The Effect of Model Uncertainty and Error on the Forecast Uncertainty

本项目研究模式制定中的不确定性和误差对集合预报系统（eps）产生的天气预报误差的影响。在EPS中，预测是通过运行一组预测模型来产生的，其中每个模型的初始条件略有不同（模型的公式也可能不同），然后对所产生的预测集合进行统计分析，以产生最佳预测，并估计预测中的误差，以及（结合实际观察）下一个预测周期的一组初始条件。这项工作是基于这样一个假设，即模型公式中的错误（主要是参数化和截断的错误）在参数化过程的尺度上（大概是在模型的截断极限或附近）将错误引入模型集成，并且这些错误通过已分解的模型动力学向上传播，直到它们在天气尺度上产生预测不确定性。由于高级传播决定了在相当长的前置时间内（例如，第三天）的预测影响，由于模型错误引起的预测错误没有任何特定的特征来区分它们与由于初始化错误引起的预测错误（在影响预测之前可能会经历相同的高级传播）。基于上述结果，pi推测，模型误差的影响可以通过调制低维向量空间中不同误差模式的大小来解释，至少是近似的，其中包含了来自所有来源的大部分预测不确定性。该研究有三个部分的议程，其中第一部分将在THORPEX交互式地面全球集成（TIGGE）数据集中存档的预测中测试假设。TIGGE档案包含由各种集合预测系统产生的预测，使用各种技术来解释模型公式和初始条件中的错误，从而允许对假设进行大量测试。第二部分包括一套“完美模式”实验，其中大气的“真实”状态将取自集合预报系统中使用的同一模式。完美的模型配置使实验中没有模型误差，因为“真实”系统可以具有与预测模型完全相同的物理和截断。这样的实验对于考虑预测误差的其他来源是有用的。第三部分包括预报实验，使用最先进的数据同化系统来吸收现实世界的观测，pi将尝试具体具有挑战性的预报案例，例如预测暖核热带气旋产生的气旋形成。除了其科学价值外，这项工作还将通过制定一项战略来提高向公众发布的天气预报的质量，从而产生社会效益。这项工作还力求提高对天气预报固有的不确定性的认识，以便在预报指南中纳入有关预报可能准确性的信息。这项工作也可能适用于气候和地球系统模型，用于产生气候预测和长期预报，以及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支持和培训，从而发展该研究领域的劳动力。

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.