Wang, L.-Y.; Wang, Y.; Hu, X.; Wang, H.; Zhou, R. Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model. Preprints2024, 2024091262. https://doi.org/10.20944/preprints202409.1262.v1
APA Style
Wang, L. Y., Wang, Y., Hu, X., Wang, H., & Zhou, R. (2024). Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model. Preprints. https://doi.org/10.20944/preprints202409.1262.v1
Chicago/Turabian Style
Wang, L., Hui Wang and Ruilin Zhou. 2024 "Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model" Preprints. https://doi.org/10.20944/preprints202409.1262.v1
Abstract
Deep-learning-based convection schemes receive wide attention due to its impressive im-provement on precipitation distribution and tropical convections of earth system simulation. But they cannot represent the stochasticity of moist physics, which will degrade the simulation of large-scale circulations, climate mean, and variability. To solve this problem, a stochastic pa-rameterization scheme based on probabilistic diffusion model named DIFF-MP is developed. The cloud-resolving data from GRIST model is coarse-grained into resolved-scale variables and sub-grid contributions due to moist physics to form the training data. DIFF-MP’s performance is compared against generative adversarial network and variational autoencoder. Results show that DIFF-MP is consistently better than the other two models on prediction error, coverage ratio, and spread-skill correlation. The standard deviation, skewness, and kurtosis of subgrid contributions generated by DIFF-MP is also closer to the testing data than the others. Interpretability experiment shows that DIFF-MP’s parameterization of moist physics is physically reasonable.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
