The Effect of Model Uncertainty and Error on the Forecast Uncertainty

模型不确定性和误差对预测不确定性的影响

• 批准号: 1237613
• 负责人: Istvan Szunyogh
• 金额: $ 36.37万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1237613&HistoricalAwards=false
• 关键词: Effect; Model; Uncertainty; Error; Forecast

This project examines the impact of uncertainty and error in model formulation on errors in weather forecasts produced by ensemble prediction systems (EPSs). In an EPS, forecasts are produced by running an ensemble of forecast models in which each model starts out with a slightly different initial condition (models can also differ in their formulation), and the resulting ensemble of forecasts is analyzed statistically to produce an optimal forecast, and estimate of the error in the forecast, and (in combination with real-world observations), a set of initial conditions for the next forecast cycle. The work is based on the hypothesis that errors in model formulation (principally errors in parameterization and truncation) introduce errors into the model integrations at the scale of the parameterized processes, presumably at or near the truncation limit of the model, and these errors are propagated upscale by resolved model dynamics until they produce forecast uncertainty at synoptic scales. Because upscale propagation determines the forecast impacts at substantial lead times (day three, for instance), forecast errors due to model errors do not have any particular characteristics that would distinguish them from forecast errors due to initialization errors (which would likely undergo the same upscale propagation before affecting the forecast). Based on the above results, the PIs conjecture that the effect of model errors could be accounted for, at least approximately, by modulating the magnitude of the different error patterns in the low-dimensional vector space which contains most of the forecast uncertainty from all sources. The research has a three part agenda, in which the first part will test the hypothesis in forecasts archived in the THORPEX Interactive Ground Global Ensemble (TIGGE) data set. The TIGGE archive contains forecasts produced by a variety of ensemble prediction systems using a variety of techniques to account for errors in model formulation and intial conditions, thus allowing numerous tests of the hypothesis. The second part consists of a suite of "perfect model" experiments, in which the "true" state of the atmosphere will be taken from the same model used in the ensemble forecast system. The perfect model configuration enables experiments in which there is no model error, as the "true" system can have exactly the same physics and truncation as the forecast model. Such experiments are useful for considering other sources of forecast errors. The third part consists of forecast experiments using a state-of-the-art data assimilation system to assimilate real-world observations, and the PIs will attempt to specific challenging forecast cases, such as prediction of cyclogenesis produced from a warm-core tropical cyclone.In addition to its scientiifc merit, the work will have societal benefit by developing a strategy to improve the quality of weather forecasts issued to the general public. The work also seeks to improve understanding of the uncertainty inherent in weather forecasts, so that information regarding the likely accuracy of forecasts can be included in forecast guidance. The work may also have applicability to climate and earth system models used to produce climate projections and long-range forecasts, and to understanding and predicting the behavior of other complex systems. In addition, the project provides support and training to a graduate student, thereby developing the workforce in this research area.

该项目研究了模型公式中的不确定性和错误对集合预测系统（EPS）产生的天气预测中错误的影响。 在EP中，预测是通过运行一个预测模型的集合来产生的，在该模型中，每个模型以略有不同的初始条件开始（模型也可能有所不同），并且在统计上分析了预测的合奏，以产生最佳预测，并在现实情况下进行预测的误差，以及与现实观察的结合）。 这项工作基于以下假设：模型公式中的错误（主要是参数化和截断的错误）将错误引入模型集成在参数化过程的规模上，大概是模型的截断限制或接近模型的截断限制，并且这些错误是通过分辨率模型繁殖的，直到它们产生预测在同步量表上的预测动力学。 由于高档传播在大量的交货时间（例如第三天）确定了预测影响，因此由于模型误差而导致的预测错误没有任何特定的特征，这些特征将它们与初始化误差所致的预测错误区分开（在影响预测之前可能会经过相同的上升繁殖）。 基于上述结果，PIS的猜想是，可以通过调节低维矢量空间中不同误差模式的幅度，从而大致可以解释模型误差的效果，该模型包含所有来源的大多数预测不确定性。 这项研究具有三部分议程，其中第一部分将在索尔佩克斯互动基础全球整体（Tigge）数据集中存档的预测中测试假设。 Tigge档案包含由多种整体预测系统产生的预测，使用多种技术来说明模型公式和Intial条件中的错误，从而允许对假设进行大量测试。 第二部分由一套“完美模型”实验组成，其中大气的“真实”状态将取自集合预测系统中使用的相同模型。 完美的模型配置实现了没有模型误差的实验，因为“ True”系统可以具有与预测模型完全相同的物理和截断。 此类实验对于考虑其他预测误差来源很有用。 第三部分包括使用最先进的数据同化系统来吸收现实世界观察的预测实验，PI将尝试针对特定的具有挑战性的预测案例，例如预测从温暖的热带旋风中产生的环环发生的预测。在其Scientiifc Merit中加上该策略来改善该策略的构成策略，从而使工作能够改善构成策略，从而使该策略构成良好的效果。 这项工作还旨在提高对天气预测中固有的不确定性的理解，因此有关预测可能准确性的信息可以包括在预测指导中。 这项工作还可能适用于用于生成气候预测和远程预测的气候和地球系统模型，并理解和预测其他复杂系统的行为。 此外，该项目为研究生提供了支持和培训，从而在该研究领域开发了劳动力。