Implementation and application of Ensemble Optimal Interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions.

The data assimilation technique is one of the important ways to reduce the uncertainty of atmospheric chemistry model input and improve the model forecast accuracy. In this paper, an ensemble optimal interpolation assimilation (EnOI) system for a regional online chemical weather numerical forecasting system (GRAPES_Meso5.1/CUACE) is developed for operational use and efficient updating of the initial fields of chemical components. A heavy haze episode in eastern China was selected, and the key factors affecting the EnOI, such as localization length-scale, ensemble size, and assimilation moment, were calibrated by sensitivity experiments. The impacts of assimilating ground-based PM2.5 observations on the model chemical initial field and PM2.5, visibility forecasts were investigated. The results show that assimilation of PM2.5 significantly reduces the uncertainty of the initial PM2.5 field. The mean error and root mean square error (RMSE) of initial PM2.5 for mainland China have all decreased by more than 75%, and the correlation coefficient could be improved to more than 0.95. Even greater improvements appear in North China. For the forecast fields, assimilation of PM2.5 improves PM2.5 and visibility forecasts throughout the lead time window of 24 h. The PM2.5 RMSE can be reduced by 10%-21% within 24 h, but the assimilation effect is most obvious in the first 12 h. The assimilation moment chosen at 1200 UTC is more effective than that at 0000 UTC for improving the forecast, because the discrepancy between simulation and observation at 1200 UTC is larger than that at 0000UTC, indicating the assimilation efficiency will be higher when the bias of the model is higher. Assimilation of PM2.5 also improves visibility forecast accuracy significantly. When the PM2.5 increment is negative, it corresponds to an increase in visibility, and when the PM2.5 analysis increment is positive, visibility decreases. It is worth noting that the improvement of visibility forecasting by assimilating PM2.5 is more obvious in the light pollution period than in the heavy pollution period, since visibility is much more affected by humidity during the heavy pollution period accompanied by low or extreme low visibility. To get further visibility improvement, especially for extreme low visibility during severe haze pollution, not only PM2.5 but also relative humidity should be simultaneously assimilated as well. [ABSTRACT FROM AUTHOR]