Deep Learning Parameterization of Vertical Wind Velocity Variability via Constrained Adversarial Training

Abstract

Abstract Atmospheric models with typical resolution in the tenths of kilometers cannot resolve the dynamics of air parcel ascent, which varies on scales ranging from tens to hundreds of meters. Small-scale wind fluctuations are thus characterized by a subgrid distribution of vertical wind velocity W with standard deviation σW. The parameterization of σW is fundamental to the representation of aerosol–cloud interactions, yet it is poorly constrained. Using a novel deep learning technique, this work develops a new parameterization for σW merging data from global storm-resolving model simulations, high-frequency retrievals of W, and climate reanalysis products. The parameterization reproduces the observed statistics of σW and leverages learned physical relations from the model simulations to guide extrapolation beyond the observed domain. Incorporating observational data during the training phase was found to be critical for its performance. The parameterization can be applied online within large-scale atmospheric models, or offline using output from weather forecasting and reanalysis products. Significance Statement Vertical air motion plays a crucial role in several atmospheric processes, such as cloud droplet and ice crystal formation. However, it often occurs at scales smaller than those resolved by standard atmospheric models, leading to uncertainties in climate predictions. To address this, we present a novel deep learning approach that synthesizes data from various sources, providing a representation of small-scale vertical wind velocity suitable for integration into atmospheric models. Our method demonstrates high accuracy when compared to observation-based retrievals, offering potential to mitigate uncertainties and enhance climate forecasting.