Abstract. We developed a carbon data assimilation system to estimate surface
carbon fluxes using the local ensemble transform Kalman filter (LETKF) and
atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of
the meteorological field based on the Goddard Earth Observing System model,
version 5 (GEOS-5). This assimilation system is inspired by the method of
Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an
observing system simulation experiment (OSSE) as evolving parameters in the
assimilation of the atmospheric CO2, using a short assimilation window
of 6 h. They included the assimilation of the standard meteorological
variables, so that the ensemble provided a measure of the uncertainty in the
CO2 transport. After introducing new techniques such as “variable
localization”, and increased observation weights near the surface, they
obtained accurate surface carbon fluxes at grid-point resolution. We
developed a new version of the local ensemble transform Kalman
filter related to the “running-in-place”
(RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation
(Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the
new assimilation system uses the “no cost smoothing” algorithm for the
LETKF (Kalnay et al., 2007b), which allows shifting the Kalman
filter solution forward or backward within an assimilation window at no cost. In the
new scheme a long “observation window” (e.g., 7 d or longer) is used to
create a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain
an accurate final analysis at 1 d. This new approach has the advantage of
being based on a short assimilation window, which makes it more accurate,
and of having been exposed to the future 7 d observations, which improves
the analysis and accelerates the spin-up. The assimilation and observation
windows are then shifted forward by 1 d, and the process is repeated.
This reduces significantly the analysis error, suggesting that the newly
developed assimilation method can be used with other Earth system models,
especially in order to make greater use of observations in conjunction with
models.
The influence of observation errors on analysis error and forecast skill investigated with an observing system simulation experiment
