IO Zarr file

Written by Minh Phan

This tutorial guides you through some of the fundamental functions you may use while exploring the Indian Ocean zarr dataset.

This tutorial utilizes the xarray library. You can also use the Zarr library which is specialized in handling this type of dataset. Xarray is more well-known in the Python developer community, hence the preference. It also works seamlessly with other popular data libraries like Pandas, NumPy, and Dask.

About our product

Our final product INDIAN_OCEAN_025GRID_DAILY.zarr is a blended dataset formated as a .zarr file, containing daily cleaned and interpolated data from fifteen variables across multiple sources, mostly from processed NASA/NOAA and Copernicus collections: * adt: sea surface height above geoid (m) * air_temp: air temperature (K) * CHL: chlorophyll-a concentration (mg/m3) * CHL_uncertainty: chlorophyll-a concentration uncertainty (%) * mlotst: mean ocean mixed layer thickness (m) * sla: sea level anomaly (m) * so: sea salinity concentration (m-3 or PSL) * sst: sea surface temperature (K) * topo: topography (m) * u_curr: u-component of total surface currents (m/s) * v_curr: v-component of total surface currents (m/s) * ug_curr: u-component of geostrophic surface currents (m/s) * vg_curr: v-component of geostrophic surface currents (m/s) * u_wind: u-component of surface wind (m/s) * v_wind: v-component of surface wind (m/s)

We also computed four additional components, namely: * curr_speed: total current speed (m/s) * curr_dir: total current direction (degrees) * wind_speed: surface wind speed (m/s) * wind_dir: surface wind direction (degrees)

All variables have been broadcasted to all fit in the temporal range we have. Therefore, not all variable data are available at any time. Check out each individual variable before use.

Import necessary libraries

! pip install cmocean

Collecting cmocean
  Using cached cmocean-3.0.3-py3-none-any.whl (222 kB)
Requirement already satisfied: matplotlib in /srv/conda/envs/notebook/lib/python3.9/site-packages (from cmocean) (3.7.1)
Requirement already satisfied: numpy in /srv/conda/envs/notebook/lib/python3.9/site-packages (from cmocean) (1.23.5)
Requirement already satisfied: packaging in /srv/conda/envs/notebook/lib/python3.9/site-packages (from cmocean) (23.1)
Requirement already satisfied: contourpy>=1.0.1 in /srv/conda/envs/notebook/lib/python3.9/site-packages (from matplotlib->cmocean) (1.0.7)
Requirement already satisfied: cycler>=0.10 in /srv/conda/envs/notebook/lib/python3.9/site-packages (from matplotlib->cmocean) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in /srv/conda/envs/notebook/lib/python3.9/site-packages (from matplotlib->cmocean) (4.39.4)
Requirement already satisfied: kiwisolver>=1.0.1 in /srv/conda/envs/notebook/lib/python3.9/site-packages (from matplotlib->cmocean) (1.4.4)
Requirement already satisfied: pillow>=6.2.0 in /srv/conda/envs/notebook/lib/python3.9/site-packages (from matplotlib->cmocean) (9.5.0)
Requirement already satisfied: pyparsing>=2.3.1 in /srv/conda/envs/notebook/lib/python3.9/site-packages (from matplotlib->cmocean) (3.0.9)
Requirement already satisfied: python-dateutil>=2.7 in /srv/conda/envs/notebook/lib/python3.9/site-packages (from matplotlib->cmocean) (2.8.2)
Requirement already satisfied: importlib-resources>=3.2.0 in /srv/conda/envs/notebook/lib/python3.9/site-packages (from matplotlib->cmocean) (5.12.0)
Requirement already satisfied: zipp>=3.1.0 in /srv/conda/envs/notebook/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib->cmocean) (3.15.0)
Requirement already satisfied: six>=1.5 in /srv/conda/envs/notebook/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib->cmocean) (1.16.0)
Installing collected packages: cmocean
Successfully installed cmocean-3.0.3

import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt # simple plotting
import holoviews as hv # simple plotting
import hvplot.xarray # simple plotting

Read data

ds = xr.open_zarr('/home/jovyan/shared/data/INDIAN_OCEAN_025GRID_DAILY.zarr/')

ds

<xarray.Dataset>
Dimensions:          (time: 16071, lat: 177, lon: 241)
Coordinates:
  * lat              (lat) float32 32.0 31.75 31.5 31.25 ... -11.5 -11.75 -12.0
  * lon              (lon) float32 42.0 42.25 42.5 42.75 ... 101.5 101.8 102.0
  * time             (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2022-12-31
Data variables: (12/19)
    CHL              (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    CHL_uncertainty  (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    adt              (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    air_temp         (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    curr_dir         (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    curr_speed       (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    ...               ...
    ug_curr          (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    v_curr           (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    v_wind           (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    vg_curr          (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    wind_dir         (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    wind_speed       (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
Attributes: (12/17)
    creator_email:              minhphan@uw.edu
    creator_name:               Minh Phan
    creator_type:               person
    date_created:               2023-07-19
    geospatial_lat_max:         32.0
    geospatial_lat_min:         -12.0
    ...                         ...
    geospatial_lon_units:       degrees_east
    source:                     Earth & Space Research (ESR), Copernicus Clim...
    summary:                    Daily mean of 0.25 x 0.25 degrees gridded dat...
    time_coverage_end:          2022-12-31T23:59:59
    time_coverage_start:        1979-01-01T00:00:00
    title:                      Climate Data for Coastal Upwelling Machine Le...

xarray.Dataset

• Dimensions:
  • time: 16071
  • lat: 177
  • lon: 241
• Coordinates: (3)
  • lat
    (lat)
    float32
    32.0 31.75 31.5 ... -11.75 -12.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([ 32.  ,  31.75,  31.5 ,  31.25,  31.  ,  30.75,  30.5 ,  30.25,  30.  ,
            29.75,  29.5 ,  29.25,  29.  ,  28.75,  28.5 ,  28.25,  28.  ,  27.75,
            27.5 ,  27.25,  27.  ,  26.75,  26.5 ,  26.25,  26.  ,  25.75,  25.5 ,
            25.25,  25.  ,  24.75,  24.5 ,  24.25,  24.  ,  23.75,  23.5 ,  23.25,
            23.  ,  22.75,  22.5 ,  22.25,  22.  ,  21.75,  21.5 ,  21.25,  21.  ,
            20.75,  20.5 ,  20.25,  20.  ,  19.75,  19.5 ,  19.25,  19.  ,  18.75,
            18.5 ,  18.25,  18.  ,  17.75,  17.5 ,  17.25,  17.  ,  16.75,  16.5 ,
            16.25,  16.  ,  15.75,  15.5 ,  15.25,  15.  ,  14.75,  14.5 ,  14.25,
            14.  ,  13.75,  13.5 ,  13.25,  13.  ,  12.75,  12.5 ,  12.25,  12.  ,
            11.75,  11.5 ,  11.25,  11.  ,  10.75,  10.5 ,  10.25,  10.  ,   9.75,
             9.5 ,   9.25,   9.  ,   8.75,   8.5 ,   8.25,   8.  ,   7.75,   7.5 ,
             7.25,   7.  ,   6.75,   6.5 ,   6.25,   6.  ,   5.75,   5.5 ,   5.25,
             5.  ,   4.75,   4.5 ,   4.25,   4.  ,   3.75,   3.5 ,   3.25,   3.  ,
             2.75,   2.5 ,   2.25,   2.  ,   1.75,   1.5 ,   1.25,   1.  ,   0.75,
             0.5 ,   0.25,   0.  ,  -0.25,  -0.5 ,  -0.75,  -1.  ,  -1.25,  -1.5 ,
            -1.75,  -2.  ,  -2.25,  -2.5 ,  -2.75,  -3.  ,  -3.25,  -3.5 ,  -3.75,
            -4.  ,  -4.25,  -4.5 ,  -4.75,  -5.  ,  -5.25,  -5.5 ,  -5.75,  -6.  ,
            -6.25,  -6.5 ,  -6.75,  -7.  ,  -7.25,  -7.5 ,  -7.75,  -8.  ,  -8.25,
            -8.5 ,  -8.75,  -9.  ,  -9.25,  -9.5 ,  -9.75, -10.  , -10.25, -10.5 ,
           -10.75, -11.  , -11.25, -11.5 , -11.75, -12.  ], dtype=float32)

  • lon
    (lon)
    float32
    42.0 42.25 42.5 ... 101.8 102.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([ 42.  ,  42.25,  42.5 , ..., 101.5 , 101.75, 102.  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1979-01-01 ... 2022-12-31

    axis :

    T

    comment :

    Data is averaged over the day

    long_name :

    time centered on the day

    standard_name :

    time

    time_bounds :

    2000-01-01 00:00:00 to 2000-01-01 23:59:59

    array(['1979-01-01T00:00:00.000000000', '1979-01-02T00:00:00.000000000',
           '1979-01-03T00:00:00.000000000', ..., '2022-12-29T00:00:00.000000000',
           '2022-12-30T00:00:00.000000000', '2022-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (19)
  • CHL
    (time, lat, lon)
    float32
    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    _ChunkSizes :

    [1, 256, 256]

    ancillary_variables :

    flags CHL_uncertainty

    coverage_content_type :

    modelResult

    input_files_reprocessings :

    Processors versions: MODIS R2022.0NRT/VIIRSN R2022.0NRT/OLCIA 07.02/VIIRSJ1 R2022.0NRT/OLCIB 07.02

    long_name :

    Chlorophyll-a concentration - Mean of the binned pixels

    standard_name :

    mass_concentration_of_chlorophyll_a_in_sea_water

    type :

    surface

    units :

    milligram m-3

    valid_max :

    1000.0

    valid_min :

    0.0

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    CHL_uncertainty

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 256, 256]

    coverage_content_type :

    qualityInformation

    long_name :

    Chlorophyll-a concentration - Uncertainty estimation

    units :

    %

    valid_max :

    32767

    valid_min :

    0

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    adt

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    comment :

    The absolute dynamic topography is the sea surface height above geoid; the adt is obtained as follows: adt=sla+mdt where mdt is the mean dynamic topography; see the product user manual for details

    grid_mapping :

    crs

    long_name :

    Absolute dynamic topography

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    air_temp

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    long_name :

    2 metre temperature

    nameCDM :

    2_metre_temperature_surface

    nameECMWF :

    2 metre temperature

    product_type :

    analysis

    shortNameECMWF :

    2t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    curr_dir

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    comments :

    Computed from total surface current velocity elements. Velocities are an average over the top 30m of the mixed layer

    depth :

    15m

    long_name :

    average direction of total surface currents

    units :

    degrees

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    curr_speed

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    comments :

    Velocities are an average over the top 30m of the mixed layer

    depth :

    15m

    long_name :

    average total surface current speed

    units :

    m s**-1

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    mlotst

    (time, lat, lon)

    float32

    dask.array<chunksize=(500, 177, 241), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 681, 1440]

    cell_methods :

    area: mean

    long_name :

