Datacube(NASA POWER Climate Data) to GeoCroissant Conversion Workflow, Metadata Generation

This notebook demonstrates the conversion of NASA POWER climate datacube (Xarray Dataset) to GeoCroissant metadata format for standardized data discovery and FAIR data principles.

GeoCroissant

Overview

  • Data Source: NASA POWER MERRA2 temporal dataset
  • Format: Zarr datacube stored on AWS S3
  • Variables: Temperature at 2 Meters (T2M) and other climate variables
  • Coverage: Global, monthly temporal resolution
  • Output: GeoCroissant JSON-LD metadata for data cataloging

Loading NASA POWER Dataset with Xarray

This cell demonstrates how to load the NASA POWER Zarr dataset using Xarray.

import xarray as xr

store = "https://nasa-power.s3.us-west-2.amazonaws.com/merra2/temporal/power_merra2_monthly_temporal_utc.zarr/"
ds = xr.open_zarr(store)

ds
<xarray.Dataset> Size: 231GB
Dimensions:          (time: 588, lat: 361, lon: 576)
Coordinates:
  * lat              (lat) float64 3kB -90.0 -89.5 -89.0 ... 89.0 89.5 90.0
  * lon              (lon) float64 5kB -180.0 -179.4 -178.8 ... 178.8 179.4
  * time             (time) datetime64[ns] 5kB 1981-01-31 ... 2029-12-31
Data variables: (12/472)
    CDD0             (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    CDD10            (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    CDD18_3          (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    DISPH            (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    EVLAND           (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    EVLAND_00        (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    ...               ...
    WS50M_MAX        (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    WS50M_MAX_AVG    (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    WS50M_MIN        (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    WS50M_MIN_AVG    (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    WS50M_RANGE      (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    Z0M              (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
Attributes: (12/37)
    acknowledgement:            The Prediction of Worldwide Energy Resources ...
    comment:                    POWER data version 10.0.0 uses the source dat...
    conventions:                CF-1.8, ACDD-1.3
    creator_email:              bradley.macpherson@nasa.gov
    creator_name:               Bradley Macpherson
    date_created:               2024-08-19T08:32:59Z
    ...                         ...
    time_coverage_end:          2029-12-31T00:00:00
    time_coverage_resolution:   P1ME
    time_coverage_start:        1981-01-01T00:00:00
    time_standard:              Universal Time Coordinated (UTC)
    title:                      POWER Universal Time Coordinated (UTC) Time S...
    version:                    POWER Version 10.0.0
    • lat
      PandasIndex
      PandasIndex(Index([-90.0, -89.5, -89.0, -88.5, -88.0, -87.5, -87.0, -86.5, -86.0, -85.5,
       ...
        85.5,  86.0,  86.5,  87.0,  87.5,  88.0,  88.5,  89.0,  89.5,  90.0],
      dtype='float64', name='lat', length=361))
