ACCESS Cryosphere Community Datapool Tools (ccdtools)

This notebook provides a general introduction to the ACCESS Cryosphere Community Datapool Tools (ccdtools) and its functionality. This notebook is designed to be executed on the NCI Gadi supercomputer.

import ccdtools
import matplotlib.pyplot as plt

Initialise and interrogate the datapool catalogue

# Initialise the datapool catalogue.
## NOTE: To build a catalog on your local machine, parse a custom YAML file to ccdtools.catalog.DataCatalog(yaml_path = "<PATH_TO_YAML_FILE>").
catalog = ccdtools.catalog.DataCatalog()

# List all datasets within the catalog.
## NOTE:  A raw pandas.DataFrame can be returned using catalog.datasets
catalog

ACCESS Cryosphere Data Catalogue

---

Number of datasets: 16
Rows: 65

	dataset	display_name	description	tags	version	subdataset	path	full_path	extension	skip_lines	no_data_value	ignore_dirs	ignore_files	loader	resolutions	static_patterns
0	measures_bedmachine_antarctica	BedMachine Antarctica	High-resolution bed topography and ice thickne...	[antarctica, bed topography, ice thickness, su...	v1	None	/g/data/av17/access-nri/cryosphere-data-pool/e...	/g/data/av17/access-nri/cryosphere-data-pool/e...	nc	0	NaN	[]	[]	default	None	[]
1	measures_bedmachine_antarctica	BedMachine Antarctica	High-resolution bed topography and ice thickne...	[antarctica, bed topography, ice thickness, su...	v2	None	/g/data/av17/access-nri/cryosphere-data-pool/e...	/g/data/av17/access-nri/cryosphere-data-pool/e...	nc	0	NaN	[]	[]	default	None	[]
2	measures_bedmachine_antarctica	BedMachine Antarctica	High-resolution bed topography and ice thickne...	[antarctica, bed topography, ice thickness, su...	v3	None	/g/data/av17/access-nri/cryosphere-data-pool/e...	/g/data/av17/access-nri/cryosphere-data-pool/e...	nc	0	NaN	[]	[]	default	None	[]
3	bedmap	Bedmap	Gridded, geospatial, and point datasets of Ant...	[antarctica, bed topography, ice thickness, su...	v1	geospatial	/g/data/av17/access-nri/cryosphere-data-pool/e...	/g/data/av17/access-nri/cryosphere-data-pool/e...	gpkg	0	-9999.0	[]	[]	default	None	[]
4	bedmap	Bedmap	Gridded, geospatial, and point datasets of Ant...	[antarctica, bed topography, ice thickness, su...	v1	points	/g/data/av17/access-nri/cryosphere-data-pool/e...	/g/data/av17/access-nri/cryosphere-data-pool/e...	csv	18	-9999.0	[]	[]	default	None	[]
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
60	measures_its_live_regional_glacier_and_ice_she...	ITS_LIVE Regional Glacier and Ice Sheet Surfac...	ITS_LIVE regional glacier and ice sheet surfac...	[antarctica, greenland, ice velocity]	v2	RGI17A	/g/data/av17/access-nri/cryosphere-data-pool/i...	/g/data/av17/access-nri/cryosphere-data-pool/i...	nc	0	NaN	[]	[]	measures_velocity	None	[_2014-2022_]
61	measures_its_live_regional_glacier_and_ice_she...	ITS_LIVE Regional Glacier and Ice Sheet Surfac...	ITS_LIVE regional glacier and ice sheet surfac...	[antarctica, greenland, ice velocity]	v2	RGI18A	/g/data/av17/access-nri/cryosphere-data-pool/i...	/g/data/av17/access-nri/cryosphere-data-pool/i...	nc	0	NaN	[]	[]	measures_velocity	None	[_2014-2022_]
62	measures_its_live_regional_glacier_and_ice_she...	ITS_LIVE Regional Glacier and Ice Sheet Surfac...	ITS_LIVE regional glacier and ice sheet surfac...	[antarctica, greenland, ice velocity]	v2	RGI19A	/g/data/av17/access-nri/cryosphere-data-pool/i...	/g/data/av17/access-nri/cryosphere-data-pool/i...	nc	0	NaN	[]	[]	measures_velocity	None	[_2014-2022_]
63	racmo2.3p2_monthly_27km_1979-2022	RACMO2.3p2 Monthly Surface Mass Balance and Cl...	Regional Atmospheric Climate Model (RACMO) ver...	[antarctica, surface mass balance, climate dat...	v1	None	/g/data/av17/access-nri/cryosphere-data-pool/s...	/g/data/av17/access-nri/cryosphere-data-pool/s...	nc	0	NaN	[]	[Height_latlon_ANT27.nc, TotIS_RACMO_ANT27_IMB...	racmo	None	[]
64	racmo2.4p1_monthly_11km_1979-2023	RACMO2.4p1 Monthly Surface Mass Balance and Cl...	Regional Atmospheric Climate Model (RACMO) ver...	[antarctica, surface mass balance, climate dat...	v1	None	/g/data/av17/access-nri/cryosphere-data-pool/s...	/g/data/av17/access-nri/cryosphere-data-pool/s...	nc	0	NaN	[]	[ANT11_masks.nc]	racmo	None	[]

