Adding new data

Written by Minh Phan

We can also add new data to our ZARR file as long as the additional dataset shape fits into our original dataset shape but one dimension. By this, we can add data along one dimension at a time, but other dimensions and all the variables (including metadata) must be identical in size. For example, if our dataset has size 100 lat x 100 lon x 200 time with five variables, the new dataset that we can append to must also have the exact five variables, and two of the dimensions be the same size (in the most logical case, we append along the time dimension, so our new data must have 100 lat x 100 lon).

Sometimes, you are also recommended to rechunk the data after appending as unequal chunk sizes may cost computational operation time.

import xarray as xr
import pandas as pd
import numpy as np

For demonstration purposes, I will not go through again process of creating another dataset, and instead provide an already cleaned dataset for us to practice on. Start by loading this cleaned dataset into our file, as well as the original dataset that we already exported (to compare and double check metadata before we export).

To keep our original dataset intact, I made a copy of our original Zarr file. Please load it instead.

Load in data

og_ds = xr.open_zarr('demonstrated data/final-sample-appending.zarr/')
new_ds = xr.open_zarr('demonstrated data/new-data-sample.zarr/')

Note that our new dataset does not have any metadata. As shown in the previous notebooks, metadata is added at the last step, so now we are going to copy all metadata from the original dataset to our new one.

Add metadata

# copy dataset metadata
new_ds.attrs = og_ds.attrs

# copy variables/dimensions metadata
# make sure that all vars in new_ds exist in og_ds
for var in new_ds.variables:
    new_ds[var].attrs = og_ds[var].attrs

# double-check
new_ds

<xarray.Dataset>
Dimensions:          (time: 2556, lat: 81, lon: 81)
Coordinates:
  * lat              (lat) float32 25.0 24.75 24.5 24.25 ... 5.75 5.5 5.25 5.0
  * lon              (lon) float32 60.0 60.25 60.5 60.75 ... 79.5 79.75 80.0
  * time             (time) datetime64[ns] 1993-01-01 1993-01-02 ... 1999-12-31
Data variables: (12/14)
    CHL              (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    CHL_uncertainty  (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    adt              (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    air_temp         (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    direction        (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    sla              (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    ...               ...
    u_curr           (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    u_wind           (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    ug_curr          (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    v_curr           (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    v_wind           (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    vg_curr          (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
Attributes: (12/17)
    creator_email:              minhphan@uw.edu
    creator_name:               Minh Phan
    creator_type:               person
    date_created:               2023-11-11
    geospatial_lat_max:         25.0
    geospatial_lat_min:         5.0
    ...                         ...
    geospatial_lon_units:       degrees_east
    source:                     OSCAR, ERA5 Reanalysis, Copernicus Climate Ch...
    summary:                    Daily mean of 0.25 x 0.25 degrees gridded dat...
    time_coverage_end:          2002-12-31T23:59:59
    time_coverage_start:        2000-01-01T00:00:00
    title:                      Sample of Climate Data for Coastal Upwelling ...

xarray.Dataset

• Dimensions:
  • time: 2556
  • lat: 81
  • lon: 81
• Coordinates: (3)
  • lat
    (lat)
    float32
    25.0 24.75 24.5 ... 5.5 5.25 5.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([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.  ], dtype=float32)

  • lon
    (lon)
    float32
    60.0 60.25 60.5 ... 79.5 79.75 80.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([60.  , 60.25, 60.5 , 60.75, 61.  , 61.25, 61.5 , 61.75, 62.  , 62.25,
           62.5 , 62.75, 63.  , 63.25, 63.5 , 63.75, 64.  , 64.25, 64.5 , 64.75,
           65.  , 65.25, 65.5 , 65.75, 66.  , 66.25, 66.5 , 66.75, 67.  , 67.25,
           67.5 , 67.75, 68.  , 68.25, 68.5 , 68.75, 69.  , 69.25, 69.5 , 69.75,
           70.  , 70.25, 70.5 , 70.75, 71.  , 71.25, 71.5 , 71.75, 72.  , 72.25,
           72.5 , 72.75, 73.  , 73.25, 73.5 , 73.75, 74.  , 74.25, 74.5 , 74.75,
           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]
    1993-01-01 ... 1999-12-31

    _ChunkSizes :

    1

    _CoordinateAxisType :

    Time

    axis :

    T

    coverage_content_type :

    coordinate

    long_name :

    reference time

    standard_name :

    time

    valid_max :

    401767.0

    valid_min :

    0.0

    array(['1993-01-01T00:00:00.000000000', '1993-01-02T00:00:00.000000000',
           '1993-01-03T00:00:00.000000000', ..., '1999-12-29T00:00:00.000000000',
           '1999-12-30T00:00:00.000000000', '1999-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (14)
  • CHL
    (time, lat, lon)
    float32
    dask.array<chunksize=(100, 81, 81), 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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    CHL_uncertainty

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    adt

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 50, 50]

    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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    air_temp

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    direction

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), meta=np.ndarray>

    

    long_name :

    10 metre wind direction

    units :

    °C

    Array Chunk Bytes 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    sla

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 50, 50]

    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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    speed

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), meta=np.ndarray>

    

    long_name :

    10 metre absolute speed

    units :

    m s**-1

    Array Chunk Bytes 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    sst

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    u_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    u_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    ug_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    v_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    v_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

    vg_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 63.97 MiB 2.50 MiB Shape (2556, 81, 81) (100, 81, 81) Dask graph 26 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (17)

  creator_email :

  minhphan@uw.edu

  creator_name :

  Minh Phan

  creator_type :

  person

  date_created :

  2023-11-11

  geospatial_lat_max :

  25.0

  geospatial_lat_min :

  5.0

  geospatial_lat_resolution :

