| Title | A 6-year-long (2013-2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC |
| Authors | Kong, Lei Tang, Xiao Zhu, Jiang Wang, Zifa Li, Jianjun Wu, Huangjian Wu, Qizhong Chen, Huansheng Zhu, Lili Wang, Wei Liu, Bing Wang, Qian Chen, Duohong Pan, Yuepeng Song, Tao Li, Fei Zheng, Haitao Jia, Guanglin Lu, Miaomiao Wu, Lin Carmichael, Gregory R. |
| Affiliation | Chinese Acad Sci, LAPC, Inst Atmospher Phys, Beijing 100029, Peoples R China Chinese Acad Sci, ICCES, Inst Atmospher Phys, Beijing 100029, Peoples R China Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing 100049, Peoples R China Chinese Acad Sci, Ctr Excellence Reg Atmospher Environm, Inst Urban Environm, Xiamen 361021, Peoples R China China Natl Environm Monitoring Ctr, Beijing 100012, Peoples R China Peking Univ, Guanghua Sch Management, Beijing 100871, Peoples R China Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China Shanghai Environm Monitoring Ctr, Shanghai 200030, Peoples R China Guangdong Environm Monitoring Ctr, State Environm Protect Key Lab Reg Air Qual Monit, Guangzhou 510308, Peoples R China Chinese Acad Sci, Hefei Inst Phys Sci, Key Lab Environm Opt & Technol, Anhui Inst Opt & Fine Mech, Hefei 230031, Peoples R China South China Univ Technol, Sch Environm & Energy, Guangzhou 510006, Peoples R China Nankai Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab Urban Ambient Air, Tianjin 300350, Peoples R China Univ Iowa, Ctr Global & Reg Environm Res, Iowa City, IA 52242 USA |
| Keywords | ENSEMBLE KALMAN FILTER TROPOSPHERIC CHEMISTRY REANALYSIS ESTIMATE PM2.5 CONCENTRATIONS CHEMICAL-TRANSPORT MODEL GROUND-LEVEL PM2.5 CARBON-MONOXIDE INTERIM REANALYSIS INITIAL CONDITIONS EAST-ASIA DATA SET |
| Issue Date | 23-Feb-2021 |
| Publisher | EARTH SYSTEM SCIENCE DATA |
| Abstract | A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM2.5, PM10, SO2, NO2, CO, and O3) for the period 2013-2018 at high spatial (15km +/- 15km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R 2 D 0 :52-0.81, CV root mean square error (RMSE) D 0 :54 mg =m3 for CO, and CV RMSE D 16 :4-39.3 mu g =m(3) for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R 2 D 0 :74-0.86 and RMSE D 16 :8-33.6 mu g =m3 in different cities). Furthermore, through the comparison to satellite-estimated PM2.5 concentrations, we show that the accuracy of the PM2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users. |
| URI | http://hdl.handle.net/20.500.11897/608904 |
| ISSN | 1866-3508 |
| DOI | 10.5194/essd-13-529-2021 |
| Indexed | SCI(E) |
| Appears in Collections: | 光华管理学院 |