    • lon
      PandasIndex
      PandasIndex(Index([  -180.0, -179.375,  -178.75, -178.125,   -177.5, -176.875,  -176.25,
       -175.625,   -175.0, -174.375,
       ...
         173.75,  174.375,    175.0,  175.625,   176.25,  176.875,    177.5,
        178.125,   178.75,  179.375],
      dtype='float64', name='lon', length=576))
    • time
      PandasIndex
      PandasIndex(DatetimeIndex(['1981-01-31', '1981-02-28', '1981-03-31', '1981-04-30',
               '1981-05-31', '1981-06-30', '1981-07-31', '1981-08-31',
               '1981-09-30', '1981-10-31',
               ...
               '2029-03-31', '2029-04-30', '2029-05-31', '2029-06-30',
               '2029-07-31', '2029-08-31', '2029-09-30', '2029-10-31',
               '2029-11-30', '2029-12-31'],
              dtype='datetime64[ns]', name='time', length=588, freq=None))
  • acknowledgement :
    The Prediction of Worldwide Energy Resources (POWER) Project is funded through the NASA Applied Sciences Program within the Earth Science Division of the Science Mission Directorate.
    comment :
    POWER data version 10.0.0 uses the source data resolution.
    conventions :
    CF-1.8, ACDD-1.3
    creator_email :
    bradley.macpherson@nasa.gov
    creator_name :
    Bradley Macpherson
    date_created :
    2024-08-19T08:32:59Z
    derived_from :
    The data products are derived from data partners and the POWER science team.
    derived_info :
    The data products are derived from data partners.
    derived_link :
    https://power.larc.nasa.gov
    geospatial_bounds :
    POLYGON((-180.0 90.0, -180.0 -90.0, 180.0 -90.0, 180.0 90.0, -180.0 90.0))
    geospatial_lat_max :
    90.0
    geospatial_lat_min :
    -90.0
    geospatial_lat_resolution :
    0.5
    geospatial_lon_max :
    179.375
    geospatial_lon_min :
    -180.0
    geospatial_lon_resolution :
    0.625
    history :
    The POWER project was established in the early 2000s to support distribution of NASA research products.
    id :
    power_merra2_monthly_temporal_utc.zarr
    institution :
    NASA Langley Research Center (LaRC)
    keywords :
    Earth Science
    keywords_vocabulary :
    NASA Global Change Master Directory (GCMD) Science Keyword Version 9.1.5
    naming_authority :
    gov.nasa.larc.power
    processing_level :
    4
    project :
    Prediction Of Worldwide Energy Resource (POWER)
    publisher_email :
    paul.w.stackhouse@nasa.gov
    publisher_name :
    Paul Stackhouse
    references :
    https://power.larc.nasa.gov
    source :
    Prediction Of Worldwide Energy Resource (POWER)
    standard_name_vocabulary :
    CF Standard Name Table v72
    summary :
    The POWER project provides solar and meteorological datasets from NASA research for support of renewable energy, building energy efficiency, and agricultural needs.
    time_coverage_duration :
    P1ME
    time_coverage_end :
    2029-12-31T00:00:00
    time_coverage_resolution :
    P1ME
    time_coverage_start :
    1981-01-01T00:00:00
    time_standard :
    Universal Time Coordinated (UTC)
    title :
    POWER Universal Time Coordinated (UTC) Time Series
    version :
    POWER Version 10.0.0

