Orals

Data assimilation is becoming more important as a method to make predictions of Earth system states. Increasingly, coupled models for different compartments of the Earth system are used. This allows for making advantage of varieties of observations, in particular remotely sensed data, in different compartments. This session focuses on weakly and strongly coupled assimilation of in situ and remotely sensed measurement data across compartments of the Earth system. Examples are data assimilation for the atmosphere-ocean system, data assimilation for the atmosphere-land system and data assimilation for the land surface-subsurface system. Optimally exploiting observations in a compartment of the terrestrial system to update also states in other compartments of the terrestrial system still has strong methodological challenges. It is not yet clear that fully coupled approaches, where data are directly used to update states in other compartments, outperform weakly coupled approaches, where states in other compartments are only updated indirectly, through the action of the model equations. Coupled data assimilation allows to determine the value of different measurement types, and the additional value of measurements to update states across compartments. Another aspect of scientific interest for weakly or fully coupled data assimilation is the software engineering related to coupling a data assimilation framework to a physical model, in order to build a computationally efficient and flexible framework.

We welcome contributions on the development and applications of coupled data assimilation systems involving models for different compartments of the Earth system like atmosphere and/or ocean and/or sea ice and/or vegetation and/or soil and/or groundwater and/or surface water bodies. Contributions could for example focus on data value with implications for monitoring network design, parameter or bias estimation or software engineering aspects. In addition, case studies which include a precise evaluation of the data assimilation performance are of high interest for the session.