    Density ocean mixed layer thickness

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_theta

    unit_long :

    Meters

    units :

    m

    Array Chunk Bytes 2.55 GiB 81.36 MiB Shape (16071, 177, 241) (500, 177, 241) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    sla

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    ancillary_variables :

    err_sla

    comment :

    The sea level anomaly is the sea surface height above mean sea surface; it is referenced to the [1993, 2012] period; see the product user manual for details

    grid_mapping :

    crs

    long_name :

    Sea level anomaly

    standard_name :

    sea_surface_height_above_sea_level

    units :

    m

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    so

    (time, lat, lon)

    float32

    dask.array<chunksize=(500, 177, 241), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 7, 341, 720]

    cell_methods :

    area: mean

    long_name :

    mean sea water salinity at 0.49 metres below ocean surface

    standard_name :

    sea_water_salinity

    unit_long :

    Practical Salinity Unit

    units :

    1e-3

    valid_max :

    28336

    valid_min :

    1

    Array Chunk Bytes 2.55 GiB 81.36 MiB Shape (16071, 177, 241) (500, 177, 241) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    sst

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    long_name :

    Sea surface temperature

    nameCDM :

    Sea_surface_temperature_surface

    nameECMWF :

    Sea surface temperature

    product_type :

    analysis

    shortNameECMWF :

    sst

    standard_name :

    sea_surface_temperature

    units :

    K

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    topo

    (lat, lon)

    float64

    dask.array<chunksize=(177, 241), meta=np.ndarray>

    

    colorBarMaximum :

    8000.0

    colorBarMinimum :

    -8000.0

    colorBarPalette :

    Topography

    grid_mapping :

    GDAL_Geographics

    ioos_category :

    Location

    long_name :

    Topography

    standard_name :

    altitude

    units :

    meters

    Array Chunk Bytes 333.26 kiB 333.26 kiB Shape (177, 241) (177, 241) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    u_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    comment :

    Velocities are an average over the top 30m of the mixed layer

    coverage_content_type :

    modelResult

    depth :

    15m

    long_name :

    zonal total surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148 ; WIND source: ECMWF ERA5 10m wind DOI: 10.24381/cds.adbb2d47 ; SST source: CMC 0.2 deg SST V2.0 DOI: 10.5067/GHCMC-4FM02

    standard_name :

    eastward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    u_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    long_name :

    10 metre U wind component

    nameCDM :

    10_metre_U_wind_component_surface

    nameECMWF :

    10 metre U wind component

    product_type :

    analysis

    shortNameECMWF :

    10u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    ug_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    comment :

    Geostrophic velocities calculated from absolute dynamic topography

    depth :

    15m

    long_name :

    zonal geostrophic surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148

    standard_name :

    geostrophic_eastward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    v_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    comment :

    Velocities are an average over the top 30m of the mixed layer

    coverage_content_type :

    modelResult

    depth :

    15m

    long_name :

    meridional total surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148 ; WIND source: ECMWF ERA5 10m wind DOI: 10.24381/cds.adbb2d47 ; SST source: CMC 0.2 deg SST V2.0 DOI: 10.5067/GHCMC-4FM02

    standard_name :

    northward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    v_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    long_name :

    10 metre V wind component

    nameCDM :

    10_metre_V_wind_component_surface

    nameECMWF :

    10 metre V wind component

    product_type :

    analysis

    shortNameECMWF :

    10v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    vg_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    comment :

    Geostrophic velocities calculated from absolute dynamic topography

    depth :

    15m

    long_name :

    meridional geostrophic surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148

    standard_name :

    geostrophic_northward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    wind_dir

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    long_name :

    10 metre wind direction

    units :

    degrees

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    wind_speed

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    long_name :

    10 metre absolute speed

    units :

    m s**-1

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([  32.0,  31.75,   31.5,  31.25,   31.0,  30.75,   30.5,  30.25,   30.0,
            29.75,
           ...
            -9.75,  -10.0, -10.25,  -10.5, -10.75,  -11.0, -11.25,  -11.5, -11.75,
            -12.0],
          dtype='float32', name='lat', length=177))

  • lon
    PandasIndex

    PandasIndex(Index([  42.0,  42.25,   42.5,  42.75,   43.0,  43.25,   43.5,  43.75,   44.0,
            44.25,
           ...
            99.75,  100.0, 100.25,  100.5, 100.75,  101.0, 101.25,  101.5, 101.75,
            102.0],
          dtype='float32', name='lon', length=241))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-01', '1979-01-02', '1979-01-03', '1979-01-04',
                   '1979-01-05', '1979-01-06', '1979-01-07', '1979-01-08',
                   '1979-01-09', '1979-01-10',
                   ...
                   '2022-12-22', '2022-12-23', '2022-12-24', '2022-12-25',
                   '2022-12-26', '2022-12-27', '2022-12-28', '2022-12-29',
                   '2022-12-30', '2022-12-31'],
                  dtype='datetime64[ns]', name='time', length=16071, freq=None))

• Attributes: (17)

  creator_email :

  minhphan@uw.edu

  creator_name :

  Minh Phan

  creator_type :

  person

  date_created :

  2023-07-19

  geospatial_lat_max :

  32.0

  geospatial_lat_min :

  -12.0

  geospatial_lat_resolution :

  0.25

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  102.0

  geospatial_lon_min :

  42.0

  geospatial_lon_resolution :

  0.25

  geospatial_lon_units :

  degrees_east

  source :

  Earth & Space Research (ESR), Copernicus Climate Change Service (C3S), Copernicus Marine Environment Monitoring Service (CMEMS), United States Geological Survey (USGS)

  summary :

  Daily mean of 0.25 x 0.25 degrees gridded data from multiple climate variables that may influence the patterns of coastal upwelling in the focused area

  time_coverage_end :

  2022-12-31T23:59:59

  time_coverage_start :

  1979-01-01T00:00:00

  title :

  Climate Data for Coastal Upwelling Machine Learning Project in Indian Ocean

We can slice data by the dimensions (latitude, longitude, time) and data variables.

# slice by latitude
# notice how we specify the range in reverse
ds.sel(lat=slice(0, -12))

<xarray.Dataset>
Dimensions:          (time: 16071, lat: 49, lon: 241)
Coordinates:
  * lat              (lat) float32 0.0 -0.25 -0.5 -0.75 ... -11.5 -11.75 -12.0
  * lon              (lon) float32 42.0 42.25 42.5 42.75 ... 101.5 101.8 102.0
  * time             (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2022-12-31
Data variables: (12/19)
    CHL              (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
    CHL_uncertainty  (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
    adt              (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
    air_temp         (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
    curr_dir         (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
    curr_speed       (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
    ...               ...
    ug_curr          (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
    v_curr           (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
    v_wind           (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
    vg_curr          (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
    wind_dir         (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
    wind_speed       (time, lat, lon) float32 dask.array<chunksize=(100, 49, 241), meta=np.ndarray>
Attributes: (12/17)
    creator_email:              minhphan@uw.edu
    creator_name:               Minh Phan
    creator_type:               person
    date_created:               2023-07-19
    geospatial_lat_max:         32.0
    geospatial_lat_min:         -12.0
    ...                         ...
    geospatial_lon_units:       degrees_east
    source:                     Earth & Space Research (ESR), Copernicus Clim...
    summary:                    Daily mean of 0.25 x 0.25 degrees gridded dat...
    time_coverage_end:          2022-12-31T23:59:59
    time_coverage_start:        1979-01-01T00:00:00
    title:                      Climate Data for Coastal Upwelling Machine Le...

xarray.Dataset

• Dimensions:
  • time: 16071
  • lat: 49
  • lon: 241
• Coordinates: (3)
  • lat
    (lat)
    float32
    0.0 -0.25 -0.5 ... -11.75 -12.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([  0.  ,  -0.25,  -0.5 ,  -0.75,  -1.  ,  -1.25,  -1.5 ,  -1.75,  -2.  ,
            -2.25,  -2.5 ,  -2.75,  -3.  ,  -3.25,  -3.5 ,  -3.75,  -4.  ,  -4.25,
            -4.5 ,  -4.75,  -5.  ,  -5.25,  -5.5 ,  -5.75,  -6.  ,  -6.25,  -6.5 ,
            -6.75,  -7.  ,  -7.25,  -7.5 ,  -7.75,  -8.  ,  -8.25,  -8.5 ,  -8.75,
            -9.  ,  -9.25,  -9.5 ,  -9.75, -10.  , -10.25, -10.5 , -10.75, -11.  ,
           -11.25, -11.5 , -11.75, -12.  ], dtype=float32)

  • lon
    (lon)
    float32
    42.0 42.25 42.5 ... 101.8 102.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([ 42.  ,  42.25,  42.5 , ..., 101.5 , 101.75, 102.  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1979-01-01 ... 2022-12-31

    axis :

    T

    comment :

    Data is averaged over the day

    long_name :

    time centered on the day

    standard_name :

    time

    time_bounds :

    2000-01-01 00:00:00 to 2000-01-01 23:59:59

    array(['1979-01-01T00:00:00.000000000', '1979-01-02T00:00:00.000000000',
           '1979-01-03T00:00:00.000000000', ..., '2022-12-29T00:00:00.000000000',
           '2022-12-30T00:00:00.000000000', '2022-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (19)
  • CHL
    (time, lat, lon)
    float32
    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    _ChunkSizes :

    [1, 256, 256]

    ancillary_variables :

    flags CHL_uncertainty

    coverage_content_type :

    modelResult

    input_files_reprocessings :

    Processors versions: MODIS R2022.0NRT/VIIRSN R2022.0NRT/OLCIA 07.02/VIIRSJ1 R2022.0NRT/OLCIB 07.02

    long_name :

    Chlorophyll-a concentration - Mean of the binned pixels

    standard_name :

    mass_concentration_of_chlorophyll_a_in_sea_water

    type :

    surface

    units :

    milligram m-3

    valid_max :

    1000.0

    valid_min :

    0.0

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    CHL_uncertainty

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 256, 256]

    coverage_content_type :

    qualityInformation

    long_name :

    Chlorophyll-a concentration - Uncertainty estimation

    units :

    %

    valid_max :

    32767

    valid_min :

    0

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    adt

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    comment :

    The absolute dynamic topography is the sea surface height above geoid; the adt is obtained as follows: adt=sla+mdt where mdt is the mean dynamic topography; see the product user manual for details

    grid_mapping :

    crs

    long_name :

    Absolute dynamic topography

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    air_temp

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    long_name :

    2 metre temperature

    nameCDM :

    2_metre_temperature_surface

    nameECMWF :

    2 metre temperature

    product_type :

    analysis

    shortNameECMWF :

    2t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    curr_dir

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    comments :

    Computed from total surface current velocity elements. Velocities are an average over the top 30m of the mixed layer

    depth :

