Articles and Gases Such As Particulate Chemical Parameterizations in the Models (Carmichael Et Al., Matter (PM), Carbon Monoxide (CO), Ozone (O3), Nitrogen 2008)
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Geosci. Model Dev., 13, 1055–1073, 2020 https://doi.org/10.5194/gmd-13-1055-2020 © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues Kyunghwa Lee1,2, Jinhyeok Yu2, Sojin Lee3, Mieun Park4,5, Hun Hong2, Soon Young Park2, Myungje Choi6, Jhoon Kim6, Younha Kim7, Jung-Hun Woo7, Sang-Woo Kim8, and Chul H. Song2 1Environmental Satellite Center, Climate and Air Quality Research Department, National Institute of Environmental Research (NIER), Incheon, Republic of Korea 2School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea 3Department of Earth and Atmospheric Sciences, University of Houston, Texas, USA 4Air Quality Forecasting Center, Climate and Air Quality Research Department, National Institute of Environmental Research (NIER), Incheon, Republic of Korea 5Environmental Meteorology Research Division, National Institute of Meteorological Sciences (NIMS), Jeju, Republic of Korea 6Department of Atmospheric Sciences, Yonsei University, Seoul, Republic of Korea 7Department of Advanced Technology Fusion, Konkuk University, Seoul, Republic of Korea 8School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea Correspondence: Chul H. Song ([email protected]) Received: 12 June 2019 – Discussion started: 18 July 2019 Revised: 30 January 2020 – Accepted: 5 February 2020 – Published: 10 March 2020 Abstract. For the purpose of providing reliable and robust compared with those of the CMAQ simulations without ICs air quality predictions, an air quality prediction system was (BASE RUN). For almost all of the species, the application developed for the main air quality criteria species in South of ICs led to improved performance in terms of correlation, Korea (PM10, PM2:5, CO, O3 and SO2). The main caveat of errors and biases over the entire campaign period. The DA the system is to prepare the initial conditions (ICs) of the RUN agreed reasonably well with the observations for PM10 Community Multiscale Air Quality (CMAQ) model simula- (index of agreement IOA D 0:60; mean bias MB D −13:54) tions using observations from the Geostationary Ocean Color and PM2:5 (IOA D 0:71; MB D −2:43) as compared to the Imager (GOCI) and ground-based monitoring networks in BASE RUN for PM10 (IOA D 0:51; MB D −27:18) and northeast Asia. The performance of the air quality predic- PM2:5 (IOA D 0:67; MB D −9:9). A significant improve- tion system was evaluated during the Korea-United States ment was also found with the DA RUN in terms of bias. Air Quality Study (KORUS-AQ) campaign period (1 May– For example, for CO, the MB of −0:27 (BASE RUN) was 12 June 2016). Data assimilation (DA) of optimal interpola- greatly enhanced to −0:036 (DA RUN). In the cases of O3 tion (OI) with Kalman filter was used in this study. One major and SO2, the DA RUN also showed better performance than advantage of the system is that it can predict not only partic- the BASE RUN. Further, several more practical issues fre- ulate matter (PM) concentrations but also PM chemical com- quently encountered in the air quality prediction system were position including five main constituents: sulfate (SO2−), ni- also discussed. In order to attain more accurate ozone predic- − C 4 trate (NO3 ), ammonium (NH4 ), organic aerosols (OAs) and tions, the DA of NO2 mixing ratios should be implemented elemental carbon (EC). In addition, it is also capable of pre- with careful consideration of the measurement artifacts (i.e., dicting the concentrations of gaseous pollutants (CO, O3 and inclusion of alkyl nitrates, HNO3 and peroxyacetyl nitrates – SO2). In this sense, this new air quality prediction system PANs – in the ground-observed NO2 mixing ratios). It was is comprehensive. The results with the ICs (DA RUN) were also discussed that, in order to ensure accurate nocturnal pre- Published by Copernicus Publications on behalf of the European Geosciences Union. 1056 K. Lee et al.: Air quality prediction system in Korea dictions of the concentrations of the ambient species, accu- Although there are various datasets representing air qual- rate predictions of the mixing layer heights (MLHs) should ity, limitations remain in the observations and model outputs. be achieved from the meteorological modeling. Several ad- Specifically, observation data are, in general, known to be vantages of the current air quality prediction system, such as more accurate than model outputs, but they have spatial and its non-static free-parameter scheme, dust episode prediction temporal limitations. These limitations will be overcome by and possible multiple implementations of DA prior to actual improving spatial and temporal coverage via future geosta- predictions, were also discussed. These configurations are all tionary satellite instruments such as the Geostationary En- possible because the current DA system is not computation- vironment Monitoring Spectrometer (GEMS) over Asia, the ally expensive. In the ongoing