sea_surface_height

sea-surface altimetry data from The Copernicus Marine Environment

Load in Python

from intake import open_catalog

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

url	http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_L4_REP_OBSERVATIONS_008_047
tags	['ocean', 'satellite']

Dataset Contents

xarray.Dataset

Dimensions:
- latitude: 720
- longitude: 1440
- nv: 2
- time: 8901

Coordinates: (7)

crs
()
int32
...
comment :
This is a container variable that describes the grid_mapping used by the data in this file. This variable does not contain any data; only information about the geographic coordinate system.
grid_mapping_name :
latitude_longitude
inverse_flattening :
298.257
semi_major_axis :
6378136.3
```
array(-2147483647, dtype=int32)
```

lat_bnds

(time, latitude, nv)

float32

dask.array<chunksize=(5, 720, 2), meta=np.ndarray>

comment :: latitude values at the north and south bounds of each pixel.
units :: degrees_north





  
      Array  Chunk 
  
  
     Bytes  51.27 MB   28.80 kB 
     Shape  (8901, 720, 2)   (5, 720, 2) 
     Count  1782 Tasks  1781 Chunks 
     Type  float32  numpy.ndarray

latitude
(latitude)
float32
-89.875 -89.625 ... 89.625 89.875
axis :
Y
bounds :
lat_bnds
long_name :
Latitude
standard_name :
latitude
units :
degrees_north
valid_max :
89.875
valid_min :
-89.875
```
array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],
      dtype=float32)
```

lon_bnds

(longitude, nv)

float32

dask.array<chunksize=(1440, 2), meta=np.ndarray>

comment :: longitude values at the west and east bounds of each pixel.
units :: degrees_east





  
      Array  Chunk 
  
  
     Bytes  11.52 kB   11.52 kB 
     Shape  (1440, 2)   (1440, 2) 
     Count  2 Tasks  1 Chunks 
     Type  float32  numpy.ndarray

longitude
(longitude)
float32
0.125 0.375 ... 359.625 359.875
axis :
X
bounds :
lon_bnds
long_name :
Longitude
standard_name :
longitude
units :
degrees_east
valid_max :
359.875
valid_min :
0.125
```
array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,
       3.59875e+02], dtype=float32)
```
nv
(nv)
int32
0 1
comment :
Vertex
units :
1
```
array([0, 1], dtype=int32)
```

time

(time)

datetime64[ns]

1993-01-01 ... 2017-05-15

axis :: T
long_name :: Time
standard_name :: time

array(['1993-01-01T00:00:00.000000000', '1993-01-02T00:00:00.000000000',
       '1993-01-03T00:00:00.000000000', ..., '2017-05-13T00:00:00.000000000',
       '2017-05-14T00:00:00.000000000', '2017-05-15T00:00:00.000000000'],
      dtype='datetime64[ns]')

Data variables: (7)

adt

(time, latitude, longitude)

float64

dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>

comment :: The absolute dynamic topography is the sea surface height above geoid; the adt is obtained as follows: adt=sla+mdt where mdt is the mean dynamic topography; see the product user manual for details
grid_mapping :: crs
long_name :: Absolute dynamic topography
standard_name :: sea_surface_height_above_geoid
units :: m





  
      Array  Chunk 
  
  
     Bytes  73.83 GB   41.47 MB 
     Shape  (8901, 720, 1440)   (5, 720, 1440) 
     Count  1782 Tasks  1781 Chunks 
     Type  float64  numpy.ndarray

err

(time, latitude, longitude)

float64

dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>

comment :: The formal mapping error represents a purely theoretical mapping error. It mainly traduces errors induced by the constellation sampling capability and consistency with the spatial/temporal scales considered, as described in Le Traon et al (1998) or Ducet et al (2000)
grid_mapping :: crs
long_name :: Formal mapping error
units :: m





  
      Array  Chunk 
  
  
     Bytes  73.83 GB   41.47 MB 
     Shape  (8901, 720, 1440)   (5, 720, 1440) 
     Count  1782 Tasks  1781 Chunks 
     Type  float64  numpy.ndarray

sla

(time, latitude, longitude)

float64

dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>

comment :: The sea level anomaly is the sea surface height above mean sea surface; it is referenced to the [1993, 2012] period; see the product user manual for details
grid_mapping :: crs
long_name :: Sea level anomaly
standard_name :: sea_surface_height_above_sea_level
units :: m





  
      Array  Chunk 
  
  
     Bytes  73.83 GB   41.47 MB 
     Shape  (8901, 720, 1440)   (5, 720, 1440) 
     Count  1782 Tasks  1781 Chunks 
     Type  float64  numpy.ndarray

ugos

(time, latitude, longitude)

float64

dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>

grid_mapping :: crs
long_name :: Absolute geostrophic velocity: zonal component
standard_name :: surface_geostrophic_eastward_sea_water_velocity
units :: m/s





  
      Array  Chunk 
  
  
     Bytes  73.83 GB   41.47 MB 
     Shape  (8901, 720, 1440)   (5, 720, 1440) 
     Count  1782 Tasks  1781 Chunks 
     Type  float64  numpy.ndarray

ugosa

(time, latitude, longitude)

float64

dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>

comment :: The geostrophic velocity anomalies are referenced to the [1993, 2012] period
grid_mapping :: crs
long_name :: Geostrophic velocity anomalies: zonal component
standard_name :: surface_geostrophic_eastward_sea_water_velocity_assuming_sea_level_for_geoid
units :: m/s





  
      Array  Chunk 
  
  
     Bytes  73.83 GB   41.47 MB 
     Shape  (8901, 720, 1440)   (5, 720, 1440) 
     Count  1782 Tasks  1781 Chunks 
     Type  float64  numpy.ndarray

vgos

(time, latitude, longitude)

float64

dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>

grid_mapping :: crs
long_name :: Absolute geostrophic velocity: meridian component
standard_name :: surface_geostrophic_northward_sea_water_velocity
units :: m/s





  
      Array  Chunk 
  
  
     Bytes  73.83 GB   41.47 MB 
     Shape  (8901, 720, 1440)   (5, 720, 1440) 
     Count  1782 Tasks  1781 Chunks 
     Type  float64  numpy.ndarray