    15m

    long_name :

    average direction of total surface currents

    units :

    degrees

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    curr_speed

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    comments :

    Velocities are an average over the top 30m of the mixed layer

    depth :

    15m

    long_name :

    average total surface current speed

    units :

    m s**-1

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    mlotst

    (time, lat, lon)

    float32

    dask.array<chunksize=(500, 49, 241), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 681, 1440]

    cell_methods :

    area: mean

    long_name :

    Density ocean mixed layer thickness

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_theta

    unit_long :

    Meters

    units :

    m

    Array Chunk Bytes 723.96 MiB 22.52 MiB Shape (16071, 49, 241) (500, 49, 241) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

    sla

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    ancillary_variables :

    err_sla

    comment :

    The sea level anomaly is the sea surface height above mean sea surface; it is referenced to the [1993, 2012] period; see the product user manual for details

    grid_mapping :

    crs

    long_name :

    Sea level anomaly

    standard_name :

    sea_surface_height_above_sea_level

    units :

    m

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    so

    (time, lat, lon)

    float32

    dask.array<chunksize=(500, 49, 241), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 7, 341, 720]

    cell_methods :

    area: mean

    long_name :

    mean sea water salinity at 0.49 metres below ocean surface

    standard_name :

    sea_water_salinity

    unit_long :

    Practical Salinity Unit

    units :

    1e-3

    valid_max :

    28336

    valid_min :

    1

    Array Chunk Bytes 723.96 MiB 22.52 MiB Shape (16071, 49, 241) (500, 49, 241) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

    sst

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    long_name :

    Sea surface temperature

    nameCDM :

    Sea_surface_temperature_surface

    nameECMWF :

    Sea surface temperature

    product_type :

    analysis

    shortNameECMWF :

    sst

    standard_name :

    sea_surface_temperature

    units :

    K

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    topo

    (lat, lon)

    float64

    dask.array<chunksize=(49, 241), meta=np.ndarray>

    

    colorBarMaximum :

    8000.0

    colorBarMinimum :

    -8000.0

    colorBarPalette :

    Topography

    grid_mapping :

    GDAL_Geographics

    ioos_category :

    Location

    long_name :

    Topography

    standard_name :

    altitude

    units :

    meters

    Array Chunk Bytes 92.26 kiB 92.26 kiB Shape (49, 241) (49, 241) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

    u_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    comment :

    Velocities are an average over the top 30m of the mixed layer

    coverage_content_type :

    modelResult

    depth :

    15m

    long_name :

    zonal total surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148 ; WIND source: ECMWF ERA5 10m wind DOI: 10.24381/cds.adbb2d47 ; SST source: CMC 0.2 deg SST V2.0 DOI: 10.5067/GHCMC-4FM02

    standard_name :

    eastward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    u_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    long_name :

    10 metre U wind component

    nameCDM :

    10_metre_U_wind_component_surface

    nameECMWF :

    10 metre U wind component

    product_type :

    analysis

    shortNameECMWF :

    10u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    ug_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    comment :

    Geostrophic velocities calculated from absolute dynamic topography

    depth :

    15m

    long_name :

    zonal geostrophic surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148

    standard_name :

    geostrophic_eastward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    v_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    comment :

    Velocities are an average over the top 30m of the mixed layer

    coverage_content_type :

    modelResult

    depth :

    15m

    long_name :

    meridional total surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148 ; WIND source: ECMWF ERA5 10m wind DOI: 10.24381/cds.adbb2d47 ; SST source: CMC 0.2 deg SST V2.0 DOI: 10.5067/GHCMC-4FM02

    standard_name :

    northward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    v_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    long_name :

    10 metre V wind component

    nameCDM :

    10_metre_V_wind_component_surface

    nameECMWF :

    10 metre V wind component

    product_type :

    analysis

    shortNameECMWF :

    10v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    vg_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    comment :

    Geostrophic velocities calculated from absolute dynamic topography

    depth :

    15m

    long_name :

    meridional geostrophic surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148

    standard_name :

    geostrophic_northward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    wind_dir

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    long_name :

    10 metre wind direction

    units :

    degrees

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    wind_speed

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 49, 241), meta=np.ndarray>

    

    long_name :

    10 metre absolute speed

    units :

    m s**-1

    Array Chunk Bytes 723.96 MiB 4.50 MiB Shape (16071, 49, 241) (100, 49, 241) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([   0.0,  -0.25,   -0.5,  -0.75,   -1.0,  -1.25,   -1.5,  -1.75,   -2.0,
            -2.25,   -2.5,  -2.75,   -3.0,  -3.25,   -3.5,  -3.75,   -4.0,  -4.25,
             -4.5,  -4.75,   -5.0,  -5.25,   -5.5,  -5.75,   -6.0,  -6.25,   -6.5,
            -6.75,   -7.0,  -7.25,   -7.5,  -7.75,   -8.0,  -8.25,   -8.5,  -8.75,
             -9.0,  -9.25,   -9.5,  -9.75,  -10.0, -10.25,  -10.5, -10.75,  -11.0,
           -11.25,  -11.5, -11.75,  -12.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  42.0,  42.25,   42.5,  42.75,   43.0,  43.25,   43.5,  43.75,   44.0,
            44.25,
           ...
            99.75,  100.0, 100.25,  100.5, 100.75,  101.0, 101.25,  101.5, 101.75,
            102.0],
          dtype='float32', name='lon', length=241))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-01', '1979-01-02', '1979-01-03', '1979-01-04',
                   '1979-01-05', '1979-01-06', '1979-01-07', '1979-01-08',
                   '1979-01-09', '1979-01-10',
                   ...
                   '2022-12-22', '2022-12-23', '2022-12-24', '2022-12-25',
                   '2022-12-26', '2022-12-27', '2022-12-28', '2022-12-29',
                   '2022-12-30', '2022-12-31'],
                  dtype='datetime64[ns]', name='time', length=16071, freq=None))

• Attributes: (17)

  creator_email :

  minhphan@uw.edu

  creator_name :

  Minh Phan

  creator_type :

  person

  date_created :

  2023-07-19

  geospatial_lat_max :

  32.0

  geospatial_lat_min :

  -12.0

  geospatial_lat_resolution :

  0.25

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  102.0

  geospatial_lon_min :

  42.0

  geospatial_lon_resolution :

  0.25

  geospatial_lon_units :

  degrees_east

  source :

  Earth & Space Research (ESR), Copernicus Climate Change Service (C3S), Copernicus Marine Environment Monitoring Service (CMEMS), United States Geological Survey (USGS)

  summary :

  Daily mean of 0.25 x 0.25 degrees gridded data from multiple climate variables that may influence the patterns of coastal upwelling in the focused area

  time_coverage_end :

  2022-12-31T23:59:59

  time_coverage_start :

  1979-01-01T00:00:00

  title :

  Climate Data for Coastal Upwelling Machine Learning Project in Indian Ocean

# slice by longitude
ds.sel(lon=slice(42, 45))

<xarray.Dataset>
Dimensions:          (time: 16071, lat: 177, lon: 13)
Coordinates:
  * lat              (lat) float32 32.0 31.75 31.5 31.25 ... -11.5 -11.75 -12.0
  * lon              (lon) float32 42.0 42.25 42.5 42.75 ... 44.5 44.75 45.0
  * time             (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2022-12-31
Data variables: (12/19)
    CHL              (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
    CHL_uncertainty  (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
    adt              (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
    air_temp         (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
    curr_dir         (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
    curr_speed       (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
    ...               ...
    ug_curr          (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
    v_curr           (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
    v_wind           (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
    vg_curr          (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
    wind_dir         (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
    wind_speed       (time, lat, lon) float32 dask.array<chunksize=(100, 177, 13), meta=np.ndarray>
Attributes: (12/17)
    creator_email:              minhphan@uw.edu
    creator_name:               Minh Phan
    creator_type:               person
    date_created:               2023-07-19
    geospatial_lat_max:         32.0
    geospatial_lat_min:         -12.0
    ...                         ...
    geospatial_lon_units:       degrees_east
    source:                     Earth & Space Research (ESR), Copernicus Clim...
    summary:                    Daily mean of 0.25 x 0.25 degrees gridded dat...
    time_coverage_end:          2022-12-31T23:59:59
    time_coverage_start:        1979-01-01T00:00:00
    title:                      Climate Data for Coastal Upwelling Machine Le...

xarray.Dataset

• Dimensions:
  • time: 16071
  • lat: 177
  • lon: 13
• Coordinates: (3)
  • lat
    (lat)
    float32
    32.0 31.75 31.5 ... -11.75 -12.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([ 32.  ,  31.75,  31.5 ,  31.25,  31.  ,  30.75,  30.5 ,  30.25,  30.  ,
            29.75,  29.5 ,  29.25,  29.  ,  28.75,  28.5 ,  28.25,  28.  ,  27.75,
            27.5 ,  27.25,  27.  ,  26.75,  26.5 ,  26.25,  26.  ,  25.75,  25.5 ,
            25.25,  25.  ,  24.75,  24.5 ,  24.25,  24.  ,  23.75,  23.5 ,  23.25,
            23.  ,  22.75,  22.5 ,  22.25,  22.  ,  21.75,  21.5 ,  21.25,  21.  ,
            20.75,  20.5 ,  20.25,  20.  ,  19.75,  19.5 ,  19.25,  19.  ,  18.75,
            18.5 ,  18.25,  18.  ,  17.75,  17.5 ,  17.25,  17.  ,  16.75,  16.5 ,
            16.25,  16.  ,  15.75,  15.5 ,  15.25,  15.  ,  14.75,  14.5 ,  14.25,
            14.  ,  13.75,  13.5 ,  13.25,  13.  ,  12.75,  12.5 ,  12.25,  12.  ,
            11.75,  11.5 ,  11.25,  11.  ,  10.75,  10.5 ,  10.25,  10.  ,   9.75,
             9.5 ,   9.25,   9.  ,   8.75,   8.5 ,   8.25,   8.  ,   7.75,   7.5 ,
             7.25,   7.  ,   6.75,   6.5 ,   6.25,   6.  ,   5.75,   5.5 ,   5.25,
             5.  ,   4.75,   4.5 ,   4.25,   4.  ,   3.75,   3.5 ,   3.25,   3.  ,
             2.75,   2.5 ,   2.25,   2.  ,   1.75,   1.5 ,   1.25,   1.  ,   0.75,
             0.5 ,   0.25,   0.  ,  -0.25,  -0.5 ,  -0.75,  -1.  ,  -1.25,  -1.5 ,
            -1.75,  -2.  ,  -2.25,  -2.5 ,  -2.75,  -3.  ,  -3.25,  -3.5 ,  -3.75,
            -4.  ,  -4.25,  -4.5 ,  -4.75,  -5.  ,  -5.25,  -5.5 ,  -5.75,  -6.  ,
            -6.25,  -6.5 ,  -6.75,  -7.  ,  -7.25,  -7.5 ,  -7.75,  -8.  ,  -8.25,
            -8.5 ,  -8.75,  -9.  ,  -9.25,  -9.5 ,  -9.75, -10.  , -10.25, -10.5 ,
           -10.75, -11.  , -11.25, -11.5 , -11.75, -12.  ], dtype=float32)

