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Title: A Binomial Stochastic Framework for Efficiently Modeling Discrete Statistics of Convective Populations

Journal Article · · Journal of Advances in Modeling Earth Systems
ORCiD logo [1]; ORCiD logo [2]
  1. Institute for Geophysics and Meteorology University of Cologne Cologne Germany
  2. Institut für Meteorologie und Geophysik Universität Wien Vienna Austria
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Abstract Understanding the coupling between convective clouds and the general circulation, as well as addressing the gray zone problem in convective parameterization, requires insight into the genesis and maintenance of spatial patterns in cumulus cloud populations. In this study, a simple toy model for recreating populations of interacting convective objects as distributed over a two‐dimensional Eulerian grid is formulated to this purpose. Key elements at the foundation of the model include i) a fully discrete formulation for capturing discrete behavior in convective properties at small population sample sizes, ii) object age‐dependence for representing life‐cycle effects, and iii) a prognostic number budget allowing for object interactions and co‐existence of multiple species. A primary goal is to optimize the computational efficiency of this system. To this purpose the object birth rate is represented stochastically through a spatially aware Bernoulli process. The same binomial stochastic operator is applied to horizontal advection of objects, conserving discreteness in object number. The applicability to atmospheric convection as well as behavior implied by the formulation is assessed. Various simple applications of the BiOMi model (Binomial Objects on Microgrids) are explored, suggesting that important convective behavior can be captured at low computational cost. This includes i) subsampling effects and associated powerlaw scaling in the convective gray zone, ii) stochastic predator‐prey behavior, iii) the downscale turbulent energy cascade, and iv) simple forms of spatial organization and convective memory. Consequences and opportunities for convective parameterization in next‐generation weather and climate models are discussed.

Research Organization:
Cleveland State Univ., Cleveland, OH (United States); Univ. of Cologne (Germany); Univ. of Vienna (Austria)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
SC0017999
OSTI ID:
1772288
Journal Information:
Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 3 Vol. 13; ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English

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