Production analysis
NOTE: This notebooks requires access to a remo output dataset at DKRZ!
This notebooks shows how to use the tools from pyremo.analysis
to evaluate and plot results from a REMO production run. The functions rely on certain conventions concerning the REMO output filenames. However, it should work for most REMO runs and here we show how to do a standard analysis that might be useful.
[1]:
%load_ext autoreload
%autoreload 2
[2]:
import numpy as np
import xarray as xr
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
Here we will use the output of an early REMO test run that provides us with production like REMO output data with a monthly temporal resolution. The data can be found here:
[3]:
path = "/work/ch0636/g300046/remo_results_056000"
Since we want to look at the whole dataset lazily, we will require a dask client that manages parallel access to the data.
[4]:
from dask.distributed import Client
client = Client()
client
[4]:
Client
Client-ecf7ebd9-5446-11ec-b2ad-0800383d349d
Connection method: Cluster object | Cluster type: distributed.LocalCluster |
Dashboard: /user/g300046/advanced//proxy/8787/status |
Cluster Info
LocalCluster
de8bbfde
Dashboard: /user/g300046/advanced//proxy/8787/status | Workers: 6 |
Total threads: 24 | Total memory: 31.25 GiB |
Status: running | Using processes: True |
Scheduler Info
Scheduler
Scheduler-b3629c79-4d67-45a2-81a1-60aeb42ebfc7
Comm: tcp://127.0.0.1:40435 | Workers: 6 |
Dashboard: /user/g300046/advanced//proxy/8787/status | Total threads: 24 |
Started: Just now | Total memory: 31.25 GiB |
Workers
Worker: 0
Comm: tcp://127.0.0.1:38018 | Total threads: 4 |
Dashboard: /user/g300046/advanced//proxy/46825/status | Memory: 5.21 GiB |
Nanny: tcp://127.0.0.1:39883 | |
Local directory: /mnt/lustre01/pf/zmaw/g300046/python/packages/pyremo/notebooks/dask-worker-space/worker-mmnmq41a |
Worker: 1
Comm: tcp://127.0.0.1:33901 | Total threads: 4 |
Dashboard: /user/g300046/advanced//proxy/43670/status | Memory: 5.21 GiB |
Nanny: tcp://127.0.0.1:41733 | |
Local directory: /mnt/lustre01/pf/zmaw/g300046/python/packages/pyremo/notebooks/dask-worker-space/worker-5ik_7wjm |
Worker: 2
Comm: tcp://127.0.0.1:35481 | Total threads: 4 |
Dashboard: /user/g300046/advanced//proxy/45233/status | Memory: 5.21 GiB |
Nanny: tcp://127.0.0.1:35663 | |
Local directory: /mnt/lustre01/pf/zmaw/g300046/python/packages/pyremo/notebooks/dask-worker-space/worker-y1pej7aj |
Worker: 3
Comm: tcp://127.0.0.1:45364 | Total threads: 4 |
Dashboard: /user/g300046/advanced//proxy/33916/status | Memory: 5.21 GiB |
Nanny: tcp://127.0.0.1:39619 | |
Local directory: /mnt/lustre01/pf/zmaw/g300046/python/packages/pyremo/notebooks/dask-worker-space/worker-lgz5vq45 |
Worker: 4
Comm: tcp://127.0.0.1:45549 | Total threads: 4 |
Dashboard: /user/g300046/advanced//proxy/41379/status | Memory: 5.21 GiB |
Nanny: tcp://127.0.0.1:44840 | |
Local directory: /mnt/lustre01/pf/zmaw/g300046/python/packages/pyremo/notebooks/dask-worker-space/worker-_9_a7nt7 |
Worker: 5
Comm: tcp://127.0.0.1:41712 | Total threads: 4 |
Dashboard: /user/g300046/advanced//proxy/36926/status | Memory: 5.21 GiB |
Nanny: tcp://127.0.0.1:35713 | |
Local directory: /mnt/lustre01/pf/zmaw/g300046/python/packages/pyremo/notebooks/dask-worker-space/worker-lyyyhsfp |
[5]:
from pyremo.analysis import analysis, obs, plot
/mnt/lustre01/pf/zmaw/g300046/python/packages/pyremo/pyremo/cmor/remo_cmor.py:16: UserWarning: no python cmor available
warnings.warn("no python cmor available")
The pyremo.analysis
module provides the RemoExperiment
class that will manage easy access to the REMO output. The class will scan the output directory according to REMO output filenaming conventions and will also open the monthly dataset.
