低噪声区资料同化及其在黑潮中的应用
Data assimilation in the low noise regime with application to the Kuroshio
摘要：
在线数据同化技术，如集合卡尔曼滤波器和粒子滤波器失去准确性显着时，提出了一个不太可能的观测。这样的观测结果可能是由异常大的测量误差引起的，或者反映了系统动态中的罕见波动。在足够长的时间跨度内，这些事件中的一个或几个很可能会发生。它们通常是底层系统最有趣特征的特征，它们的预测成为数据同化程序的主要焦点。沿着日本东海岸的黑潮或黑潮就是这样一个系统的例子。它经历了罕见的，但戏剧性的变化之间的状态小曲流期间，目前仍然接近日本海岸，和一个大曲流期间，它凸出远离海岸。由于黑潮在分配周围区域的热量和盐度方面起着重要的作用，因此对这些转变的预测具有重要意义。在这里，我们专注于一个政权，其中系统的随机强迫和观测噪声都很小。在这种情况下，大偏差理论可以用来理解为什么标准的过滤方法失败，并指导设计更有效的数据同化技术。基于我们的分析，我们提出了几种数据同化策略，能够有效地处理罕见的事件，如黑潮的过渡。这些技术进行了测试的模型上的黑潮，并表现出更好的比标准的过滤方法。
英文摘要：
On-line data assimilation techniques such as ensemble Kalman filters and particle filters lose accuracy dramatically when presented with an unlikely observation. Such an observation may be caused by an unusually large measurement error or reflect a rare fluctuation in the dynamics of the system. Over a long enough span of time it becomes likely that one or several of these events will occur. Often they are signatures of the most interesting features of the underlying system and their prediction becomes the primary focus of the data assimilation procedure. The Kuroshio or Black Current that runs along the eastern coast of Japan is an example of such a system. It undergoes infrequent but dramatic changes of state between a small meander during which the current remains close to the coast of Japan, and a large meander during which it bulges away from the coast. Because of the important role that the Kuroshio plays in distributing heat and salinity in the surrounding region, prediction of these transitions is of acute interest. Here we focus on a regime in which both the stochastic forcing on the system and the observational noise are small. In this setting large deviation theory can be used to understand why standard filtering methods fail and guide the design of the more effective data assimilation techniques. Motivated by our analysis we propose several data assimilation strategies capable of efficiently handling rare events such as the transitions of the Kuroshio. These techniques are tested on a model of the Kuroshio and shown to perform much better than standard filtering methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