65 rows × 16 columns

Various functions exist to allow users to interrogate the data catalog:

• search() - Search the data pool for a given key word. This searches the dataset, display_name, and tags fields.

• help() - Retrieve a summary of dataset-specific information for a given dataset.

• available_versions() - Retrieve a list of available versions for a given dataset.

• available_subdatasets() - Retrieve a list of available subdataets for a given dataset.

• available_resolutions() - Retrieve a list of available resolutions for a given dataset.

# Search the catalog based on display_name, tags, or dataset_name. Here, explore racmo datasets
catalog.search('racmo')

ACCESS Cryosphere Data Catalogue

---

Number of datasets: 2
Rows: 2

	dataset	display_name	description	tags	version	subdataset	path	full_path	extension	skip_lines	no_data_value	ignore_dirs	ignore_files	loader	resolutions	static_patterns
0	racmo2.3p2_monthly_27km_1979-2022	RACMO2.3p2 Monthly Surface Mass Balance and Cl...	Regional Atmospheric Climate Model (RACMO) ver...	[antarctica, surface mass balance, climate dat...	v1	None	/g/data/av17/access-nri/cryosphere-data-pool/s...	/g/data/av17/access-nri/cryosphere-data-pool/s...	nc	0	NaN	[]	[Height_latlon_ANT27.nc, TotIS_RACMO_ANT27_IMB...	racmo	None	[]
1	racmo2.4p1_monthly_11km_1979-2023	RACMO2.4p1 Monthly Surface Mass Balance and Cl...	Regional Atmospheric Climate Model (RACMO) ver...	[antarctica, surface mass balance, climate dat...	v1	None	/g/data/av17/access-nri/cryosphere-data-pool/s...	/g/data/av17/access-nri/cryosphere-data-pool/s...	nc	0	NaN	[]	[ANT11_masks.nc]	racmo	None	[]

# The help() function provides a full overview of a given dataset
catalog.help('measures_its_live_regional_glacier_and_ice_sheet_surface_velocities')

Dataset: measures_its_live_regional_glacier_and_ice_sheet_surface_velocities

Available versions:
  - v1
  - v2

Tip:
  Use catalog.help(dataset=..., version=...) for more details.

