JoanaR/multi-mode-CNN-pytorch
A PyTorch implementation of the Multi-Mode CNN to reconstruct Chlorophyll-a time series in the global ocean from oceanic and atmospheric physical drivers
This project helps oceanographers, marine biologists, and climate scientists accurately estimate Chlorophyll-a (Chl-a) levels in the global ocean over time. By taking various oceanic and atmospheric measurements like sea surface temperature, wind, and currents, it produces a detailed time series of Chl-a concentrations. This allows researchers to track phytoplankton activity, which is crucial for understanding marine ecosystems and climate change.
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Use this if you need to reconstruct missing or create new long-term Chlorophyll-a time series data for marine science applications using physical oceanographic and atmospheric drivers.
Not ideal if your primary need is real-time monitoring of Chlorophyll-a or if you lack historical physical oceanographic and atmospheric data as inputs.
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Jupyter Notebook
License
MIT
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Last pushed
May 18, 2023
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