compressed_sfc_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/compressed_sfc_fc_daily.yaml")
ds=cat.netcdf.read()Metadata
| title | compressed_sfc_fc_daily |
| location | /reanalysis/land/ERA5/daily/global |
| tags | global,reanalysis,daily |
| catalog_dir | https://raw.githubusercontent.com/kpegion/COLA-DATASETS-CATALOG/gh-pages/intake-catalogs/compressed_sfc_fc_daily.yaml |
| last updated | 2021-05-12 |
Dataset Contents
<xarray.Dataset>
Dimensions: (lgrid: 141780, time: 15341)
Coordinates:
* time (time) float64 1.979e+07 1.979e+07 1.979e+07 ... 2.02e+07 2.02e+07
Dimensions without coordinates: lgrid
Data variables:
lat (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
lon (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
cin (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
zust (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
msror (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
mssror (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
cbh (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
msr (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
msshf (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
mslhf (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
msdwswrf (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
msdwlwrf (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
msnswrf (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
msnlwrf (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
swvl1 (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
swvl2 (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
swvl3 (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
swvl4 (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
mer (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
mvimd (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
mtpr (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
cape (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
mper (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
tcw (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
stl1 (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
blh (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
10u (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
10v (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
2t (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
2d (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
stl2 (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
stl3 (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
mx2t (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
mn2t (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
skt (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
stl4 (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
fsr (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
flsr (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
lat_2 (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
lon_2 (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
mcpr (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
sp (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
tcc (time, lgrid) float32 dask.array<chunksize=(31, 141780), meta=np.ndarray>
Attributes:
CDI: Climate Data Interface version 1.8.2 (http://mpimet.mpg.de/...
history: Fri Apr 16 08:45:44 2021: cdo -z zip_1 -f nc4 -O copy compr...
Conventions: CF-1.6
description: ERA5 daily statistics computed from hourly output on N320 r...
CDO: Climate Data Operators version 1.8.2 (http://mpimet.mpg.de/...xarray.Dataset
- lgrid: 141780
- time: 15341
- time(time)float641.979e+07 1.979e+07 ... 2.02e+07
- standard_name :
- time
- long_name :
- Middle time for daily statistics application to hourly output (Day0@0600UTC < t <= Day+1@0600UTC due to ERA5 analysis cycle phasing)
- units :
- day as %Y%m%d.%f
- calendar :
- proleptic_gregorian
- axis :
- T
array([19790101.770833, 19790102.770833, 19790103.770833, ..., 20201229.770833, 20201230.770833, 20201231.770833])
- lat(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- standard_name :
- latitude
- units :
- Degrees North
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 2016 Tasks 504 Chunks Type float32 numpy.ndarray - lon(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- standard_name :
- longitude
- units :
- Degrees East
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 2016 Tasks 504 Chunks Type float32 numpy.ndarray - cin(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Convective inhibition
- units :
- J/kg
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - zust(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Friction velocity
- units :
- m/s
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - msror(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean Sfc runoff rate
- units :
- kg/m^2/s
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - mssror(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean Sub-sfc runoff rate
- units :
- kg/m^2/s
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - cbh(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Cloud base height
- units :
- m
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - msr(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean snowfall rate
- units :
- kg/m^2/s
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - msshf(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean surface sensible heat flux
- units :
- W/m^2
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - mslhf(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean surface latent heat flux
- units :
- W/m^2
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - msdwswrf(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean surface downward shortwave radiation
- units :
- W/m^2
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - msdwlwrf(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean surface downward longwave radiation
- units :
- W/m^2
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - msnswrf(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean surface net shortwave radiation
- units :
- W/m^2
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - msnlwrf(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean surface net longwave radiation
- units :
- W/m^2
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - swvl1(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 1
- units :
- m^3/m^3
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - swvl2(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 2
- units :
- m^3/m^3
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - swvl3(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 3
- units :
- m^3/m^3
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - swvl4(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 4
- units :
- m^3/m^3
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - mer(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean evaporation rate
- units :
- kg/m^2/s
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - mvimd(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean vertically integrated moisture divergence
- units :
- kg/m^2/s
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - mtpr(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean total precipitation rate
- units :
- kg/m^2/s
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - cape(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Convective available potential energy
- units :
- J/kg
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - mper(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean potential evaporation rate
- units :
- kg/m^2/s
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - tcw(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Total column water
- units :
- kg/m^2
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - stl1(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Soil temperature level 1
- units :
- K
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - blh(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Boundary layer height
- units :
- m
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - 10u(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- 10m U wind component
- units :
- m/s
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - 10v(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- 10m V wind component
- units :
- m/s
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - 2t(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- 2m temperature
- units :
- K
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - 2d(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- 2m dew point temperature
- units :
- K
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - stl2(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Soil temperature level 2
- units :
- K
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - stl3(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Soil temperature level 3
- units :
- K
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - mx2t(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Maximum temperature at 2 metres since previous post-processing
- units :
- K
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - mn2t(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Minimum temperature at 2 metres since previous post-processing
- units :
- K
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - skt(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Skin temperature
- units :
- K
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - stl4(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Soil temperature level 4
- units :
- K
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - fsr(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Forecast surface roughness
- units :
- m
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - flsr(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Forecast logarithm of surface roughness for heat
- units :
- -
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - lat_2(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- standard_name :
- latitude
- units :
- Degrees North
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 2016 Tasks 504 Chunks Type float32 numpy.ndarray - lon_2(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- standard_name :
- longitude
- units :
- Degrees East
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 2016 Tasks 504 Chunks Type float32 numpy.ndarray - mcpr(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Mean convective precipitation rate
- units :
- kg/m^2/s
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - sp(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Surface pressure
- units :
- Pa
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray - tcc(time, lgrid)float32dask.array<chunksize=(31, 141780), meta=np.ndarray>
- long_name :
- Total cloud cover
- units :
- -
Array Chunk Bytes 8.70 GB 17.58 MB Shape (15341, 141780) (31, 141780) Count 1512 Tasks 504 Chunks Type float32 numpy.ndarray
- CDI :
- Climate Data Interface version 1.8.2 (http://mpimet.mpg.de/cdi)
- history :
- Fri Apr 16 08:45:44 2021: cdo -z zip_1 -f nc4 -O copy compressed_sfc_fc_daily_197901.nc4 ../compressed_sfc_fc_daily_197901.nc4 Wed Apr 14 18:49:26 2021: cdo merge compressed_sfc_fc_daily_197901.nc4 compressed_pc_cloud_fc_daily_197901.nc4 merged/compressed_sfc_fc_daily_197901.nc4 Created Sat Aug 17 01:00:10 2019
- Conventions :
- CF-1.6
- description :
- ERA5 daily statistics computed from hourly output on N320 reduced grid with only land grid cells retained, sans Antarctica. Note the timing described in the time variable
- CDO :
- Climate Data Operators version 1.8.2 (http://mpimet.mpg.de/cdo)