Regional data-driven weather modeling with a global stretched-grid Article Swipe
Thomas N. Nipen
,
Håvard Homleid Haugen
,
Magnus Sikora Ingstad
,
Even Marius Nordhagen
,
Aram Farhad Shafiq Salihi
,
Paulina Tedesco
,
Ivar A. Seierstad
,
Jørn Kristiansen
,
Simon Lang
,
Mihai Alexe
,
Jesper Dramsch
,
Baudouin Raoult
,
Gert Mertes
,
Matthew Chantry
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.02891
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.02891
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.02891
- https://arxiv.org/pdf/2409.02891
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403160553
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403160553Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.02891Digital Object Identifier
- Title
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Regional data-driven weather modeling with a global stretched-gridWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-04Full publication date if available
- Authors
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Thomas N. Nipen, Håvard Homleid Haugen, Magnus Sikora Ingstad, Even Marius Nordhagen, Aram Farhad Shafiq Salihi, Paulina Tedesco, Ivar A. Seierstad, Jørn Kristiansen, Simon Lang, Mihai Alexe, Jesper Dramsch, Baudouin Raoult, Gert Mertes, Matthew ChantryList of authors in order
- Landing page
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https://arxiv.org/abs/2409.02891Publisher landing page
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https://arxiv.org/pdf/2409.02891Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2409.02891Direct OA link when available
- Concepts
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Grid, Meteorology, Weather Research and Forecasting Model, Computer science, Climatology, Environmental science, Geography, Geology, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2025: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.regional | 6, 31 |
| abstract_inverted_index.stations | 128 |
| abstract_inverted_index.suitable | 4 |
| abstract_inverted_index.temporal | 79 |
| abstract_inverted_index.arbitrary | 55 |
| abstract_inverted_index.dedicates | 26 |
| abstract_inverted_index.elsewhere | 40 |
| abstract_inverted_index.evaluated | 122 |
| abstract_inverted_index.forecasts | 71, 137 |
| abstract_inverted_index.maintains | 36 |
| abstract_inverted_index.naturally | 53 |
| abstract_inverted_index.networks, | 51 |
| abstract_inverted_index.producing | 70 |
| abstract_inverted_index.Artificial | 16 |
| abstract_inverted_index.Prediction | 113 |
| abstract_inverted_index.forecasts, | 166 |
| abstract_inverted_index.prediction | 66 |
| abstract_inverted_index.presented. | 11 |
| abstract_inverted_index.resolution | 28, 39, 95, 106 |
| abstract_inverted_index.Forecasting | 18 |
| abstract_inverted_index.competitive | 161 |
| abstract_inverted_index.data-driven | 1 |
| abstract_inverted_index.forecasting | 8 |
| abstract_inverted_index.introducing | 21 |
| abstract_inverted_index.measurement | 127 |
| abstract_inverted_index.operational | 107 |
| abstract_inverted_index.outperforms | 142 |
| abstract_inverted_index.performance | 117 |
| abstract_inverted_index.pre-trained | 84 |
| abstract_inverted_index.resolution. | 80 |
| abstract_inverted_index.short-range | 64, 135 |
| abstract_inverted_index.Intelligence | 17 |
| abstract_inverted_index.applications | 9 |
| abstract_inverted_index.architecture | 24 |
| abstract_inverted_index.observations | 125 |
| abstract_inverted_index.temperature. | 156 |
| abstract_inverted_index.precipitation | 162 |
| abstract_inverted_index.underestimate | 171 |
| abstract_inverted_index.stretched-grid | 23 |
| abstract_inverted_index.configurations. | 58 |
| abstract_inverted_index.multi-resolution | 56 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 14 |
| citation_normalized_percentile |