A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds

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.

A Simple Model for Tropical Convective Cloud Shield Area Growth and Decay Rates Informed by Geostationary IR, GPM, and Aqua/AIRS Satellite Data

“…The latter result is similar to the findings of Hagos et al. (2020). Might this imply that less organized convection during the later decaying stages of system lifecycles favors cloud shield sustenance and increased longevity?…”

“…In this state, as R PA increases (i.e., organization decreases), the source term also increases (top left portion of Figure 11a). The latter result is similar to the findings of Hagos et al (2020). Might this imply that less organized convection during the later decaying stages of system lifecycles favors cloud shield sustenance and increased longevity?…”

“…This result is interesting, particularly in light of Hagos et al. (2020) where it was found that if there were more convective cells for a given convective area, growth of the stratiform area was larger. Hagos et al.…”

“…For comparison purposes, after moving the first term on the RHS of Equation to the LHS, the equation becomes closer to those estimating the stratiform area time tendency, with the convective mass flux convergence term following Tiedtke (1993), Teixeira (2001) and follow‐ons, and the decay term mimicking Hagos et al. (2020), although it is important to note that these terms were used in studies that were prognosing grid‐box or fixed‐domain stratiform cloud fraction or area changes whereas Equation prognoses cloud physical area changes following Lagrangian‐tracked MCSs. Hagos et al.…”

“…where A is the cloud shield area, A c is the convective area (with the subscript "SRC" indicating this term represents the temporal generation of new convective area), M c is the convective mass flux, M s is the stratiform mass flux, ρ is the atmospheric density, and τ is a cloud shield area decay timescale. For comparison purposes, after moving the first term on the RHS of Equation 1 to the LHS, the equation becomes closer to those estimating the stratiform area time tendency, with the convective mass flux convergence term following Tiedtke (1993), Teixeira (2001) and follow-ons, and the decay term mimicking Hagos et al (2020), although it is important to note that these terms were used in studies that were prognosing grid-box or fixed-domain stratiform cloud fraction or area changes whereas Equation 1 prognoses cloud physical area changes following Lagrangian-tracked MCSs. Hagos et al ( 2020) is further similar in that radar data (off the coast of Darwin, Australia) are used to develop a simple analytical model of stratiform area, though individual MCSs were not explicitly tracked in that analysis and the source terms vary in structure.…”