Collaborative Research: CMG: Ensemble Data Assimilation Based on Control Theory

本项目涉及用于大气和海洋预报的数据同化方法的研究。工作的重点是开发将非高斯误差统计纳入集合卡尔曼滤波的方法，克服了广泛使用的操作方法所基于的假设之一的需要。该方法涉及到控制理论中的非线性优化方法的使用。一旦该方法被开发出来，新方法的性能将与现有方法进行比较。新方案将用于评估非高斯误差统计对预报的影响。如果成功，这项工作将提供一种方法，在某些条件下，特别是极端条件下，改进天气和海洋预报。

This project involves research on data assimilation methods for atmospheric and oceanic forecasting. The focus of the work is to develop methods for incorporating non-Gaussian error statistics into the Ensemble Kalman Filter, overcoming the need for one of the assumptions on which widely used operational methods are based. The approach involves the use of nonlinear optimization methods taken from control theory. Once the method has been developed, the performance of the new approach will be compared with that of existing methods. The new scheme will be used to assess the impact non-Gaussian error statistics on forecasts.If successful, the work will provide a method that may improve forecasts of weather and ocean forecasts under certain conditions, particularly extreme conditions.