and future works, more ad- Tropospheric Emissions: Monitoring of Pollution (TEMPO) vanced DA techniques such as the 3D variational (3DVAR) over North America and the Sentinel-4 over Europe. In addi- method and ensemble Kalman filter (EnK) are being tested tion, the TROPOspheric Monitoring Instrument (TROPOMI) and will be introduced to the Korean air quality prediction on board the Copernicus Sentinel-5 Precursor satellite was system (KAQPS). successfully launched into low earth orbit (LEO) on 13 Octo- ber 2017 and is providing information on the chemical com- position in the atmosphere with a higher spatial resolution of 3:5km × 7km. 1 Introduction Unlike observational data, models can provide meteoro- logical and chemical information without any spatial and Air quality has long been considered an important issue in temporal data discontinuity, but they do have an issue of inac- climate change, visibility and public health, and it is strongly curacy. The major causes of uncertainty in the results of CTM dependent upon meteorological conditions, emissions and simulations are introduced from imperfect emissions, mete- the transport of air pollutants. Air pollutants typically con- orological fields, initial conditions (ICs), and physical and sist of atmospheric particles and gases such as particulate chemical parameterizations in the models (Carmichael et al., matter (PM), carbon monoxide (CO), ozone (O3), nitrogen 2008). In order to minimize the limitations and maximize the dioxide (NO2) and sulfur dioxide (SO2). These aerosols and advantages of observation data and model outputs, there have gases play important roles in anthropogenic climate forcing, been numerous attempts to provide accurate and spatially as both directly (Bellouin et al., 2005; Carmichael et al., 2009; well as temporally continuous information on chemical com- IPCC, 2013; Scott et al., 2014) and indirectly (Breon et al., position in the atmosphere by integrating observation data 2002; IPCC, 2013; Penner et al., 2004; Scott et al., 2014) with model outputs via data assimilation (DA) techniques. influencing the global radiation budget. Among the various Although the Korean numerical weather prediction (NWP) air pollutants, PM and surface O3 are the most notorious carried out by the Korea Meteorological Administration health threats, as has been stated by several previous studies (KMA) employs various DA techniques, almost no previous (Carmichael et al., 2009; Dehghani et al., 2017; Khaniabadi efforts have been made to develop an air quality prediction et al., 2017). system with DA in South Korea. Therefore, in the present With the stated importance of atmospheric aerosols and study, the air quality prediction system named as Korean Air gases, considerable research efforts have been made to mon- Quality Prediction System version 1 (KAQPS v1) was devel- itor and quantify their amounts in the atmosphere through oped by preparing ICs via DA for the Community Multiscale satellite-, airborne- and ground-based observations as well Air Quality (CMAQ) model (Byun and Schere, 2006; Byun as chemistry-transport model (CTM) simulations. In South and Ching, 1999) using satellite- and ground-based observa- Korea, the Korean Ministry of the Environment (KMoE) tions for particulate matter (PM) and atmospheric gases such provides real-time chemical concentrations as measured by as CO, O3 and SO2. The performances of the system were ground-based observations for six air quality criteria air then demonstrated during the period of the Korea-United pollutants (PM10, PM2:5,O3, CO, SO2 and NO2) at the States Air Quality Study (KORUS-AQ) campaign (1 May– Air Korea website (https://www.airkorea.or.kr, last access: 12 June 2016) in South Korea. 8 March 2020). PM10 or (PM2:5) refers to the atmospheric In this study, the optimal interpolation (OI) method with particulate matter that has an aerodynamic diameter less than the Kalman filter was applied in order to develop the air qual- 10 (or 2.5) µm. In addition, the National Institute of Environ- ity prediction system, since this method is still useful and mental Research (NIER) of South Korea provides air qual- viable in terms of computational cost and performance. The ity predictions using multiple CTM simulations. Air quality performance of the method is almost comparable to that of predictions are another crucial element for protecting public the 3D variational (3DVAR) method, as shown in Tang et health through the forecasting of high air pollution episodes al. (2017). More complex and advanced DA techniques are in advance and alerting citizens about these high episodes. currently being and will continue to be applied to current air In this context, reliable and robust air quality forecasts are quality prediction systems. These works are now in progress. necessary to avoid any confusion caused by poor predictions In addition, this paper also discusses several practical is- given by CTM simulations. sues frequently encountered in the air quality predictions Geosci. Model Dev., 13, 1055–1073, 2020 www.geosci-model-dev.net/13/1055/2020/ K.