The impact of observing characteristics on the ability to predict ozone under varying polluted photochemical regimes
- 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
- 2Centre National de Recherches Météorologiques – Groupe d'étude de l'Atmosphère Météorologique, Météo-France and CNRS, UMR3589, Toulouse, France
- 3NILU – Norwegian Institute for Air Research, Kjeller, Norway
- 4Department of Mechanical Engineering, University of Colorado, Boulder, Colorado, USA
- 5Laboratoire d'Aérologie, Université de Toulouse, CNRS, UMR, Toulouse, France
Abstract. We conduct analyses to assess how characteristics of observations of ozone and its precursors affect air quality forecasting and research. To carry out this investigation, we use a photochemical box model and its adjoint integrated with a Lagrangian 4D-variational data assimilation system. Using this framework in conjunction with pseudo-observations, we perform an ozone precursor source inversion and estimate surface emissions. We then assess the resulting improvement in ozone air quality prediction. We use an analytical model to conduct uncertainty analyses. Using this analytical tool, we address some key questions regarding how the characteristics of observations affect ozone precursor emission inversion and in turn ozone prediction. These questions include what the effect is of choosing which species to observe, of varying amounts of observation noise, of changing the observing frequency and the observation time during the diurnal cycle, and of how these different scenarios interact with different photochemical regimes. In our investigation we use three observed species scenarios: CO and NO2; ozone, CO, and NO2; and HCHO, CO and NO2. The photochemical model was set up to simulate a range of summertime polluted environments spanning NOx-(NO and NO2)-limited to volatile organic compound (VOC)-limited conditions. We find that as the photochemical regime changes, here is a variation in the relative importance of trace gas observations to be able to constrain emission estimates and to improve the subsequent ozone forecasts. For example, adding ozone observations to an NO2 and CO observing system is found to decrease ozone prediction error under NOx- and VOC-limited regimes, and complementing the NO2 and CO system with HCHO observations would improve ozone prediction in the transitional regime and under VOC-limited conditions. We found that scenarios observing ozone and HCHO with a relative observing noise of lower than 33 % were able to achieve ozone prediction errors of lower than 5 ppbv (parts per billion by volume). Further, only observing intervals of 3 h or shorter were able to consistently achieve ozone prediction errors of 5 ppbv or lower across all photochemical regimes. Making observations closer to the prediction period and either in the morning or afternoon rush hour periods made greater improvements for ozone prediction: 0.2–0.3 ppbv for the morning rush hour and from 0.3 to 0.8 ppbv for the afternoon compared to only 0–0.1 ppbv for other times of the day. Finally, we made two complementary analyses that show that our conclusions are insensitive to the assumed diurnal emission cycle and to the choice of which VOC species emission to estimate using our framework. These questions will address how different types of observing platform, e.g. geostationary satellites or ground monitoring networks, could support future air quality research and forecasting.