EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Approximation and Optimization of Atmospheric Simulations in High Spatio-Temporal Resolution with Neural Networks

Elnaz Azmi1, Jörg Meyer1, Marcus Strobl1, Michael Weimer2, and Achim Streit1
Elnaz Azmi et al.
  • 1Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany (
  • 2Institute of Environmental Physics, University of Bremen, Bremen, Germany

Accurate forecasts of the atmosphere demand large-scale simulations with high spatio-temporal resolution. Atmospheric chemistry modeling, for example, usually requires solving a system of hundreds of coupled ordinary partial differential equations. Due to the computational complexity, large high performance computing resources are required, which is a challenge as the spatio-temporal resolution increases. Machine learning methods and specially deep learning can offer an approximation of the simulations with some factor of speed-up while using less compute resources. The goal of this study is to investigate the feasibility, opportunities but also challenges and pitfalls of replacing the compute-intensive chemistry of a state-of-the-art atmospheric chemistry model with a trained neural network model to forecast the concentration of trace gases at each grid cell and to reduce the computational complexity of the simulation. In this work, we introduce a neural network model (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 ground truth 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 compared to our ground truth dataset. We discuss appropriate metrics to evaluate the quality of models and present 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 variable. 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 simulations.

How to cite: Azmi, E., Meyer, J., Strobl, M., Weimer, M., and Streit, A.: Approximation and Optimization of Atmospheric Simulations in High Spatio-Temporal Resolution with Neural Networks, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6287,, 2023.