ea_oper_fc_daily
Load in Python
from intake import open_catalog
cat = open_catalog("https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/ea_oper_fc_daily.yaml")
ds=cat.netcdf.read()Metadata
| title | ea_oper_fc_daily |
| location | /reanalysis/land/ERA5/daily/europe |
| tags | global,reanalysis,daily |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/ea_oper_fc_daily.yaml |
| last updated | 2019-11-16 |
Dataset Contents
<xarray.Dataset>
Dimensions: (lat: 130, lon: 187, time: 5002)
Coordinates:
* lat (lat) float32 35.26931 35.55034 35.83137 ... 71.24117 71.52219
* lon (lon) float32 -11.25 -10.96875 -10.6875 ... 40.5 40.78125 41.0625
* time (time) float64 0.0 24.0 48.0 72.0 ... 4.32e+03 4.344e+03 4.368e+03
Data variables:
blh (time, lat, lon) float32 dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
cin (time, lat, lon) float32 dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
fal (time, lat, lon) float32 dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
mtpr (time, lat, lon) float32 dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
mx2t (time, lat, lon) float32 dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
sdlw (time, lat, lon) float32 dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
sdsw (time, lat, lon) float32 dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
slhf (time, lat, lon) float32 dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
snlw (time, lat, lon) float32 dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
snsw (time, lat, lon) float32 dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
sshf (time, lat, lon) float32 dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
Attributes:
CDI: Climate Data Interface version 1.8.2 (http://mpimet.mpg.de/...
Conventions: CF-1.6
history: Wed Jul 10 15:52:08 2019: /usr/local/apps/nco/4.6.7/bin/nck...
CDO: Climate Data Operators version 1.8.2 (http://mpimet.mpg.de/...
NCO: 4.6.7xarray.Dataset
- lat: 130
- lon: 187
- time: 5002
- lat(lat)float3235.26931 35.55034 ... 71.52219
- standard_name :
- latitude
- long_name :
- latitude
- units :
- degrees_north
- axis :
- Y
array([35.26931, 35.55034, 35.83137, 36.1124 , 36.39343, 36.67446, 36.95549, 37.23652, 37.51755, 37.79858, 38.07961, 38.36064, 38.64167, 38.9227 , 39.20373, 39.48476, 39.76579, 40.04683, 40.32785, 40.60888, 40.88992, 41.17094, 41.45198, 41.73301, 42.01403, 42.29507, 42.5761 , 42.85713, 43.13816, 43.41919, 43.70022, 43.98125, 44.26228, 44.54331, 44.82434, 45.10537, 45.3864 , 45.66743, 45.94846, 46.22949, 46.51052, 46.79155, 47.07258, 47.35361, 47.63464, 47.91567, 48.1967 , 48.47773, 48.75876, 49.03979, 49.32082, 49.60185, 49.88288, 50.16391, 50.44494, 50.72598, 51.007 , 51.28803, 51.56907, 51.85009, 52.13113, 52.41216, 52.69318, 52.97422, 53.25525, 53.53628, 53.81731, 54.09834, 54.37937, 54.6604 , 54.94143, 55.22246, 55.50349, 55.78452, 56.06555, 56.34658, 56.62761, 56.90864, 57.18967, 57.4707 , 57.75173, 58.03276, 58.31379, 58.59482, 58.87585, 59.15688, 59.43791, 59.71894, 59.99997, 60.281 , 60.56203, 60.84306, 61.12409, 61.40512, 61.68615, 61.96718, 62.24821, 62.52924, 62.81027, 63.0913 , 63.37233, 63.65336, 63.93439, 64.21542, 64.49645, 64.77748, 65.05851, 65.33954, 65.62057, 65.9016 , 66.18263, 66.46366, 66.74469, 67.02572, 67.30675, 67.58778, 67.86881, 68.14984, 68.43087, 68.7119 , 68.99293, 69.27396, 69.55499, 69.83602, 70.11705, 70.39808, 70.67911, 70.96014, 71.24117, 71.52219], dtype=float32) - lon(lon)float32-11.25 -10.96875 ... 41.0625
- standard_name :
- longitude
- long_name :
- longitude
- units :
- degrees_east
- axis :
- X
array([-11.25 , -10.96875, -10.6875 , -10.40625, -10.125 , -9.84375, -9.5625 , -9.28125, -9. , -8.71875, -8.4375 , -8.15625, -7.875 , -7.59375, -7.3125 , -7.03125, -6.75 , -6.46875, -6.1875 , -5.90625, -5.625 , -5.34375, -5.0625 , -4.78125, -4.5 , -4.21875, -3.9375 , -3.65625, -3.375 , -3.09375, -2.8125 , -2.53125, -2.25 , -1.96875, -1.6875 , -1.40625, -1.125 , -0.84375, -0.5625 , -0.28125, 0. , 0.28125, 0.5625 , 0.84375, 1.125 , 1.40625, 1.6875 , 1.96875, 2.25 , 2.53125, 2.8125 , 3.09375, 3.375 , 3.65625, 3.9375 , 4.21875, 4.5 , 4.78125, 5.0625 , 5.34375, 5.625 , 5.90625, 6.1875 , 6.46875, 6.75 , 7.03125, 7.3125 , 7.59375, 7.875 , 8.15625, 8.4375 , 8.71875, 9. , 9.28125, 9.5625 , 9.84375, 10.125 , 10.40625, 10.6875 , 10.96875, 11.25 , 11.53125, 11.8125 , 12.09375, 12.375 , 12.65625, 12.9375 , 13.21875, 13.5 , 13.78125, 14.0625 , 14.34375, 14.625 , 14.90625, 15.1875 , 15.46875, 15.75 , 16.03125, 16.3125 , 16.59375, 16.875 , 17.15625, 17.4375 , 17.71875, 18. , 18.28125, 18.5625 , 18.84375, 19.125 , 19.40625, 19.6875 , 19.96875, 20.25 , 20.53125, 20.8125 , 21.09375, 21.375 , 21.65625, 21.9375 , 22.21875, 22.5 , 22.78125, 23.0625 , 23.34375, 23.625 , 23.90625, 24.1875 , 24.46875, 24.75 , 25.03125, 25.3125 , 25.59375, 25.875 , 26.15625, 26.4375 , 26.71875, 27. , 27.28125, 27.5625 , 27.84375, 28.125 , 28.40625, 28.6875 , 28.96875, 29.25 , 29.53125, 29.8125 , 30.09375, 30.375 , 30.65625, 30.9375 , 31.21875, 31.5 , 31.78125, 32.0625 , 32.34375, 32.625 , 32.90625, 33.1875 , 33.46875, 33.75 , 34.03125, 34.3125 , 34.59375, 34.875 , 35.15625, 35.4375 , 35.71875, 36. , 36.28125, 36.5625 , 36.84375, 37.125 , 37.40625, 37.6875 , 37.96875, 38.25 , 38.53125, 38.8125 , 39.09375, 39.375 , 39.65625, 39.9375 , 40.21875, 40.5 , 40.78125, 41.0625 ], dtype=float32) - time(time)float640.0 24.0 ... 4.344e+03 4.368e+03
- standard_name :
- time
- calendar :
- standard
- axis :
- T
array([ 0., 24., 48., ..., 4320., 4344., 4368.])
