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<article language="en">
	<journal>
		<journal_title>Atmospheric Chemistry and Physics Discussions</journal_title>
		<journal_url>www.atmos-chem-phys-discuss.net</journal_url>
		<issn>1680-7367</issn>
		<eissn>1680-7375</eissn>
		<volume_number>9</volume_number>
		<issue_number>2</issue_number>
		<publication_year>2009</publication_year>
	</journal>
	<doi>10.5194/acpd-9-5785-2009</doi>
	<article_url>http://www.atmos-chem-phys-discuss.net/9/5785/2009/</article_url>
	<abstract_html>http://www.atmos-chem-phys-discuss.net/9/5785/2009/acpd-9-5785-2009.html</abstract_html>
	<fulltext_pdf>http://www.atmos-chem-phys-discuss.net/9/5785/2009/acpd-9-5785-2009.pdf</fulltext_pdf>
	<start_page>5785</start_page>
	<end_page>5808</end_page>
	<publication_date>2009-03-04</publication_date>
	<article_title content_type="html">Data assimilation of CALIPSO aerosol observations</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>T. T. Sekiyama</name>
		</author>
		<author numeration="2" affiliations="1">
			<name>T. Y. Tanaka</name>
		</author>
		<author numeration="3" affiliations="2">
			<name>A. Shimizu</name>
		</author>
		<author numeration="4" affiliations="3,4">
			<name>T. Miyoshi</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Meteorological Research Institute, Tsukuba, Japan</affiliation>
		<affiliation numeration="2" content_type="html">National Institute for Environmental Studies, Tsukuba, Japan</affiliation>
		<affiliation numeration="3" content_type="html">Japan Meteorological Agency, Tokyo, Japan</affiliation>
		<affiliation numeration="4" content_type="html">now at: University of Maryland, USA</affiliation>
	</affiliations>
	<abstract content_type="html">We have developed an advanced data assimilation system for a global aerosol
model with a four-dimensional ensemble Kalman filter in which the Level 1B
data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observations (CALIPSO) were successfully assimilated for the first time, to
the best of the authors&apos; knowledge. A one-month data assimilation cycle
experiment for dust, sulfate, and sea-salt aerosols was performed in May
2007. The results were validated via two independent observations: 1) the
ground-based lidar network in East Asia, managed by the National Institute
for Environmental Studies of Japan, and 2) weather reports of aeolian dust
events in Japan. Detailed four-dimensional structures of aerosol outflows
from source regions over oceans and continents for various particle types
and sizes were well reproduced. The intensity of dust emission at each grid
point was also globally corrected. These results are valuable for the
comprehensive analysis of aerosol behavior as well as aerosol forecasting.</abstract>
	<references>
		<reference numeration="1" content_type="text"> Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects, Mon. Weather Rev., 129, 420–436, 2001. </reference>
		<reference numeration="2" content_type="text"> Bouttier, F. and Courtier, P.: Data assimilation concepts and methods, Meteorological Training Course Lecture Series, ECMWF, 75~pp., 1999. </reference>
		<reference numeration="3" content_type="text"> Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistcs, J. Geophys. Res., 99(C5), 10143–10162, 1994. </reference>
		<reference numeration="4" content_type="text"> Hara, Y., Yumimoto, K., Uno, I., Shimizu, A., Sugimoto, N., Liu, Z., and Winker, D. M.: Asian dust outflow in the PBL and free atmosphere retrieved by NASA CALIPSO and an assimilated dust transport model, Atmos. Chem. Phys., 9, 1227–1239, 2009. </reference>
