LEAP Data Catalog

lat
(lat)
float32
-89.88 -89.62 ... 89.62 89.88
grids :
Uniform grid from -89.875 to 89.875 by 0.25
long_name :
Latitude
units :
degrees_north
```
array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],
      dtype=float32)
```
lon
(lon)
float32
0.125 0.375 0.625 ... 359.6 359.9
grids :
Uniform grid from 0.125 to 359.875 by 0.25
long_name :
Longitude
units :
degrees_east
```
array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,
       3.59875e+02], dtype=float32)
```

time

(time)

datetime64[ns]

1981-09-01T12:00:00 ... 2021-06-...

long_name :: Center time of the day

array(['1981-09-01T12:00:00.000000000', '1981-09-02T12:00:00.000000000',
       '1981-09-03T12:00:00.000000000', ..., '2021-06-12T12:00:00.000000000',
       '2021-06-13T12:00:00.000000000', '2021-06-14T12:00:00.000000000'],
      dtype='datetime64[ns]')

zlev
(zlev)
float32
0.0
actual_range :
0, 0
long_name :
Sea surface height
positive :
down
units :
meters
```
array([0.], dtype=float32)
```

anom

(time, zlev, lat, lon)

float32

dask.array<chunksize=(20, 1, 720, 1440), meta=np.ndarray>

long_name :: Daily sea surface temperature anomalies
units :: Celsius
valid_max :: 1200
valid_min :: -1200

	Array	Chunk
Bytes	56.13 GiB	79.10 MiB
Shape	(14532, 1, 720, 1440)	(20, 1, 720, 1440)
Dask graph	727 chunks in 2 graph layers
Data type	float32 numpy.ndarray

err

(time, zlev, lat, lon)

float32

dask.array<chunksize=(20, 1, 720, 1440), meta=np.ndarray>

long_name :: Estimated error standard deviation of analysed_sst
units :: Celsius
valid_max :: 1000
valid_min :: 0

	Array	Chunk
Bytes	56.13 GiB	79.10 MiB
Shape	(14532, 1, 720, 1440)	(20, 1, 720, 1440)
Dask graph	727 chunks in 2 graph layers
Data type	float32 numpy.ndarray

ice

(time, zlev, lat, lon)

float32

dask.array<chunksize=(20, 1, 720, 1440), meta=np.ndarray>

long_name :: Sea ice concentration
units :: %
valid_max :: 100
valid_min :: 0

	Array	Chunk
Bytes	56.13 GiB	79.10 MiB
Shape	(14532, 1, 720, 1440)	(20, 1, 720, 1440)
Dask graph	727 chunks in 2 graph layers
Data type	float32 numpy.ndarray

sst

(time, zlev, lat, lon)

float32

dask.array<chunksize=(20, 1, 720, 1440), meta=np.ndarray>

long_name :: Daily sea surface temperature
units :: Celsius
valid_max :: 4500
valid_min :: -300

	Array	Chunk
Bytes	56.13 GiB	79.10 MiB
Shape	(14532, 1, 720, 1440)	(20, 1, 720, 1440)
Dask graph	727 chunks in 2 graph layers
Data type	float32 numpy.ndarray

lat

PandasIndex

PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,
       -87.875, -87.625,
       ...
        87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,
        89.625,  89.875],
      dtype='float32', name='lat', length=720))

lon

PandasIndex

PandasIndex(Index([  0.125,   0.375,   0.625,   0.875,   1.125,   1.375,   1.625,   1.875,
         2.125,   2.375,
       ...
       357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125, 359.375,
       359.625, 359.875],
      dtype='float32', name='lon', length=1440))

time

PandasIndex

PandasIndex(DatetimeIndex(['1981-09-01 12:00:00', '1981-09-02 12:00:00',
               '1981-09-03 12:00:00', '1981-09-04 12:00:00',
               '1981-09-05 12:00:00', '1981-09-06 12:00:00',
               '1981-09-07 12:00:00', '1981-09-08 12:00:00',
               '1981-09-09 12:00:00', '1981-09-10 12:00:00',
               ...
               '2021-06-05 12:00:00', '2021-06-06 12:00:00',
               '2021-06-07 12:00:00', '2021-06-08 12:00:00',
               '2021-06-09 12:00:00', '2021-06-10 12:00:00',
               '2021-06-11 12:00:00', '2021-06-12 12:00:00',
               '2021-06-13 12:00:00', '2021-06-14 12:00:00'],
              dtype='datetime64[ns]', name='time', length=14532, freq=None))

zlev

PandasIndex

PandasIndex(Index([0.0], dtype='float32', name='zlev'))

