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” 根据该奖项，Paegle 博士将继续研究大气可预测性，特别是本地和远程初始条件的不确定性对数值预测的影响。将对全球和有限区域模型进行研究。具体来说，他将：1）使用可变分辨率全局模型进行实验，使用跨越三个数量级的分辨率，以确定可预测性的理论和实践限制以及这些限制如何依赖于局部、远程和全局不确定性。研究结果应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，该模型旨在评估分辨率增强相对于与用于初始化模型的操作预测提供的不完善边界条件相关的不确定性的“增值”。目标是量化可在内部（局部）模型域内通过提供边界条件的外部（区域或全球）模型的预测技能的改进而实现的相对预测改进。这项研究的结果对于改进短期和较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共同获得该奖项的支持。 ***