[6]:
%time exp = analysis.RemoExperiment('/work/ch0636/g300046/remo_results_056000')
100%|██████████| 3256/3256 [00:01<00:00, 2388.54it/s]
100%|██████████| 398/398 [00:00<00:00, 2595.10it/s]
CPU times: user 29.4 s, sys: 2.78 s, total: 32.1 s
Wall time: 47 s
We can now easiy access the REMO monthly output dataset.
[7]:
remo_ds = exp.ds
remo_ds
[7]:
<xarray.Dataset> Dimensions: (rlon: 433, rlat: 433, meansea: 1, height10m: 1, height2m: 1, lev_4: 1, nhyi: 28, nhym: 27, lev_5: 1, snlevs: 3, time: 456) Coordinates: * rlon (rlon) float64 -28.93 -28.82 ... 18.49 18.6 * rlat (rlat) float64 -23.93 -23.82 ... 23.49 23.6 * meansea (meansea) float64 0.0 * height10m (height10m) float64 10.0 * height2m (height2m) float64 2.0 * lev_4 (lev_4) float64 1.0 * lev_5 (lev_5) float64 27.0 * snlevs (snlevs) float64 1.0 2.0 3.0 lon (rlat, rlon) float64 -10.32 -10.23 ... 68.65 lat (rlat, rlon) float64 21.28 21.32 ... 67.86 67.8 * time (time) datetime64[ns] 1979-01-15 ... 2016-12-15 Dimensions without coordinates: nhyi, nhym Data variables: (12/132) hyai (nhyi) float64 dask.array<chunksize=(28,), meta=np.ndarray> hybi (nhyi) float64 dask.array<chunksize=(28,), meta=np.ndarray> hyam (nhym) float64 dask.array<chunksize=(27,), meta=np.ndarray> hybm (nhym) float64 dask.array<chunksize=(27,), meta=np.ndarray> rotated_latitude_longitude |S1 ... QDB (time, rlat, rlon) float32 dask.array<chunksize=(1, 433, 433), meta=np.ndarray> ... ... EVAPFL (time, rlat, rlon) float32 dask.array<chunksize=(1, 433, 433), meta=np.ndarray> TMCHFL (time, rlat, rlon) float32 dask.array<chunksize=(1, 433, 433), meta=np.ndarray> SNMLRHO (time, snlevs, rlat, rlon) float32 dask.array<chunksize=(1, 3, 433, 433), meta=np.ndarray> BLA (rlat, rlon) float32 ... FIB (rlat, rlon) float32 ... mask (rlat, rlon) int64 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 Attributes: CDI: Climate Data Interface version 1.9.6 (http://mpimet... Conventions: CF-1.6 history: preprocessing with pyremo = 0.1.0 institution: European Centre for Medium-Range Weather Forecasts CDO: Climate Data Operators version 1.9.6 (http://mpimet... _NCProperties: version=2,netcdf=4.7.4,hdf5=1.10.6 forcing_file_format: NetCDF remo_version: 2.0.0 git_branch: nc_meta git_hash: c4ee7f4 system: Linux eddy3 2.6.32-754.33.1.el6.x86_64 #1 SMP Mon A...
