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The ensemble Kalman filter (EnKF) is a widely used ensemble-based\udassimilation method, which estimates the forecast error covariance matrix\udusing a Monte Carlo approach that involves an ensemble of short-term\udforecasts. While the accuracy of the forecast error covariance matrix is\udcrucial for achieving accurate forecasts, the estimate given by the EnKF\udneeds to be improved using inflation techniques. Otherwise, the sampling\udcovariance matrix of perturbed forecast states will underestimate the true\udforecast error covariance matrix because of the limited ensemble size and\udlarge model errors, which may eventually result in the divergence of the\udfilter.\ud  \udIn this study, the forecast error covariance inflation factor is estimated\udusing a generalized cross-validation technique. The improved EnKF\udassimilation scheme is tested on the atmosphere-like Lorenz-96 model with\udspatially correlated observations, and is shown to reduce the analysis error\udand increase its sensitivity to the observations

An estimate of the inflation factor and analysis sensitivity in the ensemble Kalman filter