		<reference numeration="5" content_type="text"> Harlim, J.: Errors in the initial conditions for numerical weather prediction: A study of error growth patterns and error reduction with ensemble filtering, PhD dissertation, University of Maryland, USA, 76~pp., 2006. </reference>
		<reference numeration="6" content_type="text"> Hollingsworth, A., Engelen, R. J., Textor, C., Benedetti, A., Boucher, O., Chevallier, F., Dethof, A., Elbern, H., Eskes, H., Flemming, J., Granier, C., Kaiser, J. W., Morcrette, J. J., Rayner, P., Peuch, V. H., Rouil, L., Schultz, M. G., Simmons, A. J., and The GEMS Consortium: Toward a Monitoring and Forecasting System For Atmospheric Composition: The GEMS Project, B. Am. Meteorol. Soc., 89, 1147–1164, 2008. </reference>
		<reference numeration="7" content_type="text"> Houtekamer, P. L. and Mitchell, H. L.: Data assimilation using an ensemble Kalman filter technique, Mon. Weather Rev., 126, 796–811, 1998. </reference>
		<reference numeration="8" content_type="text"> Hunt, B. R., Kalnay, E., Kostelich, E. J., Ott, E., Patil, D. J., and coauthors: Four-dimensional ensemble Kalman filtering, Tellus A, 56, 273–277, 2004. </reference>
		<reference numeration="9" content_type="text"> Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230, 112–126, 2007. </reference>
		<reference numeration="10" content_type="text"> Liu, Z., Vaughan, M. A., Winker, D. M., Hostetler, C. A., Poole, L. R., Hlavka, D., Hart, W., and McGill, M.: Use of probability distribution functions for discriminating between cloud and aerosol in lidar backscatter data, J. Geophys. Res., 109, D15202, doi:10.1029/2004JD004732, 2004. </reference>
		<reference numeration="11" content_type="text"> Lin, C., Wang, Z., and Zhu, J.: An Ensemble Kalman Filter for severe dust storm data assimilation over China, Atmos. Chem. Phys., 8, 2975–2983, 2008a. </reference>
		<reference numeration="12" content_type="text"> Lin, C., Zhu, J., and Wang, Z.: Model bias correction for dust storm forecast using ensemble Kalman filter, J. Geophys. Res., 113, D14306, doi:10.1029/2007JD009498, 2008b. </reference>
		<reference numeration="13" content_type="text"> Miyoshi, T. and Aranami, K.: Applying a Four-dimensional Local Ensemble Transform Kalman Filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM), SOLA, 2, 128–131, doi:10.2151/sola.2006-033, 2006. </reference>
		<reference numeration="14" content_type="text"> Miyoshi, T. and Sato, Y.: Assimilating Satellite radiances with a Local Ensemble Transform Kalman Filter (LETKF) applied to the JMA Global Model (GSM), SOLA, 3, 037–040, doi:10.2151/sola.2007-010, 2007. </reference>
		<reference numeration="15" content_type="text"> Miyoshi, T. and Yamane, S: Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 resolution, Mon. Weather Rev., 135, 3841–3861, 2007. </reference>
		<reference numeration="16" content_type="text"> Miyoshi, T., Yamane, S., and Enomoto, T.: The AFES-LETKF experimental ensemble reanalysis: ALERA, SOLA, 3, 045–048, doi:10.2151/sola.2007-012, 2007a. </reference>
		<reference numeration="17" content_type="text"> Miyoshi, T., Yamane, S., and Enomoto, T.: Localization the error covariance by physical distances within a Local Ensemble Transform Kalman Filter (LETKF), SOLA, 3, 089–092, doi:10.2151/sola.2007-023, 2007b. </reference>
		<reference numeration="18" content_type="text"> Niu, T., Gong, S. L., Zhu, G. F., Liu, H. L., Hu, X. Q., Zhou, C. H., and Wang, Y. Q.: Data assimilation of dust aerosol observations for the CUACE/dust forecasting system, Atmos. Chem. Phys., 8, 3473–3482, 2008. </reference>
		<reference numeration="19" content_type="text"> Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., and coauthors: Exploiting local low dimensionality of the atmospheric dynamics for efficient Kalman filtering, online available at: http://arxiv.org/PS_cache/physics/pdf/0203/0203058v3.pdf. </reference>