  • lon
    (lon)
    float32
    42.0 42.25 42.5 ... 44.5 44.75 45.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([42.  , 42.25, 42.5 , 42.75, 43.  , 43.25, 43.5 , 43.75, 44.  , 44.25,
           44.5 , 44.75, 45.  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1979-01-01 ... 2022-12-31

    axis :

    T

    comment :

    Data is averaged over the day

    long_name :

    time centered on the day

    standard_name :

    time

    time_bounds :

    2000-01-01 00:00:00 to 2000-01-01 23:59:59

    array(['1979-01-01T00:00:00.000000000', '1979-01-02T00:00:00.000000000',
           '1979-01-03T00:00:00.000000000', ..., '2022-12-29T00:00:00.000000000',
           '2022-12-30T00:00:00.000000000', '2022-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (19)
  • CHL
    (time, lat, lon)
    float32
    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    _ChunkSizes :

    [1, 256, 256]

    ancillary_variables :

    flags CHL_uncertainty

    coverage_content_type :

    modelResult

    input_files_reprocessings :

    Processors versions: MODIS R2022.0NRT/VIIRSN R2022.0NRT/OLCIA 07.02/VIIRSJ1 R2022.0NRT/OLCIB 07.02

    long_name :

    Chlorophyll-a concentration - Mean of the binned pixels

    standard_name :

    mass_concentration_of_chlorophyll_a_in_sea_water

    type :

    surface

    units :

    milligram m-3

    valid_max :

    1000.0

    valid_min :

    0.0

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    CHL_uncertainty

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 256, 256]

    coverage_content_type :

    qualityInformation

    long_name :

    Chlorophyll-a concentration - Uncertainty estimation

    units :

    %

    valid_max :

    32767

    valid_min :

    0

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    adt

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    comment :

    The absolute dynamic topography is the sea surface height above geoid; the adt is obtained as follows: adt=sla+mdt where mdt is the mean dynamic topography; see the product user manual for details

    grid_mapping :

    crs

    long_name :

    Absolute dynamic topography

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    air_temp

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    long_name :

    2 metre temperature

    nameCDM :

    2_metre_temperature_surface

    nameECMWF :

    2 metre temperature

    product_type :

    analysis

    shortNameECMWF :

    2t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    curr_dir

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    comments :

    Computed from total surface current velocity elements. Velocities are an average over the top 30m of the mixed layer

    depth :

    15m

    long_name :

    average direction of total surface currents

    units :

    degrees

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    curr_speed

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    comments :

    Velocities are an average over the top 30m of the mixed layer

    depth :

    15m

    long_name :

    average total surface current speed

    units :

    m s**-1

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    mlotst

    (time, lat, lon)

    float32

    dask.array<chunksize=(500, 177, 13), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 681, 1440]

    cell_methods :

    area: mean

    long_name :

    Density ocean mixed layer thickness

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_theta

    unit_long :

    Meters

    units :

    m

    Array Chunk Bytes 141.07 MiB 4.39 MiB Shape (16071, 177, 13) (500, 177, 13) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

    sla

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    ancillary_variables :

    err_sla

    comment :

    The sea level anomaly is the sea surface height above mean sea surface; it is referenced to the [1993, 2012] period; see the product user manual for details

    grid_mapping :

    crs

    long_name :

    Sea level anomaly

    standard_name :

    sea_surface_height_above_sea_level

    units :

    m

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    so

    (time, lat, lon)

    float32

    dask.array<chunksize=(500, 177, 13), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 7, 341, 720]

    cell_methods :

    area: mean

    long_name :

    mean sea water salinity at 0.49 metres below ocean surface

    standard_name :

    sea_water_salinity

    unit_long :

    Practical Salinity Unit

    units :

    1e-3

    valid_max :

    28336

    valid_min :

    1

    Array Chunk Bytes 141.07 MiB 4.39 MiB Shape (16071, 177, 13) (500, 177, 13) Dask graph 33 chunks in 3 graph layers Data type float32 numpy.ndarray

    sst

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    long_name :

    Sea surface temperature

    nameCDM :

    Sea_surface_temperature_surface

    nameECMWF :

    Sea surface temperature

    product_type :

    analysis

    shortNameECMWF :

    sst

    standard_name :

    sea_surface_temperature

    units :

    K

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    topo

    (lat, lon)

    float64

    dask.array<chunksize=(177, 13), meta=np.ndarray>

    

    colorBarMaximum :

    8000.0

    colorBarMinimum :

    -8000.0

    colorBarPalette :

    Topography

    grid_mapping :

    GDAL_Geographics

    ioos_category :

    Location

    long_name :

    Topography

    standard_name :

    altitude

    units :

    meters

    Array Chunk Bytes 17.98 kiB 17.98 kiB Shape (177, 13) (177, 13) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

    u_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    comment :

    Velocities are an average over the top 30m of the mixed layer

    coverage_content_type :

    modelResult

    depth :

    15m

    long_name :

    zonal total surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148 ; WIND source: ECMWF ERA5 10m wind DOI: 10.24381/cds.adbb2d47 ; SST source: CMC 0.2 deg SST V2.0 DOI: 10.5067/GHCMC-4FM02

    standard_name :

    eastward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    u_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    long_name :

    10 metre U wind component

    nameCDM :

    10_metre_U_wind_component_surface

    nameECMWF :

    10 metre U wind component

    product_type :

    analysis

    shortNameECMWF :

    10u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    ug_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    comment :

    Geostrophic velocities calculated from absolute dynamic topography

    depth :

    15m

    long_name :

    zonal geostrophic surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148

    standard_name :

    geostrophic_eastward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    v_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    comment :

    Velocities are an average over the top 30m of the mixed layer

    coverage_content_type :

    modelResult

    depth :

    15m

    long_name :

    meridional total surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148 ; WIND source: ECMWF ERA5 10m wind DOI: 10.24381/cds.adbb2d47 ; SST source: CMC 0.2 deg SST V2.0 DOI: 10.5067/GHCMC-4FM02

    standard_name :

    northward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    v_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    long_name :

    10 metre V wind component

    nameCDM :

    10_metre_V_wind_component_surface

    nameECMWF :

    10 metre V wind component

    product_type :

    analysis

    shortNameECMWF :

    10v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    vg_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    comment :

    Geostrophic velocities calculated from absolute dynamic topography

    depth :

    15m

    long_name :

    meridional geostrophic surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148

    standard_name :

    geostrophic_northward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    wind_dir

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    long_name :

    10 metre wind direction

    units :

    degrees

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

    wind_speed

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 13), meta=np.ndarray>

    

    long_name :

    10 metre absolute speed

    units :

    m s**-1

    Array Chunk Bytes 141.07 MiB 898.83 kiB Shape (16071, 177, 13) (100, 177, 13) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([  32.0,  31.75,   31.5,  31.25,   31.0,  30.75,   30.5,  30.25,   30.0,
            29.75,
           ...
            -9.75,  -10.0, -10.25,  -10.5, -10.75,  -11.0, -11.25,  -11.5, -11.75,
            -12.0],
          dtype='float32', name='lat', length=177))

  • lon
    PandasIndex

    PandasIndex(Index([ 42.0, 42.25,  42.5, 42.75,  43.0, 43.25,  43.5, 43.75,  44.0, 44.25,
            44.5, 44.75,  45.0],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-01', '1979-01-02', '1979-01-03', '1979-01-04',
                   '1979-01-05', '1979-01-06', '1979-01-07', '1979-01-08',
                   '1979-01-09', '1979-01-10',
                   ...
                   '2022-12-22', '2022-12-23', '2022-12-24', '2022-12-25',
                   '2022-12-26', '2022-12-27', '2022-12-28', '2022-12-29',
                   '2022-12-30', '2022-12-31'],
                  dtype='datetime64[ns]', name='time', length=16071, freq=None))

• Attributes: (17)

  creator_email :

  minhphan@uw.edu

  creator_name :

  Minh Phan

  creator_type :

  person

  date_created :

  2023-07-19

  geospatial_lat_max :

  32.0

  geospatial_lat_min :

  -12.0

  geospatial_lat_resolution :

  0.25

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  102.0

  geospatial_lon_min :

  42.0

  geospatial_lon_resolution :

  0.25

  geospatial_lon_units :

  degrees_east

  source :

  Earth & Space Research (ESR), Copernicus Climate Change Service (C3S), Copernicus Marine Environment Monitoring Service (CMEMS), United States Geological Survey (USGS)

  summary :

  Daily mean of 0.25 x 0.25 degrees gridded data from multiple climate variables that may influence the patterns of coastal upwelling in the focused area

  time_coverage_end :

  2022-12-31T23:59:59

  time_coverage_start :

  1979-01-01T00:00:00

  title :

  Climate Data for Coastal Upwelling Machine Learning Project in Indian Ocean

# slice by time
ds.sel(time=slice('1998', '1999'))

<xarray.Dataset>
Dimensions:          (time: 730, lat: 177, lon: 241)
Coordinates:
  * lat              (lat) float32 32.0 31.75 31.5 31.25 ... -11.5 -11.75 -12.0
  * lon              (lon) float32 42.0 42.25 42.5 42.75 ... 101.5 101.8 102.0
  * time             (time) datetime64[ns] 1998-01-01 1998-01-02 ... 1999-12-31
Data variables: (12/19)
    CHL              (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
    CHL_uncertainty  (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
    adt              (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
    air_temp         (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
    curr_dir         (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
    curr_speed       (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
    ...               ...
    ug_curr          (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
    v_curr           (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
    v_wind           (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
    vg_curr          (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
    wind_dir         (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
    wind_speed       (time, lat, lon) float32 dask.array<chunksize=(60, 177, 241), meta=np.ndarray>
Attributes: (12/17)
    creator_email:              minhphan@uw.edu
    creator_name:               Minh Phan
    creator_type:               person
    date_created:               2023-07-19
    geospatial_lat_max:         32.0
    geospatial_lat_min:         -12.0
    ...                         ...
    geospatial_lon_units:       degrees_east
    source:                     Earth & Space Research (ESR), Copernicus Clim...
    summary:                    Daily mean of 0.25 x 0.25 degrees gridded dat...
    time_coverage_end:          2022-12-31T23:59:59
    time_coverage_start:        1979-01-01T00:00:00
    title:                      Climate Data for Coastal Upwelling Machine Le...

