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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
Historic
Publication Date:
2008
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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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 http://worldlibrary.net/


Description
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.

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

Excerpt
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