		<reference numeration="20" content_type="text"> Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., and coauthors: A local ensemble Kalman filter for atmospheric data assimilation, Tellus A, 56, 415–428, 2004. </reference>
		<reference numeration="21" content_type="text"> Shimizu, A., Sugimoto, N., Matsui, I., Arao, K., Uno, I., Murayama, T., Kagawa, N., Aoki, K., Uchiyama, A., and Yamazaki, A.: Continuous observations of Asian dust and other aerosols by polarization lidars in China and Japan during ACE-Asia, J. Geophys. Res., 109, D19S17, doi:10.1029/2002JD003253, 2004. </reference>
		<reference numeration="22" content_type="text"> Tanaka, T. Y. and Chiba, M.: Global simulation of dust aerosol with a chemical transport model, MASINGAR, J. Meteorol. Soc. Jpn., 83A, 255–278, 2005. </reference>
		<reference numeration="23" content_type="text"> Tanaka, T. Y. and Chiba, M.: A numerical study of the contributions of dust source regions to the global dust budget, Global Planet. Change, 52, 88–104, doi:10.1016/j.gloplacha.2006.02.002, 2006. </reference>
		<reference numeration="24" content_type="text"> Tanaka, T. Y., Orito, K., Sekiyama, T. T., Shibata, K., Chiba, M., and Tanaka, H.: MASINGAR, a global tropospheric aerosol chemical transport model coupled with MRI/JMA98 GCM, Pap. Meteorol. Geophys., 53, 119–138, 2003. </reference>
		<reference numeration="25" content_type="text"> Tanaka, T. Y., Kurosaki, Y., Chiba, M., Matsumura, T., Nagai, T., Yamazaki, A., Uchiyama, A., Tsunematsu, N., and Kai, K.: Trans-continental dust transport from North Africa and the Middle East to East Asia, Atmos. Environ., 39(21), 3901–3909, 2005. </reference>
		<reference numeration="26" content_type="text"> Tanaka, T. Y., Aoki, T., Takahashi, H., Shibata, K., Uchiyama, A., and Mikami, M.: Study of the sensitivity of optical properties of mineral dust to the direct aerosol radiative perturbation using a global aerosol transport model, SOLA, 3, 33–36, doi:10.2151/sola.2007-009, 2007. </reference>
		<reference numeration="27" content_type="text"> Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker, J. S.: Ensemble square root filters, Mon. Weather Rev., 131, 1485–1490, 2003. </reference>
		<reference numeration="28" content_type="text"> Uno, I., Wang, Z., Chiba, M., et al.: Dust model intercomparison (DMIP) study over Asia: Overview, J. Geophys. Res., 111, D12213, doi:10.1029/2005JD006575, 2006. </reference>
		<reference numeration="29" content_type="text"> Whitaker, J. S. and Hamill, T. M.: Ensemble data assimilation without perturbed observations, Mon. Weather Rev., 130, 1913–1924, 2002. </reference>
		<reference numeration="30" content_type="text"> Winker, D. M., Hunt, H. H., and McGill, M. J.: Initial performance assessment of CALIOP, Geophys. Res. Lett., 34, L19803, doi:10.1029/2007GL030135, 2007. </reference>
		<reference numeration="31" content_type="text"> Yumimoto, K., Uno, I., Sugimoto, N., Shimizu, A., and Satake, S.: Adjoint inverse modeling of dust emission and transport over East Asia, Geophys. Res. Lett., 34, L00806, doi:10.1029/2006GL028551, 2007. </reference>
		<reference numeration="32" content_type="text"> Yumimoto, K., Uno, I., Sugimoto, N., Shimizu, A., Liu, Z., and Winker, D. M.: Adjoint inversion modeling of Asian dust emission using lidar observations, Atmos. Chem. Phys., 8, 2869–2884, 2008. </reference>
		<reference numeration="33" content_type="text"> Zhang, J., Reid, J. S., Westphal, D. L., Baker, N. L., and Hyer, E. J.: A system for operational aerosol optical depth data assimilation over global oceans, J. Geophys. Res., 113, D10208, doi:10.1029/2007JD009065, 2008. </reference>
	</references>
</article>

