The 10,240-member ensemble Kalman filtering with an intermediate AGCM

• Published in: Geophysical research letters 2014-07, Vol.41 (14), p.5264-5271
• Main Authors: Miyoshi, Takemasa, Kondo, Keiichi, Imamura, Toshiyuki
• Format: Article
• Language: English
• Subjects: Atmosphere; Atmospheric circulation; Computer simulation; Correlation; covariance localization; data assimilation; Eigenvalues; ensemble Kalman filter; Geophysics; Kalman filters; large ensemble; Mathematical models; Permissible error; Specific humidity; Transforms
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1002/2014GL060863

The local ensemble transform Kalman filter (LETKF) with an intermediate atmospheric general circulation model (AGCM) is implemented with the Japanese 10 petaflops (floating point operations per second) “K computer” for large‐ensemble simulations of 10,240 members, 2 orders of magnitude greater than the typical ensemble size of about 100. The computational challenge includes the eigenvalue decomposition of 10,240 × 10,240 dense covariance matrices at each grid point. Using the efficient eigenvalue solver for the K computer, the LETKF computations are accelerated by a factor of 8, allowing a 3 week experiment of 10,240‐member LETKF with an intermediate AGCM for the first time. The flow‐dependent 10,240‐member ensemble revealed meaningful long‐range error correlations at continental scales. The surface pressure error correlation shows teleconnection patterns like the Pacific North American pattern. Specific humidity error correlation shows continental scale wave trains. Investigations with different ensemble sizes suggest that at least several hundred members be necessary to capture these continental scale error correlations. Key Points Local ensemble transform Kalman filter is implemented with the K computer The 10,240‐member ensemble reveals the probability distribution of the atmosphere Several hundred members are needed to capture the continental scale correlations