Intensive Campaigns Supported by Air Quality Forecasting Capability to Identify Chemical and Atmospheric Regimes Susceptible to Standard Violations

Abstract

The U.S. National Aeronautics and Space Administration (NASA) has started a series of regional scale multiple platform field measurement intensives between 2011 and 2014. These measurement campaigns are collectively called the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. The emphasis of spatial collocation of multiple space-, air- and ground-based measurements facilitate the characterization of the vertical distribution of various air pollutants. During the January 16 to February 8, 2013 DISCOVER-AQ campaign over the San Joaquin Valley (SJV), California, one of the frontier science questions addressed concerned the frequent wintertime Particulate Matter (PM) standard violation over SJV and its vicinities. This study represents our contribution of real-time air quality forecasting to support flight-planning during the campaign as well as post analysis and model evaluation for some of the air pollutants. Two sets of real-time forecasts based on a coupled National Center for Environmental Prediction (NCEP) North American Model (NAM) and the U.S. EPA Community Multi-scale Air Quality Model (CMAQ) are included in the study: (1) a 12 km horizontal resolution domain over the Conterminous U.S., and (2) a 4 km resolutiondomain over the SJV and its adjacent areas nested-in from (1) as its parent. During the campaign there were several episodes of high surface PM concentration in the lowest hundreds of meters over SJV. We performed post-analyses of meteorological parameters such as the planetary boundary height and of chemical characteristics such as verification of modeled concentration of PM components at surface levels. Comparisons of the performance of the two forecasts will shed insight on horizontal resolution requirement to capture the meteorology and chemistry characteristics in SJV air quality modeling. These analyses will aid policy and regulatory relevant decisions as well as assist in improving the model for future applications.