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This dataset contains the pretrained model weights and precomputed results for the paper "Thunderstorm nowcasting with deep learning: a multi-hazard data fusion model" submitted to Geophysical Research Letters. A preprint of the paper will be submitted. The ML code can be found at https://github.com/MeteoSwiss/c4dl-multi. Download all the files here and extract the contents to the following subdirectories in the ML code directory: Results (c4dl-results-lightningdl.zip) -> results/ Pretrained models (c4dl-models-lightningdl.zip) -> models/ Additionally, you will need the datasets from this Zenodo archive. Follow the instructions there for downloading.
The work of JL was supported by the fellowship "Seamless Artificially Intelligent Thunderstorm Nowcasts" from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The hosting institution of this fellowship is MeteoSwiss in Switzerland.
| 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). | 1 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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