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. 
Published: February 2021

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 (15 km×15 km) 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 R2=0.52–0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4–39.3 µg/m3 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 (R2=0.74–0.86 and RMSE =16.8–33.6 µg/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.