Ensemble Kalman data assimilation has proven to be a versatile tool in geophysical sciences. Besides the traditional use of combining observations with a model state to create improved initial conditions for a forecast, researchers are using EnKF to gain clearer pictures of physical phenomena, conduct sensitivity studies of the time evolution of modeled phenomena, and assisting with forecast model development by identifying model biases, helping to reveal coding and algorithm errors, and providing insight into model variability and error characteristics. This talk will illustrate as many of these uses as time allows, using NCAR’s Community Earth System Model (CESM) and the Data Assimilation Research Testbed (DART) for illustration.