Abstract:
Recent years have witnessed great progress in emulators based on neural network (NN). Current state-of-the-art emulators methods often apply shallow NN to attain high performance in physics system, which brings a faster speed processing on resource-constrained environments. Although several works have focused on improving accuracy in physics emulators, an effective and efficient method for tackling time consuming problem of existing system on high-resolution remains lacking. In this paper, we propose a optimum NN emulator of a microphysics (MPS) parameterization scheme to effectively solve the problem in numerical weather prediction (NWP) model, Korea Integrated Model (KIM) in particular. Specifically, we adopt a shallow NN to build an intelligent emulator, which can learn the feature map and estimate the vertical MPS forcing increment profiles. This study mainly relies on two technical contributions: (1) Optimization: reviewing and improving models for simulating non-linear parameters; (2) Feasibility: efficient computation with minimal loss of physical information. We validate the proposed model with four seasons (10-day forecast at 200 seconds interval) of KIM. Results indicate that the proposed single-layer network shows best performance for emulating MPS in KIM. Our analyses will provide a guideline for optimal physical parameterizations modeling.

Keywords:
cloud microphysics, latent heating rate, production rates of cloud microphysical process, WDM6, mesoscale convective system (WDM6), WRF model/KIM physics

Citation:
Madhulatha, A., Dudhia, J., Park, R.-S., and Rajeevan, M., “Simulation of Latent Heating Rate From the Microphysical Process Associated With Mesoscale Convective System Over Korean Peninsula”, Earth and Space Science, vol. 9, no. 9, 2022. doi:10.1029/2022EA002419.