Potential of error-evolving tracer forecasts for operational assimilation of PM2.5 during wildfire smoke episodes

2023 was record-breaking for wildfires in Canada with unprecedented impacts on local ecosystems as well as large scale smoke hazards. These exceptional fire impacts rose the public demand for accurate forecasts of smoke plumes as well as analysis of air quality impacts. However, fire smoke plumes are extreme air quality events with exceptionally high concentrations and related uncertainties fall outside statistical ranges. These particular conditions induce specific challenges for data assimilation algorithms, because error estimates need to capture the high uncertainties and spatial gradients. At the same time, operational forecast systems require high computational efficiency to deliver fast, yet accurate forecasts to the public.

This study explores the potential of a novel assimilation approach, called parametric Kalman filter (PKF), for operational air quality forecasting during extreme air quality events. By explicitly evolving the main error parameters, the PKF has been proven to provide accurate uncertainty estimates at very low computational costs. In this work, a dynamical propagation of error standard deviations is implemented in the Canadian atmospheric-chemical forecast model GEM-MACH. This extended forecast model is applied to a case study of Quebec wildfires in early July 2023. First results indicate that the forecast error distributions during this events can be sufficiently approximated by a passive error-tracer. It is demonstrated that vertical diffusion is a critical component for dynamical error forecasting of extreme air quality events. The error standard-deviation forecasts are used in the current objective analysis (OA) for surface air quality at ECCC (Environment and Climate Change Canada) and compared to operational OA results.