Local volume solvers for Earth system data assimilation: implementation
in the framework for Joint Effort for Data Assimilation Integration
Abstract
The Joint Effort for Data assimilation Integration (JEDI) is an
international collaboration aimed at developing an open software
ecosystem for model agnostic data assimilation. This paper considers
implementation of the model-agnostic family of the local volume solvers
in the JEDI framework. The implemented solvers include the Local
Ensemble Transform Kalman Filter (LETKF), the Gain form Ensemble
Transform Kalman Filter (GETKF), and the optimal interpolation variant
of the LETKF filter (LETKF-OI). This paper documents the implementation
choices and strategies that allow model agnostic implementation. We also
document an expansive set of localization approaches that includes
generic distance-based localization, localization based on modulated
ensemble products, but also localizations specific to ocean (based on
the Rossby radius of deformation), and land (based on the terrain
difference between observation and model grid point). Finally, we apply
the developed solvers in a limited set of experiments, including
single-observation experiments in atmosphere and ocean, and cycling
experiments for the ocean, land, and aerosol assimilation. We also
provide a proof of concept that illustrates how JEDI Ensemble Kalman
Filter solvers can be used in a strongly coupled framework providing
increments to the ocean based on the combined observations from the
ocean and the atmosphere.