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3DVAR Chemical Data Assimilation In WRF/CHEM Forecasts During CALNEX


Air quality forecasts are typically initialized using chemical species concentrations obtained from the previous day's forecasts with no regard to the observations. This approach generally leads to better forecasts compared to those initialized with chemical species concentrations obtained from climatology. Data assimilation is an essential part of weather forecasting in all major meteorological centers. However, few attempts have been made to assimilate chemical species for air quality forecasting. Chemical data assimilation in air quality modeling is both a result of problem complexity (the number of chemical species varies in the model from tens to hundreds and is the multiple of the number of atmospheric state variables) and the scarcity of observations (especially with respect to vertical profiles). This is likely to change in the near future with the proliferation of satellites and unmanned observing platforms. The presentation will concentrate on the development of background error covariances for fine aerosols, the implementation of these species in the Grid Statistical Interpolation (GSI) and evaluation of forecasts with assimilation of surface measurements of these constituents. Preliminary results show forecast improvement when data assimilation is used.

Article / Publication Data
Available Metadata
Fiscal Year
Published On
January 01, 2011
Submitted On
March 15, 2021

This publication was presented at the following:

13th Conference on Atmospheric Chemistry
American Meteorological Society


Not available


Authors who have authored or contributed to this publication.

  • Stuart A. McKeen - Not Positioned Csl
    Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder
    NOAA/Global Systems Laboratory