A Stratospheric Intrusion-Influenced Ozone Pollution Episode Associated with an Intense Horizontal-Trough Event

Ozone pollution is currently a serious issue in China. As an important source of tropospheric ozone, the stratospheric ozone has received less concern. This study uses a combination of ground-based ozone measurements, the latest ERA5 reanalysis data as well as chemistry-climate model and Lagrangian Particle Dispersion Modeling (LPDM) simulations to investigate the potential impacts of stratospheric intrusion (SI) on surface ozone pollution episodes in eastern China. Station-based observations indicate that severe ozone pollution occurred from 27 April to 28 April 2018 in eastern China, with maximal values over 140 ppbv. ERA5 meteorological and ozone data suggest that a strong horizontal-trough exists at the same time, which leads to an evident SI event and brings ozone-rich air from the stratosphere to the troposphere. Using a stratospheric ozone tracer defined by NCAR’s Community Atmosphere Model with Chemistry (CAM-Chem), we conclude that this SI event contributed about 15 ppbv (15%) to the surface ozone pollution episode during 27–28 April in eastern China. The potential impacts of SI events on surface ozone variations should be therefore considered in ozone forecast and control.

Ozone distributions over southern Lake Michigan: comparisons between ferry-based observations, shoreline-based DOAS observations and air quality forecast models

Air quality forecast models typically predict large ozone abundances over water relative to land in the Great Lakes region. While each state bordering Lake Michigan has dedicated monitoring systems, offshore measurements have been sparse, mainly executed through specific short-term campaigns. This study examines ozone abundances over Lake Michigan as measured on the Lake Express ferry, by shoreline Differential Optical Absorption Spectroscopy (DOAS) observations in southeastern Wisconsin, and as predicted by the National Air Quality Forecast System. From 2008–2009 measurements of O3, SO2, NO2 and formaldehyde were made in the summertime by DOAS at a shoreline site in Kenosha, WI. From 2008–2010 measurements of ambient ozone conducted on the Lake Express, a high-speed ferry that travels between Milwaukee, WI and Muskegon, MI up to 6 times daily from spring to fall. Ferry ozone observations over Lake Michigan were an average of 3.8 ppb higher than those measured at shoreline in Kenosha with little dependence on position of the ferry or temperature but with highest differences during evening and night. Concurrent ozone forecast images from National Weather System's National Air Quality Forecast System in the upper Midwestern region surrounding Lake Michigan were saved over the ferry ozone sampling period in 2009. The bias of the model O3 forecast was computed and evaluated with respect to ferry-based measurements. The model 1 and 8 h ozone mean biases were both 12 ppb higher than observed ozone, and maximum daily 1 h ozone mean bias was 10 ppb, indicating substantial ozone over-prediction over water. Trends in the bias with respect to location and time of day or month were also explored showing non-uniformity in model bias. Extreme ozone events were predicted by the model but not observed by ferry measurements.

Application of Multiple Linear Regression Models and Artificial Neural Networks on the Surface Ozone Forecast in the Greater Athens Area, Greece

An attempt is made to forecast the daily maximum surface ozone concentration for the next 24 hours, within the greater Athens area (GAA). For this purpose, we applied Multiple Linear Regression (MLR) models against a forecasting model based on Artificial Neural Network (ANN) approach. The availability of basic meteorological parameters is of great importance in order to forecast the ozone’s concentration levels. Modelling was based on recorded meteorological and air pollution data from thirteen monitoring sites within the GAA (network of the Hellenic Ministry of the Environment, Energy and Climate Change) over five years from 2001 to 2005. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that in every aspect, the prognostic model by far is the ANN model. This suggests that the ANN model can be used to issue warnings for the general population and mainly sensitive groups.

Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions

In order to improve the surface ozone forecast over Beijing and surrounding regions, data assimilation method integrated into a high-resolution regional air quality model and a regional air quality monitoring network are employed. Several advanced data assimilation strategies based on ensemble Kalman filter are designed to adjust O3 initial conditions, NOx initial conditions and emissions, VOCs initial conditions and emissions separately or jointly through assimilating ozone observations. As a result, adjusting precursor initial conditions demonstrates potential improvement of the 1-h ozone forecast almost as great as shown by adjusting precursor emissions. Nevertheless, either adjusting precursor initial conditions or emissions show deficiency in improving the short-term ozone forecast at suburban areas. Adjusting ozone initial values brings significant improvement to the 1-h ozone forecast, and its limitations lie in the difficulty in improving the 1-h forecast at some urban site. A simultaneous adjustment of the above five variables is found to be able to reduce these limitations and display an overall better performance in improving both the 1-h and 24-h ozone forecast over these areas. The root mean square errors of 1-h ozone forecast at urban sites and suburban sites decrease by 51% and 58% respectively compared with those in free run. Through these experiments, we found that assimilating local ozone observations is determinant for ozone forecast over the observational area, while assimilating remote ozone observations could reduce the uncertainty in regional transport ozone.

Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions

We performed ozone data assimilation by simultaneously adjusting the ozone initial conditions, precursor initial conditions and emissions based on the Ensemble Kalman Filter (EnKF) and assessed its impacts on ozone modeling and forecasting in Beijing and nearby regions. A high-resolution regional air quality model and a newly established regional monitoring network covering Beijing and its surrounding areas were employed. At each assimilation cycle, the forecast error covariance was sampled from a set of forecast ensembles that were generated by perturbing ozone precursor initial conditions, emissions, photolysis rates and deposition velocity. A model-error module and a local analysis scheme have been introduced to reduce the impact of filter divergence and spurious correlation that accompanied with EnKF. The results showed significant improvement of 1-hour ozone forecast in Beijing and its surrounding areas through separately adjusting ozone initial conditions, precursor initial conditions and emissions with ozone observations. However, adjustment of precursor initial conditions and emissions had minor effect on the 1-hour ozone forecast in suburban area. The best ozone forecast skill was obtained through jointly adjusting ozone initial conditions, NOx and VOC initial conditions and emissions. The root mean square errors of 1-hour ozone forecast at urban sites and suburban sites decreased by 54% and 59% respectively compared with those in free run. Furthermore, the specific impacts of observations from urban and suburban sites on ozone data assimilation were evaluated by implementing sensitivity experiments. Both urban and suburban sites were found to be very important for the improvement of regional ozone forecast. The importance of observational data at urban sites was particularly highlighted through its role in constraining the uncertainty of precursor initial conditions and emission rates. Further improvement of regional ozone forecast might therefore be expected with more routine regional air pollution monitoring stations.

Ozone Forecasts of the Stratospheric Polar Vortex–Splitting Event in September 2002

The Southern Hemisphere major warming event in September 2002 has led to a breakup of the vortex in the middle and higher stratosphere and to a corresponding splitting of the ozone hole. Daily 3D ozone forecasts, produced at the Royal Netherlands Meteorological Institute (KNMI) with a tracer transport and assimilation model based on the ECMWF dynamical forecasts, provided an accurate prediction of this event a week prior to the actual breakup of the vortex. The ozone forecast model contains parameterizations for gas phase and heterogeneous chemistry. Initial states for the forecast are obtained from the assimilation of near-real-time ozone data from the Global Ozone Monitoring Experiment (GOME) on European Space Agency (ESA) Remote Sensing Satellite-2 (ERS-2). In this paper, the ozone forecasts and analyses are discussed as produced before, during, and after the event. These fields are compared with ground-based Dobson, ozonesonde, and Total Ozone Mapping Spectrometer (TOMS) observations. The total ozone comparisons show that the location of the vortex edge is generally well described by the 5–7-day forecasts in September and October. The GOME assimilation compared with TOMS shows a good correspondence concerning vortex location and ozone features but also reflects clear differences in the average ozone amount between the two retrieval schemes. The assimilation system produces realistic ozone profiles, apart from a systematic underestimation of ozone around 150 hPa inside the vortex in August–October.