    • Exploring Dataset Structure and Metadata

    This cell examines the dataset structure, global attributes, and variable metadata.

    print(ds)                # Summary of dataset
print(ds.attrs)          # Global metadata
print(ds.T2M)           # Inspect one variable
print(ds.T2M.attrs)     # Variable-specific metadata
    <xarray.Dataset> Size: 231GB
Dimensions:          (time: 588, lat: 361, lon: 576)
Coordinates:
  * lat              (lat) float64 3kB -90.0 -89.5 -89.0 ... 89.0 89.5 90.0
  * lon              (lon) float64 5kB -180.0 -179.4 -178.8 ... 178.8 179.4
  * time             (time) datetime64[ns] 5kB 1981-01-31 ... 2029-12-31
Data variables: (12/472)
    CDD0             (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    CDD10            (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    CDD18_3          (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    DISPH            (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    EVLAND           (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    EVLAND_00        (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    ...               ...
    WS50M_MAX        (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    WS50M_MAX_AVG    (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    WS50M_MIN        (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    WS50M_MIN_AVG    (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    WS50M_RANGE      (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
    Z0M              (time, lat, lon) float32 489MB dask.array<chunksize=(588, 30, 30), meta=np.ndarray>
Attributes: (12/37)
    acknowledgement:            The Prediction of Worldwide Energy Resources ...
    comment:                    POWER data version 10.0.0 uses the source dat...
    conventions:                CF-1.8, ACDD-1.3
    creator_email:              bradley.macpherson@nasa.gov
    creator_name:               Bradley Macpherson
    date_created:               2024-08-19T08:32:59Z
    ...                         ...
    time_coverage_end:          2029-12-31T00:00:00
    time_coverage_resolution:   P1ME
    time_coverage_start:        1981-01-01T00:00:00
    time_standard:              Universal Time Coordinated (UTC)
    title:                      POWER Universal Time Coordinated (UTC) Time S...
    version:                    POWER Version 10.0.0
{'acknowledgement': 'The Prediction of Worldwide Energy Resources (POWER) Project is funded through the NASA Applied Sciences Program within the Earth Science Division of the Science Mission Directorate.', 'comment': 'POWER data version 10.0.0 uses the source data resolution.', 'conventions': 'CF-1.8, ACDD-1.3', 'creator_email': 'bradley.macpherson@nasa.gov', 'creator_name': 'Bradley Macpherson', 'date_created': '2024-08-19T08:32:59Z', 'derived_from': 'The data products are derived from data partners and the POWER science team.', 'derived_info': 'The data products are derived from data partners.', 'derived_link': 'https://power.larc.nasa.gov', 'geospatial_bounds': 'POLYGON((-180.0 90.0, -180.0 -90.0, 180.0 -90.0, 180.0 90.0, -180.0 90.0))', 'geospatial_lat_max': 90.0, 'geospatial_lat_min': -90.0, 'geospatial_lat_resolution': 0.5, 'geospatial_lon_max': 179.375, 'geospatial_lon_min': -180.0, 'geospatial_lon_resolution': 0.625, 'history': 'The POWER project was established in the early 2000s to support distribution of NASA research products.', 'id': 'power_merra2_monthly_temporal_utc.zarr', 'institution': 'NASA Langley Research Center (LaRC)', 'keywords': 'Earth Science', 'keywords_vocabulary': 'NASA Global Change Master Directory (GCMD) Science Keyword Version 9.1.5', 'naming_authority': 'gov.nasa.larc.power', 'processing_level': '4', 'project': 'Prediction Of Worldwide Energy Resource (POWER)', 'publisher_email': 'paul.w.stackhouse@nasa.gov', 'publisher_name': 'Paul Stackhouse', 'references': 'https://power.larc.nasa.gov', 'source': 'Prediction Of Worldwide Energy Resource (POWER)', 'standard_name_vocabulary': 'CF Standard Name Table v72', 'summary': 'The POWER project provides solar and meteorological datasets from NASA research for support of renewable energy, building energy efficiency, and agricultural needs.', 'time_coverage_duration': 'P1ME', 'time_coverage_end': '2029-12-31T00:00:00', 'time_coverage_resolution': 'P1ME', 'time_coverage_start': '1981-01-01T00:00:00', 'time_standard': 'Universal Time Coordinated (UTC)', 'title': 'POWER Universal Time Coordinated (UTC) Time Series', 'version': 'POWER Version 10.0.0'}
<xarray.DataArray 'T2M' (time: 588, lat: 361, lon: 576)> Size: 489MB
dask.array<open_dataset-T2M, shape=(588, 361, 576), dtype=float32, chunksize=(588, 30, 30), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float64 3kB -90.0 -89.5 -89.0 -88.5 ... 88.5 89.0 89.5 90.0
  * lon      (lon) float64 5kB -180.0 -179.4 -178.8 -178.1 ... 178.1 178.8 179.4
  * time     (time) datetime64[ns] 5kB 1981-01-31 1981-02-28 ... 2029-12-31
Attributes:
    base:                float32
    cell_methods:        time: mean
    definition:          The average air (dry bulb) temperature at 2 meters a...
    long_name:           Temperature at 2 Meters
    significant_digits:  2
    standard_name:       Temperature_at_2_Meters
    status:              official
    units:               C
    valid_max:           80.0
    valid_min:           -125.0
{'base': 'float32', 'cell_methods': 'time: mean', 'definition': 'The average air (dry bulb) temperature at 2 meters above the surface of the earth.', 'long_name': 'Temperature at 2 Meters', 'significant_digits': 2, 'standard_name': 'Temperature_at_2_Meters', 'status': 'official', 'units': 'C', 'valid_max': 80.0, 'valid_min': -125.0}

    Visualizing Temperature Data

    This cell creates a basic visualization of the Temperature at 2 Meters (T2M) variable for the first time step.

    ds.T2M.isel(time=0).plot()