Conventions :: CF-1.6, ACDD-1.3
cdm_data_type :: Grid
comment :: Data was converted from NetCDF-3 to NetCDF-4 format with metadata updates in November 2017.
creator_email :: oisst-help@noaa.gov
creator_url :: https://www.ncei.noaa.gov/
date_created :: 2020-05-08T19:05:13Z
date_modified :: 2020-05-08T19:05:13Z
geospatial_lat_max :: 90.0
geospatial_lat_min :: -90.0
geospatial_lat_resolution :: 0.25
geospatial_lat_units :: degrees_north
geospatial_lon_max :: 360.0
geospatial_lon_min :: 0.0
geospatial_lon_resolution :: 0.25
geospatial_lon_units :: degrees_east
history :: Final file created using preliminary as first guess, and 3 days of AVHRR data. Preliminary uses only 1 day of AVHRR data.
id :: oisst-avhrr-v02r01.19810901.nc
institution :: NOAA/National Centers for Environmental Information
instrument :: Earth Remote Sensing Instruments > Passive Remote Sensing > Spectrometers/Radiometers > Imaging Spectrometers/Radiometers > AVHRR > Advanced Very High Resolution Radiometer
instrument_vocabulary :: Global Change Master Directory (GCMD) Instrument Keywords
keywords :: Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature
keywords_vocabulary :: Global Change Master Directory (GCMD) Earth Science Keywords
metadata_link :: https://doi.org/10.25921/RE9P-PT57
naming_authority :: gov.noaa.ncei
ncei_template_version :: NCEI_NetCDF_Grid_Template_v2.0
platform :: Ships, buoys, Argo floats, MetOp-A, MetOp-B
platform_vocabulary :: Global Change Master Directory (GCMD) Platform Keywords
processing_level :: NOAA Level 4
product_version :: Version v02r01
references :: Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang et al. (2020) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version v02r01, submitted.Climatology is based on 1971-2000 OI.v2 SST. Satellite data: Pathfinder AVHRR SST and Navy AVHRR SST. Ice data: NCEP Ice and GSFC Ice.
sensor :: Thermometer, AVHRR
source :: ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfinder_AVHRR, Navy_AVHRR
standard_name_vocabulary :: CF Standard Name Table (v40, 25 January 2017)
summary :: NOAAs 1/4-degree Daily Optimum Interpolation Sea Surface Temperature (OISST) (sometimes referred to as Reynolds SST, which however also refers to earlier products at different resolution), currently available as version v02r01, is created by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over time. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs. A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY DOISST, available from Oct 1981, but there is a companion DOISST product that includes microwave satellite data, available from June 2002
time_coverage_end :: 1981-09-01T23:59:59Z
time_coverage_start :: 1981-09-01T00:00:00Z
title :: NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1 - Final

lat
(lat)
float32
-89.88 -89.62 ... 89.62 89.88
grids :
Uniform grid from -89.875 to 89.875 by 0.25
long_name :
Latitude
units :
degrees_north
```
array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],
      dtype=float32)
```
lon
(lon)
float32
0.125 0.375 0.625 ... 359.6 359.9
grids :
Uniform grid from 0.125 to 359.875 by 0.25
long_name :
Longitude
units :
degrees_east
```
array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,
       3.59875e+02], dtype=float32)
```

time

(time)

datetime64[ns]

1981-09-01T12:00:00 ... 2021-06-...

long_name :: Center time of the day

array(['1981-09-01T12:00:00.000000000', '1981-09-02T12:00:00.000000000',
       '1981-09-03T12:00:00.000000000', ..., '2021-06-12T12:00:00.000000000',
       '2021-06-13T12:00:00.000000000', '2021-06-14T12:00:00.000000000'],
      dtype='datetime64[ns]')

zlev
(zlev)
float32
0.0
actual_range :
0, 0
long_name :
Sea surface height
positive :
down
units :
meters
```
array([0.], dtype=float32)
```

anom

(time, zlev, lat, lon)

float32

dask.array<chunksize=(20, 1, 720, 1440), meta=np.ndarray>

long_name :: Daily sea surface temperature anomalies
units :: Celsius
valid_max :: 1200
valid_min :: -1200

	Array	Chunk
Bytes	56.13 GiB	79.10 MiB
Shape	(14532, 1, 720, 1440)	(20, 1, 720, 1440)
Dask graph	727 chunks in 2 graph layers
Data type	float32 numpy.ndarray

err

(time, zlev, lat, lon)

float32

dask.array<chunksize=(20, 1, 720, 1440), meta=np.ndarray>

long_name :: Estimated error standard deviation of analysed_sst
units :: Celsius
valid_max :: 1000
valid_min :: 0

	Array	Chunk
Bytes	56.13 GiB	79.10 MiB
Shape	(14532, 1, 720, 1440)	(20, 1, 720, 1440)
Dask graph	727 chunks in 2 graph layers
Data type	float32 numpy.ndarray

ice

(time, zlev, lat, lon)

float32

dask.array<chunksize=(20, 1, 720, 1440), meta=np.ndarray>

long_name :: Sea ice concentration
units :: %
valid_max :: 100
valid_min :: 0

	Array	Chunk
Bytes	56.13 GiB	79.10 MiB
Shape	(14532, 1, 720, 1440)	(20, 1, 720, 1440)
Dask graph	727 chunks in 2 graph layers
Data type	float32 numpy.ndarray

sst

(time, zlev, lat, lon)

float32

dask.array<chunksize=(20, 1, 720, 1440), meta=np.ndarray>

long_name :: Daily sea surface temperature
units :: Celsius
valid_max :: 4500
valid_min :: -300

