Traditional ensemble probabilities are computed based on the number of members that exceed a threshold at a given point divided by the total number of members. This approach has been employed for many years in coarse resolution models. However, convection-permitting ensembles of less than ~20 members are generally underdispersive, and spatial displacement at the grid-scale is often large. This issue has motivated the development of spatial filtering and neighborhood post-processing methods, such as fractional coverage and neighborhood maximum value, which account for this spatial uncertainty. Two different fractional coverage approaches for the generation of grid-point probabilities are currently being evaluated. The first method expands the traditional point probability calculation to cover a 100-km radius around a given point. The second method, which is considered experimental, applies the idea that a uniform radius is not appropriate when there is strong agreement between members. In such cases, the traditional fractional coverage approach can reduce the probabilities for these potentially well-handled events. Therefore, a variable radius approach has been developed based upon ensemble agreement scale (EAS) similarity criteria outlined in Dey et al. (2016). This approach varies the spatial filter radius size according to member-member similarity criteria. In this method, the radius size ranges from 10-km for member forecasts that are in good agreement (e.g., lake effect snow, orographic precipitation, very short-term forecasts, etc.), to 100-km when the members are more dissimilar. Results from the application of this adaptive spatial filtering technique for the calculation of point probabilities will be presented based upon several months of objective verification, and subjective feedback from the 2017 Flash Flood and Intense Rainfall Experiment.