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Ocean Clustering

Datasets, algorithms, and code to develop clustering methods for the global ocean

This project aim to create a internally consistent dataset observed and modelled ocean properties to be used when developing and testing geophysical clustering approaches. We will provide both data and code to perform the analysis. The dataset consists of monthly climologies resampled to the same 1/2° grid, both gridded in netcdf format and tabulated as hdf5 and csv files. The tabulated data are also provided subsampled by including every 2nd, 4th or 8th datapoint. These files are much smaller to download and work with.

The effort is done under the auspice of the Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES), which seeks to develop and apply quantitative models of the structure and function of marine microbial communities at seasonal and basin scales.

Included Parameters

  • Chlorophyll (Chl), PAR, Kd490, Euphotic Depth
  • remotely sensed reflectances (Rrs412, Rrs443, Rrs490, Rrs510, Rrs555, Rrs670)
  • Sea Surface Temperature (SST), Mixed Layer Depth (MLD)
  • Wind Speed (wind), Eddy Kinetik Energy (EKE), Bathymetry

Remote Sensing Data

These were downloaded from sources listed below and regridded using ... (?)

Monthly climatologies (v0.2.5 alpha)

Resolution Gridded Pandas/hdf5 Comma separated
1/2° Download cdf Download hdf Download csv
1° Download hdf Download csv
2° Download hdf Download csv
4° Download hdf Download csv

Netcdf files are identical to the 0.2 version but the csv and tab files includes invalid data. Just remove all NaN's if needed. There are many ways to access the file contents -- here are a few examples:

Using Python & csv:

pd.read_hdf("https://rsg.pml.ac.uk/shared_files/brj/CBIOMES_ecoregions/ver_0_2_5/tabulated_geospatial_montly_clim_045_090_ver_0_2_5.csv").dropna(inplace=True)

Using Julia & csv:

using CSV;
CSV.read(download("https://rsg.pml.ac.uk/shared_files/brj/CBIOMES_ecoregions/ver_0_2_5/tabulated_geospatial_montly_clim_045_090_ver_0_2_5.csv"))

Using Julia & nectdf:

path="https://rsg.pml.ac.uk/shared_files/brj/CBIOMES_ecoregions/ver_0_2_5"
file="gridded_geospatial_montly_clim_360_720_ver_0_2.nc"
run(`wget $root/$file`)

Using NCDatasets
Dataset(file)

Data Sources

Variable Source DOI
SST ESA SST-CCI L4 10.5285/62c0f97b1eac4e0197a674870afe1ee6
Chl ESA OC-CCI L4 10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
PAR NASA MODIS L3 10.5067/AQUA/MODIS/L3M/PAR/2018
Kd490 NASA MODIS L3 10.5067/AQUA/MODIS/L3M/KD/2018
euphotic_depth NASA MODIS L3 10.5067/AQUA/MODIS/L3M/ZLEE/2018
mld NOAA PMEL MIMOC 10.1002/jgrc.20122
wind NOAA NESDIS2 10.1029/2006GL027086
EKE OSCAR1 10.5067/OSCAR-03D01
bathymetry NOAA ETOPO1 10.7289/V5C8276M
Rrs412 ESA OC-CCI L4 10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
Rrs443 ESA OC-CCI L4 10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
Rrs490 ESA OC-CCI L4 10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
Rrs510 ESA OC-CCI L4 10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
Rrs555 ESA OC-CCI L4 10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
Rrs670 ESA OC-CCI L4 10.5285/9C334FBE6D424A708CF3C4CF0C6A53F5
--- --- ---

1 Calculated from geostrophic velocities 2 Blended Sea Winds from URL

Model Monthly Climatologies (v0.2.6 alpha)

These were downloaded from the source listed below and regridded using the gcmfaces toolbox plus recipes from OceanColorData.jl (these notebooks) and those included here in DataSet From DARWIN/.

Resolution Gridded Pandas/hdf5 Comma separated
1/2° Download cdf Download hdf Download csv
1° Download hdf Download csv
2° Download hdf Download csv
4° Download hdf Download csv

Rrs values have been fixed.

Source: DOI, URL, Documentation

Standard Classification Estimates

Download cdf

There are still some issues with the hdf and csv files.

Included Parameters: Longhurst, optical_water_classes