Evolving topics in data assimilation

As my postdoc mentor at GSFC in the early 1980s, Eugenia Kalnay introduced me to a wide range of topics in predictability, ensembles, and data assimilation. Many of these topics became personal research interests and have evolved and morphed into new areas of research of wider interest, and in some cases have had important impacts on operations. My postdoc research experiences in what were then new research areas included trying to answer these questions:

• How can we use ensembles to improve forecasts, skill vs. spread, etc.?

• How can we use the data assimilation cycle to identify and correct model bias?

• How should OSSEs be designed?

• Can we solve variational data assimilation with nonlinear obs operators by using conjugate gradient minimization?

• What can we do when a feature (a storm) is in the wrong place in the background?

• What can we do when only half of a feature is observed by a satellite?

• Would it be possible/better to use radiances in data assimilation instead of retrievals?

• Can we solve the 4d-VAR problem?

I will describe some advances in some of these areas and make some predictions of the future evolution of data assimilation. In principle, modern approaches solve these issues. In practice, approximations and short cuts are required and keeping these concerns in mind can help to understand results from modern systems, and point the way to improving them.