| Abstract | Tower footprints represent the surface area that influenced an atmospheric mixing ratio observation measured at one of the 13 tower locations of the INFLUX network (https://sites.psu.edu/influx/site-information/). These footprints were generated to map in space and time the sensitivity of any given hourly atmospheric observation sampled between September 2012 to September 2015 by the network. The footprints were created by coupling the Lagrangian Particle Dispersion Model (LPDM) described by Uliasz [1994] to the WRF-FDDA modeling system (http://www2.mmm.ucar.edu/wrf/users/download/get_source.html). Particles are released from the receptors in a backward in time mode with the wind fields and the turbulence generated by the Eulerian model WRF-FDDA. In a backward in time mode, particles are released from the measurement locations and travel to the surface and the boundaries. Compared to a forward mode where particles are released from the entire surface of the simulation domain, the particles in backward mode are released only from the observation locations with all of them being used to estimate fluxes, which reduces the computational cost of the simulation. Every 20 s, 35 particles are released at the position of the towers, which corresponds to 6300 particles per hour per measurement site (or receptor). At high spatial resolutions, the particle locations have to be stored at a much higher frequency compared to regional applications. As a first estimation, a particle would fly over a 1 km pixel in about 3 min (assuming a horizontal mean wind speed of 5 m/s). To avoid any gaps in the particle trajectories, particle positions were recorded every minute. At the opposite, because the domain is small (87 km wide), the integration time, i.e., the time window during which the air masses are influenced by the local surface emissions, is limited to few hours. Here particles were integrated over 12 h to ensure that particles traverse the entire domain in any meteorological situations.
The dynamical fields in LPDM are forced by mean horizontal winds (u, v, w), potential temperature and turbulent kinetic energy (TKE) from WRF-FDDA. At this resolution (1 km), turbulent motion corresponds to the closure of the energy budget at each time step. This scalar is used to quantify turbulent motion of particles as a pseudo random velocity. Based on the TKE, wind, and potential temperature, the Lagrangian model diagnoses turbulent vertical velocity and dissipation of turbulent energy. The off-line coupling between an Eulerian and a Lagrangian model solves most of the problems of nonlinearity in the advection term at the mesoscale. Most of the nonlinear processes resolved by the atmospheric model are attributed to a scalar representing the velocity of the particles. At each time step (here 20 s), particles move with a velocity interpolated from the dynamical fields of the WRF-FDDA simulation stored every 20 min.
The formalism for inferring source-receptor relationships from particle distributions is described by Seibert and Frank [2004]. At each time step, the fraction of particles (released from one receptor at one time) within some volume gives the influence of that volume on the receptor. If the volume includes the surface this will yield the influence of surface sources. If the volume includes the boundary (sides or top) it yields the influence of that part of the boundary.
Uliasz, M. (1994), Lagrangian particle modeling in mesoscale applications, in Environmental Modelling II, edited by P. Zanetti, pp. 71–102, Computational Mechanics Publications, Chicago, Ill.
Seibert, P., and A. Frank (2004), Source-receptor matrix calculation with a Lagrangian particle dispersion model in backward mode, Atmos. Chem. Phys., 4(1), 51–63, doi:10.5194/acp-4-51-2004. |
