|Title||EnKF and Hybrid Gain Ensemble Data Assimilation|
|Year of Publication||2014|
|Authors||Hamrud, M, Bonavita, M, Isaksen, L|
|Secondary Title||Technical Memorandum|
|Type of Work||Technical Memorandum|
The wish to do detailed comparisons between variational and more scalable ensemble-based data assimilation systems in a semi-operational environment has led to the development of a state of the art EnKF system at ECMWF. A broad description of the ECMWF EnKF is given in this paper, focusing on highlighting differences compared to standard EnKF practice. In particular, a discussion of the novel algorithm used to control imbalances between the mass and wind fields in the EnKF analysis is given. The scalability and computational properties of the EnKF are reviewed and the implementation choices adopted at ECMWF described. The sensitivity of the ECMWF EnKF to ensemble size, horizontal resolution and representation of model errors is also discussed. The performance of the EnKF system has been compared to a 4DVar of similar resolution. It is found that there is not a major difference between the forecast skill of the two systems. However, like the operational hybrid 4DVar-EDA, a hybrid EnKF-Variational system (which we refer to as Hybrid Gain Ensemble Data Assimilation, HG-EnDA) is capable of significantly out-performing both component systems. The HG-EnDA has been implemented with little effort following Penny (2014). Results of numerical experimentation comparing the HG-EnDA with the hybrid 4DVar-EDA used operationally at ECMWF are presented.