A hybrid nonlinear-Kalman ensemble transform filter (LKNETF) algorithm is build by combining the second-order exact particle filter NETF (nonlinear ensemble transform filter) with the local ensemble transform Kalman filter (LETKF). The hybrid filter combines the stability of the LETKF with the nonlinear properties of the NETF to obtain improved assimilation results for small ensemble sizes. Both filter components are localized in a consistent way so that the filter can be applied with high-dimensional models. The degree of filter nonlinearity is defined by a hybrid weight, which shifts the analysis between the LETKF and NETF. Since the NETF is more sensitive to sampling errors than the LETKF, the latter filter should be preferred in linear Gaussian cases. An adaptive hybrid weight can be defined based on the nonlinearity of the system so that the adaptivity yields a good filter performance in both linear and nonlinear situations. In particular the skewness and kurtosis of the ensemble can be applied to quantify the non-Gaussianity. The filter behavior is exemplified based on experiments with the chaotic Lorenz-63 und -96 models, in which the nonlinearity can be controlled by the length of the forecast phase. In these experiments the hybrid filter can yield an error reduction of up to 28% compared to the LETKF.