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:- Create a project on GCP; if this is the first time using GCP, a prompt will appear to choose a Google account to link to all GCP-related activities.
- Create a Cloud Billing account associated with the project and enable billing for the project through this account.
- Using Google Cloud IAM, add the Service Usage Consumer role to your account, which enables it to make billed requests on the behalf of the project.
- Through command line, install the Google Cloud SDK; this can be done using conda:
conda install -c conda-forge google-cloud-sdk - Initialize the
gcloudcommand line interface, logging into the account used to create the aforementioned project and selecting it as the default project; this will allow the project to be used for requester pays access through the command line:gcloud auth login gcloud init - Finally, use
gcloudto establish application default credentials; this will allow the project to be used for requester pays access through applications:gcloud auth application-default login
Metadata
| tags | ['atmosphere', 'model'] |
Dataset Contents
xarray.Dataset
- time: 1920
- x: 5120
- y: 2560
- time(time)float64101.6 100.5 101.6 ... 180.5 180.5
- long_name :
- time
- units :
- day
array([101.625 , 100.541656, 101.583344, ..., 180.416656, 180.458344, 180.5 ]) - x(x)float320.0 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)float320.0 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)float32dask.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 - CLDC(time, y, x)float32dask.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 - CWP(time, y, x)float32dask.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 - IWP(time, y, x)float32dask.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 - LHF(time, y, x)float32dask.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 - LWNS(time, y, x)float32dask.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 - LWNSC(time, y, x)float32dask.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