gpcp_cdr_daily_v1_3

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["gpcp_cdr_daily_v1_3"].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

title	Global Precipitation Climatatology Project (GPCP) Climate Data Record (CDR), Daily V1.3
url	https://climatedataguide.ucar.edu/climate-data/gpcp-daily-global-precipitation-climatology-project
tags	['atmosphere', 'model']

Dataset Contents

xarray.Dataset

Dimensions:
- latitude: 180
- longitude: 360
- nv: 2
- time: 8069

Coordinates: (6)

lat_bounds

(time, latitude, nv)

float32

dask.array<chunksize=(8069, 180, 2), meta=np.ndarray>

comment :: latitude values at the north and south bounds of each pixel.





  
      Array  Chunk 
  
  
     Bytes  11.62 MB   11.62 MB 
     Shape  (8069, 180, 2)   (8069, 180, 2) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

latitude

(latitude)

float32

-90.0 -89.0 -88.0 ... 88.0 89.0

axis :: Y
bounds :: lat_bounds
long_name :: Latitude
standard_name :: latitude
units :: degrees_north
valid_range :: [-90.0, 90.0]

array([-90., -89., -88., -87., -86., -85., -84., -83., -82., -81., -80., -79.,
       -78., -77., -76., -75., -74., -73., -72., -71., -70., -69., -68., -67.,
       -66., -65., -64., -63., -62., -61., -60., -59., -58., -57., -56., -55.,
       -54., -53., -52., -51., -50., -49., -48., -47., -46., -45., -44., -43.,
       -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31.,
       -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19.,
       -18., -17., -16., -15., -14., -13., -12., -11., -10.,  -9.,  -8.,  -7.,
        -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   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.],
      dtype=float32)

lon_bounds

(time, longitude, nv)

float32

dask.array<chunksize=(8069, 360, 2), meta=np.ndarray>

comment :: longitude values at the west and east bounds of each pixel.





  
      Array  Chunk 
  
  
     Bytes  23.24 MB   23.24 MB 
     Shape  (8069, 360, 2)   (8069, 360, 2) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

longitude
(longitude)
float32
0.0 1.0 2.0 ... 357.0 358.0 359.0
axis :
X
bounds :
lon_bounds
long_name :
Longitude
standard_name :
longitude
units :
degrees_east
valid_range :
[0.0, 360.0]
```
array([  0.,   1.,   2., ..., 357., 358., 359.], dtype=float32)
```

time

(time)

object

1997-01-01 00:00:00 ... 2018-12-31 00:00:00

axis :: T
bounds :: time_bounds
long_name :: time
standard_name :: time

array([cftime.DatetimeGregorian(1997, 1, 1, 0, 0, 0, 0),
       cftime.DatetimeGregorian(1997, 1, 2, 0, 0, 0, 0),
       cftime.DatetimeGregorian(1997, 1, 3, 0, 0, 0, 0), ...,
       cftime.DatetimeGregorian(2018, 12, 29, 0, 0, 0, 0),
       cftime.DatetimeGregorian(2018, 12, 30, 0, 0, 0, 0),
       cftime.DatetimeGregorian(2018, 12, 31, 0, 0, 0, 0)], dtype=object)

time_bounds

(time, nv)

object

dask.array<chunksize=(8069, 2), meta=np.ndarray>

comment :: time bounds for each time value





  
      Array  Chunk 
  
  
     Bytes  129.10 kB   129.10 kB 
     Shape  (8069, 2)   (8069, 2) 
     Count  2 Tasks  1 Chunks 
     Type  object  numpy.ndarray

Data variables: (1)

precip

(time, latitude, longitude)

float32

dask.array<chunksize=(517, 180, 360), meta=np.ndarray>

cell_methods :: area: mean time: mean
long_name :: NOAA Climate Data Record (CDR) of Daily GPCP Satellite-Gauge Combined Precipitation
standard_name :: lwe_precipitation_rate
units :: mm/day
valid_range :: [0.0, 100.0]





  
      Array  Chunk 
  
  
     Bytes  2.09 GB   134.01 MB 
     Shape  (8069, 180, 360)   (517, 180, 360) 
     Count  17 Tasks  16 Chunks 
     Type  float32  numpy.ndarray

Attributes: (45)
Conventions :
CF-1.6, ACDD 1.3
Metadata_Conventions :
CF-1.6, Unidata Dataset Discovery v1.0, NOAA CDR v1.0, GDS v2.0
acknowledgment :
This project was supported in part by a grant from the NOAA Climate Data Record (CDR) Program for satellites.
cdm_data_type :
Grid
cdr_program :
NOAA Climate Data Record Program for satellites, FY 2011.
cdr_variable :
precipitation
comment :
Processing computer: eagle2.umd.edu
contributor_name :
Robert Adler, Jian-Jian Wang
contributor_role :
principalInvestigator, processor and custodian
creator_email :
jjwang@umd.edu
creator_name :
Dr. Jian-Jian Wang
date_created :
2017-05-30T16:53:52Z
geospatial_lat_max :
90.0
geospatial_lat_min :
-90.0
geospatial_lat_resolution :
1 degree
geospatial_lat_units :
degrees_north
geospatial_lon_max :
360.0
geospatial_lon_min :
0.0
geospatial_lon_resolution :
1 degree
geospatial_lon_units :
degrees_east
history :
1) 2017-05-30T16:53:52Z, Dr. Jian-Jian Wang, U of Maryland, Created beta (B1) file
id :
199701/gpcp_v01r03_daily_d19970101_c20170530.nc
institution :
ACADEMIC > UMD/ESSIC > Earth System Science Interdisciplinary Center, University of Maryland
keywords :
EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > PRECIPITATION AMOUNT
keywords_vocabulary :
NASA Global Change Master Directory (GCMD) Earth Science Keywords, Version 7.0
license :
No constraints on data access or use.
metadata_link :
gov.noaa.ncdc:XXXXX
naming_authority :
gov.noaa.ncdc
platform :
GOES (Geostationary Operational Environmental Satellite), GMS (Japan Geostationary Meteorological Satellite), METEOSAT, TIROS > Television Infrared Observation Satellite, DMSP (Defense Meteorological Satellite Program)
processing_level :
NASA Level 3
product_version :
v01r03
project :
GPCP > Global Precipitation Climatology Project
publisher_email :
jjwang@umd.edu
publisher_name :
NOAA National Centers for Environmental Information (NCEI)
publisher_url :
https://www.ncei.noaa.gov
references :
Huffman et al. 1997, http://dx.doi.org/10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2; Adler et al. 2003, http://dx.doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2; Huffman et al. 2009, http://dx.doi.org/10.1029/2009GL040000; Adler et al. 2017, Global Precipitation Climatology Project (GPCP) Daily Analysis: Climate Algorithm Theoretical Basis Document (C-ATBD)
sensor :
Imager, TOVS > TIROS Operational Vertical Sounder, SSMI > Special Sensor Microwave/Imager
source :
/data1/GPCP_CDR/GPCP_Output/1DD//bin/199701/stfsg3.19970101.s
spatial_resolution :
1 degree
standard_name_vocabulary :
CF Standard Name Table (v41, 22 February 2017)
summary :
Global Precipitation Climatology Project (GPCP) Daily Version 1.3 gridded, merged satellite/gauge precipitation Climate data Record (CDR) from 1996 to present.
time_coverage_duration :
P1D
time_coverage_end :
1997-01-01T23:59:59Z
time_coverage_start :
1997-01-01T00:00:00Z
title :
Global Precipitation Climatatology Project (GPCP) Climate Data Record (CDR), Daily V1.3