Controlling model error of underdamped forecast models in sparse observational networks using a variance-limiting Kalman filter



The problem of controlling covariance overestimation due to underdamped forecast models and sparsity of the observational network in an ensemble Kalman filter setting is considered. It is shown in a variational setting that limiting the analysis-error covariance to stay below the climatological value and driving the mean towards the climatological mean for the unobserved variables can significantly improve analysis skill over standard ensemble Kalman filters. These issues are explored for a Lorenz-96 system. It is shown that, for large observation intervals, the climatological information assures that the statistical properties of the unobserved variables are recovered, providing superior analysis skill. This skill improvement is increased for larger observational noise. Copyright © 2012 Royal Meteorological Society