python xarray and zarr

Python in quarto on rstudio server on nectar

import xarray as xr 
import geopandas as gp 
import pandas as pd 
import sparse

store = 'https://ncsa.osn.xsede.org/Pangeo/pangeo-forge/gpcp-feedstock/gpcp.zarr'

ds = xr.open_dataset(store, engine='zarr', chunks={})
ds

<xarray.Dataset>
Dimensions:      (latitude: 180, nv: 2, longitude: 360, time: 9226)
Coordinates:
    lat_bounds   (latitude, nv) float32 dask.array<chunksize=(180, 2), meta=np.ndarray>
  * latitude     (latitude) float32 -90.0 -89.0 -88.0 -87.0 ... 87.0 88.0 89.0
    lon_bounds   (longitude, nv) float32 dask.array<chunksize=(360, 2), meta=np.ndarray>
  * longitude    (longitude) float32 0.0 1.0 2.0 3.0 ... 356.0 357.0 358.0 359.0
  * time         (time) datetime64[ns] 1996-10-01 1996-10-02 ... 2021-12-31
    time_bounds  (time, nv) datetime64[ns] dask.array<chunksize=(200, 2), meta=np.ndarray>
Dimensions without coordinates: nv
Data variables:
    precip       (time, latitude, longitude) float32 dask.array<chunksize=(200, 180, 360), meta=np.ndarray>
Attributes: (12/45)
    Conventions:                CF-1.6, ACDD 1.3
    Metadata_Conventions:       CF-1.6, Unidata Dataset Discovery v1.0, NOAA ...
    acknowledgment:             This project was supported in part by a grant...
    cdm_data_type:              Grid
    cdr_program:                NOAA Climate Data Record Program for satellit...
    cdr_variable:               precipitation
    ...                         ...
    standard_name_vocabulary:   CF Standard Name Table (v41, 22 February 2017)
    summary:                    Global Precipitation Climatology Project (GPC...
    time_coverage_duration:     P1D
    time_coverage_end:          1996-10-01T23:59:59Z
    time_coverage_start:        1996-10-01T00:00:00Z
    title:                      Global Precipitation Climatatology Project (G...

xarray.Dataset

• Dimensions:
  • latitude: 180
  • nv: 2
  • longitude: 360
  • time: 9226
• Coordinates: (6)
  • lat_bounds
    (latitude, nv)
    float32
    dask.array<chunksize=(180, 2), meta=np.ndarray>

    comment :

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

    Array Chunk Bytes 1.41 kiB 1.41 kiB Shape (180, 2) (180, 2) Count 2 Graph Layers 1 Chunks Type float32 numpy.ndarray

  • latitude
    (latitude)
    float32
    -90.0 -89.0 -88.0 ... 88.0 89.0

    axis :

    Y

    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
    (longitude, nv)
    float32
    dask.array<chunksize=(360, 2), meta=np.ndarray>

    comment :

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

    Array Chunk Bytes 2.81 kiB 2.81 kiB Shape (360, 2) (360, 2) Count 2 Graph Layers 1 Chunks Type float32 numpy.ndarray

  • longitude
    (longitude)
    float32
    0.0 1.0 2.0 ... 357.0 358.0 359.0

    axis :

    X

    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)
    datetime64[ns]
    1996-10-01 ... 2021-12-31

    axis :

    T

    long_name :

    time

    standard_name :

    time

    array(['1996-10-01T00:00:00.000000000', '1996-10-02T00:00:00.000000000',
           '1996-10-03T00:00:00.000000000', ..., '2021-12-29T00:00:00.000000000',
           '2021-12-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • time_bounds
    (time, nv)
    datetime64[ns]
    dask.array<chunksize=(200, 2), meta=np.ndarray>

    comment :

    time bounds for each time value

    Array Chunk Bytes 144.16 kiB 3.12 kiB Shape (9226, 2) (200, 2) Count 2 Graph Layers 47 Chunks Type datetime64[ns] numpy.ndarray

• Data variables: (1)
  • precip
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(200, 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 2.23 GiB 49.44 MiB Shape (9226, 180, 360) (200, 180, 360) Count 2 Graph Layers 47 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 :

  2017-05-30T16:52:42Z

  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) 2017-05-30T16:52:42Z, Dr. Jian-Jian Wang, U of Maryland, Created beta (B1) file

  id :

  199610/gpcp_v01r03_daily_d19961001_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/199610/stfsg3.19961001.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 :

  1996-10-01T23:59:59Z

  time_coverage_start :

  1996-10-01T00:00:00Z

  title :

  Global Precipitation Climatatology Project (GPCP) Climate Data Record (CDR), Daily V1.3