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🌊 Public Ocean Database for South Atlantic

OHW22_SA_ocean_db


📝 One line description

Bridge ocean data gaps across the Brazilian coast and make it easily available to the public.

👥 Collaborators

Danilo Augusto Silva - nilodna

Denise Fukai - denisefukai

Douglas M. Nehme - douglasnehme

Filipe Fernandes - ocefpaf

Luiza Paschoal Stein - LuizaPS

Marcela Strane - marcela-strane

Paula Marangoni - paulamarangoni

Tobias Ramalho dos Santos Ferreira - soutobias

Filipe Fernandes - ocefpaf


💁 Background

The South Atlantic is one of the least observed oceans globally. It has several socio-economic impacts for bordering countries, from less accurate daily metocean weather forecasts to problems in seasonal forecasting of food crops, for example. The growth of worldwide ocean observing programs based on autonomus equipment such as profilers and drifters has greatly increased in situ observations in the role ocean basin. But for a comprehensive understanding of the South Atlantic other long-term and multi-parameter data from the entire water column are essential. For Brazilian waters, public data from weather buoys, CTDs, ADCPs and others are still rare and those that exist are tightly dispersed across diferent institutions. This, in practice, means that these data do not exist for the community and do not bring any relevant gain to society.

🎯 Goals

Gather disperse oceaneanographic data for the South Atlantic and make it easily available for the public.

🏆 Relevance

  • Fulfillment:

  • Contribute to:

    • #13, #14, and #15 United Nations Sustainable Development Goals
      • #13: Take urgent action to combat climate change and its impacts
      • #14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development
      • #15: Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss

🔢 Datasets

🔀 Workflow

  • Gather weather buoy data;

  • Apply QC measures to the data;

  • Host the QC data into a SQL database;

  • Connect the SQL database to a API;

  • Make the API public.

🚧 Future Developments

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  • Jupyter Notebook 79.4%
  • Python 19.4%
  • Other 1.2%