A quantitative analysis of grid nudging effect on each process of PM2.5 production in the Korean Peninsula

Abstract This study investigated the effect of assimilated meteorological fields on simulated PM2.5 concentrations in the Korean Peninsula. Two different CMAQ simulations were conducted using base WRF run (BASE) and grid-nudged WRF run (GNG) which included a simple data assimilation method for the time period of April, 2009. The simulated PM2.5 and PM10 concentrations were compared with corresponding observations. The BASE PM2.5 concentrations were significantly underestimated at Anmyeondo (AMD) and six Air Quality Monitoring Station (AQMS) sites in Korea, but GNG showed improved agreement with in-situ measurements due to the effect of grid nudging. The grid nudging effect was dominant under the PBL height and it appeared more clearly under the unstable synoptic condition (April 5–8) than stable condition (April 9–13). Additional quantitative analysis was conducted using the Integrated Process Rate (IPR) in the CMAQ model to investigate the effect of varied meteorological fields on each PM2.5 production and destruction processes. The PM2.5 production rate by aerosol process in GNG was shown to be higher than that of BASE, especially near the source region (e.g., Eastern China). The increased temperature and decreased wind speed by grid nudging effect led to increase of aerosol production rates especially during the nighttime. The change of aerosol production rates were mainly caused by increased sulfate ( SO 4 2 − ) and nitrate ( NO 3 − ) production rates in the day and nighttime respectively. Also, GNG provides higher PM2.5 transport rates than BASE over the whole domain. The amount of PM2.5 scavenged by wet deposition process in GNG was smaller than that of BASE over the Yellow Sea, reflecting the decreased water vapor mixing ratio by grid nudging. Thus, it resulted in the increase of simulated PM2.5 concentrations. The results indicated that understanding the effects of grid nudging on PM2.5 concentrations is crucial to enhance the performance of PM2.5 modeling/forecasting capability over the Korean Peninsula.

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