    Extracting Year-Specific Climate Data

    This cell demonstrates how to extract data for a specific year (2020) from the full dataset.

    import xarray as xr

# Open the dataset
ds = xr.open_zarr(
    "https://nasa-power.s3.us-west-2.amazonaws.com/merra2/temporal/power_merra2_monthly_temporal_utc.zarr/"
)

# Choose the year
year = 2020

# Select only that year
ds_2020 = ds.sel(time=slice(f"{year}-01-01", f"{year}-12-31"))

# Now ds_2020['T2M'] has shape: (12, lat, lon)

print(ds_2020['T2M'])

# Optionally: save it to a new file
ds_2020[['T2M']].to_netcdf(f"T2M_monthly_{year}.nc")

# Plot first month
ds_2020['T2M'].isel(time=0).plot()
    <xarray.DataArray 'T2M' (time: 12, lat: 361, lon: 576)> Size: 10MB
dask.array<getitem, shape=(12, 361, 576), dtype=float32, chunksize=(12, 30, 30), chunktype=numpy.ndarray>
Coordinates:
  * lat      (lat) float64 3kB -90.0 -89.5 -89.0 -88.5 ... 88.5 89.0 89.5 90.0
  * lon      (lon) float64 5kB -180.0 -179.4 -178.8 -178.1 ... 178.1 178.8 179.4
  * time     (time) datetime64[ns] 96B 2020-01-31 2020-02-29 ... 2020-12-31
Attributes:
    base:                float32
    cell_methods:        time: mean
    definition:          The average air (dry bulb) temperature at 2 meters a...
    long_name:           Temperature at 2 Meters
    significant_digits:  2
    standard_name:       Temperature_at_2_Meters
    status:              official
    units:               C
    valid_max:           80.0
    valid_min:           -125.0

    GeoCroissant Converter

    import json
from datetime import datetime
import xarray as xr
import hashlib


def create_nasa_t2m_2020_croissant():
    """Create GeoCroissant metadata for NASA T2M 2020 following TTL specifications."""
    
    zarr_url = "https://nasa-power.s3.us-west-2.amazonaws.com/merra2/temporal/power_merra2_monthly_temporal_utc.zarr/"
    
    # Load dataset
    ds_full = xr.open_zarr(zarr_url)
    ds_2020 = ds_full.sel(time=slice("2020-01-01", "2020-12-31"))
    
    # Generate checksum
    hash_input = f"{zarr_url}2020T2M".encode('utf-8')
    checksum = hashlib.sha256(hash_input).hexdigest()
    
    # TTL-compliant GeoCroissant metadata
    croissant = {
        "@context": {
            "@language": "en",
            "@vocab": "https://schema.org/",
            "citeAs": "cr:citeAs",
            "column": "cr:column",
            "conformsTo": "dct:conformsTo",
            "cr": "http://mlcommons.org/croissant/",
            "geocr": "http://mlcommons.org/croissant/geo/",
            "rai": "http://mlcommons.org/croissant/RAI/",
            "dct": "http://purl.org/dc/terms/",
            "sc": "https://schema.org/",
            "data": {
                "@id": "cr:data",
                "@type": "@json"
            },
            "examples": {
                "@id": "cr:examples",
                "@type": "@json"
            },
            "dataType": {
                "@id": "cr:dataType",
                "@type": "@vocab"
            },
            "extract": "cr:extract",
            "field": "cr:field",
            "fileProperty": "cr:fileProperty",
            "fileObject": "cr:fileObject",
            "fileSet": "cr:fileSet",
            "format": "cr:format",
            "includes": "cr:includes",
            "isLiveDataset": "cr:isLiveDataset",
            "jsonPath": "cr:jsonPath",
            "key": "cr:key",
            "md5": "cr:md5",
            "parentField": "cr:parentField",
            "path": "cr:path",
            "recordSet": "cr:recordSet",
            "references": "cr:references",
            "regex": "cr:regex",
            "repeated": "cr:repeated",
            "replace": "cr:replace",
            "samplingRate": "cr:samplingRate",
            "separator": "cr:separator",
            "source": "cr:source",
            "subField": "cr:subField",
            "transform": "cr:transform"
        },
        "@type": "sc:Dataset",
        "name": "NASA POWER T2M 2020",
        "description": "Temperature at 2 Meters monthly data for 2020",
        "version": "1.0.0",
        "license": "CC-BY-4.0",
        "conformsTo": [
            "http://mlcommons.org/croissant/1.1",
            "http://mlcommons.org/croissant/geo/1.0"
        ],
        "citeAs": "@dataset{nasa_power_t2m_2020, title={NASA POWER T2M 2020}, year={2020}, url={https://nasa-power.s3.us-west-2.amazonaws.com}}",
        "datePublished": "2020-01-01",
        
