sam_ngaqua_qobs_eqx_3d

3D 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_3d"].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 gcloud command 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 gcloud to 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

Dimensions:
- time: 640
- x: 5120
- y: 2560
- z: 34

Coordinates: (4)

time
(time)
float64
100.6 100.8 100.9 ... 180.4 180.5
long_name :
time
units :
d
```
array([100.625, 100.75 , 100.875, ..., 180.25 , 180.375, 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)

z

(z)

float32

37.0 112.0 ... 25500.0 27000.0

long_name :: height
units :: m

array([   37.,   112.,   194.,   288.,   395.,   520.,   667.,   843.,  1062.,
        1331.,  1664.,  2274.,  3097.,  4119.,  5310.,  6555.,  7763.,  8931.,
       10048., 11116., 12141., 13138., 14115., 15063., 15984., 16900., 17800.,
       18700., 19800., 21000., 22500., 24000., 25500., 27000.], dtype=float32)

Data variables: (10)

PP

(time, z, y, x)

float32

dask.array<chunksize=(1, 34, 1280, 1280), meta=np.ndarray>

long_name :: Pressure Perturbation
units :: Pa





  
      Array  Chunk 
  
  
     Bytes  1.14 TB   222.82 MB 
     Shape  (640, 34, 2560, 5120)   (1, 34, 1280, 1280) 
     Count  5121 Tasks  5120 Chunks 
     Type  float32  numpy.ndarray

QN

(time, z, y, x)

float32

dask.array<chunksize=(1, 34, 1280, 1280), meta=np.ndarray>

long_name :: Non-precipitating Condensate (Water+Ice)
units :: g/kg





  
      Array  Chunk 
  
  
     Bytes  1.14 TB   222.82 MB 
     Shape  (640, 34, 2560, 5120)   (1, 34, 1280, 1280) 
     Count  5121 Tasks  5120 Chunks 
     Type  float32  numpy.ndarray

QP

(time, z, y, x)

float32

dask.array<chunksize=(1, 34, 1280, 1280), meta=np.ndarray>

long_name :: Precipitating Water (Rain+Snow)
units :: g/kg





  
      Array  Chunk 
  
  
     Bytes  1.14 TB   222.82 MB 
     Shape  (640, 34, 2560, 5120)   (1, 34, 1280, 1280) 
     Count  5121 Tasks  5120 Chunks 
     Type  float32  numpy.ndarray

QRAD

(time, z, y, x)

float32

dask.array<chunksize=(1, 34, 1280, 1280), meta=np.ndarray>

long_name :: Radiative heating rate
units :: K/day





  
      Array  Chunk 
  
  
     Bytes  1.14 TB   222.82 MB 
     Shape  (640, 34, 2560, 5120)   (1, 34, 1280, 1280) 
     Count  5121 Tasks  5120 Chunks 
     Type  float32  numpy.ndarray

QV

(time, z, y, x)

float32

dask.array<chunksize=(1, 34, 1280, 1280), meta=np.ndarray>

long_name :: Water Vapor
units :: g/kg





  
      Array  Chunk 
  
  
     Bytes  1.14 TB   222.82 MB 
     Shape  (640, 34, 2560, 5120)   (1, 34, 1280, 1280) 
     Count  5121 Tasks  5120 Chunks 
     Type  float32  numpy.ndarray

TABS

(time, z, y, x)

float32

dask.array<chunksize=(1, 34, 1280, 1280), meta=np.ndarray>

long_name :: Absolute Temperature
units :: K





  
      Array  Chunk 
  
  
     Bytes  1.14 TB   222.82 MB 
     Shape  (640, 34, 2560, 5120)   (1, 34, 1280, 1280) 
     Count  5121 Tasks  5120 Chunks 
     Type  float32  numpy.ndarray

U

(time, z, y, x)

float32

dask.array<chunksize=(1, 34, 1280, 1280), meta=np.ndarray>

long_name :: X Wind Component
units :: m/s





  
      Array  Chunk 
  
  
     Bytes  1.14 TB   222.82 MB 
     Shape  (640, 34, 2560, 5120)   (1, 34, 1280, 1280) 
     Count  5121 Tasks  5120 Chunks 
     Type  float32  numpy.ndarray

V

(time, z, y, x)

float32

dask.array<chunksize=(1, 34, 1280, 1280), meta=np.ndarray>

long_name :: Y Wind Component
units :: m/s





  
      Array  Chunk 
  
  
     Bytes  1.14 TB   222.82 MB 
     Shape  (640, 34, 2560, 5120)   (1, 34, 1280, 1280) 
     Count  5121 Tasks  5120 Chunks 
     Type  float32  numpy.ndarray

W

(time, z, y, x)

float32

dask.array<chunksize=(1, 34, 1280, 1280), meta=np.ndarray>

long_name :: Z Wind Component
units :: m/s





  
      Array  Chunk 
  
  
     Bytes  1.14 TB   222.82 MB 
     Shape  (640, 34, 2560, 5120)   (1, 34, 1280, 1280) 
     Count  5121 Tasks  5120 Chunks 
     Type  float32  numpy.ndarray

p

(z)

float32

dask.array<chunksize=(34,), meta=np.ndarray>

long_name :: pressure
units :: mb





  
      Array  Chunk 
  
  
     Bytes  136 B   136 B 
     Shape  (34,)   (34,) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

Attributes: (0)