FSS-based Evaluation on Monsoon Precipitation Forecasts in South China from Regional Models with Different Resolution

This study evaluated the forecast skill of CMA-GD 3 km and CMA-GD 1 km with hourly Rapid Update Cycle(RUC)for five monsoon precipitation events in South China from 2018 to 2020,using the fraction skill score(FSS)of the neighborhood spatial verification method.The results revealed that,among the 24-lead-hour forecasts in CMA-GD 3 km,the FSS for the 0.1 mm precipitation threshold increased linearly with the lead time from 3 to 1 hour,while there was no significant improvement in other lead times.For the 5 mm precipitation threshold,the forecast skill was highest for the latest 1-hour lead time,while the FSS showed slight variation between lead times of 24 hours and 8 hours.The FSS for 10 mm and 20 mm precipitation thresholds were similar to that of 5 mm,with the difference that the best score occurred at the 2-hour lead time.Among the 6-lead-hour forecasts in CMA-GD 1 km,the forecasts of the latest 1-hour lead time were the best choices for four precipitation thresholds.When comparing CMA-GD 3 km and CMA-GD 1 km,it was found that CMA-GD 3 km had better skill for forecasts of 0.1 mm and 5 mm precipitation at 2-hour and 1-hour lead times,while CMA-GD 1 km had better skill for all other forecasts,including the forecast of 20 mm precipitation nearly all lead hours(including 3-to 6-hour,and 1-hour lead times).The results suggest that the increased resolution of the model may be beneficial for precipitation forecasts in South China,especially for short-duration heavy precipitation over a longer lead hours.However,the limited sample size of this study calls for further evaluation using more cases to validate the results′generality.

Investigation of PM_(2.5) pollution during COVID-19 pandemic in Guangzhou,China

The COVID-19 pandemic has raised awareness about various environmental issues,includ-ing PM_(2.5) pollution.Here,PM_(2.5) pollution during the COVID-19 lockdown was traced and an-alyzed to clarify the sources and factors influencing PM_(2.5) in Guangzhou,with an emphasis on heavy pollution.The lockdown led to large reductions in industrial and traffic emissions,which significantly reduced PM_(2.5) concentrations in Guangzhou.Interestingly,the trend of PM_(2.5) concentrations was not consistent with traffic and industrial emissions,as minimum concentrations were observed in the fourth period(3/01-3/31,22.45 μg/m^(3))of the lockdown.However,the concentrations of other gaseous pollutants,e.g.,SO_(2),NO_(2) and CO,were corre-lated with industrial and traffic emissions,and the lowest values were noticed in the sec-ond period(1/24-2/0_(3))of the lockdown.Meteorological correlation analysis revealed that the decreased PM_(2.5) concentrations during COVID-19 can be mainly attributed to decreased in-dustrial and traffic emissions rather than meteorological conditions.When meteorological factors were included in the PM_(2.5) composition and backward trajectory analyses,we found that long-distance transportation and secondary pollution offset the reduction of primary emissions in the second and third stages of the pandemic.Notably,industrial PM_(2.5) emis-sions from western,southern and southeastern Guangzhou play an important role in the formation of heavy pollution events.Our results not only verify the importance of control-ling traffic and industrial emissions,but also provide targets for further improvements in PM_(2.5) pollution.