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.

这一建议侧重于两个相互关联且同样重要的问题。其中第一个是开发一种新的统计学习理论（Statistical Learning Theory，简称STLM），致力于对特定的复杂系统进行建模。二是发展一种新的数值气候模式的对流表示。了解气候和天气对科学、社会和经济都很重要。我们关注的过程（云，特别是对流）对气候和天气至关重要。该提案涉及一种改进这些进程的代表性的新方法。我们的目标是将联合收割机的数学科学家团队与专业知识在云建模和气候系统建模的大气科学家，以产生一个创新的表示对流的大气模型用于数值天气预报和气候变化研究。该项目将开发一个模拟系统，该系统模拟更现实但非常昂贵的高分辨率云系统解析模型（CSRM）在各种云状态下的统计行为。在大规模模型中使用这些CSRM框架中最简单的框架会增加今天的成本吗？的大气模型的数千倍，这使得他们的使用不切实际的许多研究。通过在大规模模型中模拟这些更现实的框架的行为，我们开发了一种新的对流参数化，大大降低了模型对流更现实的表示的成本，并提供了一个机会来解决目前被视为科学界的关键问题。通过开发面向应用的SLT，我们希望使目前正在探索的更现实的云和对流公式在计算上可行，并将其用于气候模型。该提案将计算统计科学界使用的研究与气候科学相结合。气候系统最重要的组成部分之一是云的表示。它们控制着进入和离开气候系统的能量和热量的许多方面，并与地球系统的许多组成部分（农业，天气，社会和经济）相互作用。但是云是如此复杂，以至于在用于理解气候和天气的模型中无法非常精确地处理它们。表示云所需的方程是如此复杂，以至于精确的处理将使当前的模型慢上数千或数百万倍。目前的计算气候和天气模型无法提供云的精确表示，因此需要更快的云近似处理。气候和天气模式中云的传统表示不够准确，在改进这些模式组件方面进展缓慢。这项建议采用了先进的数学方法，试图改善这种情况。这些方法（称为统计学习理论或统计学习理论）允许人们用准确和非常快速的近似来表示非常复杂的系统。我们将尝试使用近似方法来近似非常详细、复杂和昂贵的对流云模型，以产生云的精确近似，目的是使用这种近似（这些近似通常被称为气候和天气模型中的参数化）。这项研究将推动知识库在气候社区和气候社区使用。