- blh(time, lat, lon)float32dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
- long_name :
- Boundary layer height [m]
Array Chunk Bytes 486.39 MB 17.79 MB Shape (5002, 130, 187) (183, 130, 187) Count 120 Tasks 40 Chunks Type float32 numpy.ndarray - cin(time, lat, lon)float32dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
- long_name :
- Convective inhibition [J kg**-1]
Array Chunk Bytes 486.39 MB 17.79 MB Shape (5002, 130, 187) (183, 130, 187) Count 120 Tasks 40 Chunks Type float32 numpy.ndarray - fal(time, lat, lon)float32dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
- long_name :
- Forecast albedo [-]
Array Chunk Bytes 486.39 MB 17.79 MB Shape (5002, 130, 187) (183, 130, 187) Count 120 Tasks 40 Chunks Type float32 numpy.ndarray - mtpr(time, lat, lon)float32dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
- long_name :
- Mean total precipitation rate [kg m**-2 s**-1]
Array Chunk Bytes 486.39 MB 17.79 MB Shape (5002, 130, 187) (183, 130, 187) Count 120 Tasks 40 Chunks Type float32 numpy.ndarray - mx2t(time, lat, lon)float32dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
- long_name :
- Maximum temperature at 2 metres [K]
Array Chunk Bytes 486.39 MB 17.79 MB Shape (5002, 130, 187) (183, 130, 187) Count 120 Tasks 40 Chunks Type float32 numpy.ndarray - sdlw(time, lat, lon)float32dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
- long_name :
- Mean surface downward long-wave radiation flux [W m**-2]
Array Chunk Bytes 486.39 MB 17.79 MB Shape (5002, 130, 187) (183, 130, 187) Count 120 Tasks 40 Chunks Type float32 numpy.ndarray - sdsw(time, lat, lon)float32dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
- long_name :
- Mean surface downward short-wave radiation flux [W m**-2]
Array Chunk Bytes 486.39 MB 17.79 MB Shape (5002, 130, 187) (183, 130, 187) Count 120 Tasks 40 Chunks Type float32 numpy.ndarray - slhf(time, lat, lon)float32dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
- long_name :
- Mean surface latent heat flux [W m**-2]
Array Chunk Bytes 486.39 MB 17.79 MB Shape (5002, 130, 187) (183, 130, 187) Count 120 Tasks 40 Chunks Type float32 numpy.ndarray - snlw(time, lat, lon)float32dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
- long_name :
- Mean surface net long-wave radiation flux [W m**-2]
Array Chunk Bytes 486.39 MB 17.79 MB Shape (5002, 130, 187) (183, 130, 187) Count 120 Tasks 40 Chunks Type float32 numpy.ndarray - snsw(time, lat, lon)float32dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
- long_name :
- Mean surface net short-wave radiation flux [W m**-2]
Array Chunk Bytes 486.39 MB 17.79 MB Shape (5002, 130, 187) (183, 130, 187) Count 120 Tasks 40 Chunks Type float32 numpy.ndarray - sshf(time, lat, lon)float32dask.array<chunksize=(122, 130, 187), meta=np.ndarray>
- long_name :
- Mean surface sensible heat flux [W m**-2]
Array Chunk Bytes 486.39 MB 17.79 MB Shape (5002, 130, 187) (183, 130, 187) Count 120 Tasks 40 Chunks Type float32 numpy.ndarray
- CDI :
- Climate Data Interface version 1.8.2 (http://mpimet.mpg.de/cdi)
- Conventions :
- CF-1.6
- history :
- Wed Jul 10 15:52:08 2019: /usr/local/apps/nco/4.6.7/bin/ncks -O -4 -L 1 /home/rd/napd/perm/heatwave/ea_oper_fc_daily_1979.nc /home/rd/napd/perm/heatwave/ea_oper_fc_daily_1979.nc4 Wed Jul 10 15:52:05 2019: cdo -f nc import_binary /home/rd/napd/perm/heatwave/ea_oper_fc_daily_1979.ctl /home/rd/napd/perm/heatwave/ea_oper_fc_daily_1979.nc
- CDO :
- Climate Data Operators version 1.8.2 (http://mpimet.mpg.de/cdo)
- NCO :
- 4.6.7