Sensitivity of Deterministic Prediction to Model Configuration and Initial Uncertainty

确定性预测对模型配置和初始不确定性的敏感性

• 批准号: 0109241
• 负责人: Jan Paegle
• 金额: $ 25.77万
• 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: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0109241&HistoricalAwards=false
• 关键词: Sensitivity; Deterministic; Prediction; Model; Configuration

This research addresses predictability of short to medium range weather evolution. The first research component will examine reasons for the relatively small benefits of high resolution limited area models compared to more crudely resolved global models revealed in the Principal Investigator's (PI) recent studies. These studies suggest that high resolution limited area models are superior to more coarse resolution global models at 12 and 24 hours, but lose their superiority by 36 hours. The PI hypothesizes that this short period of higher accuracy may be explained by incompatibilities between the global model and limited area model that are coupled at one-way imposed lateral boundaries of the latter, and by dynamical inconsistencies between these models particularly in regard to orographic flow-blocking. It is also hypothesized that the proximity of the validation domain to the data sparse Pacific may limit accuracy of all models. The relevance of these hypotheses will be examined by repeating prior limited area experiments with a global, variable resolution model that focuses its high-resolution window upon the entire conterminous United States. This approach allows two-way global interaction between the highly resolved domain and the rest of the world and will be validated over both the western and eastern United States. The second portion the research will examine fundamental predictability issues regarding saturation of uncertainty growth, the spectral distribution of uncertainty evolution, and possible inferences for the spacing of atmospheric observations. This work follows recent studies in which initial state uncertainty is determined from the difference of two separate, equally credible atmospheric analyses obtained from the United States and European operational numerical forecast models. Those results indicate that initial state uncertainty from the larger scales contribute more to error growth than do uncertainties at the smaller scales. These results, however, were based upon studies that used relatively coarse simulations of only five days duration that did not attain uncertainty saturation. The PI will extend these investigations to higher resolution models with integrations up to two weeks in order to obtain more complete estimates of saturation of uncertainty, the spectral distribution of uncertainty evolution, and observation/analysis requirements.Successful completion of this research could lead to improved weather forecasts and provide guidance for optimal siting of observational instruments.

这项研究解决了中短期天气演变的可预测性问题。第一个研究部分将审查高分辨率有限区域模式相对于首席调查员(PI)最近研究中揭示的更粗略的全球模式好处相对较小的原因。这些研究表明，高分辨率有限区域模式在12小时和24小时优于较粗分辨率的全球模式，但在36小时后失去优势。PI假设，这一较高精度的短周期可以由全球模式和有限区域模式之间的不相容以及这些模式之间的动力不一致来解释，这些模式在单向施加的横向边界上耦合，特别是在地形流动阻塞方面。还假设验证域接近数据稀疏太平洋可能会限制所有模型的准确性。这些假设的相关性将通过重复先前的有限区域实验来检验，该实验使用一个全球可变分辨率模式，该模式将其高分辨率窗口集中在整个毗邻的美国。这种方法允许高度解析的领域与世界其他地区之间的双向全球互动，并将在美国西部和东部得到验证。第二部分，研究将审查基本的可预测性问题，有关不确定增长的饱和度，不确定演变的频谱分布，以及对大气观测间隔的可能推断。这项工作是在最近的研究之后进行的，在这些研究中，初始状态的不确定性是根据从美国和欧洲业务数值预报模式获得的两个独立的、同样可信的大气分析的差异来确定的。这些结果表明，大尺度的初始状态不确定性比小尺度的不确定性对误差增长的贡献更大。然而，这些结果是基于研究，这些研究使用了相对粗略的模拟，持续时间只有五天，没有达到不确定性饱和。PI将把这些研究扩展到更高分辨率的模式，并进行长达两周的积分，以获得对不确定度饱和度、不确定度演变的频谱分布和观测/分析要求的更完整估计。成功完成这项研究将有助于改进天气预报，并为观测仪器的最佳选址提供指导。