Predictability Characteristics of Global and Limited Area Models

全局和有限区域模型的可预测性特征

基本信息

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

项目摘要

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“全球和有限区域模型的可预测性特征”，佩格尔博士将继续对大气可预测性的调查，尤其是在本地和远程情况下，在本地和远程情况下对数值预测的不确定性的影响。将针对全球和限量区模型进行研究。具体而言，他将：1）使用跨越三个数量级的分辨率进行可变分辨率的全局模型进行实验，以确定可预测性的理论和二级限制，以及限制如何依赖局部，远程和全局不确定性。结果应该对数值模型和观察系统的设计具有实际影响。 2）使用高分辨率的本地模型执行实验，旨在通过分辨率增强来评估与与该模型初始化的操作预测相关的不确定性相关的不确定性。目标是量化可以通过提供边界条件的外部（区域或全局）模型的预测技能来改善内部（本地）模型域内实现的相对预测改进。这项研究的结果具有明显的潜力，可以改善短时和更长的时间尺度上的数值预测。中尺度动态气象和大规模动态气象计划将分享该奖项的支持。 ***