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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. |
