Assessment of bias-adjusted PM<sub>2.5</sub> air quality forecasts over the continental United States during 2007

Abstract

Abstract. To develop fine particulate matter (PM 2.5 ) air quality forecasts for the US, a National Air Quality Forecast Capability (NAQFC) system, which linked NOAA's North American Mesoscale (NAM) meteorological model with EPA's Community Multiscale Air Quality (CMAQ) model, was deployed in the developmental mode over the continental United States during 2007. This study investigates the operational use of a bias-adjustment technique called the Kalman Filter Predictor approach for improving the accuracy of the PM 2.5 forecasts at monitoring locations. The Kalman Filter Predictor bias-adjustment technique is a recursive algorithm designed to optimally estimate bias-adjustment terms using the information extracted from previous measurements and forecasts.The bias-adjustment technique is found to improve PM 2.5 forecasts (i.e. reduced errors and increased correlation coefficients) for the entire year at almost all locations. The NAQFC tends to overestimate PM 2.5 during the cool season and underestimate during the warm season in the eastern part of the continental US domain, but the opposite is true for the Pacific Coast. In the Rocky Mountain region, the NAQFC system overestimates PM 2.5 for the whole year. The biasadjusted forecasts can quickly (after 2-3 days' lag) adjust to reflect the transition from one regime to the other. The modest computational requirements and systematic improvements in forecast outputs across all seasons suggest that this technique can be easily adapted to perform bias adjustment for real-time PM 2.5 air quality forecasts.

A Retrospective Comparison of Model-Based Forecasted PM2.5Concentrations with Measurements

“…A few studies 11,[13][14][15][16] have assessed the performance of the CMAQ model in a PM 2.5 forecasting mode in the United States but were limited to specific seasons or regions. The study by McKeen et al 13 and Yu et al 15 was…”

“…The increased availability of forecast guidance based on photochemical models had stimulated interest in the assessment of the performance of these models when applied in a forecasting mode. This study reports the application and evaluation of the performance of the Community Multiscale Air Quality (CMAQ) model in predicting daily PM 2.5 air quality over the eastern United States over a 2-yr period.A few studies 11,[13][14][15][16] have assessed the performance of the CMAQ model in a PM 2.5 forecasting mode in the United States but were limited to specific seasons or regions. The study by McKeen et al 13 and Yu et al 15 was…”

Aerosols in the CALIOPE air quality modelling system: evaluation and analysis of PM levels, optical depths and chemical composition over Europe

“…Hogrefe et al, 2006;Djalalova et al, 2010). Bias-adjustment strategies range from the relatively simple mean bias and multiplicative ratio adjustments used by McKeen et al (2005) to the more complex Kalman filter techniques (Manders et al, 2009;Kang et al, 2010;Sicardi et al, 2011). In this section we provide an estimation of the spatial and seasonal distribution of the different aerosol components over Europe with our model results including an a posteriori correction.…”

Transport and mixing patterns over Central California during the carbonaceous aerosol and radiative effects study (CARES)

“…Regional-scale predictions of aerosol mass are not well correlated with observations when paired in space and time and there is a positive bias during the winter . Biases in simulated aerosol mass also vary geographically, such as the differences between the eastern and western US (Kang et al, 2010).…”