Reducing Forecast Uncertainty to Improve Understanding of Atmospheric Flow Transitions

包围圈预报已成为天气预报中不可缺少的工具。集合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底层物理（换句话说，误差估计不会强烈地投射到物理结构上）。的主要目标是显示，通过一系列的实验，证据表明，一个非线性的方法将使我们能够隔离和消除一个确定性的集合预测中的错误的一部分。实验基础是从三个日益复杂的混沌系统，其中包括大气过渡和半球结构获得的结果。使用神经网络探测预测误差的确定性成分，它可以表明，误差恢复涉及到的基本类型的流，它可以用来预测过渡的大气流量使用集合数据。这项研究将扩大这些发现，考虑日益复杂的大气模型，包括一个完整的原始方程系综建模系统。 智力优点：这项工作的完成可以更准确地预测大规模大气流动的转变，并将误差与底层状态精确地联系起来，从而深入了解此类转变的可预测性。 更广泛的影响：该研究将为神经网络和天气预测错误诊断分析的研究生提供培训。这些结果将有助于更广泛的天气预测研究界，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.