- rlon: 433
- rlat: 433
- meansea: 1
- height10m: 1
- height2m: 1
- lev_4: 1
- nhyi: 28
- nhym: 27
- lev_5: 1
- snlevs: 3
- time: 456
- rlon(rlon)float64-28.93 -28.82 -28.71 ... 18.49 18.6
- long_name :
- longitude in rotated pole grid
- standard_name :
- grid_longitude
- units :
- degrees
array([-28.925, -28.815, -28.705, ..., 18.375, 18.485, 18.595])
- rlat(rlat)float64-23.93 -23.82 -23.71 ... 23.49 23.6
- long_name :
- latitude in rotated pole grid
- standard_name :
- grid_latitude
- units :
- degrees
array([-23.925, -23.815, -23.705, ..., 23.375, 23.485, 23.595])
- meansea(meansea)float640.0
- units :
- m
array([0.])
- height10m(height10m)float6410.0
- units :
- m
array([10.])
- height2m(height2m)float642.0
- units :
- m
array([2.])
- lev_4(lev_4)float641.0
- long_name :
- hybrid level at layer interfaces
- units :
- level
array([1.])
- lev_5(lev_5)float6427.0
- long_name :
- hybrid level at layer interfaces
- units :
- level
array([27.])
- snlevs(snlevs)float641.0 2.0 3.0
- long_name :
- snow model levels
- units :
- level
array([1., 2., 3.])
- lon(rlat, rlon)float64-10.32 -10.23 ... 68.45 68.65
- standard_name :
- latitude
- long_name :
- latitude coordinate
- units :
- degrees_north
array([[-10.32442492, -10.2255746 , -10.12663631, ..., 36.46573424, 36.57217063, 36.67853788], [-10.37190898, -10.27291967, -10.17384204, ..., 36.49820705, 36.60481695, 36.71135738], [-10.41945843, -10.32033014, -10.22111315, ..., 36.53073499, 36.63751861, 36.74423241], ..., [-47.63250803, -47.49968315, -47.36633252, ..., 67.86970929, 68.07308549, 68.27546505], [-47.80633107, -47.67374728, -47.54063711, ..., 68.05786147, 68.26127198, 68.46367928], [-47.98092068, -47.84858268, -47.71571771, ..., 68.24737092, 68.45080865, 68.65323657]])
- lat(rlat, rlon)float6421.28 21.32 21.36 ... 67.86 67.8
- standard_name :
- longitude
- long_name :
- longitude coordinate
- units :
- degrees_north
array([[21.28237631, 21.32272793, 21.36295 , ..., 24.53040312, 24.50334737, 24.47614167], [21.38309201, 21.4235057 , 21.46378972, ..., 24.63636546, 24.60926381, 24.582012 ], [21.48379434, 21.52427016, 21.56461618, ..., 24.74232081, 24.7151732 , 24.68787521], ..., [60.83106195, 60.90861606, 60.98608812, ..., 67.76169805, 67.69647124, 67.63105084], [60.90133623, 60.97899696, 61.05657632, ..., 67.84564803, 67.78024163, 67.71464239], [60.97138543, 61.04915252, 61.12683895, ..., 67.92938064, 67.86379409, 67.79801544]])
- time(time)datetime64[ns]1979-01-15 ... 2016-12-15
array(['1979-01-15T00:00:00.000000000', '1979-02-15T00:00:00.000000000', '1979-03-15T00:00:00.000000000', ..., '2016-10-15T00:00:00.000000000', '2016-11-15T00:00:00.000000000', '2016-12-15T00:00:00.000000000'], dtype='datetime64[ns]')
- hyai(nhyi)float64dask.array<chunksize=(28,), meta=np.ndarray>
- long_name :
- hybrid A coefficient at layer interfaces
- units :
- Pa
Array Chunk Bytes 224 B 224 B Shape (28,) (28,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - hybi(nhyi)float64dask.array<chunksize=(28,), meta=np.ndarray>
- long_name :
- hybrid B coefficient at layer interfaces
- units :
- 1
Array Chunk Bytes 224 B 224 B Shape (28,) (28,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - hyam(nhym)float64dask.array<chunksize=(27,), meta=np.ndarray>
- long_name :
- hybrid A coefficient at layer midpoints
- units :
- Pa
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - hybm(nhym)float64dask.array<chunksize=(27,), meta=np.ndarray>
- long_name :
- hybrid B coefficient at layer midpoints
- units :
- 1
Array Chunk Bytes 216 B 216 B Shape (27,) (27,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray - rotated_latitude_longitude()|S1...