xarray.Dataset

• Dimensions:
  • time: 730
  • lat: 177
  • lon: 241
• Coordinates: (3)
  • lat
    (lat)
    float32
    32.0 31.75 31.5 ... -11.75 -12.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([ 32.  ,  31.75,  31.5 ,  31.25,  31.  ,  30.75,  30.5 ,  30.25,  30.  ,
            29.75,  29.5 ,  29.25,  29.  ,  28.75,  28.5 ,  28.25,  28.  ,  27.75,
            27.5 ,  27.25,  27.  ,  26.75,  26.5 ,  26.25,  26.  ,  25.75,  25.5 ,
            25.25,  25.  ,  24.75,  24.5 ,  24.25,  24.  ,  23.75,  23.5 ,  23.25,
            23.  ,  22.75,  22.5 ,  22.25,  22.  ,  21.75,  21.5 ,  21.25,  21.  ,
            20.75,  20.5 ,  20.25,  20.  ,  19.75,  19.5 ,  19.25,  19.  ,  18.75,
            18.5 ,  18.25,  18.  ,  17.75,  17.5 ,  17.25,  17.  ,  16.75,  16.5 ,
            16.25,  16.  ,  15.75,  15.5 ,  15.25,  15.  ,  14.75,  14.5 ,  14.25,
            14.  ,  13.75,  13.5 ,  13.25,  13.  ,  12.75,  12.5 ,  12.25,  12.  ,
            11.75,  11.5 ,  11.25,  11.  ,  10.75,  10.5 ,  10.25,  10.  ,   9.75,
             9.5 ,   9.25,   9.  ,   8.75,   8.5 ,   8.25,   8.  ,   7.75,   7.5 ,
             7.25,   7.  ,   6.75,   6.5 ,   6.25,   6.  ,   5.75,   5.5 ,   5.25,
             5.  ,   4.75,   4.5 ,   4.25,   4.  ,   3.75,   3.5 ,   3.25,   3.  ,
             2.75,   2.5 ,   2.25,   2.  ,   1.75,   1.5 ,   1.25,   1.  ,   0.75,
             0.5 ,   0.25,   0.  ,  -0.25,  -0.5 ,  -0.75,  -1.  ,  -1.25,  -1.5 ,
            -1.75,  -2.  ,  -2.25,  -2.5 ,  -2.75,  -3.  ,  -3.25,  -3.5 ,  -3.75,
            -4.  ,  -4.25,  -4.5 ,  -4.75,  -5.  ,  -5.25,  -5.5 ,  -5.75,  -6.  ,
            -6.25,  -6.5 ,  -6.75,  -7.  ,  -7.25,  -7.5 ,  -7.75,  -8.  ,  -8.25,
            -8.5 ,  -8.75,  -9.  ,  -9.25,  -9.5 ,  -9.75, -10.  , -10.25, -10.5 ,
           -10.75, -11.  , -11.25, -11.5 , -11.75, -12.  ], dtype=float32)

  • lon
    (lon)
    float32
    42.0 42.25 42.5 ... 101.8 102.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([ 42.  ,  42.25,  42.5 , ..., 101.5 , 101.75, 102.  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1998-01-01 ... 1999-12-31

    axis :

    T

    comment :

    Data is averaged over the day

    long_name :

    time centered on the day

    standard_name :

    time

    time_bounds :

    2000-01-01 00:00:00 to 2000-01-01 23:59:59

    array(['1998-01-01T00:00:00.000000000', '1998-01-02T00:00:00.000000000',
           '1998-01-03T00:00:00.000000000', ..., '1999-12-29T00:00:00.000000000',
           '1999-12-30T00:00:00.000000000', '1999-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (19)
  • CHL
    (time, lat, lon)
    float32
    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    _ChunkSizes :

    [1, 256, 256]

    ancillary_variables :

    flags CHL_uncertainty

    coverage_content_type :

    modelResult

    input_files_reprocessings :

    Processors versions: MODIS R2022.0NRT/VIIRSN R2022.0NRT/OLCIA 07.02/VIIRSJ1 R2022.0NRT/OLCIB 07.02

    long_name :

    Chlorophyll-a concentration - Mean of the binned pixels

    standard_name :

    mass_concentration_of_chlorophyll_a_in_sea_water

    type :

    surface

    units :

    milligram m-3

    valid_max :

    1000.0

    valid_min :

    0.0

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    CHL_uncertainty

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 256, 256]

    coverage_content_type :

    qualityInformation

    long_name :

    Chlorophyll-a concentration - Uncertainty estimation

    units :

    %

    valid_max :

    32767

    valid_min :

    0

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    adt

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    comment :

    The absolute dynamic topography is the sea surface height above geoid; the adt is obtained as follows: adt=sla+mdt where mdt is the mean dynamic topography; see the product user manual for details

    grid_mapping :

    crs

    long_name :

    Absolute dynamic topography

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    air_temp

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    long_name :

    2 metre temperature

    nameCDM :

    2_metre_temperature_surface

    nameECMWF :

    2 metre temperature

    product_type :

    analysis

    shortNameECMWF :

    2t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    curr_dir

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    comments :

    Computed from total surface current velocity elements. Velocities are an average over the top 30m of the mixed layer

    depth :

    15m

    long_name :

    average direction of total surface currents

    units :

    degrees

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    curr_speed

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    comments :

    Velocities are an average over the top 30m of the mixed layer

    depth :

    15m

    long_name :

    average total surface current speed

    units :

    m s**-1

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    mlotst

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 681, 1440]

    cell_methods :

    area: mean

    long_name :

    Density ocean mixed layer thickness

    standard_name :

    ocean_mixed_layer_thickness_defined_by_sigma_theta

    unit_long :

    Meters

    units :

    m

    Array Chunk Bytes 118.79 MiB 81.36 MiB Shape (730, 177, 241) (500, 177, 241) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray

    sla

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    ancillary_variables :

    err_sla

    comment :

    The sea level anomaly is the sea surface height above mean sea surface; it is referenced to the [1993, 2012] period; see the product user manual for details

    grid_mapping :

    crs

    long_name :

    Sea level anomaly

    standard_name :

    sea_surface_height_above_sea_level

    units :

    m

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    so

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 7, 341, 720]

    cell_methods :

    area: mean

    long_name :

    mean sea water salinity at 0.49 metres below ocean surface

    standard_name :

    sea_water_salinity

    unit_long :

    Practical Salinity Unit

    units :

    1e-3

    valid_max :

    28336

    valid_min :

    1

    Array Chunk Bytes 118.79 MiB 81.36 MiB Shape (730, 177, 241) (500, 177, 241) Dask graph 3 chunks in 3 graph layers Data type float32 numpy.ndarray

    sst

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    long_name :

    Sea surface temperature

    nameCDM :

    Sea_surface_temperature_surface

    nameECMWF :

    Sea surface temperature

    product_type :

    analysis

    shortNameECMWF :

    sst

    standard_name :

    sea_surface_temperature

    units :

    K

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    topo

    (lat, lon)

    float64

    dask.array<chunksize=(177, 241), meta=np.ndarray>

    

    colorBarMaximum :

    8000.0

    colorBarMinimum :

    -8000.0

    colorBarPalette :

    Topography

    grid_mapping :

    GDAL_Geographics

    ioos_category :

    Location

    long_name :

    Topography

    standard_name :

    altitude

    units :

    meters

    Array Chunk Bytes 333.26 kiB 333.26 kiB Shape (177, 241) (177, 241) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    u_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    comment :

    Velocities are an average over the top 30m of the mixed layer

    coverage_content_type :

    modelResult

    depth :

    15m

    long_name :

    zonal total surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148 ; WIND source: ECMWF ERA5 10m wind DOI: 10.24381/cds.adbb2d47 ; SST source: CMC 0.2 deg SST V2.0 DOI: 10.5067/GHCMC-4FM02

    standard_name :

    eastward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    u_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    long_name :

    10 metre U wind component

    nameCDM :

    10_metre_U_wind_component_surface

    nameECMWF :

    10 metre U wind component

    product_type :

    analysis

    shortNameECMWF :

    10u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    ug_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    comment :

    Geostrophic velocities calculated from absolute dynamic topography

    depth :

    15m

    long_name :

    zonal geostrophic surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148

    standard_name :

    geostrophic_eastward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    v_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    comment :

    Velocities are an average over the top 30m of the mixed layer

    coverage_content_type :

    modelResult

    depth :

    15m

    long_name :

    meridional total surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148 ; WIND source: ECMWF ERA5 10m wind DOI: 10.24381/cds.adbb2d47 ; SST source: CMC 0.2 deg SST V2.0 DOI: 10.5067/GHCMC-4FM02

    standard_name :

    northward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    v_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    long_name :

    10 metre V wind component

    nameCDM :

    10_metre_V_wind_component_surface

    nameECMWF :

    10 metre V wind component

    product_type :

    analysis

    shortNameECMWF :

    10v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    vg_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    comment :

    Geostrophic velocities calculated from absolute dynamic topography

    depth :

    15m

    long_name :

    meridional geostrophic surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148

    standard_name :

    geostrophic_northward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    wind_dir

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    long_name :

    10 metre wind direction

    units :

    degrees

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    wind_speed

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 177, 241), meta=np.ndarray>

    

    long_name :

    10 metre absolute speed

    units :

    m s**-1

    Array Chunk Bytes 118.79 MiB 16.27 MiB Shape (730, 177, 241) (100, 177, 241) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([  32.0,  31.75,   31.5,  31.25,   31.0,  30.75,   30.5,  30.25,   30.0,
            29.75,
           ...
            -9.75,  -10.0, -10.25,  -10.5, -10.75,  -11.0, -11.25,  -11.5, -11.75,
            -12.0],
          dtype='float32', name='lat', length=177))

  • lon
    PandasIndex

    PandasIndex(Index([  42.0,  42.25,   42.5,  42.75,   43.0,  43.25,   43.5,  43.75,   44.0,
            44.25,
           ...
            99.75,  100.0, 100.25,  100.5, 100.75,  101.0, 101.25,  101.5, 101.75,
            102.0],
          dtype='float32', name='lon', length=241))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1998-01-01', '1998-01-02', '1998-01-03', '1998-01-04',
                   '1998-01-05', '1998-01-06', '1998-01-07', '1998-01-08',
                   '1998-01-09', '1998-01-10',
                   ...
                   '1999-12-22', '1999-12-23', '1999-12-24', '1999-12-25',
                   '1999-12-26', '1999-12-27', '1999-12-28', '1999-12-29',
                   '1999-12-30', '1999-12-31'],
                  dtype='datetime64[ns]', name='time', length=730, freq=None))

• Attributes: (17)

  creator_email :

  minhphan@uw.edu

  creator_name :

  Minh Phan

  creator_type :

  person

  date_created :

  2023-07-19

  geospatial_lat_max :

  32.0

  geospatial_lat_min :

  -12.0

  geospatial_lat_resolution :

  0.25

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  102.0

  geospatial_lon_min :

  42.0

  geospatial_lon_resolution :

  0.25

  geospatial_lon_units :

  degrees_east

  source :

