A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 2: Optimization scheme and identical twin experiment of atmospheric CO 2 inversion
2016
A four-dimensional variational method (4D-Var) is a popular technique for
source/sink inversions of atmospheric constituents, but it is not without
problems. Using an icosahedral grid transport model and the 4D-Var method, a
new atmospheric greenhouse gas (GHG) inversion system has been developed. The
system combines offline forward and adjoint models with a quasi-Newton
optimization scheme. The new approach is then used to conduct identical twin
experiments to investigate optimal system settings for an atmospheric
CO2 inversion problem, and to demonstrate the validity of the new
inversion system. In this paper, the inversion problem is simplified by
assuming the prior flux errors to be reasonably well known and by designing
the prior error correlations with a simple function as a first step. It is
found that a system of forward and adjoint models with smaller model errors
but with nonlinearity has comparable optimization performance to that of
another system that conserves linearity with an exact adjoint relationship.
Furthermore, the effectiveness of the prior error correlations is
demonstrated, as the global error is reduced by about 15 % by adding
prior error correlations that are simply designed when 65 weekly flask
sampling observations at ground-based stations are used. With the optimal
setting, the new inversion system successfully reproduces the spatiotemporal
variations of the surface fluxes, from regional (such as biomass burning) to
global scales. The optimization algorithm introduced in the new system does
not require decomposition of a matrix that establishes the correlation among
the prior flux errors. This enables us to design the prior error covariance
matrix more freely.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
61
References
11
Citations
NaN
KQI