The U.S. spends tens of billions of dollars each year to reduce air pollution in order to protect human health and the environment. More than half the people in the U.S. live in areas that do not meet the health-based air quality standards established by the U.S. Environmental Protection Agency. The primary air quality problems are elevated levels of ground-level ozone(O3) and fine particulate matter (PM2.5) that can lead to respiratory and cardiovascular problems and tens of thousands of premature deaths each year. Effective targeting of air pollution controls depends on having good scientific understanding of which sources and which regions are contributing to air quality issues. OWAQ supports several areas of air pollution research that provide information and products that directly support air quality decision-makers, air quality forecasters, and the research community. The focus of this research is on improving measurements and monitoring the exchange of pollutants between the air and the Earth's surface and on developing and applying the next generation of forecasting and assessment.
The use of ensembles with the objective of improving the simulation of plume dispersion is becoming a more common practice among atmospheric modelers. One of the biggest challenges in creating an ensemble is developing the appropriate selection process to avoid the use of redundant model information that might be inadvertently included when choosing its members. The exclusion of redundant ensemble members reduces the ensemble error and it is a more efficient use of computing resources. A combination of statistical measures is being used to identify simulations that are similar in order to reduce the redundant ensemble members.
Previous research has demonstrated that for air quality forecasts of surface particulates and ozone, a combination of Kalman filter and analog post-processing techniques provide a powerful tool to increase the skill of NOAA's operational CMAQ model. Research is being conducted to examine the geographic sensitivity of air quality model forecasts helping identify the most important parameters to be included regionally for analog searches. Techniques for the calculation and display of probabilistic forecast information that could be used in real-time for the CMAQ model are also being explored.
Research is being conducted to improve the data assimilation capabilities used in the state-of-the-art integrated modeling system - the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem). The current focus is on further improvement of a hybrid data assimilation system using an Ensemble Kalman Filter approach and the Grid-point Statistical Interpolation (GSI) method, a three-dimensional variational approach (3DVAR). The impact of the data assimilation system is being evaluated for summer and winter seasons.
Fine Particulate and Aerosol Predictions
Although three-dimensional atmospheric chemistry models have been in use for over thirty years, the performance of these models in simulating the formation, composition and distribution of fine particles in the atmosphere needs much improvement. In particular, the organic component of fine particles is often significantly under predicted as compared to speciated aerosol measurements, apparently because many organic aerosol formations pathways are not included or not well represented in current atmospheric chemistry models. Research is focused on developing and employing process-level models in conjunction with field measurements to investigate the physical and chemical processes of biosphere-atmosphere exchange which influence the sources and sinks of atmospheric fine particles and incorporating the new scientific understanding from these studies into numerical three-dimensional atmospheric chemistry models.
Research is being conducted to use model simulations and model verification analysis with observations from a recent field experiment and surface networks to assess the applicability of current emission inventories used by NOAA for fine particulate simulations. Results will provide recommended upgrades or changes needed to insure consistency with available data. Model and evaluation work is being focused on identifying discrepancies between model simulations and data collected from aircraft, surface networks, and available meteorological platforms during the June-July 2013 SENEX field study.