Validation and Calibration of CAMS PM_2.5 Forecasts Using In Situ PM_2.5 Measurements in China and United States

• Main Authors: Chengbo Wu, Ke Li, Kaixu Bai
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Remote Sensing
• Subjects: PM_2.5; ensemble forecast; validation; bias correction; air pollution
• Online Access: https://www.mdpi.com/2072-4292/12/22/3813

An accurate forecast of fine particulate matter (PM_2.5) concentration in the forthcoming days is crucial since it can be used as an early warning for the prevention of general public from hazardous PM_2.5 pollution events. Though the European Copernicus Atmosphere Monitoring Service (CAMS) provides global PM_2.5 forecasts up to the next 120 h at a 3 h time interval, the data accuracy of this product had not been well evaluated. By using hourly PM_2.5 concentration data that were sampled in China and United States (US) between 2017 and 2018, the data accuracy and bias levels of CAMS PM_2.5 concentration forecast over these two countries were examined. Ground-based validation results indicate a relatively low accuracy of raw PM_2.5 forecasts given the presence of large and spatially varied modeling biases, especially in northwest China and the western United States. Specifically, the PM_2.5 forecasts in China showed a mean correlation value ranging 0.31–0.45 (0.24–0.42 in US) and RMSE of 38–83 (8.30–16.76 in US) μg/m³, as the forecasting time horizons increased from 3 h to 120 h. Additionally, the data accuracy was found to not only decrease with the increase of forecasting time horizons but also exhibit an evident diurnal cycle. This implies the current CAMS forecasting model failed to resolve the local processes that modulate the diurnal variability of PM_2.5. Moreover, the data accuracy varied between seasons, as accurate PM_2.5 forecasts were more likely to be derived in the autumn in China, whereas these were more likely in spring in the US. To improve the data accuracy of the raw PM_2.5 forecasts, a statistical bias correction model was then established using the random forest method to account for large modeling biases. The cross-validation results clearly demonstrated the effectiveness and benefits of the proposed bias correction model, as the diurnal varied and temporally increasing modeling biases were substantially reduced after the calibration. Overall, the calibrated CAMS PM_2.5 forecasts could be used as a promising data source to prevent general public from severe PM_2.5 pollution events given the improved data accuracy.