        # Standard spatial coverage using schema.org
        "spatialCoverage": {
            "@type": "Place",
            "geo": {
                "@type": "GeoShape",
                "box": "-90.0 -180.0 90.0 179.375"
            }
        },
        
        # GeoCroissant spatial properties
        "geocr:coordinateReferenceSystem": "EPSG:4326",
        "geocr:spatialResolution": {
            "@type": "QuantitativeValue",
            "value": 0.5,
            "unitText": "degrees"
        },
        "geocr:temporalResolution": {
            "@type": "QuantitativeValue", 
            "value": 1,
            "unitText": "month"
        },
        
        # Temporal coverage
        "temporalCoverage": "2020-01-01/2020-12-31",
        
        "keywords": ["temperature", "climate", "nasa power", "t2m", "2020"],
        
        "distribution": [
            {
                "@type": "cr:FileObject",
                "@id": "zarr-data",
                "name": "zarr-data",
                "contentUrl": zarr_url,
                "encodingFormat": "application/zarr",
                "md5": checksum[:32]
            }
        ],
        
        "recordSet": [
            {
                "@type": "cr:RecordSet",
                "name": "t2m_data",
                "field": [
                    {
                        "@type": "cr:Field",
                        "name": "T2M",
                        "description": "Temperature at 2 Meters",
                        "dataType": "sc:Float",
                        "source": {
                            "fileObject": {
                                "@id": "zarr-data"
                            }
                        }
                    },
                    {
                        "@type": "cr:Field", 
                        "name": "latitude",
                        "description": "Latitude coordinate",
                        "dataType": "sc:Float",
                        "source": {
                            "fileObject": {
                                "@id": "zarr-data"
                            }
                        }
                    },
                    {
                        "@type": "cr:Field",
                        "name": "longitude",
                        "description": "Longitude coordinate", 
                        "dataType": "sc:Float",
                        "source": {
                            "fileObject": {
                                "@id": "zarr-data"
                            }
                        }
                    },
                    {
                        "@type": "cr:Field",
                        "name": "time",
                        "description": "Time coordinate",
                        "dataType": "sc:Text",
                        "source": {
                            "fileObject": {
                                "@id": "zarr-data"
                            }
                        }
                    }
                ]
            }
        ]
    }
    
