[A-AS03] Extreme Events and Mesoscale Weather: Observations and Modeling

[AAS03-P13] Aerosol impacts on summer precipitation forecast over the North China Plain by using Thompson Aerosol-Aware scheme in WRF

*Chunwei Guo¹、Dan Chen¹、Min Chen¹ (1.Institute of Urban Meteorology, China Meteorological Administration)

キーワード：aerosol-aware scheme, statistical analysis, TS scores, polluted-precipitation cases

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