Gibbs Sea Water Oceanographic Toolbox of TEOS-10 implemented in Rust
Gibbs Sea Water Oceanographic Toolbox of TEOS-10 implemented in Rust - Published in JOSS (2024)
OceanBioME.jl
OceanBioME.jl: A flexible environment for modelling the coupled interactions between ocean biogeochemistry and physics - Published in JOSS (2023)
MITgcm.jl
MITgcm.jl: a Julia Interface to the MITgcm - Published in JOSS (2024)
DIVAnd
DIVAnd performs an n-dimensional variational analysis of arbitrarily located observations
python-stratify
Vectorized interpolators for Nd atmospheric and oceanographic data
seager19
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.
workshop_data_reuse
Data reuse saves time and accelerates the pace of scientific discovery. But how do you actually reuse the data that you put in repositories? This is a 4 hour workshop tailored to the ocean sciences. We aim to teach the basics of data reuse using ERDDAP servers maintained by ocean science specific repo's and programs and pulling these data into the Python environment.
pyosoaa
pyOSOAA is a python interface for the Ocean Successive Orders with Atmosphere - Advanced (OSOAA) radiative transfer.
https://github.com/axiom-data-science/docker-erddap
A feature full Tomcat (SSL over APR, etc.) running ERDDAP