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ocean-sciences
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21 search results found
Divand.jl
⭐
57
DIVAnd performs an n-dimensional variational analysis of arbitrarily located observations
Diva
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30
DIVA (Data-Interpolating Variational Analysis) is a software tool dedicated to the spatial interpolation of in situ data in oceanography.
Oceanbiome.jl
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30
🌊 🦠 🌿 A fast and flexible modelling environment written in Julia for modelling the coupled interactions between ocean biogeochemistry, carbonate chemistry, and physics
Python Stratify
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24
Vectorized interpolators for Nd atmospheric and oceanographic data
Glider_toolbox
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24
MATLAB/Octave scripts to manage data collected by a glider fleet, including data download, data processing and product and figure generation, both in real time and delayed time.
Divapythontools
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20
Interface to run Diva software tool (spatial interpolation).
Swot_simulator
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20
SWOT Simulator for Ocean Science
Searvey
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16
Sea state observational data retrieval
Pyosoaa
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15
pyOSOAA is a python interface for the Ocean Successive Orders with Atmosphere - Advanced (OSOAA) radiative transfer.
Docker Erddap
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15
A feature full Tomcat (SSL over APR, etc.) running ERDDAP
Gsw Rs
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14
Unofficial Gibbs Sea Water Oceanographic Toolbox of TEOS-10 in Rust
Diva Workshops
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14
Code, data and instructions for the Diva workshops
Osd Analysis
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13
Repository for all Ocean Sampling Day related source code with information on how-to acquire OSD data
Pyfesom
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9
python library and tools for pre and post processing of the FESOM ocean model files
Documentation
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8
GHER documentation
Gravitymachine Research
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8
Scale-free Vertical Tracking Microscopy (a.k.a Gravity Machine) repo.
Api Python Client
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7
Provides easy access to ONC data in Python
Oceanexplorer
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7
Easy exploration of the NOAA world ocean atlas
Xsarsea
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7
scientific functions to compute radar or geophysical parameters from satellite images over ocean
Uvic2.9
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6
This is the base code of the University of Victoria (UVic) Earth System Climate Model version 2.9 with the Model of Ocean Biogeochemistry and Isotopes (MOBI) version 2.1.
Seager19
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5
Replication of Seager et al. (2019) Nat. Clim. Chan. They used a simple-as-possible coupled model to explain the bias in the nino3.4 trend in climate models (CMIP5). This repository replicates/reproduces their work, shows that it also applies to CMIP6, and varies some of the parameters.
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Data Analysis Ocean Sciences (6)
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Ocean Sciences Diva (4)
Remote Sensing Ocean Sciences (4)
Data Visualization Ocean Sciences (4)
Julia Ocean Sciences (3)
1-21 of 21 search results
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