Approximation and Optimization of Global Environmental Simulations with Neural Networks

Abstract

Solving a system of hundreds of chemical differential equations in environmental simulations has a major computational complexity, and thereby requires high performance computing resources, which is a challenge as the spatio-temporal resolution increases. Machine learning methods and specially deep learning can offer an approximation of simulations with some factor of speed-up while using less compute resources. In this work, we introduce a neural network based approach (ICONET) to forecast trace gas concentrations without executing the traditional compute-intensive atmospheric simulations. ICONET is equipped with a multifeature Long Short Term Memory (LSTM) model to forecast atmospheric chemicals iteratively in time. We generated the training and test dataset, our target dataset for ICONET, by execution of an atmospheric chemistry simulation in ICON-ART. Applying the ICONET trained model to forecast a test dataset results in a good fit of the forecast values to our target dataset. We discussed appropriate metrics to evaluate the quality of models and presented the quality of the ICONET forecasts with RMSE and KGE metrics. The variety in the nature of trace gases limits the model's learning and forecast skills according to the respective trace gas. In addition to the quality of the ICONET forecasts, we described the computational efficiency of ICONET as its run time speed-up in comparison to the run time of the ICON-ART simulation. The ICONET forecast showed a speed-up factor of 3.1 over the run time of the atmospheric chemistry simulation of ICON-ART, which is a significant achievement, especially when considering the importance of ensemble simulation.