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: 0721658
• 负责人: Philip Rasch
• 金额: $ 16万
• 依托单位: University Corporation For Atmospheric Res
• 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 2010-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0721658&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.

该提案重点关注两个相互关联且同等重要的问题。第一个是开发一种新的统计学习理论（SLT），致力于对特定的复杂系统进行建模。第二个是为数值气候模型开发一种新的对流表示。了解气候和天气对于科学、社会和经济都很重要。我们关注的过程（云，特别是对流）对于气候和天气至关重要。该提案涉及一种改进这些过程的表示的新方法。我们的目标是将具有 SLT 专业知识的数学科学家团队和具有云建模和气候系统建模专业知识的大气科学家团队结合起来，为用于数值天气预报和气候变化研究的大气模型中的对流提供创新的表示。该项目将开发一个 SLT 系统，模拟各种云状态下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。即使在大型模型中使用最简单的 CSRM 框架也会使当今大气模型的成本增加数千倍，这使得它们对于许多研究来说不切实际。通过在大型模型中模拟这些更真实的框架的行为，我们开发了一种新的 SLT 参数化，大大降低了模型对流的更真实表示的成本，并提供了解决当前科学界视为关键问题的机会。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的研究与气候科学结合起来。气候系统最重要的组成部分之一是云的表现。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。但云是如此复杂，以至于无法在用于了解气候和天气的模型中非常精确地处理它们。表示云所需的方程非常复杂，精确的处理会使当前模型的速度减慢数千或数百万倍。当前的计算气候和天气模型无法精确表示云，因此需要更快地对云进行近似处理。气候和天气模型中云的传统表示不够准确，并且改进这些模型组件的进展缓慢。该提案采用先进的统计数学方法来尝试改善这种情况。这些方法（称为统计学习理论或 SLT）允许人们以准确且非常快速的近似值来表示非常复杂的系统。我们将尝试使用 SLT 来近似非常详细、复杂且昂贵的对流云模型，以生成准确的云近似值，目的是使用该近似值（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 社区和气候社区使用的知识库。