A variational ensemble Kalman filtering method for data assimilation using 2D and 3D version of COHERENS model

作者：Amour Idrissa^*; Kauranne Tuomo

来源：International Journal for Numerical Methods in Fluids, 2017, 83(6): 544-558.

DOI：10.1002/fld.4276

• data assimilation
• COHERENS
• variational and ensemble Kalman filters
• forecasting
• decoupled implementation
• suspended matter

Decoupled implementation of data assimilation methods has been rarely studied. The variational ensemble Kalman filter has been implemented such that it needs not to communicate directly with the model, but only through input and output devices. In this work, an open multi-functional three-dimensional (3D) model, the coupled hydrodynamical-ecological model for regional and shelf seas (COHERENS), has been used. Assimilation of the total suspended matter (TSM) is carried out in 154km(2) lake Sakylan Pyhajarvi. Observations of TSM were derived from high-resolution satellite images of turbidity and chrolophyll-a. For demonstrating the method, we have used a low-resolution model grid of 1km. The model was run for a period from May 16 to September 14. We have run the COHERENS model with two-dimensional (2D) mode time steps and 3D mode time steps. This allows COHERENS to switch between 2D and 3D modes in a single run for computational efficiency. We have noticed that there is not much difference between these runs. This is because satellite images depict the derived TSM for the surface layer only. The use of additional 3D data might change this conclusion and improve the results. We have found that in this study, the use of a large ensemble size does not guarantee higher performance. The successful implementation of decoupled variational ensemble Kalman filter method opens the way for other methods and evolution models to enjoy the benefits without having to spend substantial effort in merging the model and assimilation codes together, which can be a difficult task.

• 出版日期2017-2-28