sam_ngaqua_qobs_eqx_2d

2D fields from a near-global Aquaplanet Simulation with the System for Atmospheric Modeling

Load in Python

from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/atmosphere.yaml") ds = cat["sam_ngaqua_qobs_eqx_2d"].to_dask()

Working with requester pays data

Several of the datasets within the cloud data catalog are contained in requester pays storage buckets. This means that a user requesting data must provide their own billing project (created and authenticated through Google Cloud Platform) to be billed for the charges associated with accessing a dataset. To set up an GCP billing project and use it for authentication in applications:

Metadata

tags ['atmosphere', 'model']

Dataset Contents

Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • time: 1920
    • x: 5120
    • y: 2560
    • time
      (time)
      float64
      101.6 100.5 101.6 ... 180.5 180.5
      long_name :
      time
      units :
      day
      array([101.625   , 100.541656, 101.583344, ..., 180.416656, 180.458344,
             180.5     ])
    • x
      (x)
      float32
      0.0 4000.0 ... 20476000.0
      units :
      m
      array([0.0000e+00, 4.0000e+03, 8.0000e+03, ..., 2.0468e+07, 2.0472e+07,
             2.0476e+07], dtype=float32)
    • y
      (y)
      float32
      0.0 4000.0 ... 10236000.0
      units :
      m
      array([0.0000e+00, 4.0000e+03, 8.0000e+03, ..., 1.0228e+07, 1.0232e+07,
             1.0236e+07], dtype=float32)
    • CLD
      (time, y, x)
      float32
      dask.array<chunksize=(1, 2560, 5120), meta=np.ndarray>
      long_name :
      Cloud Frequency
      units :
      %
      Array Chunk
      Bytes 100.66 GB 52.43 MB
      Shape (1920, 2560, 5120) (1, 2560, 5120)
      Count 1921 Tasks 1920 Chunks
      Type float32 numpy.ndarray
      5120 2560 1920
    • CLDC
      (time, y, x)
      float32
      dask.array<chunksize=(1, 2560, 5120), meta=np.ndarray>
      long_name :
      Cloud cover (Instantaneous)
      units :
      Array Chunk
      Bytes 100.66 GB 52.43 MB
      Shape (1920, 2560, 5120) (1, 2560, 5120)
      Count 1921 Tasks 1920 Chunks
      Type float32 numpy.ndarray
      5120 2560 1920
    • CWP
      (time, y, x)
      float32
      dask.array<chunksize=(1, 2560, 5120), meta=np.ndarray>
      long_name :
      Cloud Water Path
      units :
      mm
      Array Chunk
      Bytes 100.66 GB 52.43 MB
      Shape (1920, 2560, 5120) (1, 2560, 5120)
      Count 1921 Tasks 1920 Chunks
      Type float32 numpy.ndarray
      5120 2560 1920
    • IWP
      (time, y, x)
      float32
      dask.array<chunksize=(1, 2560, 5120), meta=np.ndarray>
      long_name :
      Ice Path
      units :
      mm
      Array Chunk
      Bytes 100.66 GB 52.43 MB
      Shape (1920, 2560, 5120) (1, 2560, 5120)
      Count 1921 Tasks 1920 Chunks
      Type float32 numpy.ndarray
      5120 2560 1920
    • LHF
      (time, y, x)
      float32
      dask.array<chunksize=(1, 2560, 5120), meta=np.ndarray>
      long_name :
      Latent Heat Flux
      units :
      W/m2
      Array Chunk
      Bytes 100.66 GB 52.43 MB
      Shape (1920, 2560, 5120) (1, 2560, 5120)
      Count 1921 Tasks 1920 Chunks
      Type float32 numpy.ndarray
      5120 2560 1920
    • LWNS
      (time, y, x)
      float32
      dask.array<chunksize=(1, 2560, 5120), meta=np.ndarray>
      long_name :
      Net LW at the surface
      units :
      W/m2
      Array Chunk
      Bytes 100.66 GB 52.43 MB
      Shape (1920, 2560, 5120) (1, 2560, 5120)
      Count 1921 Tasks 1920 Chunks
      Type float32 numpy.ndarray
      5120 2560 1920
    • LWNSC
      (time, y, x)
      float32
      dask.array<chunksize=(1, 2560, 5120), meta=np.ndarray>
      long_name :
      Net clear-sky LW at the surface
      units :
      W/m2
      Array Chunk
      Bytes 100.66 GB 52.43 MB
      Shape (1920, 2560, 5120) (1, 2560, 5120)
      Count 1921 Tasks 1920 Chunks
      Type float32 numpy.ndarray