# Version-specific information about a given dataset can be obtained by specifying a given version
catalog.help('measures_its_live_regional_glacier_and_ice_sheet_surface_velocities', version = 'v1')

Dataset: measures_its_live_regional_glacier_and_ice_sheet_surface_velocities

Available versions:
  - v1
  - v2

Version: v1

Available subdatasets:
  - ALA
  - ANT
  - CAN
  - GRE
  - HMA
  - ICE
  - PAT
  - SRA

Supported catalog keywords:
  - subdataset : yes
  - resolution : yes
  - static  : yes

Example usage:
  catalog.load_dataset('measures_its_live_regional_glacier_and_ice_sheet_surface_velocities', version = 'v1', subdataset = '...', resolution = '...', static = True)

# Find versions of a given dataset
catalog.available_versions('racmo2.3p2_monthly_27km_1979-2022')

['v1']

# Find available subdatasets for bedmap. By default, the latest version is used.
catalog.available_subdatasets('bedmap')

['geospatial', 'points', 'gridded']

# Get available resolutions for a given dataset/version/subdataset. If not specified, the latest version is used.
catalog.available_resolutions('measures_its_live_regional_glacier_and_ice_sheet_surface_velocities', version = 'v1', subdataset = 'ALA')

{'annual': {'120m': 'G0120', '240m': 'G0240'},
 'static': {'120m': 'G0120', '240m': 'G0240'}}

Loading Datasets

To load a dataset, we use the load_dataset function. This accepts various keyword arguments, depending on the dataset. At a minimum it requires dataset_name.

Other arguments include:

• version [str] - this is the version string to be loaded (e.g. “v1”, “v2”, “v3”). By default, it uses the latest version.

• subdataset [str]- this is the name of the subdataset to be loaded

• resolution [str] - this is the requested resolution to be loaded, if resolutions are available for the given dataset.

• composite [bool] - should the composite file be loaded, or the annual series. Default = False

# Load a simple dataset
bedmachine_v1 = catalog.load_dataset('measures_bedmachine_antarctica', version = 'v1')
bedmachine_v1

/g/data/xp65/public/apps/med_conda/envs/analysis3-25.12/lib/python3.11/site-packages/distributed/diagnostics/nvml.py:14: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml

<xarray.Dataset> Size: 4GB
Dimensions:    (y: 13333, x: 13333)
Coordinates:
  * y          (y) int32 53kB 3333000 3332500 3332000 ... -3332500 -3333000
  * x          (x) int32 53kB -3333000 -3332500 -3332000 ... 3332500 3333000
Data variables:
    mapping    |S1 1B ...
    mask       (y, x) int8 178MB dask.array<chunksize=(1905, 1905), meta=np.ndarray>
    firn       (y, x) float32 711MB dask.array<chunksize=(953, 953), meta=np.ndarray>
    surface    (y, x) float32 711MB dask.array<chunksize=(953, 953), meta=np.ndarray>
    thickness  (y, x) float32 711MB dask.array<chunksize=(953, 953), meta=np.ndarray>
    bed        (y, x) float32 711MB dask.array<chunksize=(953, 953), meta=np.ndarray>
    errbed     (y, x) float32 711MB dask.array<chunksize=(1334, 1334), meta=np.ndarray>
    source     (y, x) int8 178MB dask.array<chunksize=(1905, 1905), meta=np.ndarray>
    geoid      (y, x) int16 356MB dask.array<chunksize=(1334, 1334), meta=np.ndarray>
Attributes: (12/17)
    Conventions:                 CF-1.7
    Title:                       BedMachine Antarctica
    Author:                      Mathieu Morlighem
    version:                     05-Nov-2019 (v1.38)
    nx:                          13333.0
    ny:                          13333.0
    ...                          ...
    ymax:                        3333000
    spacing:                     500
    no_data:                     -9999.0
    license:                     No restrictions on access or use
    Data_citation:               Morlighem M. et al., (2019), Deep glacial tr...
    Notes:                       Data processed at the Department of Earth Sy...

xarray.Dataset

• Dimensions:
  • y: 13333
  • x: 13333
• Coordinates: (2)
  • y
    (y)
    int32
    3333000 3332500 ... -3333000

    long_name :

    Cartesian y-coordinate

    standard_name :

    projection_y_coordinate

    units :

    meter

    array([ 3333000,  3332500,  3332000, ..., -3332000, -3332500, -3333000],
          dtype=int32)

