era5_hourly_reanalysis_single_levels_sa

ERA5 hourly estimates of variables on single levels

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["era5_hourly_reanalysis_single_levels_sa"].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

url	https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels
tags	['ocean', 'model', 'atmosphere']

Dataset Contents

xarray.Dataset

Dimensions:
- latitude: 721
- longitude: 1440
- time: 350640

Coordinates: (3)

latitude
(latitude)
float32
90.0 89.75 89.5 ... -89.75 -90.0
long_name :
latitude
units :
degrees_north
```
array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)
```

longitude

(longitude)

float32

0.0 0.25 0.5 ... 359.5 359.75

long_name :: longitude
units :: degrees_east

array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
       3.5975e+02], dtype=float32)

time

(time)

datetime64[ns]

1979-01-01 ... 2018-12-31T23:00:00

long_name :: time

array(['1979-01-01T00:00:00.000000000', '1979-01-01T01:00:00.000000000',
       '1979-01-01T02:00:00.000000000', ..., '2018-12-31T21:00:00.000000000',
       '2018-12-31T22:00:00.000000000', '2018-12-31T23:00:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (17)

asn

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Snow albedo
units :: (0 - 1)





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

d2m

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: 2 metre dewpoint temperature
units :: K





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

e

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Evaporation
standard_name :: lwe_thickness_of_water_evaporation_amount
units :: m of water equivalent





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

mn2t

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Minimum temperature at 2 metres since previous post-processing
units :: K





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

mx2t

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Maximum temperature at 2 metres since previous post-processing
units :: K





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

ptype

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Precipitation type
units :: code table (4.201)





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

ro

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Runoff
units :: m





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

sd

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Snow depth
standard_name :: lwe_thickness_of_surface_snow_amount
units :: m of water equivalent





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

sro

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Surface runoff
units :: m





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

ssr

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Surface net solar radiation
standard_name :: surface_net_downward_shortwave_flux
units :: J m**-2





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

t2m

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: 2 metre temperature
units :: K





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

tcc

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Total cloud cover
standard_name :: cloud_area_fraction
units :: (0 - 1)





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

tcrw

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Total column rain water
units :: kg m**-2





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

tp

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Total precipitation
units :: m





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

tsn

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: Temperature of snow layer
standard_name :: temperature_in_surface_snow
units :: K





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

u10

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: 10 metre U wind component
units :: m s**-1





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

v10

(time, latitude, longitude)

float32

dask.array<chunksize=(31, 721, 1440), meta=np.ndarray>

long_name :: 10 metre V wind component
units :: m s**-1





  
      Array  Chunk 
  
  
     Bytes  1.46 TB   128.74 MB 
     Shape  (350640, 721, 1440)   (31, 721, 1440) 
     Count  11312 Tasks  11311 Chunks 
     Type  float32  numpy.ndarray

Attributes: (2)
Conventions :
CF-1.6
history :
2019-09-20 05:15:01 GMT by grib_to_netcdf-2.10.0: /opt/ecmwf/eccodes/bin/grib_to_netcdf -o /cache/data7/adaptor.mars.internal-1568954670.105603-18230-3-5ca6e0df-a562-42ba-8b0b-948b2e1815bd.nc /cache/tmp/5ca6e0df-a562-42ba-8b0b-948b2e1815bd-adaptor.mars.internal-1568954670.1062171-18230-2-tmp.grib