	Array	Chunk
Bytes	56.13 GiB	79.10 MiB
Shape	(14532, 1, 720, 1440)	(20, 1, 720, 1440)
Dask graph	727 chunks in 2 graph layers
Data type	float32 numpy.ndarray

lat

PandasIndex

PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,
       -87.875, -87.625,
       ...
        87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,
        89.625,  89.875],
      dtype='float32', name='lat', length=720))

lon

PandasIndex

PandasIndex(Index([  0.125,   0.375,   0.625,   0.875,   1.125,   1.375,   1.625,   1.875,
         2.125,   2.375,
       ...
       357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125, 359.375,
       359.625, 359.875],
      dtype='float32', name='lon', length=1440))

time

PandasIndex

PandasIndex(DatetimeIndex(['1981-09-01 12:00:00', '1981-09-02 12:00:00',
               '1981-09-03 12:00:00', '1981-09-04 12:00:00',
               '1981-09-05 12:00:00', '1981-09-06 12:00:00',
               '1981-09-07 12:00:00', '1981-09-08 12:00:00',
               '1981-09-09 12:00:00', '1981-09-10 12:00:00',
               ...
               '2021-06-05 12:00:00', '2021-06-06 12:00:00',
               '2021-06-07 12:00:00', '2021-06-08 12:00:00',
               '2021-06-09 12:00:00', '2021-06-10 12:00:00',
               '2021-06-11 12:00:00', '2021-06-12 12:00:00',
               '2021-06-13 12:00:00', '2021-06-14 12:00:00'],
              dtype='datetime64[ns]', name='time', length=14532, freq=None))

zlev

PandasIndex

PandasIndex(Index([0.0], dtype='float32', name='zlev'))

Conventions :: CF-1.6, ACDD-1.3
cdm_data_type :: Grid
comment :: Data was converted from NetCDF-3 to NetCDF-4 format with metadata updates in November 2017.
creator_email :: oisst-help@noaa.gov
creator_url :: https://www.ncei.noaa.gov/
date_created :: 2020-05-08T19:05:13Z
date_modified :: 2020-05-08T19:05:13Z
geospatial_lat_max :: 90.0
geospatial_lat_min :: -90.0
geospatial_lat_resolution :: 0.25
geospatial_lat_units :: degrees_north
geospatial_lon_max :: 360.0
geospatial_lon_min :: 0.0
geospatial_lon_resolution :: 0.25
geospatial_lon_units :: degrees_east
history :: Final file created using preliminary as first guess, and 3 days of AVHRR data. Preliminary uses only 1 day of AVHRR data.
id :: oisst-avhrr-v02r01.19810901.nc
institution :: NOAA/National Centers for Environmental Information
instrument :: Earth Remote Sensing Instruments > Passive Remote Sensing > Spectrometers/Radiometers > Imaging Spectrometers/Radiometers > AVHRR > Advanced Very High Resolution Radiometer
instrument_vocabulary :: Global Change Master Directory (GCMD) Instrument Keywords
keywords :: Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature
keywords_vocabulary :: Global Change Master Directory (GCMD) Earth Science Keywords
metadata_link :: https://doi.org/10.25921/RE9P-PT57
naming_authority :: gov.noaa.ncei
ncei_template_version :: NCEI_NetCDF_Grid_Template_v2.0
platform :: Ships, buoys, Argo floats, MetOp-A, MetOp-B
platform_vocabulary :: Global Change Master Directory (GCMD) Platform Keywords
processing_level :: NOAA Level 4
product_version :: Version v02r01
references :: Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang et al. (2020) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version v02r01, submitted.Climatology is based on 1971-2000 OI.v2 SST. Satellite data: Pathfinder AVHRR SST and Navy AVHRR SST. Ice data: NCEP Ice and GSFC Ice.
sensor :: Thermometer, AVHRR
source :: ICOADS, NCEP_GTS, GSFC_ICE, NCEP_ICE, Pathfinder_AVHRR, Navy_AVHRR
standard_name_vocabulary :: CF Standard Name Table (v40, 25 January 2017)
summary :: NOAAs 1/4-degree Daily Optimum Interpolation Sea Surface Temperature (OISST) (sometimes referred to as Reynolds SST, which however also refers to earlier products at different resolution), currently available as version v02r01, is created by interpolating and extrapolating SST observations from different sources, resulting in a smoothed complete field. The sources of data are satellite (AVHRR) and in situ platforms (i.e., ships and buoys), and the specific datasets employed may change over time. At the marginal ice zone, sea ice concentrations are used to generate proxy SSTs. A preliminary version of this file is produced in near-real time (1-day latency), and then replaced with a final version after 2 weeks. Note that this is the AVHRR-ONLY DOISST, available from Oct 1981, but there is a companion DOISST product that includes microwave satellite data, available from June 2002
time_coverage_end :: 1981-09-01T23:59:59Z
time_coverage_start :: 1981-09-01T00:00:00Z
title :: NOAA/NCEI 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1 - Final