Machine Learning Parameterization for Super-droplet Cloud Microphysics Scheme

Machine learning approaches have been widely used for improving the representation of subgrid scale parameterizations in Earth System Models. In our study we target the Cloud Microphysics parameterization, in particular the two-moment bulk scheme of the ICON (Icosahedral Non-hydrostatic) Model. 

 

Cloud microphysics parameterization schemes suffer from an accuracy/speed tradeoff. The simplest schemes, often heavy with assumptions (such as the bulk moment schemes) are most common in operational weather prediction models. Conversely, the more complex schemes with fewer assumptions –e.g. Lagrangian schemes such as the super-droplet method (SDM)– are computationally expensive and used only within research and development. SDM allows easy representation of complex scenarios with multiple hydrometeors and can also be used for simulating cloud-aerosol interactions. To bridge this gap and to make the use of more complex microphysical schemes feasible within operational models, we use a data-driven approach. 

 

Here we train a neural network to mimic the behavior of SDM simulations in a warm-rain scenario in a dimensionless control volume. The network behaves like a dynamical system that converts cloud droplets to rain droplets–represented as bulk moments–with only the current system state as the input. We use a multi-step training loss to stabilize the network over long integration periods, especially in cases with extremely low cloud water to start with. We find that the network is stable across various initial conditions and in many cases, emulates the SDM simulations better than the traditional bulk moment schemes. Our network also performs better than any previous ML-based attempts to learn from SDM. This opens the possibility of using the trained network as a proxy for imitating the computationally expensive SDM within operational weather prediction models with minimum computational overhead.