    # Save metadata
    with open("NASA_T2M_2020_croissant.json", "w") as f:
        json.dump(croissant, f, indent=2)
    
    return croissant


# Execute
croissant = create_nasa_t2m_2020_croissant()
    import json

# Read and print the metadata
with open('NASA_T2M_2020_croissant.json', 'r') as f:
    metadata = json.load(f)

# Pretty print the metadata
print(json.dumps(metadata, indent=2))
    {
  "@context": {
    "@language": "en",
    "@vocab": "https://schema.org/",
    "citeAs": "cr:citeAs",
    "column": "cr:column",
    "conformsTo": "dct:conformsTo",
    "cr": "http://mlcommons.org/croissant/",
    "geocr": "http://mlcommons.org/croissant/geo/",
    "rai": "http://mlcommons.org/croissant/RAI/",
    "dct": "http://purl.org/dc/terms/",
    "sc": "https://schema.org/",
    "data": {
      "@id": "cr:data",
      "@type": "@json"
    },
    "examples": {
      "@id": "cr:examples",
      "@type": "@json"
    },
    "dataType": {
      "@id": "cr:dataType",
      "@type": "@vocab"
    },
    "extract": "cr:extract",
    "field": "cr:field",
    "fileProperty": "cr:fileProperty",
    "fileObject": "cr:fileObject",
    "fileSet": "cr:fileSet",
    "format": "cr:format",
    "includes": "cr:includes",
    "isLiveDataset": "cr:isLiveDataset",
    "jsonPath": "cr:jsonPath",
    "key": "cr:key",
    "md5": "cr:md5",
    "parentField": "cr:parentField",
    "path": "cr:path",
    "recordSet": "cr:recordSet",
    "references": "cr:references",
    "regex": "cr:regex",
    "repeated": "cr:repeated",
    "replace": "cr:replace",
    "samplingRate": "cr:samplingRate",
    "separator": "cr:separator",
    "source": "cr:source",
    "subField": "cr:subField",
    "transform": "cr:transform"
  },
  "@type": "sc:Dataset",
  "name": "NASA POWER T2M 2020",
  "description": "Temperature at 2 Meters monthly data for 2020",
  "version": "1.0.0",
  "license": "CC-BY-4.0",
  "conformsTo": [
    "http://mlcommons.org/croissant/1.1",
    "http://mlcommons.org/croissant/geo/1.0"
  ],
  "citeAs": "@dataset{nasa_power_t2m_2020, title={NASA POWER T2M 2020}, year={2020}, url={https://nasa-power.s3.us-west-2.amazonaws.com}}",
  "datePublished": "2020-01-01",
  "spatialCoverage": {
    "@type": "Place",
    "geo": {
      "@type": "GeoShape",
      "box": "-90.0 -180.0 90.0 179.375"
    }
  },
  "geocr:coordinateReferenceSystem": "EPSG:4326",
  "geocr:spatialResolution": {
    "@type": "QuantitativeValue",
    "value": 0.5,
    "unitText": "degrees"
  },
  "geocr:temporalResolution": {
    "@type": "QuantitativeValue",
    "value": 1,
    "unitText": "month"
  },
  "temporalCoverage": "2020-01-01/2020-12-31",
  "keywords": [
    "temperature",
    "climate",
    "nasa power",
    "t2m",
    "2020"
  ],
  "distribution": [
    {
      "@type": "cr:FileObject",
      "@id": "zarr-data",
      "name": "zarr-data",
      "contentUrl": "https://nasa-power.s3.us-west-2.amazonaws.com/merra2/temporal/power_merra2_monthly_temporal_utc.zarr/",
      "encodingFormat": "application/zarr",
      "md5": "cf42f8c8b1d4152d36857216bdfc5056"
    }
  ],
  "recordSet": [
    {
      "@type": "cr:RecordSet",
      "name": "t2m_data",
      "field": [
        {
          "@type": "cr:Field",
          "name": "T2M",
          "description": "Temperature at 2 Meters",
          "dataType": "sc:Float",
          "source": {
            "fileObject": {
              "@id": "zarr-data"
            }
          }
        },
        {
          "@type": "cr:Field",
          "name": "latitude",
          "description": "Latitude coordinate",
          "dataType": "sc:Float",
          "source": {
            "fileObject": {
              "@id": "zarr-data"
            }
          }
        },
        {
          "@type": "cr:Field",
          "name": "longitude",
          "description": "Longitude coordinate",
          "dataType": "sc:Float",
          "source": {
            "fileObject": {
              "@id": "zarr-data"
            }
          }
        },
        {
          "@type": "cr:Field",
          "name": "time",
          "description": "Time coordinate",
          "dataType": "sc:Text",
          "source": {
            "fileObject": {
              "@id": "zarr-data"
            }
          }
        }
      ]
    }
  ]
}

    Validating GeoCroissant Metadata

    !mlcroissant validate --jsonld=NASA_T2M_2020_croissant.json

    Temperature Data Visualization

    Single Month Temperature Plot

    import json
import xarray as xr
import matplotlib.pyplot as plt


def plot_temperature(metadata_file: str = "NASA_T2M_2020_croissant.json", month: int = 1):
    """Plot temperature for a specific month using croissant metadata."""
    
