  This document describes in detail a new hybrid ensemble-variational algorithm
that generalizes existing ensemble or variational data assimilation approaches
and would enable joint state/parameter estimations in ECMWF's Integrated
Forecasting System (IFS). The proposed methodology is intended to serve as the
basis for a prototype Copernicus CO2 emission monitoring service. The main
characteristics of the system are: 1) 4D hybridization of ensemble information
with full-rank statistical modeling by combining an ensemble-based increment
with an adjoint-based increment propagation, allowing one to increase current
spatial resolution and/or include forward model processes missing from the
adjoint integration; 2) combination of tangent-linear and adjoint solvers with
ensemble-based approximations of transport Jacobians to construct a long-window
4D-Var with timescales relevant to greenhouse gas source inversion. The
proposed methodology is non-intrusive in the sense that the main structure of
the current incremental 4D-Var algorithm remains unchanged, while the
additional computational cost associated with the source inversion component is
minimized.
