Space weather forecasters and researchers have made progress in using adaptive approaches to improve forecast accuracy. These approaches include ensemble modeling, where a series of model inputs are generated representing the range of possible values, and data assimilation, where recent measurements are used to update the simulation. Ensemble forecasts combine many model predictions to create an ensemble that is more accurate than separate models, and allow the modeler to characterize how the uncertainty in model inputs result in output uncertainty. Another way to handle model output uncertainty is through data assimilation, where a physical model's accuracy can be improved by including, or assimilating additional information and data. These approaches, and other probabilistic methods, pave the wave for more accurate future forecasts, and make optimal use of all available information.