- grid_mapping_name :
- rotated_latitude_longitude
- grid_north_pole_latitude :
- 39.25
- grid_north_pole_longitude :
- -162.0
array(b'', dtype='|S1')
- QDB(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- specific humidity surface
- units :
- kg/kg
- code :
- 112
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- QDB
- description :
- specific humidity surface
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - QDBL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- specific humidity surface (land)
- units :
- kg/kg
- code :
- 84
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- QDBL
- description :
- specific humidity surface (land)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - QDBW(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- specific humidity surface (water)
- units :
- kg/kg
- code :
- 85
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- QDBW
- description :
- specific humidity surface (water)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - QDBI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- specific humidity surface (ice)
- units :
- kg/kg
- code :
- 86
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- QDBI
- description :
- specific humidity surface (ice)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - PS(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- Surface pressure
- units :
- Pa
- code :
- 134
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- PS
- description :
- Surface pressure
- layer :
- 1.0
- cf_name :
- ps
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TS(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface temperature
- units :
- K
- code :
- 139
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TS
- description :
- surface temperature (mean over gridbox)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TSL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface temperature (land)
- units :
- K
- code :
- 54
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TSL
- description :
- surface temperature (land)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TSW(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface temperature (water)
- units :
- K
- code :
- 55
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TSW
- description :
- surface temperature (water)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TSI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface temperature (ice)
- units :
- K
- code :
- 56
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TSI
- description :
- surface temperature (ice)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - WS(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- soil wetness
- units :
- m
- code :
- 140
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- WS
- description :
- soil wetness
- layer :
- 1.0
- cf_name :
- mrso
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - SN(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- snow depth
- units :
- m
- code :
- 141
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- SN
- description :
- snow depth
- layer :
- 1.0
- cf_name :
- snw
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - APRL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- large scale precipitation
- units :
- mm
- code :
- 142
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- APRL
- description :
- large scale precipitation
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - APRC(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- convective precipitation
- units :
- mm
- code :
- 143
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- APRC
- description :
- convective precipitation
- layer :
- 1.0
- cf_name :
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Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - APRS(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- snow fall
- units :
- mm
- code :
- 144
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- APRS
- description :
- snow fall
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - PSRED(time, meansea, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- mean sea level pressure
- units :
- Pa
- code :
- 151
- leveltype :
- 102
- grid_mapping :
- rotated_latitude_longitude
- variable :
- PSRED
- description :
- mean sea level pressure
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - RUNOFF(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface runoff
- units :
- mm
- code :
- 160
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- RUNOFF
- description :
- total runoff (surface runoff PLUS drainage !!)
- layer :
- 1.0
- cf_name :
- mrro
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - DRAIN(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- drainage
- units :
- mm
- code :
- 53
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- DRAIN
- description :
- drainage (part of 160)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - ACLCOV(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- total cloud cover
- units :
- fract.
