Reducing Forecast Uncertainty to Improve Understanding of Atmospheric Flow Transitions

集合预报已成为天气预报中不可缺少的工具。系综预测中的一个问题是，无论采用哪种方法，预测误差都不能很好地映射到基本物理上(换句话说，误差估计不会强烈地投射到物理结构上)。主要目的是通过一系列实验证明，非线性方法将使我们能够隔离和消除集合预测中的确定性误差部分。实验基础来自于三个日益复杂的混沌系统的结果，这些混沌系统结合了大气跃迁和半球结构。利用神经网络探测预报误差的确定性分量，可以表明误差恢复与基本的气流类型有关，并且可以用于利用集合数据预报大气气流的转变。这项研究将在这些发现的基础上扩展，以考虑日益复杂的大气模型，包括一个完整的原始方程集合建模系统。智力优势：这项工作的完成将导致更准确地预测大尺度大气流动中的转变，并将误差与基本状态精确地联系起来，从而深入了解这种转变的可预测性。更广泛的影响：该研究将为研究生提供神经网络和天气预报误差诊断分析方面的培训。这一结果将有助于更广泛的天气预报研究界，并将对社会和政策考虑产生潜在的好处。

Ensemble prediction has become an indispensable tool in weather forecasting. One of the issues in ensemble prediction is that, regardless of the method, the prediction error does not map well to the underlying physics (in other words, error estimates do not project strongly onto physical structures). The main objective is to show, through a series of experiments, evidence that a nonlinear approach will allow us to isolate and eliminate a deterministic portion of the error in an ensemble prediction. The experimental basis is provided from results obtained from three increasingly sophisticated chaotic systems, which incorporate atmospheric transitions and hemispheric structure. Using neural networks to probe the deterministic component of forecast error, it can be shown that the error recovery relates to the underlying type of flow and that it can be used to forecast transitions in the atmospheric flow using ensemble data. This research will expand on these findings to consider increasingly sophisticated atmospheric models including a full primitive equation ensemble modeling system. Intellectual merit: Completion of this work result in more accurate prediction of transitions in the large-scale atmospheric flow and precisely relate the error to the underlying state, thereby gaining insight into the predictability of such transitions. Broader impacts: The research will provide training to a graduate student in neural networks and diagnostic analysis of weather prediction errors. The results will aid the broader weather prediction research community and will be of potential benefit for societal and policy considerations.