Effects of aerosols on the forecasting of Mei-yu frontal storms over the Yangtze-Huai River valley

The evolutions of Mei-yu frontal storms during the Mei-yu season in 2018 were simulated in the Weather Research and Forecasting with Chemistry (WRF/Chem) model to investigate the effects of aerosols on the prediction of clouds and precipitation. Two groups of daily launched forecast experiments were conducted with a duration of 17 days, by turning the chemistry-aerosol module on (WRF/Chem) and off (WRF), and additional cloud droplet number concentration (Nd) experiments were perturbed in WRF. Comparison between the two groups of experiments suggested that activating the chemistry-aerosol module improved the forecast skill for precipitation exceeding 5 mm/h during the Mei-yu season, while had no positive forecast skill on precipitation exceeding 0.1 mm/h in terms of both traditional and neighborhood spatial verifications. In contrast to the uniform Nd distribution adopted in WRF, the inhomogeneous Nd distribution in WRF/Chem agreed better with in-situ aircraft observations collected during the field campaign. As Nd increases in WRF experiments, the warm clouds decreased and deep convective clouds significantly increased with more convective updraft mass flux and net heat budget. Scavenging in Mei-yu frontal storms generally reduced the median Nd in the WRF/Chem experiment. The fewer and larger cloud particles in WRF/Chem promoted the conversion of cloud to rain and the accretion of cloud by rain, thereby leading to invigoration of warm clouds and precipitation efficiency, which explains the improved forecast skill for precipitation exceeding 5 mm/h. Enhanced warm-rain processes caused fewer cloud particles for further ascent and ultimately resulted in suppression of deep convective clouds. The preliminary findings in this study highlights the potential significance of incorporating chemistry-aerosol schemes in the operational forecasting of Mei-yu rainfall.

Automated summary

The effects of aerosols on cloud and precipitation prediction were investigated by simulating the evolution of Mei-yu frontal storms during the 2018 Mei-yu season using the Weather Research and Forecasting with Chemistry (WRF/Chem) model. Activating the chemistry-aerosol module improved the forecast skill for precipitation exceeding 5 mm/h during the Mei-yu season, while had no positive forecast skill on precipitation exceeding 0.1 mm. The inhomogeneous distribution of cloud droplet number concentration in WRF/Chem agreed better with in-situ observations, and the conversion of cloud to rain and accretion of cloud by rain in WRF/Chem contributed to improved precipitation efficiency.