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. This paper investigates the relative importance of turbulence andaerosol effects on the broadening of the droplet size distribution(DSD) during the early stage of cloud and raindrop formation. Aparcel–DNS (direct numerical simulation) hybrid approach is developedto seamlessly simulate the evolution of cloud droplets in an ascendingcloud parcel. The results show that turbulence and cloud condensation nuclei (CCN) hygroscopicityare key to the efficient formation of large droplets. The ultragiantaerosols can quickly form embryonic drizzle drops and thus determinethe onset time of autoconversion. However, due to their scarcity innatural clouds, their contribution to the total mass of drizzle dropsis insignificant. In the meantime, turbulence sustains the formationof large droplets by effectively accelerating the collisions of smalldroplets. The DSD broadening through turbulent collisions issignificant and therefore yields a higher autoconversion rate comparedto that in a nonturbulent case. It is argued that the level ofautoconversion is heavily determined by turbulence intensity. Thispaper also presents an in-cloud seeding scenario designed toscrutinize the effect of aerosols in terms of number concentration andsize. It is found that seeding more aerosols leads to highercompetition for water vapor, reduces the mean droplet radius, andtherefore slows down the autoconversion rate. On the other hand,increasing the seeding particle size can buffer such a negativefeedback. Despite the fact that the autoconversion rate is prominently alteredby turbulence and seeding, bulk variables such as liquid water content(LWC) stays nearly identical among all cases. Additionally, the lowestautoconversion rate is not co-located with the smallest mean dropletradius. The finding indicates that the traditional Kessler-type orSundqvist-type autoconversion parameterizations, which depend on theLWC or mean radius, cannot capture the drizzle formationprocess very well. Properties related to the width or the shape of the DSD arealso needed, suggesting that the scheme of Berry and Reinhardt ( 1974 ) isconceptually better. It is also suggested that a turbulence-dependentrelative-dispersion parameter should be considered.

Impact of aerosols and turbulence on cloud droplet growth: an in-cloud seeding case study using a parcel–DNS (direct numerical simulation) approach