A Machine‐Learning‐Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection
نویسندگان
چکیده
A machine-learning-assisted stochastic cloud population model is coupled with the Advanced Research Weather and Forecasting (WRF) to represent fluctuations in cloud-base mass flux associated life cycles interactions among cumulus convection cells. In this model, size distribution of convective cells are related their previous state change total area via a transition function. The tendency turn assumed depend on that resolved by host WRF model. function represented single hidden-layer neural network trained evolution cell distributions 1 km grid-spacing simulation run over Australian Monsoon region. At every grid point predicts from which random sample fed an entraining parcel calculates precipitation as well liquid water potential temperature moisture tendencies. These tendencies averaged provided Several regional simulations performed tropical mid-latitude domains test approach scale-aware parameterization. It shown such could be new promising path simulating realistic statistics propagation Madden-Julian Oscillation, while maintaining depictions diurnal cycle both land ocean.
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ژورنال
عنوان ژورنال: Journal of Advances in Modeling Earth Systems
سال: 2022
ISSN: ['1942-2466']
DOI: https://doi.org/10.1029/2021ms002808