gmet_v1

Full GMET version 1 (Newman) met ensemble in zarr format

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["gmet_v1"].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:
- ensemble: 100
- lat: 224
- lon: 464
- time: 12054

Coordinates: (4)

ensemble

(ensemble)

int64

0 1 2 3 4 5 6 ... 94 95 96 97 98 99

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])

lat
(lat)
float64
25.06 25.19 25.31 ... 52.81 52.94
long_name :
latitude
units :
degrees_north
```
array([25.0625, 25.1875, 25.3125, ..., 52.6875, 52.8125, 52.9375])
```
lon
(lon)
float64
-124.9 -124.8 ... -67.19 -67.06
long_name :
longitude
units :
degrees_east
```
array([-124.9375, -124.8125, -124.6875, ...,  -67.3125,  -67.1875,  -67.0625])
```

time

(time)

datetime64[ns]

1980-01-01 ... 2012-12-31

standard_name :: time

array(['1980-01-01T00:00:00.000000000', '1980-01-02T00:00:00.000000000',
       '1980-01-03T00:00:00.000000000', ..., '2012-12-29T00:00:00.000000000',
       '2012-12-30T00:00:00.000000000', '2012-12-31T00:00:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (7)

elevation

(lat, lon)

float64

dask.array<chunksize=(224, 464), meta=np.ndarray>

long_name :: Terrain Elevation
standard_name :: elevation
units :: meters





  
      Array  Chunk 
  
  
     Bytes  831.49 kB   831.49 kB 
     Shape  (224, 464)   (224, 464) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

mask

(lat, lon)

int32

dask.array<chunksize=(224, 464), meta=np.ndarray>

comment :: 0 value indicates cell is not active
long_name :: domain mask
note :: unitless





  
      Array  Chunk 
  
  
     Bytes  415.74 kB   415.74 kB 
     Shape  (224, 464)   (224, 464) 
     Count  2 Tasks  1 Chunks 
     Type  int32  numpy.ndarray

pcp

(ensemble, time, lat, lon)

float64

dask.array<chunksize=(1, 366, 224, 464), meta=np.ndarray>

long_name :: Daily estimated precipitation accumulation
standard_name :: precipitation
units :: kg m-2





  
      Array  Chunk 
  
  
     Bytes  1.00 TB   304.32 MB 
     Shape  (100, 12054, 224, 464)   (1, 366, 224, 464) 
     Count  3301 Tasks  3300 Chunks 
     Type  float64  numpy.ndarray

t_max

(ensemble, time, lat, lon)

float64

dask.array<chunksize=(1, 366, 224, 464), meta=np.ndarray>

long_name :: Daily estimated maximum temperature
units :: degC





  
      Array  Chunk 
  
  
     Bytes  1.00 TB   304.32 MB 
     Shape  (100, 12054, 224, 464)   (1, 366, 224, 464) 
     Count  3301 Tasks  3300 Chunks 
     Type  float64  numpy.ndarray

t_mean

(ensemble, time, lat, lon)

float64

dask.array<chunksize=(1, 366, 224, 464), meta=np.ndarray>

long_name :: Daily estimated mean temperature
units :: degC





  
      Array  Chunk 
  
  
     Bytes  1.00 TB   304.32 MB 
     Shape  (100, 12054, 224, 464)   (1, 366, 224, 464) 
     Count  3301 Tasks  3300 Chunks 
     Type  float64  numpy.ndarray

t_min

(ensemble, time, lat, lon)

float64

dask.array<chunksize=(1, 366, 224, 464), meta=np.ndarray>

long_name :: Daily estimated maximum temperature
units :: degC





  
      Array  Chunk 
  
  
     Bytes  1.00 TB   304.32 MB 
     Shape  (100, 12054, 224, 464)   (1, 366, 224, 464) 
     Count  3301 Tasks  3300 Chunks 
     Type  float64  numpy.ndarray

t_range

(ensemble, time, lat, lon)

float64

dask.array<chunksize=(1, 366, 224, 464), meta=np.ndarray>

long_name :: Daily estimated diurnal temperature range
units :: degC





  
      Array  Chunk 
  
  
     Bytes  1.00 TB   304.32 MB 
     Shape  (100, 12054, 224, 464)   (1, 366, 224, 464) 
     Count  3301 Tasks  3300 Chunks 
     Type  float64  numpy.ndarray

Attributes: (6)
history :
Version 1.0 of ensemble dataset, created December 2014.Mon Oct 23 23:59:02 2017jhamman: corrected mask and added t_min and tmax as ds['t_mean'] +/- 0.5 * ds['t_range']
institution :
National Center for Atmospheric Research (NCAR), Boulder, CO USA
nco_openmp_thread_number :
1
references :
Newman et al. 2015: Gridded Ensemble Precipitation and Temperature Estimates for the Contiguous United States. J. Hydrometeorology
source :
Generated using version 1.0 of CONUS ensemble code base
title :
CONUS daily 12-km gridded ensemble precipitation and temperature dataset for 1980-2012