Aerosol Impacts on Summer Precipitation Forecast over the North China Plain by Using Thompson Aerosol-Aware Scheme in WRF: Statistical Analysis and Significant Threat Score Improvements in Polluted Condition During June to August 2018

The North China Plain (NCP) is one of the most polluted areas in China, and the impacts of such high-levels of aerosols on clouds and precipitation remain an interesting issue with great uncertainties. From the perspective of regional numerical weather prediction, how to consider the role of aerosols is still a controversial issue considering the balance between the complexities/uncertainties of the model (aerosol-radiation-cloud interactions) and computational costs. To evaluate the reliability of the Thompson aerosol-aware schemes and promote operational applications in the Weather Research and Forecasting model (WRF), summer precipitation forecast with three experiments that considered no aerosol-radiation-cloud interactions (Thomp), aerosol-cloud interactions only (ThompAI) and full aerosol-radiation-cloud interactions (ThompAD) from June to August 2018 were carried out. Detailed statistics of the 3-h and 24-h cumulative precipitation skills were calculated for two groups, which included the entire summer season and selected polluted-precipitation events (26 days) only, were analyzed. The results showed that the overall Threat Scores (TS) for the 26 polluted-precipitation events were lower than those of the other group (the whole summer season)for either Thomp, ThompAI or ThompAD, which revealed that the forecasting abilities for polluted-precipitation cases in the model were obviously lower than those for the nonpolluted precipitation cases, indicating deficiencies in parameterizing the special conditions on polluted days. Nevertheless, for both groups, after using the aerosol-aware Thompson schemes (either ThompAI or ThompAD), the TS scores for most magnitudes (0.1, 1, 5, 10, and 25 mm) were significantly increased compared to the original Thomp experiment, with the exception being for precipitation amounts >50 mm. Meanwhile the BIAS of each magnitude also increased for both ThompAI and ThompAD due to the spatial increase in areas with precipitation compared to the original Thomp experiment. The TS improvements were more significant in the ThompAD experiment than in the ThompAI experiment. Compared with the original Thomp experiment, the ThompAD improvement ratios for the 24-h cumulative precipitation TSs were 3.01%, 4.91%, 2.05%, 13.58%, 7.80% for 0.1 mm, 1 mm, 5 mm, 10 mm, 25 mm precipitation magnitudes respectively; and the improvements were significantly higher when the statistics window was changed from 24-h to 3-h, with 3-h cumulative precipitation TS improvements reaching 10.23%, 5.09%, 11.50%, 17.4%, 14.30%, respectively, indicating the impacts that considering aerosol-radiation-cloud interactions in changing the precipitation patterns (fall zone and amounts) in the model were more effective for 3-h windows compared to 24-h windows. Case studies of selected light-rain, medium-scale rain and heavy rain events further revealed the positive impacts of the Thompson aerosol-aware schemes, and the precipitated areas were much closer to the observations. The reason why ThompAD did not show obvious advantages for large-scale precipitation (50 mm magnitude) may be due to the complex relationship involving aerosol-cold cloud interactions in the model and the randomness caused by inadequate an individual number of cases. The statistics for positive precipitation forecast skill improvements for the NCP region provide confidence in the application of the Thompson aerosol -aware scheme in the WRF model in the future.