A new statistical wake meandering (SWM) model is proposed that improves on existing models in the literature. Compared to the existing SWM models, the proposed model has a closed description that does not require simulations to create look-up tables while maintaining applicability to a wide range of flow conditions. The proposed SWM model is compared to the predictions of the Dynamic Wake Meandering (DWM) model and to wind speed measurements from a scanning Doppler lidar mounted on the nacelle of a utility-scale wind turbine for validation. The results show that the proposed model has a similar performance as the DWM model for the effect of wake meandering on the mean velocity deficit and the turbulence intensity, while being significantly faster to compute.