  Earth & Space Research (ESR), Copernicus Climate Change Service (C3S), Copernicus Marine Environment Monitoring Service (CMEMS), United States Geological Survey (USGS)

  summary :

  Daily mean of 0.25 x 0.25 degrees gridded data from multiple climate variables that may influence the patterns of coastal upwelling in the focused area

  time_coverage_end :

  2022-12-31T23:59:59

  time_coverage_start :

  1979-01-01T00:00:00

  title :

  Climate Data for Coastal Upwelling Machine Learning Project in Indian Ocean

# slice by variable
ds[['u_curr', 'u_wind']]

<xarray.Dataset>
Dimensions:  (time: 16071, lat: 177, lon: 241)
Coordinates:
  * lat      (lat) float32 32.0 31.75 31.5 31.25 ... -11.25 -11.5 -11.75 -12.0
  * lon      (lon) float32 42.0 42.25 42.5 42.75 ... 101.2 101.5 101.8 102.0
  * time     (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2022-12-31
Data variables:
    u_curr   (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
    u_wind   (time, lat, lon) float32 dask.array<chunksize=(100, 177, 241), meta=np.ndarray>
Attributes: (12/17)
    creator_email:              minhphan@uw.edu
    creator_name:               Minh Phan
    creator_type:               person
    date_created:               2023-07-19
    geospatial_lat_max:         32.0
    geospatial_lat_min:         -12.0
    ...                         ...
    geospatial_lon_units:       degrees_east
    source:                     Earth & Space Research (ESR), Copernicus Clim...
    summary:                    Daily mean of 0.25 x 0.25 degrees gridded dat...
    time_coverage_end:          2022-12-31T23:59:59
    time_coverage_start:        1979-01-01T00:00:00
    title:                      Climate Data for Coastal Upwelling Machine Le...

xarray.Dataset

• Dimensions:
  • time: 16071
  • lat: 177
  • lon: 241
• Coordinates: (3)
  • lat
    (lat)
    float32
    32.0 31.75 31.5 ... -11.75 -12.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([ 32.  ,  31.75,  31.5 ,  31.25,  31.  ,  30.75,  30.5 ,  30.25,  30.  ,
            29.75,  29.5 ,  29.25,  29.  ,  28.75,  28.5 ,  28.25,  28.  ,  27.75,
            27.5 ,  27.25,  27.  ,  26.75,  26.5 ,  26.25,  26.  ,  25.75,  25.5 ,
            25.25,  25.  ,  24.75,  24.5 ,  24.25,  24.  ,  23.75,  23.5 ,  23.25,
            23.  ,  22.75,  22.5 ,  22.25,  22.  ,  21.75,  21.5 ,  21.25,  21.  ,
            20.75,  20.5 ,  20.25,  20.  ,  19.75,  19.5 ,  19.25,  19.  ,  18.75,
            18.5 ,  18.25,  18.  ,  17.75,  17.5 ,  17.25,  17.  ,  16.75,  16.5 ,
            16.25,  16.  ,  15.75,  15.5 ,  15.25,  15.  ,  14.75,  14.5 ,  14.25,
            14.  ,  13.75,  13.5 ,  13.25,  13.  ,  12.75,  12.5 ,  12.25,  12.  ,
            11.75,  11.5 ,  11.25,  11.  ,  10.75,  10.5 ,  10.25,  10.  ,   9.75,
             9.5 ,   9.25,   9.  ,   8.75,   8.5 ,   8.25,   8.  ,   7.75,   7.5 ,
             7.25,   7.  ,   6.75,   6.5 ,   6.25,   6.  ,   5.75,   5.5 ,   5.25,
             5.  ,   4.75,   4.5 ,   4.25,   4.  ,   3.75,   3.5 ,   3.25,   3.  ,
             2.75,   2.5 ,   2.25,   2.  ,   1.75,   1.5 ,   1.25,   1.  ,   0.75,
             0.5 ,   0.25,   0.  ,  -0.25,  -0.5 ,  -0.75,  -1.  ,  -1.25,  -1.5 ,
            -1.75,  -2.  ,  -2.25,  -2.5 ,  -2.75,  -3.  ,  -3.25,  -3.5 ,  -3.75,
            -4.  ,  -4.25,  -4.5 ,  -4.75,  -5.  ,  -5.25,  -5.5 ,  -5.75,  -6.  ,
            -6.25,  -6.5 ,  -6.75,  -7.  ,  -7.25,  -7.5 ,  -7.75,  -8.  ,  -8.25,
            -8.5 ,  -8.75,  -9.  ,  -9.25,  -9.5 ,  -9.75, -10.  , -10.25, -10.5 ,
           -10.75, -11.  , -11.25, -11.5 , -11.75, -12.  ], dtype=float32)

  • lon
    (lon)
    float32
    42.0 42.25 42.5 ... 101.8 102.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([ 42.  ,  42.25,  42.5 , ..., 101.5 , 101.75, 102.  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1979-01-01 ... 2022-12-31

    axis :

    T

    comment :

    Data is averaged over the day

    long_name :

    time centered on the day

    standard_name :

    time

    time_bounds :

    2000-01-01 00:00:00 to 2000-01-01 23:59:59

    array(['1979-01-01T00:00:00.000000000', '1979-01-02T00:00:00.000000000',
           '1979-01-03T00:00:00.000000000', ..., '2022-12-29T00:00:00.000000000',
           '2022-12-30T00:00:00.000000000', '2022-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (2)
  • u_curr
    (time, lat, lon)
    float32
    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    comment :

    Velocities are an average over the top 30m of the mixed layer

    coverage_content_type :

    modelResult

    depth :

    15m

    long_name :

    zonal total surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148 ; WIND source: ECMWF ERA5 10m wind DOI: 10.24381/cds.adbb2d47 ; SST source: CMC 0.2 deg SST V2.0 DOI: 10.5067/GHCMC-4FM02

    standard_name :

    eastward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

    u_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 177, 241), meta=np.ndarray>

    

    long_name :

    10 metre U wind component

    nameCDM :

    10_metre_U_wind_component_surface

    nameECMWF :

    10 metre U wind component

    product_type :

    analysis

    shortNameECMWF :

    10u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 2.55 GiB 16.27 MiB Shape (16071, 177, 241) (100, 177, 241) Dask graph 161 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([  32.0,  31.75,   31.5,  31.25,   31.0,  30.75,   30.5,  30.25,   30.0,
            29.75,
           ...
            -9.75,  -10.0, -10.25,  -10.5, -10.75,  -11.0, -11.25,  -11.5, -11.75,
            -12.0],
          dtype='float32', name='lat', length=177))

  • lon
    PandasIndex

    PandasIndex(Index([  42.0,  42.25,   42.5,  42.75,   43.0,  43.25,   43.5,  43.75,   44.0,
            44.25,
           ...
            99.75,  100.0, 100.25,  100.5, 100.75,  101.0, 101.25,  101.5, 101.75,
            102.0],
          dtype='float32', name='lon', length=241))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-01', '1979-01-02', '1979-01-03', '1979-01-04',
                   '1979-01-05', '1979-01-06', '1979-01-07', '1979-01-08',
                   '1979-01-09', '1979-01-10',
                   ...
                   '2022-12-22', '2022-12-23', '2022-12-24', '2022-12-25',
                   '2022-12-26', '2022-12-27', '2022-12-28', '2022-12-29',
                   '2022-12-30', '2022-12-31'],
                  dtype='datetime64[ns]', name='time', length=16071, freq=None))

• Attributes: (17)

  creator_email :

  minhphan@uw.edu

  creator_name :

  Minh Phan

  creator_type :

  person

  date_created :

  2023-07-19

  geospatial_lat_max :

  32.0

  geospatial_lat_min :

  -12.0

  geospatial_lat_resolution :

  0.25

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  102.0

  geospatial_lon_min :

  42.0

  geospatial_lon_resolution :

  0.25

  geospatial_lon_units :

  degrees_east

  source :

  Earth & Space Research (ESR), Copernicus Climate Change Service (C3S), Copernicus Marine Environment Monitoring Service (CMEMS), United States Geological Survey (USGS)

  summary :

  Daily mean of 0.25 x 0.25 degrees gridded data from multiple climate variables that may influence the patterns of coastal upwelling in the focused area

  time_coverage_end :

  2022-12-31T23:59:59

  time_coverage_start :

  1979-01-01T00:00:00

  title :

  Climate Data for Coastal Upwelling Machine Learning Project in Indian Ocean

# combine multiple slicing options all at once
ds[['u_curr', 'u_wind']].sel(time=slice('1998', '1999'), 
                             lat=slice(0, -12), 
                             lon=slice(42, 45))

<xarray.Dataset>
Dimensions:  (time: 730, lat: 49, lon: 13)
Coordinates:
  * lat      (lat) float32 0.0 -0.25 -0.5 -0.75 ... -11.25 -11.5 -11.75 -12.0
  * lon      (lon) float32 42.0 42.25 42.5 42.75 43.0 ... 44.25 44.5 44.75 45.0
  * time     (time) datetime64[ns] 1998-01-01 1998-01-02 ... 1999-12-31
Data variables:
    u_curr   (time, lat, lon) float32 dask.array<chunksize=(60, 49, 13), meta=np.ndarray>
    u_wind   (time, lat, lon) float32 dask.array<chunksize=(60, 49, 13), meta=np.ndarray>
Attributes: (12/17)
    creator_email:              minhphan@uw.edu
    creator_name:               Minh Phan
    creator_type:               person
    date_created:               2023-07-19
    geospatial_lat_max:         32.0
    geospatial_lat_min:         -12.0
    ...                         ...
    geospatial_lon_units:       degrees_east
    source:                     Earth & Space Research (ESR), Copernicus Clim...
    summary:                    Daily mean of 0.25 x 0.25 degrees gridded dat...
    time_coverage_end:          2022-12-31T23:59:59
    time_coverage_start:        1979-01-01T00:00:00
    title:                      Climate Data for Coastal Upwelling Machine Le...

xarray.Dataset

• Dimensions:
  • time: 730
  • lat: 49
  • lon: 13
• Coordinates: (3)
  • lat
    (lat)
    float32
    0.0 -0.25 -0.5 ... -11.75 -12.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([  0.  ,  -0.25,  -0.5 ,  -0.75,  -1.  ,  -1.25,  -1.5 ,  -1.75,  -2.  ,
            -2.25,  -2.5 ,  -2.75,  -3.  ,  -3.25,  -3.5 ,  -3.75,  -4.  ,  -4.25,
            -4.5 ,  -4.75,  -5.  ,  -5.25,  -5.5 ,  -5.75,  -6.  ,  -6.25,  -6.5 ,
            -6.75,  -7.  ,  -7.25,  -7.5 ,  -7.75,  -8.  ,  -8.25,  -8.5 ,  -8.75,
            -9.  ,  -9.25,  -9.5 ,  -9.75, -10.  , -10.25, -10.5 , -10.75, -11.  ,
           -11.25, -11.5 , -11.75, -12.  ], dtype=float32)