- code :
- 164
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- ACLCOV
- description :
- total cloud cover
- layer :
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Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - U10(time, height10m, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- 10m u-velocity
- units :
- m/s
- code :
- 165
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
- variable :
- U10
- description :
- 10m u-velocity
- layer :
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- cf_name :
- uas
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - V10(time, height10m, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- 10m v-velocity
- units :
- m/s
- code :
- 166
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
- variable :
- V10
- description :
- 10m v-velocity
- layer :
- 1.0
- cf_name :
- vas
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TEMP2(time, height2m, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- 2m temperature
- units :
- K
- code :
- 167
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TEMP2
- description :
- 2m temperature
- layer :
- 1.0
- cf_name :
- tas
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - DEW2(time, height2m, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- 2m dew point temperature
- units :
- K
- code :
- 168
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
- variable :
- DEW2
- description :
- 2m dew point temperature
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TSURF(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface temperature (land)
- units :
- K
- code :
- 169
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TSURF
- description :
- surface temperature (land) (see also 54)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TD(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- deep soil temperature
- units :
- K
- code :
- 170
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TD
- description :
- deep soil temperature
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - WIND10(time, height10m, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- 10m windspeed
- units :
- m/s
- code :
- 171
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
- variable :
- WIND10
- description :
- 10m windspeed
- layer :
- 1.0
- cf_name :
- sfcWind
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - AZ0(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface roughness length
- units :
- m
- code :
- 173
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- AZ0
- description :
- surface roughness length
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - AZ0L(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface roughness length (land)
- units :
- m
- code :
- 72
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- AZ0L
- description :
- surface roughness length (land)
- layer :
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Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - AZ0W(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface roughness length (water)
- units :
- m
- code :
- 73
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- AZ0W
- description :
- surface roughness length (water)
- layer :
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Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - AZ0I(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface roughness length (ice)
- units :
- m
- code :
- 74
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- AZ0I
- description :
- surface roughness length (ice)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - ALB(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface background albedo
- units :
- fract.
- code :
- 174
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- ALB
- description :
- surface background albedo
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - ALBEDO(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface albedo
- units :
- fract.
- code :
- 175
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- ALBEDO
- description :
- surface albedo
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - ALSOL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface albedo (land)
- units :
- fract.
- code :
- 75
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- ALSOL
- description :
- surface albedo (land)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - ALSOW(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface albedo (water)
- units :
- fract.
- code :
- 76
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- ALSOW
- description :
- surface albedo (water)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - ALSOI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface albedo (ice)
- units :
- fract.
- code :
- 77
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- ALSOI
- description :
- surface albedo (ice)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - SRADS(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- net surface solar radiation
- units :
- W/m²
- code :
- 176
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- SRADS
- description :
- net surface solar radiation
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TRADS(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- net surface thermal radiation
- units :
- W/m²
- code :
- 177
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TRADS
- description :
- net surface thermal radiation
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - SRAD0(time, lev_4, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- net top solar radiation
- units :
- W/m²
- code :
- 178
- leveltype :
- 109
- grid_mapping :
- rotated_latitude_longitude
- variable :
- SRAD0
- description :
- net top solar radiation
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TRAD0(time, lev_4, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- top thermal radiation (OLR)
- units :
- W/m²
- code :
- 179
- leveltype :
- 109
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TRAD0
- description :
- top thermal radiation (OLR)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - USTR(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface u-stress
- units :
- Pa
- code :
- 180
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- USTR
- description :
- surface u-stress
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - USTRL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface u-stress (land)
- units :
- Pa
- code :
- 57
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- USTRL
- description :
- surface u-stress (land)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - USTRW(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface u-stress (water)
- units :
- Pa
- code :
- 58
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- USTRW
- description :
- surface u-stress (water)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - USTRI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface u-stress (ice)
- units :
- Pa
- code :
- 59
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- USTRI
- description :
- surface u-stress (ice)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - VSTR(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface v-stress
- units :
- Pa
- code :
- 181
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- VSTR
- description :
- surface v-stress
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - VSTRL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface v-stress (land)
- units :
- Pa
- code :
- 60
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- VSTRL
- description :