    # Load metadata
    with open(metadata_file, 'r', encoding='utf-8') as f:
        metadata = json.load(f)
    
    # Get Zarr URL
    zarr_url = None
    for dist in metadata.get('distribution', []):
        if dist.get('encodingFormat') == 'application/zarr':
            zarr_url = dist.get('contentUrl', '')
            break
    
    if not zarr_url:
        raise ValueError("No Zarr URL found in metadata")
    
    # Load data
    print(f"Loading data for month {month}...")
    ds_full = xr.open_zarr(zarr_url)
    ds_2020 = ds_full.sel(time=slice("2020-01-01", "2020-12-31"))
    month_data = ds_2020['T2M'].isel(time=month-1)
    
    # Get month metadata
    month_names = ['January', 'February', 'March', 'April', 'May', 'June',
                   'July', 'August', 'September', 'October', 'November', 'December']
    
    # Create plot
    plt.figure(figsize=(15, 10))
    
    # Plot temperature map
    month_data.plot(
        cmap='RdBu_r', 
        cbar_kwargs={'label': 'Temperature (°C)'}
    )
    
    # Add title and labels
    plt.title(f'T2M Temperature - {month_names[month-1]} 2020', fontsize=16)
    plt.xlabel('Longitude', fontsize=12)
    plt.ylabel('Latitude', fontsize=12)
    
    plt.show()
    
    # Print statistics
    print(f"\n{month_names[month-1]} Statistics:")
    print(f"  Mean: {float(month_data.mean()):.2f}°C")
    print(f"  Min: {float(month_data.min()):.2f}°C")
    print(f"  Max: {float(month_data.max()):.2f}°C")
    print(f"  Std: {float(month_data.std()):.2f}°C")


# Usage
if __name__ == "__main__":
    plot_temperature(month=1)
    Loading data for month 1...

January Statistics:
  Mean: 4.21°C
  Min: -41.95°C
  Max: 35.23°C
  Std: 19.88°C

    All Months Temperature Plot

    This cell creates a comprehensive visualization showing temperature patterns across all 12 months of 2020.

    import json
import xarray as xr
import matplotlib.pyplot as plt


def plot_all_months(metadata_file: str = "NASA_T2M_2020_croissant.json"):
    """Plot temperature for all 12 months using croissant metadata."""
    
    # Load metadata
    with open(metadata_file, 'r', encoding='utf-8') as f:
        metadata = json.load(f)
    
    # Get Zarr URL
    zarr_url = None
    for dist in metadata.get('distribution', []):
        if dist.get('encodingFormat') == 'application/zarr':
            zarr_url = dist.get('contentUrl', '')
            break
    
    if not zarr_url:
        raise ValueError("No Zarr URL found in metadata")
    
    # Load data
    print(f"Loading data for all months...")
    ds_full = xr.open_zarr(zarr_url)
    ds_2020 = ds_full.sel(time=slice("2020-01-01", "2020-12-31"))
    
    # Get month names
    month_names = ['January', 'February', 'March', 'April', 'May', 'June',
                   'July', 'August', 'September', 'October', 'November', 'December']
    
    # Create subplots for all 12 months
    fig, axes = plt.subplots(3, 4, figsize=(20, 15))
    axes = axes.flatten()
    
    # Plot each month
    for month in range(12):
        month_data = ds_2020['T2M'].isel(time=month)
        
        # Plot temperature map
        month_data.plot(
            ax=axes[month],
            cmap='RdBu_r', 
            cbar_kwargs={'label': 'Temperature (°C)'}
        )
        
        # Add title
        axes[month].set_title(f'{month_names[month]} 2020', fontsize=12)
        axes[month].set_xlabel('Longitude')
        axes[month].set_ylabel('Latitude')
    
    # Add overall title
    plt.suptitle('T2M Temperature - All Months 2020', fontsize=18)
    
    # Adjust layout
    plt.tight_layout()
    plt.show()


# Usage
if __name__ == "__main__":
    plot_all_months()
    Loading data for all months...