LEAP Data Catalog

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)
Dask graph	1 chunks in 2 graph layers
Data 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)
Dask graph	1 chunks in 2 graph layers
Data 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)
Dask graph	47 chunks in 2 graph layers
Data type	datetime64[ns] numpy.ndarray

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)
Dask graph	47 chunks in 2 graph layers
Data type	float32 numpy.ndarray

latitude

PandasIndex

PandasIndex(Index([-90.0, -89.0, -88.0, -87.0, -86.0, -85.0, -84.0, -83.0, -82.0, -81.0,
       ...
        80.0,  81.0,  82.0,  83.0,  84.0,  85.0,  86.0,  87.0,  88.0,  89.0],
      dtype='float32', name='latitude', length=180))

longitude

PandasIndex

PandasIndex(Index([  0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,
       ...
       350.0, 351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0, 359.0],
      dtype='float32', name='longitude', length=360))

time

PandasIndex

PandasIndex(DatetimeIndex(['1996-10-01', '1996-10-02', '1996-10-03', '1996-10-04',
               '1996-10-05', '1996-10-06', '1996-10-07', '1996-10-08',
               '1996-10-09', '1996-10-10',
               ...
               '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
               '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
               '2021-12-30', '2021-12-31'],
              dtype='datetime64[ns]', name='time', length=9226, freq=None))

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

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)
Dask graph	1 chunks in 2 graph layers
Data 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)
Dask graph	1 chunks in 2 graph layers
Data 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)
Dask graph	47 chunks in 2 graph layers
Data type	datetime64[ns] numpy.ndarray

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)
Dask graph	47 chunks in 2 graph layers
Data type	float32 numpy.ndarray

latitude

PandasIndex

PandasIndex(Index([-90.0, -89.0, -88.0, -87.0, -86.0, -85.0, -84.0, -83.0, -82.0, -81.0,
       ...
        80.0,  81.0,  82.0,  83.0,  84.0,  85.0,  86.0,  87.0,  88.0,  89.0],
      dtype='float32', name='latitude', length=180))

longitude

PandasIndex

PandasIndex(Index([  0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,
       ...
       350.0, 351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0, 359.0],
      dtype='float32', name='longitude', length=360))

time

PandasIndex

PandasIndex(DatetimeIndex(['1996-10-01', '1996-10-02', '1996-10-03', '1996-10-04',
               '1996-10-05', '1996-10-06', '1996-10-07', '1996-10-08',
               '1996-10-09', '1996-10-10',
               ...
               '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
               '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
               '2021-12-30', '2021-12-31'],
              dtype='datetime64[ns]', name='time', length=9226, freq=None))

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