A backward-time stochastic Lagrangian air quality model

We describe a backward-time Lagrangian air quality model based on time-reversed, stochastic particle trajectories. The model simulates the transport of air parcels backward in time using ensembles of fictitious particles with stochastic motions generated from the Stochastic Time-Inverted Lagrangian Transport model (STILT). Due to the fact that STILT was originally developed out of the HYSPLIT lineage, the model leverages previous work (Stein et al., 2000) that implemented within HYSPLIT a chemical scheme (CB4). Chemical transformations according to the CB4 scheme are calculated along trajectories identified by the backward-time simulations. This approach opens up several key advantages: 1) exclusive focus upon air parcels that affect the receptor's air quality; 2) the separation of transport processes—elucidated by backward-time trajectories—from chemical reactions that enables implications of multiple emission scenarios to be probed; 3) the potential to incorporate detailed sub-gridscale mixing and transport phenomena that are not tied to Eulerian gridcells.