  • x
    (x)
    int32
    -3333000 -3332500 ... 3333000

    long_name :

    Cartesian x-coordinate

    standard_name :

    projection_x_coordinate

    units :

    meter

    array([-3333000, -3332500, -3332000, ...,  3332000,  3332500,  3333000],
          dtype=int32)

• Data variables: (9)
  • mapping
    ()
    |S1
    ...

    grid_mapping_name :

    polar_stereographic

    latitude_of_projection_origin :

    -90.0

    standard_parallel :

    -71.0

    straight_vertical_longitude_from_pole :

    0.0

    semi_major_axis :

    6378273.0

    inverse_flattening :

    298.2794050428205

    false_easting :

    0.0

    false_northing :

    0.0

    [1 values with dtype=|S1]

  • mask
    (y, x)
    int8
    dask.array<chunksize=(1905, 1905), meta=np.ndarray>

    long_name :

    mask

    grid_mapping :

    mapping

    valid_range :

    [0 4]

    flag_values :

    [0 1 2 3 4]

    flag_meanings :

    ocean ice_free_land grounded_ice floating_ice lake_vostok

    source :

    Antarctic Digital Database (rock outcrop) and Jeremie Mouginot (pers. comm., grounding lines)

    Array Chunk Bytes 169.53 MiB 3.46 MiB Shape (13333, 13333) (1905, 1905) Dask graph 49 chunks in 2 graph layers Data type int8 numpy.ndarray

  • firn
    (y, x)
    float32
    dask.array<chunksize=(953, 953), meta=np.ndarray>

    long_name :

    firn air content

    units :

    meters

    grid_mapping :

    mapping

    source :

    FAC model from Ligtenberg et al., 2011, forced by RACMO2.3p2 (van Wessem et al., 2018)

    Array Chunk Bytes 678.13 MiB 3.46 MiB Shape (13333, 13333) (953, 953) Dask graph 196 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface
    (y, x)
    float32
    dask.array<chunksize=(953, 953), meta=np.ndarray>

    long_name :

    ice surface elevation

    standard_name :

    surface_altitude

    units :

    meters

    grid_mapping :

    mapping

    source :

    REMA (Byrd Polar and Climate Research Center and the Polar Geospatial Center)

    Array Chunk Bytes 678.13 MiB 3.46 MiB Shape (13333, 13333) (953, 953) Dask graph 196 chunks in 2 graph layers Data type float32 numpy.ndarray

  • thickness
    (y, x)
    float32
    dask.array<chunksize=(953, 953), meta=np.ndarray>

    long_name :

    ice thickness

    standard_name :

    land_ice_thickness

    units :

    meters

    grid_mapping :

    mapping

    source :

    Mathieu Morlighem

    Array Chunk Bytes 678.13 MiB 3.46 MiB Shape (13333, 13333) (953, 953) Dask graph 196 chunks in 2 graph layers Data type float32 numpy.ndarray

  • bed
    (y, x)
    float32
    dask.array<chunksize=(953, 953), meta=np.ndarray>

    long_name :

    bed topography

    standard_name :

    bedrock_altitude

    units :

    meters

    grid_mapping :

    mapping

    source :

    IBCSO and Mathieu Morlighem

    Array Chunk Bytes 678.13 MiB 3.46 MiB Shape (13333, 13333) (953, 953) Dask graph 196 chunks in 2 graph layers Data type float32 numpy.ndarray

  • errbed
    (y, x)
    float32
    dask.array<chunksize=(1334, 1334), meta=np.ndarray>

    long_name :

    bed topography/ice thickness error

    units :

    meters

    grid_mapping :

    mapping

    source :

    Mathieu Morlighem

    Array Chunk Bytes 678.13 MiB 6.79 MiB Shape (13333, 13333) (1334, 1334) Dask graph 100 chunks in 2 graph layers Data type float32 numpy.ndarray

