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- Helmholtz-Zentrum Hereon
In weather and climate models, physical processes that can’t be explicitly resolved are often parameterized. Among them is cloud microphysics that often works in tandem with the convective parameterization to control the formation of clouds and rain.
Existing parameterization schemes available for cloud microphysics suffer from an accuracy/speed trade-off. The most accurate schemes based on Lagrangian droplet methods are computationally expensive and are only used for research and development. On the other hand, more widely used approaches such as bulk moment schemes simplify the particle size distributions into the total mass and number density of cloud and rain droplets. While these approximations are fairly realistic in many cases, they struggle to represent more complex microphysical scenarios.
We develop a machine learning based parameterization to emulate the warm rain formation process in the Super droplet scheme (a type of Lagrangian scheme) in a dimensionless control volume. We show that the ML based emulator matches the Lagrangian simulations better than the bulk moment schemes, especially in the cases of very skewed droplet distributions. Compared to previous attempts in emulating warm rain, our ML model shows a better performance. The ML model inference runs fast thereby reducing the computational time otherwise needed for Lagrangian schemes. Thus, we have developed an ML based emulator that is more accurate than the commonly used schemes with only a small computational overhead, hence, making it possible to indirectly use Lagrangian schemes in operational weather models.