Due to the low number of submissions the session will not be held this year. Submitted abstracts have been transferred to other appropriate sessions.



Data assimilation is the science of environmental state-estimation, where observations and model information are combined to produce an analysis, or estimate of the current state of the system. Applications include

• Forecasting: Using recent observations to improve initial conditions for short-term predictions

• Diagnosis, including parameter estimation: Testing and improving models by comparing predictions to observations

• Retrospective analysis: Learning more about how the Earth works, by using models to interpret/extend different types of data

This session will be devoted to the presentation and discussion on the use of observations and data assimilation techniques, in meteorology and oceanography. We welcome submissions on algorithmic developments in data assimilation (including variational, ensemble and other techniques); quantification and treatment of uncertainty in data assimilation; observation impact; observation network design and related topics.