TY - JOUR
T1 - Structure of forecast error covariance in coupled atmosphere-chemistry data assimilation
AU - Park, S. K.
AU - Lim, S.
AU - Zupanski, M.
N1 - Publisher Copyright:
© Author(s) 2015.
PY - 2015/5/5
Y1 - 2015/5/5
N2 - In this study, we examined the structure of an ensemble-based coupled atmosphere-chemistry forecast error covariance. The Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), a coupled atmosphere-chemistry model, was used to create an ensemble error covariance. The control variable includes both the dynamical and chemistry model variables. A synthetic single observation experiment was designed in order to evaluate the cross-variable components of a coupled error covariance. The results indicate that the coupled error covariance has important cross-variable components that allow a physically meaningful adjustment of all control variables. The additional benefit of the coupled error covariance is that a cross-component impact is allowed; e.g., atmospheric observations can exert an impact on chemistry analysis, and vice versa. Given the realistic structure of ensemble forecast error covariance produced by the WRF-Chem, we anticipate that the ensemble-based coupled atmosphere-chemistry data assimilation will respond similarly to assimilation of real observations.
AB - In this study, we examined the structure of an ensemble-based coupled atmosphere-chemistry forecast error covariance. The Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), a coupled atmosphere-chemistry model, was used to create an ensemble error covariance. The control variable includes both the dynamical and chemistry model variables. A synthetic single observation experiment was designed in order to evaluate the cross-variable components of a coupled error covariance. The results indicate that the coupled error covariance has important cross-variable components that allow a physically meaningful adjustment of all control variables. The additional benefit of the coupled error covariance is that a cross-component impact is allowed; e.g., atmospheric observations can exert an impact on chemistry analysis, and vice versa. Given the realistic structure of ensemble forecast error covariance produced by the WRF-Chem, we anticipate that the ensemble-based coupled atmosphere-chemistry data assimilation will respond similarly to assimilation of real observations.
UR - http://www.scopus.com/inward/record.url?scp=84929149162&partnerID=8YFLogxK
U2 - 10.5194/gmd-8-1315-2015
DO - 10.5194/gmd-8-1315-2015
M3 - Article
AN - SCOPUS:84929149162
SN - 1991-959X
VL - 8
SP - 1315
EP - 1320
JO - Geoscientific Model Development
JF - Geoscientific Model Development
IS - 5
ER -