Asst. Professor Brian Powell
Ocean models provide an estimate of the ocean state filtered by discrete dynamical equations. Observations provide sparse information about the ocean at a variety of temporal and spatial length scales. Using one to inform the other allows us to understand and estimate the ocean more fully, but accomplishing this requires proper formulation of the problem. Using Bayesian Inference, we can derive a method for state estimation: using the observed data to improve the model’s estimate of the ocean. In this talk, I will discuss the philosophy of combining the the modeled and observed estimates of the ocean and what can be done with this estimation. Examples from around the Pacific ocean will be shown.
The seminar will be followed by drinks and finger food in the staff room. All attendees are welcome!