Data assimilation of CALIPSO aerosol observations

Abstract. 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' 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 corrected by this data assimilation system. These results are valuable for the comprehensive analysis of aerosol behavior as well as aerosol forecasting.

[1]  Teresa L. Campos,et al.  Evaluating Model Performance of an Ensemble-based Chemical Data Assimilation System During INTEX-B Field Mission , 2007 .

[2]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[3]  Takemasa Miyoshi,et al.  The AFES-LETKF Experimental Ensemble Reanalysis: ALERA , 2007 .

[4]  清孝 柴田,et al.  大気大循環モデルMRI/JMA98と結合した全球対流圏エーロゾル化学輸送モデル MASINGAR , 2003 .

[5]  P. Houtekamer,et al.  Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .

[6]  Jeffrey S. Reid,et al.  A system for operational aerosol optical depth data assimilation over global oceans , 2008 .

[7]  M. Juckes,et al.  The ASSET intercomparison of ozone analyses: method and first results , 2006 .

[8]  Caiyan Lin,et al.  An Ensemble Kalman Filter for severe dust storm data assimilation over China , 2008 .

[9]  Craig H. Bishop,et al.  Adaptive sampling with the ensemble transform Kalman filter , 2001 .

[10]  Masaru Chiba,et al.  Global Simulation of Dust Aerosol with a Chemical Transport Model, MASINGAR( ADEC-Aeolian Dust Experiment on Climate Impact-) , 2005 .

[11]  Nobuo Sugimoto,et al.  Dust model intercomparison (DMIP) study over Asia: Overview , 2006 .

[12]  Masaru Chiba,et al.  A numerical study of the contributions of dust source regions to the global dust budget , 2006 .

[13]  T. Sekiyama,et al.  MASINGAR, a global tropospheric aerosol chemical transport model coupled with MRI/JMA98 GCM: Model description , 2003 .

[14]  D. Winker,et al.  Initial performance assessment of CALIOP , 2007 .

[15]  Nobuo Sugimoto,et al.  Adjoint inverse modeling of dust emission and transport over East Asia , 2007 .

[16]  Y. Q. Wang,et al.  Data assimilation of dust aerosol observations for the CUACE/dust forecasting system , 2007 .

[17]  Study of the Sensitivity of Optical Properties of Mineral Dust to the Direct Aerosol Radiative Perturbation Using a Global Aerosol Transport Model , 2007 .

[18]  J. Whitaker,et al.  Ensemble Data Assimilation without Perturbed Observations , 2002 .

[19]  Istvan Szunyogh,et al.  Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter , 2005, physics/0511236.

[20]  Takemasa Miyoshi,et al.  Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution , 2007 .

[21]  David M. Winker,et al.  Use of probability distribution functions for discriminating between cloud and aerosol in lidar backscatter data , 2004 .

[22]  A. Hollingsworth,et al.  Toward a Monitoring and Forecasting System For Atmospheric Composition: The GEMS Project , 2008 .

[23]  E. Kalnay,et al.  Four-dimensional ensemble Kalman filtering , 2004 .

[24]  Christopher K. Wikle,et al.  Atmospheric Modeling, Data Assimilation, and Predictability , 2005, Technometrics.

[25]  Takemasa Miyoshi,et al.  Applying a Four-dimensional Local Ensemble Transform Kalman Filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM) , 2006 .

[26]  J. Whitaker,et al.  Ensemble Square Root Filters , 2003, Statistical Methods for Climate Scientists.

[27]  Jiang Zhu,et al.  Model bias correction for dust storm forecast using ensemble Kalman filter , 2008 .

[28]  Takemasa Miyoshi,et al.  Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF) , 2007 .

[29]  Zhaoyan Liu,et al.  Adjoint inversion modeling of Asian dust emission using lidar observations , 2008 .

[30]  Istvan Szunyogh,et al.  Exploiting Local Low Dimensionality of the Atmospheric Dynamics for Efficient Ensemble Kalman Filtering , 2002 .

[31]  Akihiro Uchiyama,et al.  Possible transcontinental dust transport from North Africa and the Middle East to East Asia , 2005 .

[32]  Nobuo Sugimoto,et al.  Continuous observations of Asian dust and other aerosols by polarization lidars in China and Japan during ACE-Asia , 2004 .

[33]  Zhaoyan Liu,et al.  Asian dust outflow in the PBL and free atmosphere retrieved by NASA CALIPSO and an assimilated dust transport model , 2008 .

[34]  Istvan Szunyogh,et al.  A Local Ensemble Kalman Filter for Atmospheric Data Assimilation , 2002 .

[35]  Yoshiaki Sato,et al.  Assimilating Satellite Radiances with a Local Ensemble Transform Kalman Filter (LETKF) Applied to the JMA Global Model (GSM) , 2007 .

[36]  John Harlim.  Errors in the Initial Conditions for Numerical Weather Prediction: A Study of Error Growth Patterns and Error Reduction with Ensemble Filtering , 2006 .