carlos-gg/dl4ds
Deep Learning for empirical DownScaling. Python package with state-of-the-art and novel deep learning algorithms for empirical/statistical downscaling of gridded data
This tool helps climate scientists, meteorologists, and environmental modelers generate detailed, high-resolution environmental maps and forecasts from coarser data. You input low-resolution gridded datasets, like climate models or satellite images, along with optional auxiliary data, and it outputs a refined, high-resolution version. It's designed for professionals who need more granular spatial detail for analysis or decision-making.
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Use this if you need to transform broad-scale environmental data into fine-grained, localized information for research, impact assessment, or localized forecasting.
Not ideal if your primary need is to simply visualize existing gridded data or if you lack historical high-resolution reference data for training.
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99
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Language
Jupyter Notebook
License
Apache-2.0
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Last pushed
Jul 30, 2024
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