TY - CHAP
T1 - Chapter 3.6 Application of four-dimensional variational (4DVAR) data assimilation for optimal estimation of mineral dust and CO emissions in eastern Asia
AU - Yumimoto, Keiya
AU - Uno, Itsushi
N1 - Funding Information:
This work was partly supported by the Global Environmental Research Fund, Ministry of Environment, Japan and a grant-in-aid for scientific research under Grant No. 17360259 from the Ministry of Education, Culture, Sports, Science and Technology, Japan. The CO observation data at Rishiri were provided by Dr. H. Tanimoto of the National Institute of Environmental Studies (NIES); data for Ryori and Yonaguni were provided by the World Data Centre for Greenhouse Gases (WDCGG, http://gaw.kishou.go.jp/wdcgg.html ): CO data from R/V Ronald H. Brown were provided by ACE-ASIA ( http://www.joss.ucar.edu/ace-asia/dm/data_access_frame.html ). LIDAR observation data were provided by Dr. N. Sugimoto and Dr. A. Shimizu of the National Institute of Environmental Studies (NIES).
PY - 2007
Y1 - 2007
N2 - A four-dimensional variational (4DVAR) data assimilation system was developed for a regional chemical transport model (CTM). In this study, we applied it to inverse modeling of CO emissions and mineral dust emission flux over East Asia, and demonstrated the feasibility of our assimilation system. In CO inverse modeling, three ground-based observations were used for estimating CO emission over East Asia. Assimilated results showed better agreement with observations; the RMS differences were reduced by 16-27%. CO emission over industrialized east central China between Shanghai and Beijing has increased markedly, and the results show that the annual anthropogenic (fossil and biofuel combustion) CO emission over China are 147 Tg. In dust inverse modeling, NIES LIDAR observations were used. The assimilated results better reflects the presence of the elevated dust layer and improved the under-prediction of dust concentrations. We obtained an 18% increase in calculated dust emissions through data assimilations, especially over the Mongolian region, indicating that the observed high-dense dust layer might originate in that region. These data assimilation results indicate that the 4DVAR method is very powerful for unification of observation and numerical modeling by CTM.
AB - A four-dimensional variational (4DVAR) data assimilation system was developed for a regional chemical transport model (CTM). In this study, we applied it to inverse modeling of CO emissions and mineral dust emission flux over East Asia, and demonstrated the feasibility of our assimilation system. In CO inverse modeling, three ground-based observations were used for estimating CO emission over East Asia. Assimilated results showed better agreement with observations; the RMS differences were reduced by 16-27%. CO emission over industrialized east central China between Shanghai and Beijing has increased markedly, and the results show that the annual anthropogenic (fossil and biofuel combustion) CO emission over China are 147 Tg. In dust inverse modeling, NIES LIDAR observations were used. The assimilated results better reflects the presence of the elevated dust layer and improved the under-prediction of dust concentrations. We obtained an 18% increase in calculated dust emissions through data assimilations, especially over the Mongolian region, indicating that the observed high-dense dust layer might originate in that region. These data assimilation results indicate that the 4DVAR method is very powerful for unification of observation and numerical modeling by CTM.
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U2 - 10.1016/S1474-8177(07)06036-6
DO - 10.1016/S1474-8177(07)06036-6
M3 - Chapter
AN - SCOPUS:44349182589
SN - 9780444529879
T3 - Developments in Environmental Science
SP - 318
EP - 328
BT - Air Pollution Modeling and Its Application XVIII
A2 - Borrego, Carlos
A2 - Renner, Eberhard
ER -