Sea Surface Altimetry Data Analysis¶

For this example we will use gridded sea-surface altimetry data from The Copernicus Marine Environment. This is a widely used dataset in physical oceanography and climate.

The dataset has been extracted from Copernicus and stored in google cloud storage in xarray-zarr format. It is catalogues in the Pangeo Cloud Catalog at https://catalog.pangeo.io/browse/master/ocean/sea_surface_height/

[1]:

import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
import hvplot.xarray
plt.rcParams['figure.figsize'] = (15,10)
%matplotlib inline

Initialize Dataset¶

Here we load the dataset from the zarr store. Note that this very large dataset initializes nearly instantly, and we can see the full list of variables and coordinates, including metadata for each variable.

[2]:

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()
ds

[2]:

<xarray.Dataset>
Dimensions:    (time: 8901, latitude: 720, longitude: 1440, nv: 2)
Coordinates:
    crs        int32 ...
    lat_bnds   (time, latitude, nv) float32 dask.array<chunksize=(5, 720, 2), meta=np.ndarray>
  * latitude   (latitude) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88
    lon_bnds   (longitude, nv) float32 dask.array<chunksize=(1440, 2), meta=np.ndarray>
  * longitude  (longitude) float32 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9
  * nv         (nv) int32 0 1
  * time       (time) datetime64[ns] 1993-01-01 1993-01-02 ... 2017-05-15
Data variables:
    adt        (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>
    err        (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>
    sla        (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>
    ugos       (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>
    ugosa      (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>
    vgos       (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>
    vgosa      (time, latitude, longitude) float64 dask.array<chunksize=(5, 720, 1440), meta=np.ndarray>
Attributes: (12/43)
    Conventions:                     CF-1.6
    Metadata_Conventions:            Unidata Dataset Discovery v1.0
    cdm_data_type:                   Grid
    comment:                         Sea Surface Height measured by Altimetry...
    contact:                         servicedesk.cmems@mercator-ocean.eu
    creator_email:                   servicedesk.cmems@mercator-ocean.eu
    ...                              ...
    summary:                         SSALTO/DUACS Delayed-Time Level-4 sea su...
    time_coverage_duration:          P1D
    time_coverage_end:               1993-01-01T12:00:00Z
    time_coverage_resolution:        P1D
    time_coverage_start:             1992-12-31T12:00:00Z
    title:                           DT merged all satellites Global Ocean Gr...

xarray.Dataset

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

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	48.89 MiB	28.12 kiB
Shape	(8901, 720, 2)	(5, 720, 2)
Count	1782 Tasks	1781 Chunks
Type	float32	numpy.ndarray

latitude
(latitude)
float32
-89.88 -89.62 ... 89.62 89.88
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	11.25 kiB	11.25 kiB
Shape	(1440, 2)	(1440, 2)
Count	2 Tasks	1 Chunks
Type	float32	numpy.ndarray

longitude
(longitude)
float32
0.125 0.375 0.625 ... 359.6 359.9
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