Articles | Volume 13, issue 8
Atmos. Chem. Phys., 13, 4265–4278, 2013
Atmos. Chem. Phys., 13, 4265–4278, 2013

Research article 25 Apr 2013

Research article | 25 Apr 2013

A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM2.5 prediction

Z. Li1,2, Z. Zang2, Q. B. Li2,3, Y. Chao2,5, D. Chen2, Z. Ye2, Y. Liu4, and K. N. Liou2,3 Z. Li et al.
  • 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
  • 2Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, California, USA
  • 3Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California, USA
  • 4Brookhaven National Laboratory, Upton, New York, USA
  • 5Remote Sensing Solutions, Inc., Pasadena, California, USA

Abstract. A three-dimensional variational data assimilation (3-DVAR) algorithm for aerosols in a WRF/Chem model is presented. The WRF/Chem model uses the MOSAIC (Model for Simulating Aerosol Interactions and Chemistry) scheme, which explicitly treats eight major species (elemental/black carbon, organic carbon, nitrate, sulfate, chloride, ammonium, sodium and the sum of other inorganic, inert mineral and metal species) and represents size distributions using a sectional method with four size bins. The 3-DVAR scheme is formulated to take advantage of the MOSAIC scheme in providing comprehensive analyses of species concentrations and size distributions. To treat the large number of state variables associated with the MOSAIC scheme, this 3-DVAR algorithm first determines the analysis increments of the total mass concentrations of the eight species, defined as the sum of the mass concentrations across all size bins, and then distributes the analysis increments over four size bins according to the background error variances. The number concentrations for each size bin are adjusted based on the ratios between the mass and number concentrations of the background state. Additional flexibility is incorporated to further lump the eight mass concentrations, and five lumped species are used in the application presented. The system is evaluated using the analysis and prediction of PM2.5 in the Los Angeles basin during the CalNex 2010 field experiment, with assimilation of surface PM2.5 and speciated concentration observations. The results demonstrate that the data assimilation significantly reduces the errors in comparison with a simulation without data assimilation and improved forecasts of the concentrations of PM2.5 as well as individual species for up to 24 h. Some implementation difficulties and limitations of the system are discussed.

Final-revised paper