  • source
    (y, x)
    int8
    dask.array<chunksize=(1905, 1905), meta=np.ndarray>

    long_name :

    data source

    grid_mapping :

    mapping

    valid_range :

    [ 1 10]

    flag_values :

    [ 1 2 3 4 5 6 7 10]

    flag_meanings :

    REMA_IBCSO mass_conservation interpolation hydrostatic streamline_diffusion gravity seismic multibeam

    source :

    Mathieu Morlighem

    Array Chunk Bytes 169.53 MiB 3.46 MiB Shape (13333, 13333) (1905, 1905) Dask graph 49 chunks in 2 graph layers Data type int8 numpy.ndarray

  • geoid
    (y, x)
    int16
    dask.array<chunksize=(1334, 1334), meta=np.ndarray>

    long_name :

    EIGEN-EC4 Geoid - WGS84 Ellipsoid difference

    standard_name :

    geoid_height_above_reference_ellipsoid

    units :

    meters

    grid_mapping :

    mapping

    geoid :

    eigen-6c4 (Forste et al 2014)

    Array Chunk Bytes 339.07 MiB 3.39 MiB Shape (13333, 13333) (1334, 1334) Dask graph 100 chunks in 2 graph layers Data type int16 numpy.ndarray

• Attributes: (17)

  Conventions :

  CF-1.7

  Title :

  BedMachine Antarctica

  Author :

  Mathieu Morlighem

  version :

  05-Nov-2019 (v1.38)

  nx :

  13333.0

  ny :

  13333.0

  Projection :

  Polar Stereographic South (71S,0E)

  proj4 :

  +init=epsg:3031

  sea_water_density (kg m-3) :

  1027.0

  ice_density (kg m-3) :

  917.0

  xmin :

  -3333000

  ymax :

  3333000

  spacing :

  500

  no_data :

  -9999.0

  license :

  No restrictions on access or use

  Data_citation :

  Morlighem M. et al., (2019), Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet, Nature Geoscience (accepted)

  Notes :

  Data processed at the Department of Earth System Science, University of California, Irvine

# Load a specific subdataset. The 'type' returned depends on the dataset.
measures_basins_imbie = catalog.load_dataset('measures_antarctic_boundaries', version = 'v2', subdataset = 'basins_imbie')
measures_basins_imbie

	NAME	Regions	Subregions	TYPE	geometry
0	NaN	Islands	NaN	IS	MULTIPOLYGON (((-217776.532 2130382.522, -2168...
1	A-Ap	East	A-Ap	GR	POLYGON ((1233919.752 1935020.761, 1237354.55 ...
2	Ap-B	East	Ap-B	GR	POLYGON ((1267480.529 1935993.733, 1261072.64 ...
3	B-C	East	B-C	GR	POLYGON ((1672255.018 -94566.965, 1625433.485 ...
4	C-Cp	East	C-Cp	GR	POLYGON ((2594276.532 -575938.016, 2594114.248...
5	Cp-D	East	Cp-D	GR	POLYGON ((2317315.872 -603916.511, 2321147.611...
6	D-Dp	East	D-Dp	GR	POLYGON ((1419962.788 -1124912.594, 1435793.14...
7	Dp-E	East	Dp-E	GR	POLYGON ((458049.88 -1586925.021, 458790.209 -...
8	E-Ep	East	E-Ep	GR	POLYGON ((385888.335 -1128116.87, 385900.076 -...
9	Ep-F	West	Ep-F	GR	POLYGON ((-578695.182 -1285897.642, -578945.60...
10	F-G	West	F-G	GR	POLYGON ((-1099110.89 -966140.129, -1089377.46...
11	G-H	West	G-H	GR	POLYGON ((-1515456.671 -545301.309, -1515230.3...
12	H-Hp	West	H-Hp	GR	POLYGON ((-1665853.445 206829.399, -1662779.24...
13	Hp-I	Peninsula	Hp-I	GR	MULTIPOLYGON (((-1981550.026 606779.819, -1980...
14	I-Ipp	Peninsula	I-Ipp	GR	POLYGON ((-2418949.539 1418837.511, -2418949.6...
15	Ipp-J	Peninsula	Ipp-J	GR	POLYGON ((-1584579.956 902423, -1584579.956 90...
16	J-Jpp	West	J-Jpp	GR	POLYGON ((-1187659.179 23095.994, -1199482.277...
17	Jpp-K	East	Jpp-K	GR	POLYGON ((-621202.971 771276.873, -620950.97 7...
18	K-A	East	K-A	GR	POLYGON ((-681761.563 1361890.885, -681258.113...

