GPCP Version 2.3 Combined Precipitation Dataset (Final)

  1. main
  2. obs
  3. gridded
  4. atm
  5. precip
  6. GPCP Version 2.3 Combined Precipitation Dataset (Final)

Load in Python

from intake import open_catalog

cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/gpcp.yaml")
ds=cat.netcdf.read()

Metadata

title GPCP Version 2.3 Combined Precipitation Dataset (Final)
location /shared/obs/gridded/GPCP
tags gridded, obs, atm, precip, monthly, global
catalog_dir https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/gpcp.yaml
last updated 2020-05-30

Dataset Contents

Show/Hide data repr Show/Hide attributes
xarray.Dataset
    • lat: 72
    • lon: 144
    • nv: 2
    • time: 496
    • lat
      (lat)
      float32
      -88.75 -86.25 ... 86.25 88.75
      units :
      degrees_north
      actual_range :
      [ 88.75 -88.75]
      long_name :
      Latitude
      standard_name :
      latitude
      axis :
      Y
      array([-88.75, -86.25, -83.75, -81.25, -78.75, -76.25, -73.75, -71.25, -68.75,
       -66.25, -63.75, -61.25, -58.75, -56.25, -53.75, -51.25, -48.75, -46.25,
       -43.75, -41.25, -38.75, -36.25, -33.75, -31.25, -28.75, -26.25, -23.75,
       -21.25, -18.75, -16.25, -13.75, -11.25,  -8.75,  -6.25,  -3.75,  -1.25,
         1.25,   3.75,   6.25,   8.75,  11.25,  13.75,  16.25,  18.75,  21.25,
        23.75,  26.25,  28.75,  31.25,  33.75,  36.25,  38.75,  41.25,  43.75,
        46.25,  48.75,  51.25,  53.75,  56.25,  58.75,  61.25,  63.75,  66.25,
        68.75,  71.25,  73.75,  76.25,  78.75,  81.25,  83.75,  86.25,  88.75],
      dtype=float32)
    • lon
      (lon)
      float32
      1.25 3.75 6.25 ... 356.25 358.75
      units :
      degrees_east
      long_name :
      Longitude
      actual_range :
      [ 1.25 358.75]
      standard_name :
      longitude
      axis :
      X
      array([  1.25,   3.75,   6.25,   8.75,  11.25,  13.75,  16.25,  18.75,  21.25,
        23.75,  26.25,  28.75,  31.25,  33.75,  36.25,  38.75,  41.25,  43.75,
        46.25,  48.75,  51.25,  53.75,  56.25,  58.75,  61.25,  63.75,  66.25,
        68.75,  71.25,  73.75,  76.25,  78.75,  81.25,  83.75,  86.25,  88.75,
        91.25,  93.75,  96.25,  98.75, 101.25, 103.75, 106.25, 108.75, 111.25,
       113.75, 116.25, 118.75, 121.25, 123.75, 126.25, 128.75, 131.25, 133.75,
       136.25, 138.75, 141.25, 143.75, 146.25, 148.75, 151.25, 153.75, 156.25,
       158.75, 161.25, 163.75, 166.25, 168.75, 171.25, 173.75, 176.25, 178.75,
       181.25, 183.75, 186.25, 188.75, 191.25, 193.75, 196.25, 198.75, 201.25,
       203.75, 206.25, 208.75, 211.25, 213.75, 216.25, 218.75, 221.25, 223.75,
       226.25, 228.75, 231.25, 233.75, 236.25, 238.75, 241.25, 243.75, 246.25,
       248.75, 251.25, 253.75, 256.25, 258.75, 261.25, 263.75, 266.25, 268.75,
       271.25, 273.75, 276.25, 278.75, 281.25, 283.75, 286.25, 288.75, 291.25,
       293.75, 296.25, 298.75, 301.25, 303.75, 306.25, 308.75, 311.25, 313.75,
       316.25, 318.75, 321.25, 323.75, 326.25, 328.75, 331.25, 333.75, 336.25,
       338.75, 341.25, 343.75, 346.25, 348.75, 351.25, 353.75, 356.25, 358.75],
      dtype=float32)
    • time
      (time)
      datetime64[ns]
      1979-01-01 ... 2020-04-01
      long_name :
      Time
      delta_t :
      0000-01-00 00:00:00
      avg_period :
      0000-01-00 00:00:00
      standard_name :
      time
      axis :
      T
      actual_range :
      [65378. 80444.]
      array(['1979-01-01T00:00:00.000000000', '1979-02-01T00:00:00.000000000',
       '1979-03-01T00:00:00.000000000', ..., '2020-02-01T00:00:00.000000000',
       '2020-03-01T00:00:00.000000000', '2020-04-01T00:00:00.000000000'],
      dtype='datetime64[ns]')
    • time_bnds
      (time, nv)
      datetime64[ns]
      ...
      comment :
      time bounds for each time value
      array([['1979-01-01T00:00:00.000000000', '1979-02-01T00:00:00.000000000'],
       ['1979-02-01T00:00:00.000000000', '1979-03-01T00:00:00.000000000'],
       ['1979-03-01T00:00:00.000000000', '1979-04-01T00:00:00.000000000'],
       ...,
       ['2020-02-01T00:00:00.000000000', '2020-03-01T00:00:00.000000000'],
       ['2020-03-01T00:00:00.000000000', '2020-04-01T00:00:00.000000000'],
       ['2020-04-01T00:00:00.000000000', '2020-05-01T00:00:00.000000000']],
      dtype='datetime64[ns]')
    • lat_bnds
      (lat, nv)
      float32
      ...
      units :
      degrees_north
      comment :
      latitude values at the north and south bounds of each pixel.
      array([[-90. , -87.5],
       [-87.5, -85. ],
       [-85. , -82.5],
       [-82.5, -80. ],
       [-80. , -77.5],
