Development of Korean Air Chemistry Modeling System version 2.0 (K_ACheMS v2.0) and its applications to operational forecasts and reanalysis products

Abstract

Firstly, concentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10 %, 19 %, 29 %, and 10 % in the predictability of PM2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations. Secondly, real-time operational air quality forecasts began in 2022 using the Korean Air Chemistry Modeling System version 2.0 (K_ACheMS v2.0). The current operational air quality forecasts of the K_ACheMS v2.0 has been driven by the Weather Research and Forecasting (WRF) model simulation utilizing National Oceanic and Atmospheric Administration (NOAA) real-time Global Forecast System (GFS) products for both initial and boundary conditions. However, several researches reported that this positive bias in predicted ground-level wind speeds from the WRF model simulations. In this study, therefore, two additional meteorological modeling systems have been incorporated into the operational K_ACheMS v2.0, in addition to the GFS-WRF meteorological modeling system: (i) the GFS-WRF meteorological modeling system with bias-corrected U- and V-wind components via eXtreme Gradient Boosting (XGBoost) and (ii) the system directly uses the Global Data Assimilation and Prediction System (GDAPS) product, which is the main operational weather prediction model of Korea Meteorological Administration (KMA) based on the Unified Model (UM) developed by the UK Met Office, into the CMAQ-GIST model. The newly incorporated two meteorological modeling systems have contributed to the improvement of PM2.5 predictability. The real-time PM2.5 forecasts of K_ACheMS v2.0 using GFS-WRF with XGBoost exhibited the highest HR of 76.88 % due to the effectiveness of bias-corrected wind speeds in capturing high PM2.5 transport. The real-time PM2.5 forecasts of K_ACheMS v2.0 using UM-GDAPS showed the superior performances, yielding the highest IOA of 0.79 and R of 0.64. Thirdly, chemical reanalysis data, produced by assimilating observations into chemistry transport model backgrounds, can provide systematically best-estimated and spatiotemporally continuous information on atmospheric chemical components. The multi-year chemical reanalysis products can be applied to detailed diagnostic analysis of severe air quality episodes, multi-year trend analysis of air pollution, construction of detailed pollution map to estimate human mortality and morbidity due to air pollution, etc. With these purposes, several institutes have produced their chemical reanalysis products. For example, European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmospheric Modeling and Monitoring Service (CAMS) and National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO) have been establishing their global air quality reanalysis fields, named CAMS reanalysis (CAMSRA) and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), respectively. However, both reanalysis products have limitations in application to regional-scale air quality studies due to low spatial resolutions and poor parameterizations for regional atmospheric environments. Therefore, in this study, the K_ACheMS v2.0 has been extended to regional chemical reanalysis system. The technical elements to construct the chemical reanalysis products are exactly the same as those of the air quality prediction system. Only the difference between the two systems is the frequency of the data assimilation (e.g., 24-hr intervals for prediction; 3-hr intervals for reanalysis). Ground-based observations, such as PM2.5, PM10, CO, ozone, NO2, and SO2 in South Korea, China, and Japan, are assimilated into the CMAQ-GIST model every 3 hours. Geostationary satellite-retrieved AODs from Advanced Himawari Imager (AHI) and Geostationary Ocean Color Imager (GOCI) are also assimilated. The produced chemical reanalysis data, named K_ACheMS RA, for 2016 were compared with other chemical reanalysis products, such as CAMSRA, MERRA-2, Tropospheric Chemistry Reanalysis version 2 (TCR-2), and Chinese Air Quality reanalysis (CAQRA). Additionally, the validation of K_ACheMS RA was performed by comparing with the acid deposition monitoring network in East Asia (EANET) observations, which is independent observations not used in data assimilation.