CZ16_1_2000m_Temp_year

Load in Python

from intake import open_catalog

cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/CZ16_1_2000m_Temp_year.yaml")
ds=cat.netcdf.read()

Metadata

title	CZ16_1_2000m_Temp_year
location	/project/atlantic_var/OBS/OHC_Cheng/OHC_Cheng/DATA
tags	PLACEHOLDER
catalog_dir	https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/CZ16_1_2000m_Temp_year.yaml
last updated	2017-08-26

Dataset Contents

<xarray.Dataset>
Dimensions:    (depth_std: 41, lat: 180, lon: 360, time: 927)
Coordinates:
  * lat        (lat) float32 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon        (lon) float32 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0
  * depth_std  (depth_std) float32 1.0 5.0 10.0 20.0 ... 1700.0 1800.0 2000.0
Dimensions without coordinates: time
Data variables:
    temp       (time, lat, lon, depth_std) float32 dask.array<chunksize=(1, 180, 360, 41), meta=np.ndarray>
Attributes:
    Title:           IAP 3-Dimentional Subsurface Temperature Dataset Using O...
    StartYear:       1940
    StartMonth:      1
    StartDay:        1
    EndYear:         1940
    EndMonth:        1
    EndDay:          30
    Period:          1
    GridProjection:  Mercator, gridded
    GridPoints:      360x180
    Creator:         Lijing Cheng From IAP,CAS,P.R.China
    Reference:       Cheng and Zhu 2016. Journal of Climate; Cheng et al. 201...

xarray.Dataset

Dimensions:
- depth_std: 41
- lat: 180
- lon: 360
- time: 927

Coordinates: (3)

lat

(lat)

float32

-89.5 -88.5 -87.5 ... 88.5 89.5

long_name :: latitude
units :: degree_north
standard_name :: latitude

array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],
      dtype=float32)

lon
(lon)
float32
1.0 2.0 3.0 ... 358.0 359.0 360.0
long_name :
longitude
units :
degree_east
standard_name :
longitude
```
array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)
```

depth_std

(depth_std)

float32

1.0 5.0 10.0 ... 1800.0 2000.0

long_name :: standard depth
units :: m
standard_name :: depth_std

array([1.0e+00, 5.0e+00, 1.0e+01, 2.0e+01, 3.0e+01, 4.0e+01, 5.0e+01, 6.0e+01,
       7.0e+01, 8.0e+01, 9.0e+01, 1.0e+02, 1.2e+02, 1.4e+02, 1.6e+02, 1.8e+02,
       2.0e+02, 2.5e+02, 3.0e+02, 3.5e+02, 4.0e+02, 4.5e+02, 5.0e+02, 5.5e+02,
       6.0e+02, 6.5e+02, 7.0e+02, 7.5e+02, 8.0e+02, 8.5e+02, 9.0e+02, 1.0e+03,
       1.1e+03, 1.2e+03, 1.3e+03, 1.4e+03, 1.5e+03, 1.6e+03, 1.7e+03, 1.8e+03,
       2.0e+03], dtype=float32)

Data variables: (1)

temp

(time, lat, lon, depth_std)

float32

dask.array<chunksize=(1, 180, 360, 41), meta=np.ndarray>

long_name :: temperature
units :: centigrade
standard_name :: Temperature

	Array	Chunk
Bytes	9.85 GB	10.63 MB
Shape	(927, 180, 360, 41)	(1, 180, 360, 41)
Count	3708 Tasks	927 Chunks
Type	float32	numpy.ndarray

Attributes: (12)
Title :
IAP 3-Dimentional Subsurface Temperature Dataset Using Objective Analysis
StartYear :
1940
StartMonth :
1
StartDay :
1
EndYear :
1940
EndMonth :
1
EndDay :
30
Period :
1
GridProjection :
Mercator, gridded
GridPoints :
360x180
Creator :
Lijing Cheng From IAP,CAS,P.R.China
Reference :
Cheng and Zhu 2016. Journal of Climate; Cheng et al. 2017. Science Advances