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License: CC BY
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ZENODO
Software . 2024
License: CC BY
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ZENODO
Software . 2024
License: CC BY
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ZENODO
Software . 2024
License: CC BY
Data sources: Datacite
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Source code for Hybrid WRFCHEM with Physics and AI Schemes (PhotoChemistry)

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 17 Aug 2024 English Publisher:Zenodo
Authors: Xia, Zihan;

doi: 10.5281/zenodo.15614698 , 10.5281/zenodo.13335902 , 10.5281/zenodo.13335901

Source code for Hybrid WRFCHEM with Physics and AI Schemes (PhotoChemistry)

Abstract

In this hybrid version of WRF-Chem with both physics and AI schemes, we have successfully established an artificial intelligence photochemistry scheme based on the Multi-head Self-Attention algorithm. This scheme replaces the conventional numerical scheme for the SAPRC-99 mechanism and enables atmospheric models to achieve fast, accurate, and stable simulations of photochemistry processes.

Related Organizations
Keywords

Atmospheric sciences, Machine learning

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Average