Articles | Volume 15, issue 10
Atmos. Chem. Phys., 15, 5627–5644, 2015
Atmos. Chem. Phys., 15, 5627–5644, 2015

Research article 21 May 2015

Research article | 21 May 2015

Development of a custom OMI NO2 data product for evaluating biases in a regional chemistry transport model

G. Kuhlmann1,2, Y. F. Lam1,3, H. M. Cheung1, A. Hartl1, J. C. H. Fung4, P. W. Chan5, and M. O. Wenig6 G. Kuhlmann et al.
  • 1School of Energy and Environment, City University of Hong Kong, Hong Kong, China
  • 2Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
  • 3Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China
  • 4Department of Mathematics, The Hong Kong University of Science & Technology, Hong Kong, China
  • 5Hong Kong Observatory, Hong Kong, China
  • 6Meteorologisches Institut, Ludwig-Maximilians-Universität, Munich, Germany

Abstract. In this paper, we present the custom Hong Kong NO2 retrieval (HKOMI) for the Ozone Monitoring Instrument (OMI) on board the Aura satellite which was used to evaluate a high-resolution chemistry transport model (CTM) (3 km × 3 km spatial resolution). The atmospheric chemistry transport was modelled in the Pearl River Delta (PRD) region in southern China by the Models-3 Community Multiscale Air Quality (CMAQ) modelling system from October 2006 to January 2007. In the HKOMI NO2 retrieval, tropospheric air mass factors (AMFs) were recalculated using high-resolution ancillary parameters of surface reflectance, a priori NO2 and aerosol profiles, of which the latter two were taken from the CMAQ simulation. We tested the influence of the ancillary parameters on the data product using four different aerosol parametrizations. Ground-level measurements by the PRD Regional Air Quality Monitoring (RAQM) network were used as additional independent measurements.

The HKOMI retrieval increases estimated tropospheric NO2 vertical column densities (VCD) by (+31 ± 38)%, when compared to NASA's standard product (OMNO2-SP), and improves the normalized mean bias (NMB) between satellite and ground observations by 26 percentage points from −41 to −15%. The individual influences of the parameters are (+11.4 ± 13.4)% for NO2 profiles, (+11.0 ± 20.9)% for surface reflectance and (+6.0 ± 8.4)% for the best aerosol parametrization. The correlation coefficient r is low between ground and satellite observations (r = 0.35). The low r and the remaining NMB can be explained by the low model performance and the expected differences when comparing point measurements with area-averaged satellite observations.

The correlation between CMAQ and the RAQM network is low (r ≈ 0.3) and the model underestimates the NO2 concentrations in the northwestern model domain (Foshan and Guangzhou). We compared the CMAQ NO2 time series of the two main plumes with our best OMI NO2 data set (HKOMI-4). The model overestimates the NO2 VCDs by about 15% in Hong Kong and Shenzhen, while the correlation coefficient is satisfactory (r = 0.56). In Foshan and Guangzhou, the correlation is low (r = 0.37) and the model underestimates the VCDs strongly (NMB = −40%). In addition, we estimated that the OMI VCDs are also underestimated by about 10 to 20% in Foshan and Guangzhou because of the influence of the model parameters on the AMFs.

In this study, we demonstrate that the HKOMI NO2 retrieval reduces the bias of the satellite observations and how the data set can be used to study the magnitude of NO2 concentrations in a regional model at high spatial resolution of 3 × 3 km2. The low bias was achieved with recalculated AMFs using updated surface reflectance, aerosol profiles and NO2 profiles. Since unbiased concentrations are important, for example, in air pollution studies, the results of this paper can be very helpful in future model evaluation studies.

Short summary
Regional NO2 distributions can be simulated by models or retrieved from satellite observations. We developed a custom OMI NO2 data product for the Pearl River delta region which reduces biases compared to the standard product. The product is used for the evaluation of a regional air quality model for which it is a useful addition to ground measurements. The unbiased NO2 data product can be very helpful for air pollution studies in urban areas.
Final-revised paper