HR: 14:10h
AN: A33F-03 [Abstracts]
TI: Uncertainty Analysis of STEM-2K1 Air Quality Forecasts for ICARTT
AU: * Chai, T
EM: tchai@cgrer.uiowa.edu
AF: University of Iowa, CGRER, Iowa City, IA 52242
United States
AU: Carmichael, G R
EM: gcarmich@engineering.uiowa.edu
AF: University of Iowa, CGRER, Iowa City, IA 52242
United States
AU: Tang, Y
EM: ytang@cgrer.uiowa.edu
AF: University of Iowa, CGRER, Iowa City, IA 52242
United States
AU: Sandu, A
EM: asandu7@cs.vt.edu
AF: Virginia Polytechnic Institute and State University, 660 McBryde Hall, Blacksburg, VA 24061
United States
AU: Constantinescu, E M
EM: emconsta@vt.edu
AF: Virginia Polytechnic Institute and State University, 660 McBryde Hall, Blacksburg, VA 24061
United States
AU: Skorton, J S
EM: jskorton@stanford.edu
AF: Stanford University, P.O. Box 15975, Stanford, CA 94309
United States
AB:
Air quality forecasts(hindcasts) of a regional Chemical Transport
Model (CTM) STEM-2K1 (Sulfur Transport Eulerian Model)
for ICARTT (International Consortium for Atmospheric Research on
Transport and Transformation) period has been evaluated
using two different methods. In the NMC approach, variations between
the model forecasts resulted from using different meteorological
fields are used as surrogates to model errors. In the observational
method, the model representativeness due to limited model resolutions
is taken into account when comparing the model results with the observations.
As model errors are often correlated among different species and between
their neighboring grid points, the model error covariance matrices have
been calculated. The results are then used to verify a flow-dependent
background error covariance model that is proposed for data assimilation
applications aiming to provide improved air quality forecasts.
In the end, the uncertainty analysis results have been implemented
into the STEM-2K1 four-dimensional variational data assimilation (4D-Var)
system. Results are presented and compared with the previous forecasts.
DE: 0368 Troposphere: constituent transport and chemistry
DE: 3315 Data assimilation
DE: 3355 Regional modeling
SC: Atmospheric Sciences [A]
MN: Fall Meeting 2005