Open AccessEnKF and Hybrid Gain Ensemble Data Assimilation. Part I: EnKF Implementation
Author(s) -
Mats Hamrud,
Massimo Bonavita,
Lars Isaksen
Publication year - 2015
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/mwr-d-14-00333.1
Subject(s) - data assimilation , scalability , ensemble kalman filter , computer science , meteorology , environmental science , climatology , geology , database , artificial intelligence , geography , kalman filter , extended kalman filter
The desire to do detailed comparisons between variational and more scalable ensemble-based data assimilation systems in a semioperational 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. A comparison with 4DVar will be found in Part II of this two-part study.