Research Example 4:
Improving Chemical Weather Predictions

Predicting chemical weather is of growing importance to society. Chemical transport models have become essential tools for providing science-based input into best alternatives for reducing urban pollution levels, designing cost-effective emission control strategies, siting of facilities, interpreting observational data, and assessing how we have altered the chemistry of the global environment. The forecasting of chemical weather has become a new application area, providing important information to the public, decision makers, and researchers. National weather services throughout the world are broadening their traditional role of mesoscale weather prediction to also include prediction of other environmental phenomena (e.g., plumes from biomass burning, volcanic eruptions, dust storms, and urban air pollution) that could potentially affect the health and welfare of the public. Currently hundreds of cities worldwide are providing real-time air quality forecasts.

While chemical weather prediction and weather prediction are closely aligned, there are important differences between the two. One important difference is that weather prediction is typically focused on severe, adverse weather conditions (e.g., storms), while the meteorology of adverse air quality conditions frequently is associated with benign weather. Boundary-layer structure and wind direction are perhaps the two most poorly determined meteorological variables for chemical weather prediction. Meteorological observations are critical to effectively predicting air quality, yet meteorological observing systems are typically designed to support prediction of severe weather and not the subtleties of adverse air quality. Research needs associated with the meteorological elements of air quality prediction have recently been assessed (Dabberdt et al., 2004a). The additional processes associated with emissions, chemical transformations, and removal also differentiate chemical weather prediction from weather forecasting. Because many important pollutants (e.g., ozone and fine particulate sulfate) are secondary in nature (i.e., formed via chemical reactions in the atmosphere), chemical weather models must include a rich description of the photochemical oxidant cycle. It is also important to note that the chemical and removal processes are highly coupled to meteorological variables (e.g., temperature and water vapor), as are many of the emission terms. In the case of windblown soils, emission rates correlate with surface winds, and evaporative emissions correlate with temperature. In the case of emissions associated with heating and air conditioning, the demand responds to ambient temperature.

The chemical observation networks were designed to support compliance and regulatory functions and not prediction. Chemical weather prediction places greater emphasis on real-time access to data, with broader spatial

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