Data Assimilation - Methodology UQ

Data assimilation is the process of fusing information from imperfect
models, noisy measurements, and priors, to produce an optimal
representation of the state of a physical system. Data assimilation can
be interpreted and carried out in a Bayesian framework. Practical
methods for large-scale systems include suboptimal and the ensemble Kalman
filter approaches, optimal interpolation, and three and four dimensional
variational methods.

This working group will focus on emerging problems that include, but are
not limited to: new computational algorithms, modeling of model errors,
impact of observations, and quantification of posterior uncertainties. There
will be strong ties between theory and applications investigated throughout
the program.