7B.2    Application of the randomized incremental optimal technique (RIOT) for parallelization of 4D-Var in WRFDA-Chem

 

Guerrette, Jonathan, National Oceanic and Atmospheric Administration, Nicolas Bousserez, and Daven Henze, University of Colorado at Boulder

 

Atmospheric pollutants (e.g., carbon dioxide, methane, aerosols) have quantifiable impacts on weather, climate, and human health, often on the time scale of a few days.  While operational weather prediction improves significantly by applying data assimilation (DA) to initial state, the current chemical state also depends strongly on surface fluxes, whose relationship to large-scale observations can only be approximated by a 4D model.  WRFDA-Chem uses incremental four-dimensional variational data assimilation (4D-Var) to quantify emissions of atmospheric chemical tracers in limited area domains for research applications on single-week timescales.  4D-Var is computationally expensive for real-time forecasting, partially due to its iterative minimization procedure, which is divided into inner and outer loops.  The recent proliferation of randomized methods for matrix decomposition enables the parallelization of the inner loop.  We utilize an ensemble of adjoint integrations in the randomized incremental optimal technique (RIOT) in WRFDA-Chem.  We achieve wall-time reductions of nearly an order of magnitude, with some increase in the required core-hours.  This new development can be extended to any application of WRFDA 4D-Var, independent of observation and control variable combinations.  Together, WRFDA-Chem and RIOT might enable simultaneous meteorological and chemical DA in order to improve both types of forecasts.