Deep learning for twelve hour precipitation forecasts
Can AI learn from atmospheric data and improve weather forecasting? The neural network MetNet-2 achieves this by forecasting the fast changing variable of precipitation up to 12 h ahead more accurately and efficiently than traditional models based on hand-coded physics.
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-09-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-32483-x |
| _version_ | 1811274809901842432 |
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| author | Lasse Espeholt Shreya Agrawal Casper Sønderby Manoj Kumar Jonathan Heek Carla Bromberg Cenk Gazen Rob Carver Marcin Andrychowicz Jason Hickey Aaron Bell Nal Kalchbrenner |
| author_facet | Lasse Espeholt Shreya Agrawal Casper Sønderby Manoj Kumar Jonathan Heek Carla Bromberg Cenk Gazen Rob Carver Marcin Andrychowicz Jason Hickey Aaron Bell Nal Kalchbrenner |
| author_sort | Lasse Espeholt |
| collection | DOAJ |
| description | Can AI learn from atmospheric data and improve weather forecasting? The neural network MetNet-2 achieves this by forecasting the fast changing variable of precipitation up to 12 h ahead more accurately and efficiently than traditional models based on hand-coded physics. |
| first_indexed | 2024-04-12T23:25:55Z |
| format | Article |
| id | doaj.art-3ce233e8b2ea4d9f8ce0eb1005f589c3 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-12T23:25:55Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-3ce233e8b2ea4d9f8ce0eb1005f589c32022-12-22T03:12:24ZengNature PortfolioNature Communications2041-17232022-09-0113111010.1038/s41467-022-32483-xDeep learning for twelve hour precipitation forecastsLasse Espeholt0Shreya Agrawal1Casper Sønderby2Manoj Kumar3Jonathan Heek4Carla Bromberg5Cenk Gazen6Rob Carver7Marcin Andrychowicz8Jason Hickey9Aaron Bell10Nal Kalchbrenner11Google Research, Google IncGoogle Research, Google IncGoogle Research, Google IncGoogle Research, Google IncGoogle Research, Google IncGoogle Research, Google IncGoogle Research, Google IncGoogle Research, Google IncGoogle Research, Google IncGoogle Research, Google IncGoogle Research, Google IncGoogle Research, Google IncCan AI learn from atmospheric data and improve weather forecasting? The neural network MetNet-2 achieves this by forecasting the fast changing variable of precipitation up to 12 h ahead more accurately and efficiently than traditional models based on hand-coded physics.https://doi.org/10.1038/s41467-022-32483-x |
| spellingShingle | Lasse Espeholt Shreya Agrawal Casper Sønderby Manoj Kumar Jonathan Heek Carla Bromberg Cenk Gazen Rob Carver Marcin Andrychowicz Jason Hickey Aaron Bell Nal Kalchbrenner Deep learning for twelve hour precipitation forecasts Nature Communications |
| title | Deep learning for twelve hour precipitation forecasts |
| title_full | Deep learning for twelve hour precipitation forecasts |
| title_fullStr | Deep learning for twelve hour precipitation forecasts |
| title_full_unstemmed | Deep learning for twelve hour precipitation forecasts |
| title_short | Deep learning for twelve hour precipitation forecasts |
| title_sort | deep learning for twelve hour precipitation forecasts |
| url | https://doi.org/10.1038/s41467-022-32483-x |
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