Impact of meteorological inputs on wild-fire smoke predictions over the Contiguous United States

Wild-fire smoke is an important source of particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5). The NOAA Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model is linked with the NOAA Environmental Modeling System (NEMS) Non-hydrostatic Multi-scale Model on the Arakawa staggered B-grid (NMM-B) to provide numerical guidance for operational forecasts of wild-fire smoke across the country. The locations and extent of wildfire smoke are detected through the National Environmental Satellite, Data, and Information System (NESDIS) Hazard Mapping System (HMS) and the emissions are then calculated using the U.S. Forecast Service's Blue Sky Framework. The HYSPLIT model utilizes various meteorological inputs predicted by NMM-B to calculate transport, dispersion, and deposition, and then provides temporally and spatially varying air pollutant concentrations. In the current smoke operational forecasting system, NMM-B outputs at 12-km horizontal resolution are used to drive HYSPLIT smoke predictions. Meanwhile, NMM-B provides meteorological operational forecasts at a 4-km resolution over the Contiguous United States (CONUS). In this study, the nested NMM-B outputs at 4-km resolution are implemented into the smoke predictions. We use the Grid2grid Forecast Verification System developed at National Centers for Environmental Prediction (NCEP) / Environmental Modeling Center (EMC) to evaluate one month smoke predictions (i.e., August 2013) with NOAA Geostationary Operational Environmental Satellite (GOES) East Aerosol/Smoke products. In addition, we examine the impact of two different planetary boundary layer (PBL) parameterization schemes ((i.e., Mellor-Yamada-Janjic (MYJ) versus Global Forecast System (GFS) on the smoke transport and dispersion. In specific, we investigate how these PBL schemes, one using a local closure and the other relying on a bulk mixing approach, influence stability-related parameters (e.g., friction velocity, convective velocity), vertical mixing coefficient, and disposition velocity. The study is expected to provide useful information to improve future smoke predictions.