Application of a statistical post-processing technique to a gridded, operational, air quality forecast

An automated air quality forecast bias correction scheme based on the short-term persistence of model bias with respect to recent observations is described. The scheme has been implemented in the operational Met Office five day regional air quality forecast for the UK. It has been evaluated against routine hourly pollution observations for a year-long hindcast. The results demonstrate the value of the scheme in improving performance. For the first day of the forecast the post-processing reduces the bias from 7.02 to 0.53 μg m−3 for O3, from −4.70 to −0.63 μg m−3 for NO2, from −4.00 to −0.13 μg m−3 for PM2.5 and from −7.70 to −0.25 μg m−3 for PM10. Other metrics also improve for all species. An analysis of the variation of forecast skill with lead-time is presented and demonstrates that the post-processing increases forecast skill out to five days ahead.