  0.25

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  80.0

  geospatial_lon_min :

  60.0

  geospatial_lon_resolution :

  0.25

  geospatial_lon_units :

  degrees_east

  source :

  OSCAR, ERA5 Reanalysis, Copernicus Climate Change Service (C3S), Copernicus Marine Environment Monitoring Service (CMEMS)

  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 :

  2002-12-31T23:59:59

  time_coverage_start :

  2000-01-01T00:00:00

  title :

  Sample of Climate Data for Coastal Upwelling Machine Learning Project in Indian Ocean

Appending data

new_ds.to_zarr('demonstrated data/final-sample-appending.zarr/', append_dim='time', mode='a')

<xarray.backends.zarr.ZarrStore at 0x7f1455c51dd0>

Final result

xr.open_zarr('demonstrated data/final-sample-appending.zarr/')

<xarray.Dataset>
Dimensions:          (time: 3287, lat: 81, lon: 81)
Coordinates:
  * lat              (lat) float32 25.0 24.75 24.5 24.25 ... 5.75 5.5 5.25 5.0
  * lon              (lon) float32 60.0 60.25 60.5 60.75 ... 79.5 79.75 80.0
  * time             (time) datetime64[ns] 2000-01-01 2000-01-02 ... 1999-12-31
Data variables: (12/14)
    CHL              (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    CHL_uncertainty  (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    adt              (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    air_temp         (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    direction        (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    sla              (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    ...               ...
    u_curr           (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    u_wind           (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    ug_curr          (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    v_curr           (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    v_wind           (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
    vg_curr          (time, lat, lon) float32 dask.array<chunksize=(100, 81, 81), meta=np.ndarray>
Attributes: (12/17)
    creator_email:              minhphan@uw.edu
    creator_name:               Minh Phan
    creator_type:               person
    date_created:               2023-11-11
    geospatial_lat_max:         25.0
    geospatial_lat_min:         5.0
    ...                         ...
    geospatial_lon_units:       degrees_east
    source:                     OSCAR, ERA5 Reanalysis, Copernicus Climate Ch...
    summary:                    Daily mean of 0.25 x 0.25 degrees gridded dat...
    time_coverage_end:          2002-12-31T23:59:59
    time_coverage_start:        2000-01-01T00:00:00
    title:                      Sample of Climate Data for Coastal Upwelling ...

xarray.Dataset

• Dimensions:
  • time: 3287
  • lat: 81
  • lon: 81
• Coordinates: (3)
  • lat
    (lat)
    float32
    25.0 24.75 24.5 ... 5.5 5.25 5.0

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([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.  ], dtype=float32)

  • lon
    (lon)
    float32
    60.0 60.25 60.5 ... 79.5 79.75 80.0

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([60.  , 60.25, 60.5 , 60.75, 61.  , 61.25, 61.5 , 61.75, 62.  , 62.25,
           62.5 , 62.75, 63.  , 63.25, 63.5 , 63.75, 64.  , 64.25, 64.5 , 64.75,
           65.  , 65.25, 65.5 , 65.75, 66.  , 66.25, 66.5 , 66.75, 67.  , 67.25,
           67.5 , 67.75, 68.  , 68.25, 68.5 , 68.75, 69.  , 69.25, 69.5 , 69.75,
           70.  , 70.25, 70.5 , 70.75, 71.  , 71.25, 71.5 , 71.75, 72.  , 72.25,
           72.5 , 72.75, 73.  , 73.25, 73.5 , 73.75, 74.  , 74.25, 74.5 , 74.75,
           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]
    2000-01-01 ... 1999-12-31

    _ChunkSizes :

    1

    _CoordinateAxisType :

    Time

    axis :

    T

    coverage_content_type :

    coordinate

    long_name :

    reference time

    standard_name :

    time

    valid_max :

    401767.0

    valid_min :

    0.0

    array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000',
           '2000-01-03T00:00:00.000000000', ..., '1999-12-29T00:00:00.000000000',
           '1999-12-30T00:00:00.000000000', '1999-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (14)
  • CHL
    (time, lat, lon)
    float32
    dask.array<chunksize=(100, 81, 81), 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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    CHL_uncertainty

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    adt

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 50, 50]

    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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    air_temp

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    direction

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), meta=np.ndarray>

    

    long_name :

    10 metre wind direction

    units :

    °C

    Array Chunk Bytes 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    sla

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), meta=np.ndarray>

    

    _ChunkSizes :

    [1, 50, 50]

    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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    speed

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), meta=np.ndarray>

    

    long_name :

    10 metre absolute speed

    units :

    m s**-1

    Array Chunk Bytes 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    sst

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    u_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    u_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    ug_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    v_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    v_wind

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

    vg_curr

    (time, lat, lon)

    float32

    dask.array<chunksize=(100, 81, 81), 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 82.27 MiB 2.50 MiB Shape (3287, 81, 81) (100, 81, 81) Dask graph 33 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (17)

  creator_email :

  minhphan@uw.edu

  creator_name :

  Minh Phan

  creator_type :

  person

  date_created :

  2023-11-11

  geospatial_lat_max :

  25.0

  geospatial_lat_min :

  5.0

  geospatial_lat_resolution :

  0.25

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  80.0

  geospatial_lon_min :

  60.0

  geospatial_lon_resolution :

  0.25

  geospatial_lon_units :

  degrees_east

  source :

  OSCAR, ERA5 Reanalysis, Copernicus Climate Change Service (C3S), Copernicus Marine Environment Monitoring Service (CMEMS)

  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 :

  2002-12-31T23:59:59

  time_coverage_start :

  2000-01-01T00:00:00

  title :

  Sample of Climate Data for Coastal Upwelling Machine Learning Project in Indian Ocean