论文标题
实时预测失败和由于数据差距引起的恢复的动力学
Dynamics of real-time forecasting failure and recovery due to data gaps
论文作者
论文摘要
实时预测对社会很重要。它使用连续数据流来更新预测，以持续准确。但是，数据源很容易受到攻击或事故的影响，并且对数据差距引起的预测失败和恢复的动态知之甚少。作为第一个系统的研究，基于洛伦兹的模型预测系统被各种长度和时机的数据差距破坏了。发现数据同化时间是最重要的因素。即使在数据同化恢复后很长时间，也发现预测精度也不会返回原始精度。
Real-time forecasting is important to the society. It uses continuous data streams to update forecasts for sustained accuracy. But the data source is vulnerable to attacks or accidents and the dynamics of forecasting failure and recovery due to data gaps is poorly understood. As the first systematic study, a Lorenz model-based forecasting system was disrupted with data gaps of various lengths and timing. The restart time of data assimilation is found to be the most important factor. The forecasting accuracy is found not returning to the original even long after the data assimilation recovery.