  • lon
    (lon)
    float32
    42.0 42.25 42.5 ... 44.5 44.75 45.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([42.  , 42.25, 42.5 , 42.75, 43.  , 43.25, 43.5 , 43.75, 44.  , 44.25,
           44.5 , 44.75, 45.  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1998-01-01 ... 1999-12-31

    axis :

    T

    comment :

    Data is averaged over the day

    long_name :

    time centered on the day

    standard_name :

    time

    time_bounds :

    2000-01-01 00:00:00 to 2000-01-01 23:59:59

    array(['1998-01-01T00:00:00.000000000', '1998-01-02T00:00:00.000000000',
           '1998-01-03T00:00:00.000000000', ..., '1999-12-29T00:00:00.000000000',
           '1999-12-30T00:00:00.000000000', '1999-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (2)
  • u_curr
    (time, lat, lon)
    float32
    dask.array<chunksize=(60, 49, 13), meta=np.ndarray>

    comment :

    Velocities are an average over the top 30m of the mixed layer

    coverage_content_type :

    modelResult

    depth :

    15m

    long_name :

    zonal total surface current

    source :

    SSH source: CMEMS SSALTO/DUACS SEALEVEL_GLO_PHY_L4_MY_008_047 DOI: 10.48670/moi-00148 ; WIND source: ECMWF ERA5 10m wind DOI: 10.24381/cds.adbb2d47 ; SST source: CMC 0.2 deg SST V2.0 DOI: 10.5067/GHCMC-4FM02

    standard_name :

    eastward_sea_water_velocity

    units :

    m s-1

    valid_max :

    3.0

    valid_min :

    -3.0

    Array Chunk Bytes 1.77 MiB 248.83 kiB Shape (730, 49, 13) (100, 49, 13) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

    u_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(60, 49, 13), meta=np.ndarray>

    

    long_name :

    10 metre U wind component

    nameCDM :

    10_metre_U_wind_component_surface

    nameECMWF :

    10 metre U wind component

    product_type :

    analysis

    shortNameECMWF :

    10u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 1.77 MiB 248.83 kiB Shape (730, 49, 13) (100, 49, 13) Dask graph 8 chunks in 3 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([   0.0,  -0.25,   -0.5,  -0.75,   -1.0,  -1.25,   -1.5,  -1.75,   -2.0,
            -2.25,   -2.5,  -2.75,   -3.0,  -3.25,   -3.5,  -3.75,   -4.0,  -4.25,
             -4.5,  -4.75,   -5.0,  -5.25,   -5.5,  -5.75,   -6.0,  -6.25,   -6.5,
            -6.75,   -7.0,  -7.25,   -7.5,  -7.75,   -8.0,  -8.25,   -8.5,  -8.75,
             -9.0,  -9.25,   -9.5,  -9.75,  -10.0, -10.25,  -10.5, -10.75,  -11.0,
           -11.25,  -11.5, -11.75,  -12.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([ 42.0, 42.25,  42.5, 42.75,  43.0, 43.25,  43.5, 43.75,  44.0, 44.25,
            44.5, 44.75,  45.0],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1998-01-01', '1998-01-02', '1998-01-03', '1998-01-04',
                   '1998-01-05', '1998-01-06', '1998-01-07', '1998-01-08',
                   '1998-01-09', '1998-01-10',
                   ...
                   '1999-12-22', '1999-12-23', '1999-12-24', '1999-12-25',
                   '1999-12-26', '1999-12-27', '1999-12-28', '1999-12-29',
                   '1999-12-30', '1999-12-31'],
                  dtype='datetime64[ns]', name='time', length=730, freq=None))

• Attributes: (17)

  creator_email :

  minhphan@uw.edu

  creator_name :

  Minh Phan

  creator_type :

  person

  date_created :

  2023-07-19

  geospatial_lat_max :

  32.0

  geospatial_lat_min :

  -12.0

  geospatial_lat_resolution :

  0.25

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  102.0

  geospatial_lon_min :

  42.0

  geospatial_lon_resolution :

  0.25

  geospatial_lon_units :

  degrees_east

  source :

  Earth & Space Research (ESR), Copernicus Climate Change Service (C3S), Copernicus Marine Environment Monitoring Service (CMEMS), United States Geological Survey (USGS)

  summary :

  Daily mean of 0.25 x 0.25 degrees gridded data from multiple climate variables that may influence the patterns of coastal upwelling in the focused area

  time_coverage_end :

  2022-12-31T23:59:59

  time_coverage_start :

  1979-01-01T00:00:00

  title :

  Climate Data for Coastal Upwelling Machine Learning Project in Indian Ocean

We can also graph the data right from slicing, especially heatmaps from 2D arrays, or line charts. This is especially useful when we want to inspect elements on the go.

# make sure that the array you slice for a heatmap visualization is a 2D array
heatmap_arr = ds['wind_speed'].sel(time='2000-01-02')
heatmap_arr

<xarray.DataArray 'wind_speed' (lat: 177, lon: 241)>
dask.array<getitem, shape=(177, 241), dtype=float32, chunksize=(177, 241), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float32 32.0 31.75 31.5 31.25 ... -11.25 -11.5 -11.75 -12.0
  * lon      (lon) float32 42.0 42.25 42.5 42.75 ... 101.2 101.5 101.8 102.0
    time     datetime64[ns] 2000-01-02
Attributes:
    long_name:  10 metre absolute speed
    units:      m s**-1

xarray.DataArray

'wind_speed'

• lat: 177
• lon: 241

• dask.array<chunksize=(177, 241), meta=np.ndarray>

  Array Chunk Bytes 166.63 kiB 166.63 kiB Shape (177, 241) (177, 241) Dask graph 1 chunks in 3 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • lat
    (lat)
    float32
    32.0 31.75 31.5 ... -11.75 -12.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([ 32.  ,  31.75,  31.5 ,  31.25,  31.  ,  30.75,  30.5 ,  30.25,  30.  ,
            29.75,  29.5 ,  29.25,  29.  ,  28.75,  28.5 ,  28.25,  28.  ,  27.75,
            27.5 ,  27.25,  27.  ,  26.75,  26.5 ,  26.25,  26.  ,  25.75,  25.5 ,
            25.25,  25.  ,  24.75,  24.5 ,  24.25,  24.  ,  23.75,  23.5 ,  23.25,
            23.  ,  22.75,  22.5 ,  22.25,  22.  ,  21.75,  21.5 ,  21.25,  21.  ,
            20.75,  20.5 ,  20.25,  20.  ,  19.75,  19.5 ,  19.25,  19.  ,  18.75,
            18.5 ,  18.25,  18.  ,  17.75,  17.5 ,  17.25,  17.  ,  16.75,  16.5 ,
            16.25,  16.  ,  15.75,  15.5 ,  15.25,  15.  ,  14.75,  14.5 ,  14.25,
            14.  ,  13.75,  13.5 ,  13.25,  13.  ,  12.75,  12.5 ,  12.25,  12.  ,
            11.75,  11.5 ,  11.25,  11.  ,  10.75,  10.5 ,  10.25,  10.  ,   9.75,
             9.5 ,   9.25,   9.  ,   8.75,   8.5 ,   8.25,   8.  ,   7.75,   7.5 ,
             7.25,   7.  ,   6.75,   6.5 ,   6.25,   6.  ,   5.75,   5.5 ,   5.25,
             5.  ,   4.75,   4.5 ,   4.25,   4.  ,   3.75,   3.5 ,   3.25,   3.  ,
             2.75,   2.5 ,   2.25,   2.  ,   1.75,   1.5 ,   1.25,   1.  ,   0.75,
             0.5 ,   0.25,   0.  ,  -0.25,  -0.5 ,  -0.75,  -1.  ,  -1.25,  -1.5 ,
            -1.75,  -2.  ,  -2.25,  -2.5 ,  -2.75,  -3.  ,  -3.25,  -3.5 ,  -3.75,
            -4.  ,  -4.25,  -4.5 ,  -4.75,  -5.  ,  -5.25,  -5.5 ,  -5.75,  -6.  ,
            -6.25,  -6.5 ,  -6.75,  -7.  ,  -7.25,  -7.5 ,  -7.75,  -8.  ,  -8.25,
            -8.5 ,  -8.75,  -9.  ,  -9.25,  -9.5 ,  -9.75, -10.  , -10.25, -10.5 ,
           -10.75, -11.  , -11.25, -11.5 , -11.75, -12.  ], dtype=float32)

  • lon
    (lon)
    float32
    42.0 42.25 42.5 ... 101.8 102.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([ 42.  ,  42.25,  42.5 , ..., 101.5 , 101.75, 102.  ], dtype=float32)

  • time
    ()
    datetime64[ns]
    2000-01-02

    axis :

    T

    comment :

    Data is averaged over the day

    long_name :

    time centered on the day

    standard_name :

    time

    time_bounds :

    2000-01-01 00:00:00 to 2000-01-01 23:59:59

    array('2000-01-02T00:00:00.000000000', dtype='datetime64[ns]')

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([  32.0,  31.75,   31.5,  31.25,   31.0,  30.75,   30.5,  30.25,   30.0,
            29.75,
           ...
            -9.75,  -10.0, -10.25,  -10.5, -10.75,  -11.0, -11.25,  -11.5, -11.75,
            -12.0],
          dtype='float32', name='lat', length=177))

  • lon
    PandasIndex

    PandasIndex(Index([  42.0,  42.25,   42.5,  42.75,   43.0,  43.25,   43.5,  43.75,   44.0,
            44.25,
           ...
            99.75,  100.0, 100.25,  100.5, 100.75,  101.0, 101.25,  101.5, 101.75,
            102.0],
          dtype='float32', name='lon', length=241))

• Attributes: (2)

  long_name :

  10 metre absolute speed

  units :

  m s**-1

heatmap_arr.plot.imshow()

# contour map with no filling
heatmap_arr.plot.contour()

# contour map with color filling
heatmap_arr.plot.contourf()

heatmap_arr.plot.surface()

Line plots

ds['wind_speed'].mean(dim=['lat', 'lon']).plot(figsize=(10, 5))

We can add in parameters to customize our graphs, as additional arguments are passed to the underlying matplotlib plot() function.

ds['air_temp'].mean(dim=['lat', 'lon']).sel(time=slice('2007', '2009')).plot.line('r-o', figsize=(10,5), markersize=1)

Histogram

# creating a new Axe object if there is no currently
# available one
ax = plt.gca() 
ds['wind_dir'].plot.hist(ax = ax)
ax.set_xlabel('10 metre wind direction (degrees east)')
ax.set_ylabel('frequency')
ax.set_title('Daily average wind direction distribution over covered area (1979-2022)')

Text(0.5, 1.0, 'Daily average wind direction distribution over covered area (1979-2022)')

# scroll along the axes to stretch the graph
heatmap_arr.hvplot().options(cmap='bgy', width=600, height=500)

Resampling

With xarray

If your xarray version works just fine with resample(), here’s the most straightforward way to resample your data temporally. It may take a long while for the data to finish resampling, especially if your dataset is big and your resampling frequency is small.

ds_resampled = ds['CHL'].resample(time='1M').mean()

Without xarray

If not, which was the case we encountered for a bit during the documentation, we developed a roundabout using Pandas’ equivalent function with the same name, which also happened to also be the underlying function that the library itself depends on (of course, more optimized), to help us perform resampling on our dataset.

