Uncertainty predictions and data assimilation for ocean and fluid flows are discussed within the context of Dynamically Orthogonal (DO) field equations and their adaptive error subspaces. These stochastic partial differential equations provide prior probabilities for novel nonlinear data assimilation methods which are derived and illustrated. The use of these nonlinear data assimilation methods and DO equations for targeted observations, i.e. for predicting the optimal sampling plans, is discussed. Numerical aspects are summarized, including new consistent schemes and test cases for the discretization of DO equations. Examples are provided using time-dependent ocean and fluid flows in two spatial dimensions. Co-authors from our MSEAS group at MIT: Thomas Sondergaard, Themis Sapsis, Matt Ueckermann and Tapovan Lolla