Impact of Cloud Assimilation on Air Quality Simulations

Regulatory emission reduction decisions that impact public health are based on numerical model simulations. Thus, it is critical that these models accurately represent the physical and chemical atmosphere. A major source of error in the air quality modeling systems is inaccurate representation of cloud fields. Clouds have a significant role in air quality simulations as they modulate biogenic hydrocarbon emissions, photolysis rates, impact boundary-layer development, lead to deep vertical mixing of pollutants and precursors, and induce aqueous phase chemistry. Thus, poor representation of clouds impacts the photochemical model's ability in properly simulating the air quality.

In the current study, we quantify the impact of improved model cloud fields on air quality simulations. Simulated cloud fields are improved by assimilating Geostationary Operational Environmental Satellite (GOES) derived cloud fields within Weather Research and Forecasting (WRF) model. The technique dynamically supports cloud formation/dissipation within WRF by using the observations to identify model cloud errors, estimate a target vertical velocity and moisture, and adjust the flow field accordingly. WRF simulations using this technique were performed over the period of August-September 2013 (NASA’s Discover-AQ field campaign). The cloud assimilation on the average improved model cloud simulation by 15%. The cloud correction not only improved the spatial and temporal distribution of clouds, it also improved boundary layer temperature, humidity, and wind speed. These improvements in meteorological fields directly impacted the air quality simulations and altered trace gas concentrations. For air quality simulations, WRF/SMOKE/CMAQ modeling system was used. Due to the radiative impact of cloud correction, isoprene and monoterpene emissions over the southeastern U.S. were reduced by 10-20%. This reduction in volatile organic compounds (VOC) concentrations along with reduced photolysis rates, improved ozone predictions in many places over continental United States. Due to large cloud errors over east/southeastern U.S., ozone predictions in this region were significantly improved. Preliminary results from this study will be presented.