AGS-PRF: Physics-Constrained Machine Learning-Based Models for Climate Simulations with Data Assimilation and Uncertainty Quantification

Despite recent computational advancements, climate models still cannot explicitly resolve key physical processes like turbulence, convection, and clouds. These unresolved processes must be accounted for by parameterization schemes. The inability of these schemes to mimic reality has hindered the ability of model simulations to capture various observed phenomena, leading model biases and uncertainties. Recently, machine learning-based techniques have greatly improved these schemes. Nevertheless, the standard machine learning-based methods are built solely on idealized computational models without considering the wealth of observational data available. Thus, inherent inaccuracies of the parameterization schemes continue to undermine the performance of climate models. This project aims to improve machine-learning-based parameterization schemes and, thereby, enhance the performance of existing climate models. The proposer will incorporate observations and physical laws into machine learning techniques to make parameterization schemes more accurate and provide uncertainty estimates to capture the chaotic nature of the climate system. The proposed work will train a young postdoctoral scholar.

Specifically, the proposal will utilize data from satellite observations and high-resolution simulations to improve machine learning schemes. Physics-constraints will be imposed either through the incorporation of a physical loss term or by considering specific machine learning tools such as Neural Networks with fixed output layer that strongly imposes the known physical constraint. Uncertainty quantification in machine learning schemes will be implemented using ensemble learning or Bayesian approaches such as Hamiltonian Monte Carlo sampling schemes. The knowledge gained in this project could have an impact across climate science, including the advancement of global climate models' development and of machine learning application to Big Data in climate science, as well as the development of novel computational probabilistic methods for complex multi-scale and multi-physics real-world systems.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.