Improving the atmospheric dispersion forecasts over Washington, D.C. using UrbanNet observations: A study with HYSPLIT model

Abstract

UrbanNet data have been used to enhance the predictions of the Weather Research and Forecasting (WRF) model through observational nudging to improve temperature and wind predictions. HYSPLIT was utilized to understand the impact of using the observationally nudged WRF fields in dispersion modeling. The meteorological observations collected from the National Weather Service monitoring stations located at two major airports in Washington metropolitan area were also assimilated into WRF modeling. The results showed that observational nudging successfully adjusted WRF wind fields towards the observations and significantly reduced the forecast temperature bias at nighttime. The comparison of HYSPLIT simulations with and without the enhancement of the WRF model using UrbanNet and airport data showed significant differences in the pattern and direction of the dispersion plume especially during the early morning hours. Furthermore, ingesting the data from the closer airport to the downtown area in WRF provided HYSPLIT simulations very similar to the ones using UrbanNet data. This provides strong evidence that local data are essential to adjust weather prediction models routinely used to drive dispersion models. There was also evidence of increased mixing height when using local data collected in the downtown, mainly resulting from the increased surface heating in the city.