# Construct a simple figure

# Coarsen bed topogrpahy for improved render time
bed = bedmachine_v1.bed.coarsen(x = 100, y = 100, boundary = 'trim').mean()

# Plot bed topography
fig, ax = plt.subplots()
bed.plot(ax=ax)

# Add grounding line
measures_basins_imbie.plot(ax = ax, edgecolor = 'red', facecolor = 'none')

<Axes: xlabel='Cartesian x-coordinate [meter]', ylabel='Cartesian y-coordinate [meter]'>

/g/data/xp65/public/apps/med_conda/envs/analysis3-25.12/lib/python3.11/site-packages/dask/_task_spec.py:758: FutureWarning: In a future version, xarray will not decode the variable 'date' into a timedelta64 dtype based on the presence of a timedelta-like 'units' attribute by default. Instead it will rely on the presence of a timedelta64 'dtype' attribute, which is now xarray's default way of encoding timedelta64 values.
To continue decoding into a timedelta64 dtype, either set `decode_timedelta=True` when opening this dataset, or add the attribute `dtype='timedelta64[ns]'` to this variable on disk.
To opt-in to future behavior, set `decode_timedelta=False`.
  return self.func(*new_argspec, **kwargs)
/g/data/xp65/public/apps/med_conda/envs/analysis3-25.12/lib/python3.11/site-packages/dask/_task_spec.py:758: FutureWarning: In a future version, xarray will not decode the variable 'dt' into a timedelta64 dtype based on the presence of a timedelta-like 'units' attribute by default. Instead it will rely on the presence of a timedelta64 'dtype' attribute, which is now xarray's default way of encoding timedelta64 values.
To continue decoding into a timedelta64 dtype, either set `decode_timedelta=True` when opening this dataset, or add the attribute `dtype='timedelta64[ns]'` to this variable on disk.
To opt-in to future behavior, set `decode_timedelta=False`.
  return self.func(*new_argspec, **kwargs)

# Load composite velocity data
vel_data = catalog.load_dataset('measures_its_live_regional_glacier_and_ice_sheet_surface_velocities', version = 'v1', subdataset = 'ANT', resolution = '240m', static = True)
vel_data

<xarray.Dataset> Size: 25GB
Dimensions:        (y: 18392, x: 22896)
Coordinates:
  * y              (y) float64 147kB 2.26e+06 2.259e+06 ... -2.154e+06
  * x              (x) float64 183kB -2.678e+06 -2.678e+06 ... 2.817e+06
Data variables: (12/14)
    chip_size_max  (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>
    count          (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>
    date           (y, x) timedelta64[ns] 3GB dask.array<chunksize=(144, 358), meta=np.ndarray>
    dt             (y, x) timedelta64[ns] 3GB dask.array<chunksize=(144, 358), meta=np.ndarray>
    ice            (y, x) float32 2GB dask.array<chunksize=(288, 358), meta=np.ndarray>
    mapping        float32 4B ...
    ...             ...
    v              (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>
    v_err          (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>
    vx             (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>
    vx_err         (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>
    vy             (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>
    vy_err         (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>
Attributes:
    Conventions:              CF-1.8
    GDAL_AREA_OR_POINT:       Area
    author:                   Alex S. Gardner, JPL/NASA
    date_created:             05-May-2019 15:53:24
    institution:              NASA Jet Propulsion Laboratory (JPL), Californi...
    motion_coordinates:       map
    motion_detection_method:  feature
    scene_pair_type:          optical
    title:                    autoRIFT surface velocities

xarray.Dataset

• Dimensions:
  • y: 18392
  • x: 22896
• Coordinates: (2)
  • y
    (y)
    float64
    2.26e+06 2.259e+06 ... -2.154e+06

