The ensemble Kalman filter assumes observations to be Gaussian random variables with a pre-specified mean and variance. In practice, observations may also have detection limits, for instance when a gauge has a minimum or maximum value. In such cases, most data assimilation schemes discard out-of-range values, treating them as “not a number,” with the loss of possibly useful qualitative information. The current work focuses on the development of a data assimilation scheme that tackles observations with a detection limit. We present the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) and test its performance against the Partial Deterministic Ensemble Kalman Filter (PDEnKF) of Borup et al. Both are designed to assimilate out-of-range observations explicitly: the out-of-range values are qualitative by nature (inequalities), but one can postulate a probability distribution for them and then update the ensemble members accordingly. The EnKF-SQ is tested within the framework of twin experiments, using both linear and nonlinear toy models. Different sensitivity experiments are conducted to assess the influence of the ensemble size, observation detection limit and number of observations on the performance of the filter. Our numerical results show that assimilating qualitative observations using the proposed scheme improves the overall forecast mean, making it viable for testing on more realistic applications such as sea-ice models.