Exploring outer-loop land-atmosphere coupling

The land-atmosphere coupling approach in current state-of-the-art NWP systems is based on weakly coupled data assimilation systems for individual Earth-system components. Atmospheric and land surface analyses are performed separately, and results are fed back into the next data assimilation window based on a model forecast. This can lead to imbalanced initial conditions or shocks and the observations cannot be fully harnessed for all components when assimilated only into one Earth system component within the same window.

The CopERnIcus climate change Service Evolution (CERISE) project aims to advance coupled surface-atmosphere assimilation in the preparation of the next generations global and regional reanalysis systems. As for the land, ECMWF’s activities are towards a unified Land Data Assimilation System (LDAS) based on the Simplified Extended Kalman Filter (SEKF) that incorporates multi-layer soil moisture analysis in operations and is currently being extended to other variables, making it suitable for improved coupling.

As part of CERISE, a “quasi-strongly coupled data assimilation” is being developed based on "outer land-atmosphere coupling" approach. Aim is to activate the SEKF as part of several 4D-Var outer loops and return the updated land analyses to initialize the atmosphere and the land of next outer loops within the same assimilation window. Initial efforts have focused on infrastructure developments to enable running the SEKF within the 4D-Var non-linear trajectory.

This work presents the preliminary results of scientific activities, numerical experimentation, and preliminary results to identify the proper number of outer loops required for optimal coupled land-atmosphere assimilation by testing different coupling configurations. For variables that are subject to strong diurnal cycles – such as soil and skin temperature - balanced initial conditions between the different outer loops can be advantageous. The new infrastructure has the capability to improve the exploitation of interface observations (e.g. land surface temperature) so that they can simultaneously influence the analysis of multiple Earth system components.