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    units :

    m

    array([ 2259727.5,  2259487.5,  2259247.5, ..., -2153632.5, -2153872.5,
           -2154112.5])

  • x
    (x)
    float64
    -2.678e+06 -2.678e+06 ... 2.817e+06

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    units :

    m

    array([-2678287.5, -2678047.5, -2677807.5, ...,  2816032.5,  2816272.5,
            2816512.5])

• Data variables: (14)
  • chip_size_max
    (y, x)
    float32
    dask.array<chunksize=(144, 358), meta=np.ndarray>

    grid_mapping :

    mapping

    long_name :

    maximum_allowable_search_chip

    standard_name :

    chip_size_max

    units :

    m

    Array Chunk Bytes 1.57 GiB 201.38 kiB Shape (18392, 22896) (144, 358) Dask graph 8192 chunks in 2 graph layers Data type float32 numpy.ndarray

  • count
    (y, x)
    float32
    dask.array<chunksize=(144, 358), meta=np.ndarray>

    grid_mapping :

    mapping

    long_name :

    image_pair_count

    standard_name :

    count

    units :

    count

    Array Chunk Bytes 1.57 GiB 201.38 kiB Shape (18392, 22896) (144, 358) Dask graph 8192 chunks in 2 graph layers Data type float32 numpy.ndarray

  • date
    (y, x)
    timedelta64[ns]
    dask.array<chunksize=(144, 358), meta=np.ndarray>

    grid_mapping :

    mapping

    long_name :

    error_weighted_image_pair_center_date

    standard_name :

    date

    Array Chunk Bytes 3.14 GiB 402.75 kiB Shape (18392, 22896) (144, 358) Dask graph 8192 chunks in 2 graph layers Data type timedelta64[ns] numpy.ndarray

  • dt
    (y, x)
    timedelta64[ns]
    dask.array<chunksize=(144, 358), meta=np.ndarray>

    grid_mapping :

    mapping

    long_name :

    error weighted average time separation between image-pairs

    standard_name :

    dt

    Array Chunk Bytes 3.14 GiB 402.75 kiB Shape (18392, 22896) (144, 358) Dask graph 8192 chunks in 2 graph layers Data type timedelta64[ns] numpy.ndarray

  • ice
    (y, x)
    float32
    dask.array<chunksize=(288, 358), meta=np.ndarray>

    flag_meanings :

    non-ice ice

    flag_values :

    [0 1]

    grid_mapping :

    mapping

    long_name :

    ice_mask

    standard_name :

    ice

    Array Chunk Bytes 1.57 GiB 402.75 kiB Shape (18392, 22896) (288, 358) Dask graph 4096 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mapping
    ()
    float32
    ...

    GeoTransform :

    -2678407.5 240.0 0 2259847.5 0 -240.0

    false_easting :

    0.0

    false_northing :

    0.0

    grid_mapping_name :

    polar_stereographic

    inverse_flattening :

    298.257223563

    latitude_of_projection_origin :

    -90.0

    scale_factor_at_projection_origin :

    1.0

    semi_major_axis :

    6378137.0

    spatial_epsg :

    3031.0

    spatial_ref :

    PROJCS["WGS 84 / Antarctic Polar Stereographic",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Polar_Stereographic"],PARAMETER["latitude_of_origin",-71],PARAMETER["central_meridian",0],PARAMETER["scale_factor",1],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","3031"]]

    straight_vertical_longitude_from_pole :

    0.0

    proj4text :

    +proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs

    crs_wkt :

