Multi-modal graph neural networks for localized off-grid weather forecasting

Qidong Yang, Jonathan Giezendanner, Daniel Salles Civitarese, Johannes Jakubik, Eric Schmitt, Anirban Chandra, Jeremy Vila, Detlef Hohl, Christopher Hill, Campbell D Watson, Sherrie Wang

Keywords: Weather forecasting, Graph Neural Network, Multi-modal, off-grid weather forecasting, heterogeneous graph neural network, climate, climate change, sustainability

TL;DR: This paper proposes a graph neural network that improves off-grid weather predictions by combining local and global weather data, especially for wind forecasting in challenging terrain.

Abstract:

Urgent applications like wildfire management and renewable energy generation require precise, localized weather forecasts near the Earth's surface.
However, weather forecast products from machine learning or numerical weather models are currently generated on a global regular grid, on which a naive interpolation cannot accurately reflect fine-grained weather patterns close to the ground.
In this work, we train a heterogeneous graph neural network (GNN) end-to-end to downscale gridded forecasts to off-grid locations of interest.
This multi-modal GNN takes advantage of local historical weather observations (e.g., wind vector, temperature) to correct the gridded weather forecast at different lead times towards locally accurate forecasts.
Each data modality is modeled as a different type of node in the graph.
Using message passing, the node at the prediction location aggregates information from its heterogeneous neighbor nodes.
Experiments using weather stations across the Northeastern United States show that our model outperforms a range of data-driven and non-data-driven off-grid forecasting methods.
Our approach demonstrates how the gap between global large-scale weather models and locally accurate predictions can be bridged to inform localized decision-making.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 12035