Forecasts generated by numerical weather prediction (NWP) models continue to improve, but they are far from perfect. Forecast errors can be separated into those caused by shortcomings in the model (e.g., discretization and parameterization errors) and those caused by an imperfect estimate of the state of the atmosphere, oceans, and land surface (i.e., the initial condition) given to the model. The goal of data assimilation is to eliminate the second class of errors to the greatest extent possible, given the observations at hand. Data assimilation is often treated as a statistical problem: Given a set of observations valid at some time, a previous forecast also valid at that time, and information about their error characteristics, what is the initial condition that is most likely to be closest to reality?

Morphological data assimilation is a complementary approach that seeks to use visual information, satellite data in particular, to help define the state of the atmosphere. The connection between satellite imagery and initial conditions in the form a model expects is made through the use of potential vorticity and its inversion. This seminar will provide an overview of the various data assimilation concepts in current use and show how the addition of visual information may improve the initial conditions of NWP models, thereby improving their forecasts.

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