       [-77.5, -75. ],
       [-75. , -72.5],
       [-72.5, -70. ],
       [-70. , -67.5],
       [-67.5, -65. ],
       [-65. , -62.5],
       [-62.5, -60. ],
       [-60. , -57.5],
       [-57.5, -55. ],
       [-55. , -52.5],
       [-52.5, -50. ],
       [-50. , -47.5],
       [-47.5, -45. ],
       [-45. , -42.5],
       [-42.5, -40. ],
       [-40. , -37.5],
       [-37.5, -35. ],
       [-35. , -32.5],
       [-32.5, -30. ],
       [-30. , -27.5],
       [-27.5, -25. ],
       [-25. , -22.5],
       [-22.5, -20. ],
       [-20. , -17.5],
       [-17.5, -15. ],
       [-15. , -12.5],
       [-12.5, -10. ],
       [-10. ,  -7.5],
       [ -7.5,  -5. ],
       [ -5. ,  -2.5],
       [ -2.5,   0. ],
       [  0. ,   2.5],
       [  2.5,   5. ],
       [  5. ,   7.5],
       [  7.5,  10. ],
       [ 10. ,  12.5],
       [ 12.5,  15. ],
       [ 15. ,  17.5],
       [ 17.5,  20. ],
       [ 20. ,  22.5],
       [ 22.5,  25. ],
       [ 25. ,  27.5],
       [ 27.5,  30. ],
       [ 30. ,  32.5],
       [ 32.5,  35. ],
       [ 35. ,  37.5],
       [ 37.5,  40. ],
       [ 40. ,  42.5],
       [ 42.5,  45. ],
       [ 45. ,  47.5],
       [ 47.5,  50. ],
       [ 50. ,  52.5],
       [ 52.5,  55. ],
       [ 55. ,  57.5],
       [ 57.5,  60. ],
       [ 60. ,  62.5],
       [ 62.5,  65. ],
       [ 65. ,  67.5],
       [ 67.5,  70. ],
       [ 70. ,  72.5],
       [ 72.5,  75. ],
       [ 75. ,  77.5],
       [ 77.5,  80. ],
       [ 80. ,  82.5],
       [ 82.5,  85. ],
       [ 85. ,  87.5],
       [ 87.5,  90. ]], dtype=float32)
    • lon_bnds
      (lon, nv)
      float32
      ...
      units :
      degrees_east
      comment :
      longitude values at the west and east bounds of each pixel.
      array([[  0. ,   2.5],
       [  2.5,   5. ],
       [  5. ,   7.5],
       ...,
       [352.5, 355. ],
       [355. , 357.5],
       [357.5, 360. ]], dtype=float32)
    • precip
      (time, lat, lon)
      float32
      ...
      long_name :
      Average Monthly Rate of Precipitation
      valid_range :
      [ 0. 100.]
      units :
      mm/day
      precision :
      32767
      var_desc :
      Precipitation
      dataset :
      GPCP Version 2.3 Combined Precipitation Dataset
      level_desc :
      Surface
      statistic :
      Mean
      parent_stat :
      Mean
      actual_range :
      [ 0. 47.3274]
      [5142528 values with dtype=float32]
  • Conventions :
    CF-1.0
    curator :
    Dr. Jian-Jian Wang ESSIC, University of Maryland College Park College Park, MD 20742 USA Phone: +1 301-405-4887
    citation :
    Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, 2003: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979 - Present). J. Hydrometeor., 4(6), 1147-1167.
    title :
    GPCP Version 2.3 Combined Precipitation Dataset (Final)
    platform :
    NOAA POES (Polar Orbiting Environmental Satellites)
    source_obs :
    CDR RSS SSMI/SSMIS Tbs over ocean CDR SSMI/SSMIS rainrates over land (Ferraro) Geo-IR (Xie) calibrated by SSMI/SSMIS rainrates for sampling TOVS/AIRS empirical precipitation estimates at higher latitudes (ocean and land) GPCC gauge analysis to bias correct satellite estimates over land and merge with satellite based on sampling OLR Precipitation Index (OPI) (Xie) used for period before 1988
    documentation :
    http://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html
    version :
    V2.3
    Acknowledgement :
    contributor_name :
    Robert Adler University of Maryland George Huffman NASA Goddard Space Flight Center David Bolvin NASA Goddard Space Flight Center/SSAI Eric Nelkin NASA Goddard Space Flight Center/SSAI Udo Schneider GPCC, Deutscher Wetterdienst Andreas Becker GPCC, Deutscher Wetterdienst Long Chiu George Mason University Mathew Sapiano University of Maryland Pingping Xie Climate Prediction Center, NWS, NOAA Ralph Ferraro NESDIS, NOAA Jian-Jian Wang University of Maryland Guojun Gu University of Maryland
    dataset_title :
    Global Precipitation Climatology Project (GPCP) Monthly Analysis Product
    description :
    https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00970
    source :
    https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-monthly/access/
    source_documentation :
    https://www.ncdc.noaa.gov/cdr/atmospheric/precipitation-gpcp-monthly
    NCO :
    4.6.9
    history :
    Generated at NOAA/ESRL PSD Sep 9 2016 based on data from source and stored in single netCDF4 file
    References :
    http://www.psl.noaa.gov/data/gridded/data.gpcp.html
    data_comment :
    Interim data covers 2020/03 through latest.