，目的是使用此近似值（这些近似值通常称为气候和天气模型中的参数化）。这项研究将推动SLT社区和气候社区中使用的知识库。