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An Ensemble Kalman Filter for Severe Dust Storm Data Assimilation Over China : Volume 8, Issue 11 (17/06/2008)

By Lin, C.

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Book Id: WPLBN0003977858
Format Type: PDF Article :
File Size: Pages 9
Reproduction Date: 2015

Title: An Ensemble Kalman Filter for Severe Dust Storm Data Assimilation Over China : Volume 8, Issue 11 (17/06/2008)  
Author: Lin, C.
Volume: Vol. 8, Issue 11
Language: English
Subject: Science, Atmospheric, Chemistry
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Zhu, J., Wang, Z., & Lin, C. (2008). An Ensemble Kalman Filter for Severe Dust Storm Data Assimilation Over China : Volume 8, Issue 11 (17/06/2008). Retrieved from

Description: LAPC and NZC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. An Ensemble Kalman Filter (EnKF) data assimilation system was developed for a regional dust transport model. This paper applied the EnKF method to investigate modeling of severe dust storm episodes occurring in March 2002 over China based on surface observations of dust concentrations to explore the impact of the EnKF data assimilation systems on forecast improvement. A series of sensitivity experiments using our system demonstrates the ability of the advanced EnKF assimilation method using surface observed PM10 in North China to correct initial conditions, which leads to improved forecasts of dust storms. However, large errors in the forecast may arise from model errors (uncertainties in meteorological fields, dust emissions, dry deposition velocity, etc.). This result illustrates that the EnKF requires identification and correction model errors during the assimilation procedure in order to significantly improve forecasts. Results also show that the EnKF should use a large inflation parameter to obtain better model performance and forecast potential. Furthermore, the ensemble perturbations generated at the initial time should include enough ensemble spreads to represent the background error after several assimilation cycles.

An Ensemble Kalman Filter for severe dust storm data assimilation over China

Evensen, G.: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10 143–10 162, 1994.; Evensen, G.: The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation, Ocean Dynamics, 53, 343–367, 2003.; Evensen, G.: Data Assimilation: The Ensemble Kalman Tilter, Springer, German, 2006.; Gong, S. L., Zhang, X. Y., Zhao, T. L., McKendry, I. G., Jaffe, D. A., and Lu, N. M.: Characterization of soil dust aerosol in China and its transport and distribution during 2001 ACE-Asia: 2. Model simulation and validation, J. Geophys. Res., 108, 4262, doi:10.1029/2002JD002633, 2003.; Han, Z. W., Ueda, H., Matsuda, K., Zhang, R. J., Arao, K., Kanai, Y., and Hasome, H.: Model study on particle size segregation and deposition during Asian dust events in March 2002, J. Geophys. Res., 109, doi:10.1029/2004JD004920, 2004.; Hanea, R. G., Velders, G. J. M., Segers, A. J., Verlaan, M., and Heemink, A. W.: A Hybrid Kalman Filter Algorithm for Large-Scale Atmospheric Chemistry Data Assimilation, Mon. Wea. Rev., 135, 140–151, 2007.; Houtekamer, P. L. and Mitchell, H. L.: Data assimilation using an ensemble Kalman filter technique, Mon. Wea. Rev., 126, 796–811, 1998.; Houtekamer, P. L. and Mitchell, H. L.: A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Wea. Rev., 129, 123–137, 2001.; Houtekamer, P. L., Mitchell, H. L., Pellerin, G., Buehner, M., Charron, M., Spacek, L., and Hansen, B.: Atmospheric Data Assimilation with an Ensemble Kalman Filter: Results with Real Observations, Mon. Wea. Rev., 133, 604–620, 2005.; Lorenc, A. C.: The potential of the ensemble Kalman filter for NWP – a comparison with 4D-Var, Q. J. R. Meteorol. Soc., 129, 3183–3203, 2003.; Liu, M. L., Westphal, D. L., Wang, S. G., Shimizu, A., Sugimoto, N., Zhou, J., and Chen, Y.: A high-resolution numerical study of the Asian dust storms of April 2001, J. Geophys. Res., 108, 8653, doi:10.1029/2002JD003178, 2003.; Lu, H. and Shao Y. P.: Toward quantitative prediction of dust strorms: an integrated wind erosion modeling system and its application, Environmental Modeling & Software, 16, 233–249, 2001.; Mitchell, H. L. and Houtekamer, P. L.: An Adaptive Ensemble Kalman Filter, Mon. Wea. Rev., 28, 416–433, 2000.; Mitchell, H. L., Houtekamer, P. L., and Pelerin, G.: Ensemble size, balance, and model error representation in en ensemble Kalman filter, Mon. Wea. Rev., 130, 2791–2808, 2002.; Mori, I., Nishikawa, M., Quan, H., and Morita, M.: Estimation of the concentration and chemical composition of kosa aerosols at their origin, Atmos. Environ., 36, 4569–4575, 2002.; Murayama, T., Sugimoto, N., Uno. I., Kinoshita, K., Aoki, K., Hagiwara, N., Liu, Z., Matsui, I., Sakai, T., Shibata, T., Arao, K., Shon, B. J., Won, J. G., Yoon, S. C., Li, T., Zhou, J., Hu, H., Abo, M., Iokibe, K., Koga, R., and Iwasaka, Y.: Ground-Based Network Observation of Asian Dust Events of April 1998 in East Asia, J. Geophys. Res., 106, 18 345–18 359, 2001.; 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 CUACE/Dust forecasting system, Atmos. Chem. Phys. Discuss., 7, 8309–8332, 2007.; Shao, Y.: A model of mineral dust emission, J. Geophys. Res., 106, 20 239–20 254, 2001.; Park, S. U. and In, H. J.: Parameterization of dust emission for the simulation of the yellow sand (Asian dust) event observed in March 2002 in Korea, J. Geophys. Res., 108, 4618, doi:10.1029/2003JD003484, 2003.; Shao, Y. P., Yang,~Y., Wang,~J. J., Song,~Z. X., Leslie,~L M., Dong,~C. H., Zhang,~Z. H., Lin,~Z. H., Kanai,~Y., Yabuki,~S., and Chun,~Y.: Northeast Asian dust storms: Real-time numerical prediction and validation, J. Geophys. Res., 108, 4691, doi:10.1029/2003JD003667, 20


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