This dataset provides surface grid fields for six conventional air pollutants (i.e. PM2.5, PM10, SO2, NO2, CO and O3), as well as surface wind speed (u, v), atmospheric pressure (psfc), relative humidity (RH), and temperature (temp) fields simulated by WRF models. The spatial and temporal resolutions are 15km and 1 hour, respectively. The time period of the dataset is from 2013 to 2019.
The data set was generated by the Chemical Data Assimilation System (ChemDAS) developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences. Based on the Ensemble Kalman Filter (EnKF) and the Nested Air Quality Prediction Model System (NAQPMS), the system assimilated more than 1000 ground air quality monitoring points of the China Meteorological Administration. This method has overcome the problems of instability, insufficient adjustment, and negative assimilation effects in atmospheric chemical data assimilation, and developed advanced algorithms for collaborative assimilation of multiple atmospheric pollutants, including automatic quality control methods for monitoring data and adaptive mode error estimation.
The data set was generated by the Chemical Data Assimilation System (ChemDAS) developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, and assimilated more than 1000 ground air quality monitoring points of the China Meteorological Administration.
The dataset is evaluated through cross validation and independent data validation.
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CSTR:https://cstr.cn/31253.11.sciencedb.00053
10.11922/sciencedb.00053