An interpretable machine learning model for seasonal precipitation forecasting
Abstract Seasonal climate forecasting is important for societal welfare, as it supports decision-makers in taking proactive steps to mitigate risks from adverse climate conditions or to take advantage of favorable ones. Here, we introduce TelNet, a sequence-to-sequence machine learning model for sho...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-025-02207-2 |
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