Bias Correction for Forecasting PM2.5 Concentrations Using Measurement Data from Monitoring Stations by Region

Abstract

The model and forecasting performances were evaluated to investigate the effectiveness of bias correction for forecasting PM2.5 concentrations for the period May 2012 to December 2014. Measured concentrations of PM2.5 and major components were obtained from five monitoring stations by region in the Korean Peninsula, and predicted concentrations were obtained from PM2.5 simulations using WRF model v3.4.1 and the CMAQ modeling system v4.7.1. Underestimation was prevalent at all stations for all components except NO3-. The effect of bias correction was pronounced at the Gangwon station, where the difference in PM2.5 between measured and predicted concentrations was largest. The performances for SO42- and the unresolved other component were primarily improved, whereas the performance for NO3-, which was originally overestimated, was degraded. The accuracy of the four-level forecast was moderate at 58% overall, but the probability of detection (POD) of high-concentration events was low at 23%. Bias correction improved the accuracy and POD to 68% and 52%, respectively; however, the rate of false detection of high-concentration events increased as well.

Keywords

• cmaq/wrf
• major components
• mean fractional bias
• ratio adjustment
• forecasting performance