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

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arxiv 2409.02891 v1 pith:SH6YPMEB submitted 2024-09-04 physics.ao-ph cs.LG

Thomas Nils Nipen , H{aa}vard Homleid Haugen , Magnus Sikora Ingstad , Even Marius Nordhagen , Aram Farhad Shafiq Salihi , Paulina Tedesco , Ivar Ambj{o}rn Seierstad , J{o}rn Kristiansen
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Simon Lang Mihai Alexe Jesper Dramsch Baudouin Raoult Gert Mertes Matthew Chantry
This is my paper · ORCID
classification physics.ao-phcs.LG
keywords modelresolutionweatherforecastsmepsregionaldata-drivenensemble
verification ladder T0 review T1 audit T2 compute T3 formal
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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. The model is pre-trained on 43 years of global ERA5 data at 31 km resolution and is further refined using 3.3 years of 2.5 km resolution operational analyses from the MetCoOp Ensemble Prediction System (MEPS). The performance of the model is evaluated using surface observations from measurement stations across Norway and is compared to short-range weather forecasts from MEPS. The DDM outperforms both the control run and the ensemble mean of MEPS for 2 m temperature. The model also produces competitive precipitation and wind speed forecasts, but is shown to underestimate extreme events.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 5 citations worldwide. Full citation record

  1. Accurate Mediterranean Sea forecasting via graph-based deep learning

    physics.ao-ph 2025-06 conditional novelty 7.0 of 10

    SeaCast, a graph neural network, makes 15-day Mediterranean Sea forecasts that outperform the operational MedFS system over the evaluated period, while producing a forecast in 20 seconds on one GPU.

  2. Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism

    cs.LG 2025-07 conditional novelty 6.0 of 10

    An MLP-based weather model called WeatherMixer trains efficiently with a new Jigsaw parallelization scheme that shards data and model across GPUs, reaching 11 PFLOPs on 256 GPUs with 72% scaling efficiency.

  3. HRRRCast: a data-driven emulator for regional weather forecasting at convection allowing scales

    physics.ao-ph 2025-07 conditional novelty 6.0 of 10

    A diffusion-based neural network trained on HRRR analysis beats HRRR forecast skill on 20 dBZ composite reflectivity across CONUS and is competitive at 30 dBZ.

  4. Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model

    physics.ao-ph 2025-10 conditional novelty 5.0 of 10

    Combining HiRA neighborhood verification with threshold-weighted CRPS shows that AI-vs-NWP rankings for extreme precipitation depend strongly on neighborhood size.

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