Predictability Characteristics of Global and Limited Area Models

全局和有限区域模型的可预测性特征

• 批准号: 9423311
• 负责人: Jan Paegle
• 金额: $ 28.82万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-05-01 至 1999-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9423311&HistoricalAwards=false
• 关键词: Predictability; Characteristics; Global; Limited; Area

9408047 Paegle "Predictability Characteristics of Global and Limited Area Models" Under this award, Dr. Paegle will continue his investigations of atmospheric predictability, and in particular, the effects of uncertainties in the initial conditions, both local and remote, on numerical predictions. Studies will be carried out for global and limited-area models. Specifically, he will: 1) conduct experiments with a variable resolution global model, using resolutions spanning three orders of magnitude, to ascertain theoretical and ppactical limits of predictability and how the limits depend on local, remote, and global uncertainties. The results should have practical implications for the design of numerical models and observing systems. 2) perform experiments with a high resolution local model designed to assess the "value- added" by resolution enhancement relative to the uncertainty associated with imperfect boundary conditions provided from operational forecasts used to initialize the model. The goal is to quantify the relative forecast improvement which can be realized within the inner (local) model domain from improvements in the forecast skill of the outer (regional or global) model that supplies the boundary conditions. The results of this research have obvious potential for improving numerical weather prediction on both short and longer time scales. The Mesoscale Dynamic Meteorology and Large-scale Dynamic Meteorology Programs will share in the support of this award. ***

9408047 Paegle“全球和有限区域模式的可预测性特征”根据该奖项，Paegle博士将继续他对大气可预测性的研究，特别是对本地和远程初始条件不确定性对数值预测的影响。将对全球模式和有限区域模式进行研究。具体来说，他将：1)使用跨越三个数量级的分辨率进行变分辨率全球模型的实验，以确定可预测性的理论和实践极限，以及这些极限如何依赖于本地、远程和全球的不确定性。研究结果对数值模型和观测系统的设计具有实际意义。2)使用高分辨率局部模型进行实验，以评估相对于初始化模型时使用的业务预报提供的不完善边界条件所带来的不确定性，分辨率增强所带来的“附加价值”。目标是量化在内部（局部）模型领域内可以实现的相对预测改进，这些改进来自于提供边界条件的外部（区域或全局）模型的预测技能的改进。本研究结果对于提高短、长时间尺度的数值天气预报具有明显的潜力。中尺度动态气象学和大尺度动态气象学项目将分享该合同的支持。＊＊＊