Abstract：Daily ozone in 2019 over the 55 cities in the Yangtze River Delta (YRD) region was forecasted by using the operational Regional Atmospheric Environmental Modeling System for eastern China (RAEMS). The results showed that the performance was consistent in different forecast lengths of 1~4d. Good agreement of modelled ozone with the observed was found through indices of correlation coefficient (r), FACx, etc. Correlation coefficients over most cities were over 0.7. At the same time, forecast was generally overestimated. The high consistence in temporal trend suggested that the single factor dynamical learning linear method of with interception of zero and learning period of 5~7d was the most appropriate approach for the improvement of ozone forecast over YRD. The forecast performance was significantly improved. Mean bias decreased from over 25 to 0.4μg/m3. Mean error, normalized mean error and root mean square error (RMSE) was 30% decreased, among which RMSE decreased from 42μg/m3 to 27μg/m3. Performance indices except for r at over 90% cities were improved. For ozone pollution, CSI of O3-8h and O3-1h was 28% and 17% increased, respectively and the missed detection rate and false alarm rate was balanced. In general, the application of the improvement method resolved the systematical overestimation of ozone forecast over the YRD region.
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