Stochastic Variation of Parameterized Physical Processes in Idealized Forecast Ensembles: Stochastic Physics

理想化预测系综中参数化物理过程的随机变化：随机物理

• 批准号: 0221542
• 负责人: Daniel Wilks
• 金额: --
• 依托单位: Cornell Univ - State: AWDS MADE PRIOR MAY 2010
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-11-01 至 2005-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0221542&HistoricalAwards=false
• 关键词: Stochastic; Variation; Parameterized; Physical; Processes

Recently, it has been shown that one major source of errors in numerical weather predictions is generated by using comparatively deterministic functions of resolvable variables to represent the unresolved sub-grid processes. One effort in improving ensemble predictions involves perturbing the physical parameterizations of the model. To tackle the problem, the PI will study the sub-grid scale signatures in various verification measures and use a combination of them to improve the ensemble forecasts. An idealized model with low-dimension configurations will be used for the study. The research focus will be on the interactions of the resolvable variables and unresolved processes with particular attention on the effects of different perturbation amplitudes, correlation scales for the random element of physical parameterizations, and possible signatures of these errors in appropriate diagnostics. The overall goal of the research is to provide knowledge on the structure of perturbations in physical parameterizations and to represent them properly in ensemble forecasts. The research study has the potential of generating useful information for operational weather prediction.

最近，研究表明，数值天气预报误差的一个主要来源是使用可分解变量的相对确定性函数来表示未分解的子网格过程。改进集合预报的一个努力涉及扰动模型的物理参数化。为了解决这个问题，PI将研究各种验证措施中的次网格尺度特征，并结合使用它们来改善集合预报。一个理想化的模型与低维配置将用于研究。研究重点将是可解析变量和未解析过程的相互作用，特别注意不同扰动幅度的影响，物理参数化随机元素的相关尺度，以及这些错误在适当诊断中可能的签名。这项研究的总体目标是提供关于物理参数化中扰动结构的知识，并在集合预报中适当地表示它们。这项研究有可能为业务天气预报提供有用的信息。