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2024

Regional data-driven weather modeling with a global stretched-grid

Nipen, Thomas Nils, Haugen, Håvard Homleid, Ingstad, Magnus Sikora et al.

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A data-driven model (DDM) suitable for regional weather forecasting applications is presented.

The model extends the Artificial Intelligence Forecasting System by introducing a stretched-grid architecture that dedicates higher resolution over a regional area of interest and maintains a lower resolution elsewhere on the globe.
The model is based on graph neural networks, which naturally affords arbitrary multi-resolution grid configurations.
The model is applied to short-range weather prediction for the Nordics, producing forecasts at 2.5 km spatial and 6 h temporal resolution.

Regional data-driven weather modeling with a global stretched-grid · Around

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