Application of a Lightning Data Assimilation Technique to Improve Transport and Dispersion Simulations

A major factor limiting the simulation of transport and dispersion (T&D) is the accuracy of the meteorological data used to drive the T&D. Further, a critical uncertainty within meteorological models is the simulation of deep convection. This study aims to improve T&D simulations by improving the accuracy of convective rainfall through data assimilation in the meteorological model used to drive the T&D. Specifically, we use a lightning assimilation technique that is applied in the Kain-Fritsch (KF) parameterization within the Weather Research and Forecasting (WRF) model. The assimilation has a simple approach that triggers deep convection where lightning is present and suppresses deep convection where lightning is absent. WRF simulations with three domains (27-9-3 km) were performed for the October 2010 Colorado Springs Tracer Experiment (COSTEX). One simulation was made with lighting assimilation applied on the outer two domains (LTGA) and another was performed without assimilation (CTRL). We find that the use of lightning assimilation considerably improves the mean absolute error, mean bias, and spatial correlation of precipitation when compared to stage-IV observations. Dispersion simulations for three COSTEX tracer releases were then calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Results show that the improved meteorology on the outer two domains in LTGA improves the T&D on the fine-scale (3 km) domain. Specifically, statistical parameters such as correlation, fractional bias, figure of merit in space, and the Kolomogorov-Smirnov parameter are all improved when lightning assimilation is used. This results in an improvement in the HYSPLIT “final rank” score, which is an overall measure of model robustness, from 1.42 (CTRL) to 1.86 (LTGA). These initial findings suggest that using lightning data assimilation improves T&D simulations. In addition, they also highlight the usefulness of meteorological data assimilation in improving T&D modeling. To add confidence to the technique and overall results, an additional independent experiment is being performed and those results will also be discussed at the meeting.