An implementation of the particle flow filter in an atmospheric model

Abstract The particle flow filter (PFF) shows promise for fully nonlinear data assimilation (DA) in high-dimensional systems. However, its application in atmospheric models has been relatively unexplored. In this study, we develop a new algorithm, PFF-DART, in order to conduct DA for high-dimensional atmospheric models. PFF-DART combines the PFF and the two-step ensemble filtering algorithm in the Data Assimilation Research Testbed (DART), exploiting the highly parallel structure of DART. To evaluate the performance of PFF-DART, we conduct an observing system simulation experiment (OSSE) in a simplified atmospheric general circulation model and compare the performance of PFF-DART with an existing linear and Gaussian DA method. Using the PFF-DART algorithm, we demonstrate, for the first time, the capability of the PFF to yield stable results in a yearlong cycling DA OSSE. Moreover, PFF-DART retains the important ability of the PFF to improve the assimilation of nonlinear and non-Gaussian observations. Finally, we emphasize that PFF-DART is a versatile algorithm that can be integrated with numerous other non-Gaussian DA techniques. This quality makes it a promising method for further investigation within a more sophisticated numerical weather prediction model in the future. Significance Statement Data assimilation is an important tool in weather forecasting. However, fundamental issues arising from linear and Gaussian approximations still exist, which can limit our utilization of observations. The particle flow filter is a promising method that avoids these approximations, but efficient algorithms for this method have yet to be developed. In this study, we develop an algorithm for the particle flow filter that can be implemented in high-dimensional geophysical models. We have demonstrated, for the first time, that the particle flow filter runs efficiently for a yearlong experiment in an atmospheric model. The new algorithm also improves the results over a commonly used data assimilation method. The results in this work demonstrate the potential usage of the particle flow filter in the weather forecasting and other high-dimensional forecasting problems.