A novel air quality model will help air quality forecasters predict surface ozone levels up to 48-hours in advance and with fewer resources, according to a team of meteorologists.The method, called regression in self-organizing map (REGiS), weighs and combines statistical air quality models by pairing them with predicted weather patterns to create probabilistic ozone forecasts. Unlike current chemical transport models, REGiS can predict ozone levels up to 48 hours in advance without requiring significant computational power.