A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds. (20th March 2020)

• Record Type: Journal Article
• Authors: Hagos, Samson; Feng, Zhe; Plant, Robert S.; Protat, Alain
• Is Part Of: Journal of advances in modeling earth systems. Volume 12:Number 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-20
• Subjects: convective clouds -- stratiform clouds -- machine learning -- population dynamics -- tropical convection -- organization; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2019MS001798
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Traditional parameterizations of the interaction between convection and the environment have relied on an assumption that the slowly varying large‐scale environment is in statistical equilibrium with a large number of small and short‐lived convective clouds. They fail to capture nonequilibrium transitions such as the diurnal cycle and the formation of mesoscale convective systems as well as observed precipitation statistics and extremes. Informed by analysis of radar observations, cloud‐permitting model simulation, theory, and machine learning, this work presents a new stochastic cloud population dynamics model for characterizing the interactions between convective and stratiform clouds, with the goal of informing the representation of these interactions in global climate models. Fifteen wet seasons of precipitating cloud observations by a C‐band radar at Darwin, Australia are fed into a machine learning algorithm to obtain transition functions that close a set of coupled equations relating large‐scale forcing, mass flux, the convective cell size distribution, and the stratiform area. Under realistic large‐scale forcing, the derived transition functions show that, on the one hand, interactions with stratiform clouds act to dampen the variability in the size and number of convective cells and therefore in the convective mass flux. On the other, for a given convective area fraction, a larger number of smaller cells is more favorable for the growth of stratiform area than a smaller number of larger cells. The combination of these two factors gives rise to solutions with a few convective cells embedded in a large stratiform area, reminiscent of mesoscale convective systems. Plain Language Summary: The work presents a stochastic cloud population model for convective and stratiform clouds for ultimate application to the development of parameterizations of those clouds in high‐resolution regional and global climate models. Machine learning is applied on precipitation radar observations to derive transition functions that represent interactions between convective cells and stratiform area. The model shows that interactions with stratiform clouds limit the variability in the size and number of convective clouds. While the size distribution of the convective cells also influences the size of stratiform area. Specifically, for the same total convective area, many small convective cells are more favorable for the formation of stratiform clouds than fewer larger convective cells. Those interactions coupled with the sensitivity of convective mass flux to cell size lead to solutions with a few cells embedded in a stratiform area as in MCSs and to damped convective mass flux variability. Key Points: A new cloud population model for characterizing the interactions between convective and stratiform clouds is developed The model is informed by application of machine learning on radar observations and a cloud‐permitting model simulation The model shows that stratiform clouds act to dampen the variability in the size and number of convective cells and therefore convective mass flux variability