Interpretation of Probabilistic Surface Ozone Forecasts: A Case Study for Philadelphia

Abstract

The use of probabilistic forecasting has been growing in a variety of disciplines because of its potential to emphasize the degree of uncertainty inherent in a prediction. Interpretation of probabilistic forecasts, however, is oftentimes difficult deterring users who may benefit from such forecasts. To encourage broader use of probabilistic forecasts in the field of air quality, a process for interpreting forecasts from a statistical probabilistic air quality surface ozone model REGiS is demonstrated. Four procedures to convert probabilistic to deterministic forecasts are explored for Philadelphia metropolitan area. These procedures calibrate the predicted probability of daily maximum 8-hour average ozone exceeding a standard value by 1) estimating climatological relative frequency, 2) establishing a probability of an exceedance threshold as 50%, 3) maximizing the threat score, and 4) determining the unit bias ratio. REGiS is trained using 2000-2011 ozone season (May 1 to September 30) data, calibrated using 2012-2014 data, and evaluated using 2015-2018 data. Assessment of the calibration data with the Pierce Skill Score suggests an exceedance threshold based on climatological relative frequency for the conversion from probabilistic to deterministic forecasts. Calibrated REGiS generally compares well to predictions from the US national air quality model and operational ”expert” forecasts over the evaluation time period. For other probabilistic models and situations, different procedures of converting probabilistic to deterministic forecasts may be more beneficial. The methods presented in this paper represent an approach for operational air quality forecasters seeking to use probabilistic model output to support forecasts designed to protect public health.