Operational bias correction for PM2.5 using the AIRPACT air quality forecast system in the Pacific Northwest.

A bias correction scheme based on a Kalman filter (KF) method has been developed and implemented for the AIRPACT air quality forecast system which operates daily for the Pacific Northwest. The KF method was used to correct hourly rolling 24-h average PM2.5 concentrations forecast at each monitoring site within the AIRPACT domain and the corrected forecasts were evaluated using observed daily PM2.5 24-h average concentrations from 2017 to 2018. The evaluation showed that the KF method reduced mean daily bias from approximately -50% to ±6% on a monthly averaged basis, and the corrected results also exhibited much smaller mean absolute errors typically less than 20%. These improvements were also apparent for the top 10 worst PM2.5 days during the 2017-2018 test period, including months with intensive wildfire events. Significant differences in AIRPACT performance among urban, suburban, and rural monitoring sites were greatly reduced in the KF bias correction forecasts. The daily 24-h average bias corrections for each monitoring site were interpolated to model grid points using three different interpolation schemes: cubic spline, Gaussian Kriging, and linear Kriging. The interpolated results were more accurate than the original AIRPACT forecasts, and both Kriging methods were better than the cubic spline method. The Gaussian method yielded smaller mean biases and the linear method yielded smaller absolute errors. The KF bias correction method has been implemented operationally using both Kriging interpolation methods for routine output on the AIRPACT website (http://lar.wsu.edu/airpact). This method is relatively easy to implement, but very effective to improve air quality forecast performance.Implications: Current chemical transport models, including CMAQ, used for air quality forecasting can have large errors and uncertainties in simulated PM2.5 concentrations. In this paper, we describe a relatively simple bias correction scheme applied to the AIRPACT air quality forecast system for the Pacific Northwest. The bias correction yields much more accurate and reliable PM2.5 results compared to the normal forecast system. As such, the operational bias corrected forecasts will provide a much better basis for daily air quality management by agencies within the region. The bias corrected results also highlight issues to guide further improvements to the normal forecast system.