j2g

OSTSM/Jason-2 Geodetic Phase Global Ocean Along track CMEMS Sea Surface Height L3 product

Load in Python

from intake import open_catalog

cat = open_catalog("https://raw.githubusercontent.com/pangeo-data/pangeo-datastore/master/intake-catalogs/ocean/altimetry.yaml")
ds  = cat["j2g"].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

tags	['altimetry', 'ocean', 'observations']
url	http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_L3_REP_OBSERVATIONS_008_062

Dataset Contents

xarray.Dataset

Dimensions:
- time: 3384775

Coordinates: (3)

latitude

(time)

float64

dask.array<chunksize=(3384775,), meta=np.ndarray>

long_name :: Latitude of measurement
standard_name :: latitude
units :: degrees_north





  
      Array  Chunk 
  
  
     Bytes  27.08 MB   27.08 MB 
     Shape  (3384775,)   (3384775,) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

longitude

(time)

float64

dask.array<chunksize=(3384775,), meta=np.ndarray>

long_name :: Longitude of measurement
standard_name :: longitude
units :: degrees_east





  
      Array  Chunk 
  
  
     Bytes  27.08 MB   27.08 MB 
     Shape  (3384775,)   (3384775,) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

time

(time)

datetime64[ns]

2017-07-11T11:11:25.429912064 ... 2017-09-14T06:06:59.062456064

axis :: T
long_name :: Time of measurement
standard_name :: time

array(['2017-07-11T11:11:25.429912064', '2017-07-11T11:11:26.427781888',
       '2017-07-11T11:11:27.425651968', ..., '2017-09-14T06:06:57.066715904',
       '2017-09-14T06:06:58.064585984', '2017-09-14T06:06:59.062456064'],
      dtype='datetime64[ns]')

Data variables: (8)

cycle

(time)

int16

dask.array<chunksize=(3384775,), meta=np.ndarray>

long_name :: Cycle the measurement belongs to
units :: 1





  
      Array  Chunk 
  
  
     Bytes  6.77 MB   6.77 MB 
     Shape  (3384775,)   (3384775,) 
     Count  2 Tasks  1 Chunks 
     Type  int16  numpy.ndarray

dac

(time)

float64

dask.array<chunksize=(3384775,), meta=np.ndarray>

comment :: The sla in this file is already corrected for the dac; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[dac]; see the product user manual for details
long_name :: Dynamic Atmospheric Correction
units :: m





  
      Array  Chunk 
  
  
     Bytes  27.08 MB   27.08 MB 
     Shape  (3384775,)   (3384775,) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

lwe

(time)

float64

dask.array<chunksize=(3384775,), meta=np.ndarray>

comment :: The sla in this file is already corrected for the lwe; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]-[lwe]; see the product user manual for details
long_name :: Long wavelength error
units :: m





  
      Array  Chunk 
  
  
     Bytes  27.08 MB   27.08 MB 
     Shape  (3384775,)   (3384775,) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

mdt

(time)

float64

dask.array<chunksize=(3384775,), meta=np.ndarray>

comment :: The mean dynamic topography is the sea surface height above geoid; it is used to compute the absolute dynamic tyopography adt=sla+mdt
long_name :: Mean dynamic topography
standard_name :: sea_surface_height_above_geoid
units :: m





  
      Array  Chunk 
  
  
     Bytes  27.08 MB   27.08 MB 
     Shape  (3384775,)   (3384775,) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

ocean_tide

(time)

float64

dask.array<chunksize=(3384775,), meta=np.ndarray>

comment :: The sla in this file is already corrected for the ocean_tide; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[ocean_tide]; see the product user manual for details
long_name :: Ocean tide model
units :: m





  
      Array  Chunk 
  
  
     Bytes  27.08 MB   27.08 MB 
     Shape  (3384775,)   (3384775,) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

sla_filtered

(time)

float64

dask.array<chunksize=(3384775,), meta=np.ndarray>

comment :: The sea level anomaly is the sea surface height above mean sea surface height; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[dac]+[ocean_tide]-[lwe]; see the product user manual for details
long_name :: Sea level anomaly filtered not-subsampled with dac, ocean_tide and lwe correction applied
standard_name :: sea_surface_height_above_sea_level
units :: m





  
      Array  Chunk 
  
  
     Bytes  27.08 MB   27.08 MB 
     Shape  (3384775,)   (3384775,) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

sla_unfiltered

(time)

float64

dask.array<chunksize=(3384775,), meta=np.ndarray>

comment :: The sea level anomaly is the sea surface height above mean sea surface height; the uncorrected sla can be computed as follows: [uncorrected sla]=[sla from product]+[dac]+[ocean_tide]-[lwe]; see the product user manual for details
long_name :: Sea level anomaly not-filtered not-subsampled with dac, ocean_tide and lwe correction applied
standard_name :: sea_surface_height_above_sea_level
units :: m





  
      Array  Chunk 
  
  
     Bytes  27.08 MB   27.08 MB 
     Shape  (3384775,)   (3384775,) 
     Count  2 Tasks  1 Chunks 
     Type  float64  numpy.ndarray

track

(time)

int16

dask.array<chunksize=(3384775,), meta=np.ndarray>

long_name :: Track in cycle the measurement belongs to
units :: 1





  
      Array  Chunk 
  
  
     Bytes  6.77 MB   6.77 MB 
     Shape  (3384775,)   (3384775,) 
     Count  2 Tasks  1 Chunks 
     Type  int16  numpy.ndarray

Attributes: (24)
Conventions :
CF-1.6
Metadata_Conventions :
Unidata Dataset Discovery v1.0
cdm_data_type :
Swath
comment :
Sea surface height measured by altimeters referenced to the [1993, 2012] period; with additional corrections; the proposed sla is already corrected for dac, ocean_tide and lwe; [uncorrected sla]=[sla from product]+[dac]+[ocean_tide]-[lwe]
contact :
servicedesk.cmems@mercator-ocean.eu
creator_email :
servicedesk.cmems@mercator-ocean.eu
creator_name :
CMEMS - Sea Level Thematic Assembly Center
creator_url :
http://marine.copernicus.eu
history :
2019-03-28T02:27:35Z: Creation
institution :
CLS, CNES
keywords :
Oceans > Ocean Topography > Sea Surface Height
keywords_vocabulary :
NetCDF COARDS Climate and Forecast Standard Names
license :
http://marine.copernicus.eu/web/27-service-commitments-and-licence.php
platform :
OSTM/Jason-2 Long Repeat Orbit
processing_level :
L3
product_version :
2019
project :
COPERNICUS MARINE ENVIRONMENT MONITORING SERVICE (CMEMS)
references :
http://marine.copernicus.eu
software_version :
6.2_DUACS_DT2018_baseline
source :
OSTM/Jason-2 Long Repeat Orbit measurements
ssalto_duacs_comment :
The reference mission used for the altimeter inter-calibration processing is Topex/Poseidon between 1993-01-01 and 2002-04-23, Jason-1 between 2002-04-24 and 2008-10-18, OSTM/Jason-2 between 2008-10-19 and 2016-06-25, Jason-3 since 2016-06-25.
standard_name_vocabulary :
NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table v37
summary :
SSALTO/DUACS Delayed-Time Level-3 sea surface height measured by OSTM/Jason-2 Long Repeat Orbit altimetry observations over Global Ocean.
title :
DT OSTM/Jason-2 Long Repeat Orbit Global Ocean Along track SSALTO/DUACS Sea Surface Height L3 product