Inline Coupling of WRF–HYSPLIT: Model Development and Evaluation Using Tracer Experiments


Fong Ngan, Ariel Stein, and Roland Draxler


J. Appl. Meteor. Climatol., 54, 1162–1176, 2015, DOI:10.1175/JAMC-D-14-0247.1


Abstract - The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), a Lagrangian dispersion model, has been coupled (inline) to the the Weather Research and Forecasting (WRF) Model meteorological model in such a way that the HYSPLIT calculation is run as part of the WRF-ARW prediction calculation. This inline version of HYSPLIT takes advantage of the higher temporal frequency of WRF-ARW variables relative to what would be available for the offline approach. Furthermore, the dispersion calculation uses the same vertical coordinate system as WRF-ARW, resulting in a more consistent depiction of the state of the atmosphere and the dispersion simulation. Both inline and the offline HYSPLIT simulations were conducted for two tracer experiments at quite different model spatial resolutions: the Cross Appalachian Tracer Experiment (CAPTEX) in regional scale (at 9-km grid spacing) and the Atmospheric Studies in Complex Terrain (ASCOT) in finescale (at 333.3-m grid spacing). A comparison of the model with the measured values showed that the results of the two approaches were very similar for all six releases in CAPTEX. For the ASCOT experiments, the cumulative statistical score of the inline simulations was better than or equal to offline runs in four of five releases. Although the use of the inline approach did not provide any advantage over the offline method for the regional spatial scale and medium-range temporal scale represented by the CAPTEX experiment, the inline HYSPLIT was able to improve the simulation of the dispersion when compared with the offline version for the fine spatial and temporal resolutions over the complex-terrain area represented by ASCOT. The improvement of the inline over the offline calculation is attributed to the elimination of temporal and vertical interpolation of the meteorological data as compared with the offline version.


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