Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning

25 Feb 2020Ashesh ChattopadhyayAdam SubelPedram Hassanzadeh

To make weather/climate modeling computationally affordable, small-scale processes are usually represented in terms of the large-scale, explicitly-resolved processes using physics-based or semi-empirical parameterization schemes. Another approach, computationally more demanding but often more accurate, is super-parameterization (SP), which involves integrating the equations of small-scale processes on high-resolution grids embedded within the low-resolution grids of large-scale processes... (read more)

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