Abstract
Abstract. Cloud and precipitation processes remain among the largest sources of uncertainties in weather and climate modelling, and considerable attention has been paid to improving the representation of the cloud and precipitation processes in numerical models in the last several decades. In this study, we develop a weighted ensemble (named EN) scheme by employing several widely used autoconversion (ATC) schemes to represent the ATC from cloud water to rainwater. One unique feature of the EN approach is that the ATC rate is a weighted mean value based on the calculations from several ATC schemes within a microphysics scheme with a negligible increase in computation cost. The EN scheme is compared with the several commonly used ATC schemes by performing real case simulations. In terms of accumulated rainfall and extreme hourly rainfall rate, the EN scheme provides better simulations than by using the single Berry–Reinhardt scheme, which was originally used in the Thompson scheme. It is worth emphasizing, in the present study, that we only pay attention to the ATC process from cloud water into rainwater with the purpose of improving the modelling of the extreme rainfall events over southern China. Actually, any (source and sink) term in a cloud microphysics scheme can be treated with the same approach. The ensemble method proposed herein appears to have important implications for developing cloud microphysics schemes in numerical models, especially for the models with variable grid resolution, which would be expected to improve the representation of cloud microphysical processes in the weather and climate models.
Highlights
Cloud and precipitation processes and associated feedbacks have been confirmed to cause the largest uncertainties in weather and climate modelling by the Intergovernmental Panel on Climate Change (IPCC) (Houghton et al, 2001)
A raindrop is initiated by the ATC process in warm clouds, which plays a significant role in the onset of a rainfall event
One unique feature of the EN approach is that the ATC rate is a mean value based on the calculations from several widely used ATC schemes
Summary
Cloud and precipitation processes and associated feedbacks have been confirmed to cause the largest uncertainties in weather and climate modelling by the Intergovernmental Panel on Climate Change (IPCC) (Houghton et al, 2001). Owing to the complex microphysical processes in clouds and their interactions with dynamical and thermodynamic processes, considerable attention has been devoted to developing cloud microphysics schemes in the numerical weather and climate models in the last several decades, which is summarized in several review articles J. Yin et al.: Representation of the autoconversion is a significant microphysical process in warm clouds. A raindrop is initiated by the ATC process in warm clouds, which plays a significant role in the onset of a rainfall event. The ATC process has an important influence on cloud microphysical properties by bridging aerosols, cloud droplets, and raindrops (White et al, 2017). An appropriate representation of the ATC process is helpful for our understanding of cloud micro- and macro-properties as well as precipitation processes
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