Visibility associated with air pollution has been studied in many countries, where empirical equations are applied to prescript the relationships among meteorology, air quality and visibility. In order to improve the next-day visibility prediction, more accurate meteorology and air quality forecasts are required. In this study, investigation of various implementations on improving local meteorological forecast for visibility prediction was performed including: 1) urban canopy model WRF (UCM-WRF), 2) land use update, and 3) sea surface temperature (SST) update. It is expected that the resulted meteorology not only improves the prediction of air quality, but also provides better inputs for the next-day visibility forecast.

It was found that the daily diurnal pattern of surface temperature was better captured using UCM-WRF than WRF model. Great improvements were observed on both wind speed and wind direction predictions, which are particularly important for air quality forecast in the urban environment. Besides the implementation of UCM-WRF, SST modification and update also helps to improve the accuracy of meteorological forecast, especially during the cold surge events. In addition, this study also found that the UCM-WRF model is quite sensitive to the prescribed land use characteristics; improving land use inputs with SST update would allow better capturing the local sea breeze condition. Overall, the improvement on meteorological forecast has resulted in better prediction of air quality in the complex terrain region.

In terms of visibility prediction, several ways of calculating visibility using gridded inputs were tested, which includes single grid (SG), PATH integrated (PATH) and AREA integrated (AREA) methods, attempting to reveal the influence of spatial resolution in the calculation. It has been found that by updating the dry mass extinction coefficient (MEE) using the modeling results with bias correction under PATH method greatly improve the uncertainty of visibility calculation.