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Abstract. Ever since its inception, the ensemble Kalman filter (EnKF) has elicited many heuristic approaches that sought to improve it. One such method is covariance localization, which alleviates spurious correlations due to finite ensemble sizes by using relevant spatial correlation information. Adaptive localization techniques account for how correlations change in time and space, in order to obtain improved covariance estimates. This work develops a Bayesian approach to adaptive Schur-product localization for the deterministic ensemble Kalman filter (DEnKF) and extends it to support multiple radii of influence. We test the proposed adaptive localization using the toy Lorenz'96 problem and a more realistic 1.5-layer quasi-geostrophic model. Results with the toy problem show that the multivariate approach informs us that strongly observed variables can tolerate larger localization radii. The univariate approach leads to markedly improved filter performance for the realistic geophysical model, with a reduction in error by as much as 33 %. 

Abstract. A flexible and highly extensible data assimilation testing suite, namedDATeS, is described in this paper. DATeS aims to offer a unified testingenvironment that allows researchers to compare different data assimilationmethodologies and understand their performance in various settings. The coreof DATeS is implemented in Python and takes advantage of its object-orientedcapabilities. The main components of the package (the numerical models, thedata assimilation algorithms, the linear algebra solvers, and the timediscretization routines) are independent of each other, which offers greatflexibility to configure data assimilation applications. DATeS can interfaceeasily with large third-party numerical models written in Fortran or in C,and with a plethora of external solvers.