EAGER: Scalable Climate Modeling using Message-Passing Recurrent Neural Networks

Real-world climate models have often been heavily reliant on large-scale physics-driven climate models involving millions of unknown parameters and sparse real-world measurements to accurately calibrate these models. This proposal aims to develop Message Passing Recurrent Neural Networks (MPRNN), a deep graph neural framework for accurate, scalable and efficient climate modeling from sparse spatio-temporal sensor measurements. Unlike fine-grained physics based climate models that model continuous behavior across space and time, MPRNNs leverage a discrete, distributed collection of heterogeneous recurrent neural networks established at different spatial nodes that simulate the underlying physics-based model and communicate using message passing algorithms to generate real-time spatio-temporal climate maps. This proposal makes fundamental contributions across several sub-disciplines including computer science, complex systems, and climate sciences including: (i) designing scalable graph machine learning frameworks for modeling complex climate systems, (ii) simulating underlying partial differential equations based physics-informed spatio-temporal models using message passing algorithms and graph neural networks, and (iii) designing a general purpose library for efficiently implementing MPRNN based simulators for complex climate systems. This proposal aims to demonstrate the efficacy of MPRNN on multiple climate modeling efforts including modeling gravity waves, environmental pollution forecasting and understanding the localized impact of climate variations in dense urban environments. This proposal builds upon prior work by the investigators that provides a baseline implementation of MPRNN for pollution forecasting and gravity wave modeling. This line of work has a broader impact on various stakeholders of climate research, including climate modeling researchers and policy experts in tackling important issues like climate change, air pollution and gravity waves. By incorporating physics-based domain structure into deep graph models, this proposal can enable climate experts to effectively emulate the behavior of large physics based simulation models using scalable deep graph based climate models. 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.