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Searvey Improvement #59
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Hello, my name is Flora, and I'm Master’s degree student in physical oceanography in Brazil. I was interested in your proposal because I already worked with numerical models during my graduation, and have some questions about it. |
Hi Flora, thank you for expressing your interest in this project. Searvey is developed with the aim for it to be a light package which serves as a one-stop shop for ocean data retrieval from various sources for the purpose of ocean model validation. The idea is to return the data as close to original data source as possible. We leverage existing APIs provided by data sources (e.g. for COOPS and IOC) or light packages built on top of those APIs (e.g. dataretrieval). So we're not developing web APIs, we're developing a Python API (simple to use functions from a ocean modeler perspective) to use existing data source APIs. The initial focus was on water level data, but we'd like to expand that to include any available variables (such as like currents, water temperature, and salinity) for There is an ongoing effort to also unify the retrieval API between different sources supported by I'd say there are two main goals with this project:
Please feel free to reach out if you have any further questions. |
Hello, my name is Abdu, I am a computer science student in the UK. I am interested in this because of its impact and importance to the environment. |
@abdu558 sure, the more sources we can cover the more useful the tool becomes |
Thank you Soroosh for the quick response. I have submitted a proposal, hope you like it. Thanks, |
Project Description
Searvey is a package designed for on-demand data retrieval from various sources, currently including: - U.S. Center for Operational Oceanographic Products and Services (CO-OPS). - Flanders Marine Institute (VLIZ). - Intergovernmental Oceanographic Commission (IOC). - U.S. Geological Survey (USGS). This project aims to enhance the capabilities of Searvey by incorporating additional observational resources and expanding the range of oceanic variables covered. The objective is to improve our capacity for evaluating numerical models by expanding the available observational data.
Expected Outcomes
1- Extended on-demand data retrieval for water levels from additional sources such as the Canadian Hydrographic Service (CHS) and the Australian Bureau of Meteorology (BOM). 2- Extended on-demand data retrieval to include more variables such as currents, water temperature, and salinity.
Skills required
Python; Libraries: Xarray
Mentor(s)
Soroosh Mani (@SorooshMani-NOAA), Atieh Alipour (@AtiehAlipour-NOAA )
Expected Project Size
175
What is the difficulty of the project?
Intermediate
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