    PROJCS["WGS 84 / Antarctic Polar Stereographic",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Polar_Stereographic"],PARAMETER["latitude_of_origin",-71],PARAMETER["central_meridian",0],PARAMETER["scale_factor",1],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","3031"]]

    standard_parallel :

    -71.0

    [1 values with dtype=float32]

  • ocean
    (y, x)
    float32
    dask.array<chunksize=(288, 358), meta=np.ndarray>

    flag_meanings :

    non-ocean ocean

    flag_values :

    [0 1]

    grid_mapping :

    mapping

    long_name :

    ocean_mask

    standard_name :

    ocean

    Array Chunk Bytes 1.57 GiB 402.75 kiB Shape (18392, 22896) (288, 358) Dask graph 4096 chunks in 2 graph layers Data type float32 numpy.ndarray

  • rock
    (y, x)
    float32
    dask.array<chunksize=(288, 358), meta=np.ndarray>

    flag_meanings :

    non-rock rock

    flag_values :

    [0 1]

    grid_mapping :

    mapping

    long_name :

    rock_mask

    standard_name :

    rock

    Array Chunk Bytes 1.57 GiB 402.75 kiB Shape (18392, 22896) (288, 358) Dask graph 4096 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v
    (y, x)
    float32
    dask.array<chunksize=(144, 358), meta=np.ndarray>

    grid_mapping :

    mapping

    map_scale_corrected :

    1

    quality_cutoff :

    7.0

    standard_name :

    land_ice_surface_velocity

    units :

    meter/year

    Array Chunk Bytes 1.57 GiB 201.38 kiB Shape (18392, 22896) (144, 358) Dask graph 8192 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v_err
    (y, x)
    float32
    dask.array<chunksize=(144, 358), meta=np.ndarray>

    grid_mapping :

    mapping

    standard_name :

    velocity_err

    units :

    meter/year

    Array Chunk Bytes 1.57 GiB 201.38 kiB Shape (18392, 22896) (144, 358) Dask graph 8192 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vx
    (y, x)
    float32
    dask.array<chunksize=(144, 358), meta=np.ndarray>

    grid_mapping :

    mapping

    map_scale_corrected :

    1

    quality_cutoff :

    7.0

    standard_name :

    land_ice_surface_x_velocity

    units :

    meter/year

    Array Chunk Bytes 1.57 GiB 201.38 kiB Shape (18392, 22896) (144, 358) Dask graph 8192 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vx_err
    (y, x)
    float32
    dask.array<chunksize=(144, 358), meta=np.ndarray>

    grid_mapping :

    mapping

    standard_name :

    x_velocity_err

    units :

    meter/year

    Array Chunk Bytes 1.57 GiB 201.38 kiB Shape (18392, 22896) (144, 358) Dask graph 8192 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vy
    (y, x)
    float32
    dask.array<chunksize=(144, 358), meta=np.ndarray>

    grid_mapping :

    mapping

    map_scale_corrected :

    1

    quality_cutoff :

    7.0

    standard_name :

    land_ice_surface_y_velocity

    units :

    meter/year

    Array Chunk Bytes 1.57 GiB 201.38 kiB Shape (18392, 22896) (144, 358) Dask graph 8192 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vy_err
    (y, x)
    float32
    dask.array<chunksize=(144, 358), meta=np.ndarray>

    grid_mapping :

    mapping

    standard_name :

    y_velocity_err

    units :

    meter/year

    Array Chunk Bytes 1.57 GiB 201.38 kiB Shape (18392, 22896) (144, 358) Dask graph 8192 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (9)

  Conventions :

  CF-1.8

  GDAL_AREA_OR_POINT :

  Area

  author :

  Alex S. Gardner, JPL/NASA

  date_created :

  05-May-2019 15:53:24

  institution :

  NASA Jet Propulsion Laboratory (JPL), California Institute of Technology

  motion_coordinates :

  map

  motion_detection_method :

  feature

  scene_pair_type :

  optical

  title :

  autoRIFT surface velocities