Accelerating Weather Forecasting: A Neural Network-Based Emulation of ISORROPIA

Atmospheric composition is an essential part of weather, climate and Earth system modeling. However, modeling atmospheric composition is a computationally expensive and time-consuming task that requires a significant amount of energy. As models scale to finer spatial and temporal resolutions, maintaining real-time performance becomes increasingly challenging. To address this, optimization and acceleration techniques are essential. One promising approach is the use of deep neural networks, which have demonstrated the capability to efficiently approximate complex systems with high accuracy. Predictions using these neural networks are notably faster compared to traditional methods, significantly reducing the computational burden. In this study, we present the development of a surrogate model designed to emulate ISORROPIA, a traditional model used for calculating the concentrations of chemical compounds in the ICON-ART (ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases) model. Specifically, ISORROPIA is an aerosol thermodynamic equilibrium model used by ART that requires substantial computational resources, occupying a significant portion of the overall calculation time, making it particularly well-suited for emulation. The methodology involved generating a comprehensive dataset using the traditional model, which served as the training data for the neural network. This dataset encompassed a wide range of chemical concentrations and conditions, ensuring the neural network could effectively learn the underlying patterns and relationships for real-life scenarios. A simple feedforward architecture was used and fine-tuned with the primary goal of maintaining a low approximation error while also striving to achieve the lowest possible inference timing. After training, the new neural network model was compared to ISORROPIA on ICON-ART simulation data. The results demonstrated that the neural network model successfully achieved the desired outcomes, maintaining low approximation error across the globe and efficient inference timing.