GEM-Chemistry Modelling and Data Assimilation System:
A High Resolution Study
A contribution to ACCENT-TROPOSAT-2, Task Group 2
Centre for Earth and Space Science, York University
4700 Keele Street, Toronto, Ontario, Canada, M3J 1P3
We propose to perform model validation and assimilate satellite data using GEM-chemistry modelling system developed by MAQNet and Meteorological Service of Canada.
The chemical weather model is based on the Canadian operational weather prediction model, the Global Environmental Multiscale (GEM) model (Côté et al. 1998). It is a global variable resolution primitive equation model using semi-implicit and semi-Lagrangian numerical techniques. The model can be run on a variable resolution regional grid with horizontal spacing ~15km. The meso-global version (~50km) will be available in 2005. Tropospheric air quality and stratospheric chemistry packages have been implanted on-line in GEM.
In terms of assimilation GEM can use both 3-D Var (operational at the Canadian Meteorological Centre since 1997) and 4D Var. The 3-D Var system (Gauthier et al. 1999) can use either a non-separable and spectral representation of the error covariances, or a low-dependent representation from an ensemble perturbation method (Buehner 2004). The 3-D Var has also been modified to include an arbitrary number of chemical species in addition to the meteorological variables. At present the cross-error covariance between the chemical and meteorological variables is set to zero, but the code is designed to include the cross-error correlation whenever needed. The meteorological 4-D Var should become operational in 2005. It includes the TLM and adjoint of; 1) a simplified planetary boundary layer, 2) Kain-Fritsch moist convective parameterization, and 3) sub-gridscale orographic gravity wave drag. The 4-D Var online chemistry is currently under development at York University under the MAQNet grant.
Our unique contribution to AT2 will be in the area of high resolution model runs for selected case studies. We plan to utilize the high temporal and spatial (~50km global uniform and ~15km global variable) resolution model domains to characterize various biogenic and anthropogenic sources using data assimilation and inverse modeling techniques.
Technology developed under CFCAS grants are in public domain and will be available to EU scientists.
Dr. John C. McConnell, York University, Toronto, Canada
Dr. Richard Ménard, Meteorological Service of Canada, Dorval, Canada
Dr. Joanna Struzewska, Warsaw University of Technology, Warsaw, Poland
Canadian Foundation for Climate and Atmospheric Sciences (CFCAS)
National Science and Engineering Research Council, Canada (NSERC)