Air quality forecasting for winter‐time PM2.5 episodes occurring in multiple cities in central and southern Chile. Issue 1 (15th January 2016) - Saide, Pablo E.; Mena‐Carrasco, Marcelo; Tolvett, Sebastian; Hernandez, Pablo; Carmichael, Gregory R.
Abstract: Episodic air quality degradation due to particles occurs in multiple cities in central and southern Chile during the austral winter reaching levels up to 300–800 µg/m 3 hourly PM2.5, which can be associated with severe effects on human health. An air quality prediction system is developed to predict such events in near real time up to 3 days in advance for nine cities with regular air quality monitoring: Santiago, Rancagua, Curicó, Talca, Chillan, Los Ángeles, Temuco, Valdivia, and Osorno. The system uses the Weather Research and Forecasting with Chemistry model configured with a nested 2 km grid‐spacing domain to predict weather and inert tracers. The tracers are converted to hourly PM2.5 concentrations using an observationally based calibration which is substantially less computationally intensive than a full chemistry model. The conversion takes into account processes occurring in these cities, including higher likelihood of episode occurrence during weekends and during colder days, the latter related to increased wood‐burning‐stove activity for heating. The system is calibrated and evaluated for April–August 2014 where it has an overall skill of 53–72% of episodes accurately forecasted (61–76% for the best initialization) which is better than persistence for most stations. Forecasts one, two, and three days in advance all have skill in forecasting events but often present large variability within them due to different meteorological initializations. The system is being implemented in Chile to assist authority decisions not only to warn the population but also to take contingency‐based emission restrictions to try to avoid severe pollution events. Key Points: Development and testing for a system predicting severe PM2.5 episodes in south‐central Chile Forecasts are produced with skill up to 3 days in advance The system is being implemented in near real time for management of episodes