# resampling is having issues, so this is a temporary solution using Pandas 
# to perform resampling by month on one range...

# choose a small range just in case overloadding happens...

ds_to_resample = ds['CHL'].sel(lat=slice(10, 5), lon=slice(75, 80)).to_dataframe()

df_resampled = ds_to_resample.groupby([pd.Grouper(freq='1M', level='time'),
                        pd.Grouper(level='lon'),
                        pd.Grouper(level='lat')]).mean()

ds_resampled = xr.Dataset.from_dataframe(df_resampled)

We can see that after resampling, our time dimension size is reduced from 8523 (days) to 280 (months). Resampling is successful!

# original ds_to_resample object before converting to dataframe
ds['CHL'].sel(lat=slice(10, 5), lon=slice(75, 80))

<xarray.DataArray 'CHL' (time: 16071, lat: 21, lon: 21)>
dask.array<getitem, shape=(16071, 21, 21), dtype=float32, chunksize=(100, 21, 21), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float32 10.0 9.75 9.5 9.25 9.0 8.75 ... 6.0 5.75 5.5 5.25 5.0
  * lon      (lon) float32 75.0 75.25 75.5 75.75 76.0 ... 79.25 79.5 79.75 80.0
  * time     (time) datetime64[ns] 1979-01-01 1979-01-02 ... 2022-12-31
Attributes:
    _ChunkSizes:                [1, 256, 256]
    ancillary_variables:        flags CHL_uncertainty
    coverage_content_type:      modelResult
    input_files_reprocessings:  Processors versions: MODIS R2022.0NRT/VIIRSN ...
    long_name:                  Chlorophyll-a concentration - Mean of the bin...
    standard_name:              mass_concentration_of_chlorophyll_a_in_sea_water
    type:                       surface
    units:                      milligram m-3
    valid_max:                  1000.0
    valid_min:                  0.0

xarray.DataArray

'CHL'

• time: 16071
• lat: 21
• lon: 21

• dask.array<chunksize=(100, 21, 21), meta=np.ndarray>

  Array Chunk Bytes 27.04 MiB 172.27 kiB Shape (16071, 21, 21) (100, 21, 21) Dask graph 161 chunks in 3 graph layers Data type float32 numpy.ndarray

• Coordinates: (3)
  • lat
    (lat)
    float32
    10.0 9.75 9.5 9.25 ... 5.5 5.25 5.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([10.  ,  9.75,  9.5 ,  9.25,  9.  ,  8.75,  8.5 ,  8.25,  8.  ,  7.75,
            7.5 ,  7.25,  7.  ,  6.75,  6.5 ,  6.25,  6.  ,  5.75,  5.5 ,  5.25,
            5.  ], dtype=float32)

  • lon
    (lon)
    float32
    75.0 75.25 75.5 ... 79.5 79.75 80.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([75.  , 75.25, 75.5 , 75.75, 76.  , 76.25, 76.5 , 76.75, 77.  , 77.25,
           77.5 , 77.75, 78.  , 78.25, 78.5 , 78.75, 79.  , 79.25, 79.5 , 79.75,
           80.  ], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1979-01-01 ... 2022-12-31

    axis :

    T

    comment :

    Data is averaged over the day

    long_name :

    time centered on the day

    standard_name :

    time

    time_bounds :

    2000-01-01 00:00:00 to 2000-01-01 23:59:59

    array(['1979-01-01T00:00:00.000000000', '1979-01-02T00:00:00.000000000',
           '1979-01-03T00:00:00.000000000', ..., '2022-12-29T00:00:00.000000000',
           '2022-12-30T00:00:00.000000000', '2022-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([10.0, 9.75,  9.5, 9.25,  9.0, 8.75,  8.5, 8.25,  8.0, 7.75,  7.5, 7.25,
            7.0, 6.75,  6.5, 6.25,  6.0, 5.75,  5.5, 5.25,  5.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([ 75.0, 75.25,  75.5, 75.75,  76.0, 76.25,  76.5, 76.75,  77.0, 77.25,
            77.5, 77.75,  78.0, 78.25,  78.5, 78.75,  79.0, 79.25,  79.5, 79.75,
            80.0],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-01', '1979-01-02', '1979-01-03', '1979-01-04',
                   '1979-01-05', '1979-01-06', '1979-01-07', '1979-01-08',
                   '1979-01-09', '1979-01-10',
                   ...
                   '2022-12-22', '2022-12-23', '2022-12-24', '2022-12-25',
                   '2022-12-26', '2022-12-27', '2022-12-28', '2022-12-29',
                   '2022-12-30', '2022-12-31'],
                  dtype='datetime64[ns]', name='time', length=16071, freq=None))

• Attributes: (10)

  _ChunkSizes :

  [1, 256, 256]

  ancillary_variables :

  flags CHL_uncertainty

  coverage_content_type :

  modelResult

  input_files_reprocessings :

  Processors versions: MODIS R2022.0NRT/VIIRSN R2022.0NRT/OLCIA 07.02/VIIRSJ1 R2022.0NRT/OLCIB 07.02

  long_name :

  Chlorophyll-a concentration - Mean of the binned pixels

  standard_name :

  mass_concentration_of_chlorophyll_a_in_sea_water

  type :

  surface

  units :

  milligram m-3

  valid_max :

  1000.0

  valid_min :

  0.0

ds_resampled

<xarray.Dataset>
Dimensions:  (time: 528, lon: 21, lat: 21)
Coordinates:
  * time     (time) datetime64[ns] 1979-01-31 1979-02-28 ... 2022-12-31
  * lon      (lon) float32 75.0 75.25 75.5 75.75 76.0 ... 79.25 79.5 79.75 80.0
  * lat      (lat) float32 5.0 5.25 5.5 5.75 6.0 6.25 ... 9.0 9.25 9.5 9.75 10.0
Data variables:
    CHL      (time, lon, lat) float32 nan nan nan nan ... 3.221 2.768 nan 1.353

xarray.Dataset

• Dimensions:
  • time: 528
  • lon: 21
  • lat: 21
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1979-01-31 ... 2022-12-31

    array(['1979-01-31T00:00:00.000000000', '1979-02-28T00:00:00.000000000',
           '1979-03-31T00:00:00.000000000', ..., '2022-10-31T00:00:00.000000000',
           '2022-11-30T00:00:00.000000000', '2022-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float32
    75.0 75.25 75.5 ... 79.5 79.75 80.0

    array([75.  , 75.25, 75.5 , 75.75, 76.  , 76.25, 76.5 , 76.75, 77.  , 77.25,
           77.5 , 77.75, 78.  , 78.25, 78.5 , 78.75, 79.  , 79.25, 79.5 , 79.75,
           80.  ], dtype=float32)

  • lat
    (lat)
    float32
    5.0 5.25 5.5 5.75 ... 9.5 9.75 10.0

    array([ 5.  ,  5.25,  5.5 ,  5.75,  6.  ,  6.25,  6.5 ,  6.75,  7.  ,  7.25,
            7.5 ,  7.75,  8.  ,  8.25,  8.5 ,  8.75,  9.  ,  9.25,  9.5 ,  9.75,
           10.  ], dtype=float32)

• Data variables: (1)
  • CHL
    (time, lon, lat)
    float32
    nan nan nan nan ... 2.768 nan 1.353

    array([[[       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            ...,
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan]],
    
           [[       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
    ...
            [0.2705628 , 0.25798783, 0.25561604, ..., 1.4041072 ,
             1.4939387 , 1.8776367 ],
            [0.32376617, 0.2919578 , 0.28656372, ..., 1.7322022 ,
             1.8477138 , 1.8143107 ],
            [0.30853522, 0.3436496 , 0.3281598 , ..., 2.5401766 ,
                    nan, 1.3396835 ]],
    
           [[0.23880072, 0.24270938, 0.24283718, ..., 0.20560512,
             0.22738725, 0.3117161 ],
            [0.2311706 , 0.2247422 , 0.23030376, ..., 0.21133746,
             0.24978474, 0.3144625 ],
            [0.22994642, 0.2216043 , 0.21443008, ..., 0.23380966,
             0.27335462, 0.33697778],
            ...,
            [0.2801102 , 0.27076766, 0.28496662, ..., 1.4887002 ,
             1.4666241 , 1.7220622 ],
            [0.3205243 , 0.2954306 , 0.31341806, ..., 1.9154898 ,
             1.42821   , 1.6312736 ],
            [0.45734957, 0.3265505 , 0.33142075, ..., 2.7675717 ,
                    nan, 1.3527689 ]]], dtype=float32)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1979-01-31', '1979-02-28', '1979-03-31', '1979-04-30',
                   '1979-05-31', '1979-06-30', '1979-07-31', '1979-08-31',
                   '1979-09-30', '1979-10-31',
                   ...
                   '2022-03-31', '2022-04-30', '2022-05-31', '2022-06-30',
                   '2022-07-31', '2022-08-31', '2022-09-30', '2022-10-31',
                   '2022-11-30', '2022-12-31'],
                  dtype='datetime64[ns]', name='time', length=528, freq='M'))

  • lon
    PandasIndex

    PandasIndex(Index([ 75.0, 75.25,  75.5, 75.75,  76.0, 76.25,  76.5, 76.75,  77.0, 77.25,
            77.5, 77.75,  78.0, 78.25,  78.5, 78.75,  79.0, 79.25,  79.5, 79.75,
            80.0],
          dtype='float32', name='lon'))

  • lat
    PandasIndex

    PandasIndex(Index([ 5.0, 5.25,  5.5, 5.75,  6.0, 6.25,  6.5, 6.75,  7.0, 7.25,  7.5, 7.75,
            8.0, 8.25,  8.5, 8.75,  9.0, 9.25,  9.5, 9.75, 10.0],
          dtype='float32', name='lat'))

• Attributes: (0)

… and as a matter of fact, we can graph this data, too!

CHL_month = ds_resampled.mean(dim=['lat', 'lon']).hvplot(label='monthly resampling').options(color='red', )

CHL_day = ds['CHL'].sel(lat=slice(10, 5), lon=slice(75, 80)).mean(dim=['lat', 'lon']).hvplot(label='daily resampling').options(color='blue')

(CHL_day*CHL_month).options(title='Monthly vs Daily resampling of chlorophyll-a levels', xlabel='year')