In order to facilitate future decision-making regarding regional
carbon fluxes, it is essential to better quantify uncertainty in inverse carbon
flux models. At Colorado State University, research is being performed in order
to better quantify sources and sinks and associated uncertainties on a
mesoscale level, through a coupled atmospheric (RAMS and PCTM) and terrestrial
carbon flux (SiB3) model (Denning, 2003).
Carbon-dioxide flux and mixing ratio data were collected from a ring of
towers (WLEF tall tower and nearby smaller towers) in northern Wisconsin over the
summer of 2004. The fully coupled
terrestrial-atmospheric model, SiB/RAMS, will be forced with 2004 reanalysis
data to predict fine scale weather in the vicinity of these towers for the
summer of 2004. Relevant portions of this simulated weather, including wind
fields and pertinent turbulence components, are extracted and used to create
backward-in-time Lagrangian Particle Dispersion Modeled (LPDM) influence
functions. Pseudo spatial carbon-dioxide
mixing ratio and flux data created by SiB/Rams is then used as input to several
different estimation routines in order to try and predict pseudo tower data at
different heights. Different temporal
and spatial aggregation lengths are considered as means of data reduction.
Particular attention will be paid to Ensemble Kalman Filter (EnKF) techniques
as well as geo-statistical methods as a means of estimation.
Author: A.E. Schuh, M. Ulliaz, S. Denning, and D. Zupanski (aschuh at atmos dot colostate dot edu)
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