Air quality forecasts produced by the National Air Quality Forecast Capability (NAQFC), human air quality forecasters, and persistence are evaluated for predictive skill and economic value when used to inform decisions regarding pollutant emission and exposure. Surface ozone forecasts and observations were collected from 40 monitors representing eight forecast regions throughout Washington, D.C.; Virginia; and Maryland over the 2005-09 ozone seasons (April-October). The skill of the forecasts are quantified using discrete statistics, such as correlation, mean bias, and root-mean-square error, and categorical statistics, such as exceedance hit rate, false alarm rate, and critical success index. The value of the forecasts are quantified using a decision model based on costs to protect the public against a poor air quality event and the losses incurred if no protective measures are taken. The results indicate that the most skillful forecast method is not necessarily the most valuable forecast method. Air shed managers need to consider multiple forecast methods when deciding on multiple protective measures, because a single measure of forecast skill can often hide the user's sensitivity to forecast error for a specific decision.
All Science Journal Classification (ASJC) codes
- Global and Planetary Change
- Social Sciences (miscellaneous)
- Atmospheric Science