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A machine learning assisted stochastic cloud population model as a parameterization of cumulus convection

Hagos, S., Chen, J., Barber, K., Sakaguchi, K., Plant, R. S. ORCID:, Feng, Z. and Xiao, H. (2022) A machine learning assisted stochastic cloud population model as a parameterization of cumulus convection. Journal of Advances in Modeling Earth Systems, 14 (7). e2021MS002808. ISSN 1942-2466

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To link to this item DOI: 10.1029/2021MS002808


A machine-learning-assisted stochastic cloud population model is coupled with the Advanced Research Weather Research and Forecasting (WRF) model to represent fluctuations in cloud-base mass flux associated with the life cycles and interactions among cumulus convection cells. In this cloud population model, the size distribution and associated cloud-base mass flux of the convective cells are related to their previous state and to the change in the total convective area via a transition function. The convective area tendency in turn is assumed to depend on the cloud-base mass flux that is resolved by the host WRF model. The transition function is represented by a single hidden-layer neural network trained by the evolution of convective cell size distributions in a 1 km grid-spacing WRF simulation run over the Australian Monsoon region. At every grid point of the host model, the cloud population model predicts the cell size and cloud-base mass flux distributions from which a random sample of cells is fed to an entraining parcel model that calculates precipitation as well as the associated liquid water potential temperature and total moisture tendencies. These tendencies are averaged over the cells and provided to the host model. Several regional simulations are performed over tropical and mid-latitude domains to test this as a potential approach to scale-aware parameterization. It is shown that such an approach could be a new promising path to simulating realistic precipitation statistics and propagation of precipitation associated with the Madden-Julian Oscillation, while maintaining realistic depictions of the diurnal cycle over both land and ocean.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:102757
Publisher:American Geophysical Union


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