wrf50_erai

Daily meteorology from 50km WRF simulation forced with ERA-Interim

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["wrf50_erai"].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

Dataset Contents

xarray.Dataset

Dimensions:
- ni: 71
- nj: 107
- time: 13483

Coordinates: (3)

lat

(ni, nj)

float32

dask.array<chunksize=(71, 107), meta=np.ndarray>

FieldType :: 104
MemoryOrder :: XY
description :: LATITUDE, SOUTH IS NEGATIVE
stagger :
units :: degree_north





  
      Array  Chunk 
  
  
     Bytes  30.39 kB   30.39 kB 
     Shape  (71, 107)   (71, 107) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

lon

(ni, nj)

float32

dask.array<chunksize=(71, 107), meta=np.ndarray>

FieldType :: 104
MemoryOrder :: XY
description :: LONGITUDE, WEST IS NEGATIVE
stagger :
units :: degree_east





  
      Array  Chunk 
  
  
     Bytes  30.39 kB   30.39 kB 
     Shape  (71, 107)   (71, 107) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

time

(time)

datetime64[ns]

1979-01-01T11:30:00 ... 2015-11-30T11:30:00

FieldType :: 104
MemoryOrder :: 0
description :: minutes since 1979-01-01 00:00:00
stagger :

array(['1979-01-01T11:30:00.000000000', '1979-01-02T11:30:00.000000000',
       '1979-01-03T11:30:00.000000000', ..., '2015-11-28T11:30:00.000000000',
       '2015-11-29T11:28:00.000000000', '2015-11-30T11:30:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (15)

DIV

(time, ni, nj)

float32