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.

这项研究解决了短期到中期天气演变的可预测性。 第一个研究部分将研究主要研究者（PI）最近的研究中发现的高分辨率有限区域模型与更粗略解决的全球模型相比相对较小的好处的原因。 这些研究表明，高分辨率有限区域模型在12和24小时上级更粗分辨率的全球模型，但在36小时失去其优越性。 PI假设，这一短时间内的高精度可以解释为全球模型和有限区域模型之间的不兼容性，耦合在后者的单向强加的横向边界，以及这些模型之间的动力学不一致，特别是在地形流阻塞。 还假设验证域与数据稀疏的太平洋的接近度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进行检查，该实验使用全球可变分辨率模型，该模型将其高分辨率窗口集中在整个美国本土。 这种方法允许高分辨率域和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进行验证。 第二部分的研究将探讨基本的可预测性问题的饱和度的不确定性增长，光谱分布的不确定性演变，以及可能的推断大气观测的间隔。 这项工作遵循最近的研究，其中初始状态的不确定性是从美国和欧洲的业务数值预报模型获得的两个独立的，同样可信的大气分析的差异。 这些结果表明，初始状态的不确定性，从较大的尺度比在较小的尺度上的不确定性的误差增长的贡献。 然而，这些结果是基于使用仅持续五天的相对粗略的模拟的研究，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，以获得更完整的不确定性饱和度估计、不确定性演变的光谱分布和观测/分析要求，这项研究的成功完成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。