- surface v-stress (land)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - VSTRW(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface v-stress (water)
- units :
- Pa
- code :
- 61
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- VSTRW
- description :
- surface v-stress (water)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - VSTRI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface v-stress (ice)
- units :
- Pa
- code :
- 62
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- VSTRI
- description :
- surface v-stress (ice)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - EVAP(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface evaporation
- units :
- mm
- code :
- 182
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- EVAP
- description :
- surface evaporation
- layer :
- 1.0
- cf_name :
- evspsbl
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - EVAPL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface evaporation (land)
- units :
- mm
- code :
- 63
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- EVAPL
- description :
- surface evaporation (land)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - EVAPW(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface evaporation (water)
- units :
- mm
- code :
- 64
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- EVAPW
- description :
- surface evaporation (water)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - EVAPI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface evaporation (ice)
- units :
- mm
- code :
- 65
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- EVAPI
- description :
- surface evaporation (ice)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TDCL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- soil temperature
- units :
- K
- code :
- 183
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TDCL
- description :
- soil temperature 5.700m thickness
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TCLFS(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface thermal cloud forcing
- units :
- W/m²
- code :
- 190
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TCLFS
- description :
- surface thermal cloud forcing
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TCLF0(time, lev_5, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- top thermal cloud forcing
- units :
- W/m²
- code :
- 192
- leveltype :
- 109
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TCLF0
- description :
- top thermal cloud forcing
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - WL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- skin reservoir content
- units :
- m
- code :
- 194
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- WL
- description :
- skin reservoir content
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - VGRAT(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- vegetation ratio
- code :
- 198
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- VGRAT
- description :
- vegetation ratio
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - VLT(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- leaf area index
- code :
- 200
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- VLT
- description :
- leaf area index
- units :
- m²/m²
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - T2MAX(time, height2m, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- maximum 2m-temperature
- units :
- K
- code :
- 201
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
- variable :
- T2MAX
- description :
- maximum 2m-temperature
- layer :
- 1.0
- cf_name :
- tasmax
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - T2MIN(time, height2m, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- minimum 2m-temperature
- units :
- K
- code :
- 202
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
- variable :
- T2MIN
- description :
- minimum 2m-temperature
- layer :
- 1.0
- cf_name :
- tasmin
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - SRAD0U(time, lev_5, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- top solar radiation upward
- units :
- W/m²
- code :
- 203
- leveltype :
- 109
- grid_mapping :
- rotated_latitude_longitude
- variable :
- SRAD0U
- description :
- top solar radiation upward
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - SRADSU(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface solar radiation upward
- units :
- W/m²
- code :
- 204
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- SRADSU
- description :
- surface solar radiation upward
- layer :
- 1.0
- cf_name :
- rsus
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TRADSU(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface thermal radiation upward
- units :
- W/m²
- code :
- 205
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TRADSU
- description :
- surface thermal radiation upward
- layer :
- 1.0
- cf_name :
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Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TSN(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- snow temperature
- units :
- K
- code :
- 206
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TSN
- description :
- snow temperature (see description below)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TD3(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- soil temperature
- units :
- K
- code :
- 207
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TD3
- description :
- soil temperature 0.065m thickness
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TD4(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- soil temperature
- units :
- K
- code :
- 208
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TD4
- description :
- soil temperature 0.254m thickness
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TD5(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- soil temperature
- units :
- K
- code :
- 209
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TD5
- description :
- soil temperature 0.913m thickness
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - SEAICE(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- sea ice cover
- units :
- fract.
- code :
- 210
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- SEAICE
- description :
- sea ice cover
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - SICED(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- sea ice depth
- units :
- m
- code :
- 211
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- SICED
- description :
- sea ice depth
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TSMAX(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- maximum surface temperature
- units :
- K
- code :
- 214
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TSMAX
- description :
- maximum surface temperature
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TSMIN(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- minimum surface temperature
- units :
- K
- code :
- 215
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TSMIN
- description :
- minimum surface temperature
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - WIMAX(time, height10m, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- maximum 10m-wind speed
- units :
- m/s
- code :
- 216
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
- variable :
- WIMAX
- description :
- maximum 10m-wind speed (w/o gusts)
- layer :
- 1.0
- cf_name :
- sfcWindmax
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - SNMEL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- snow melt
- units :
- mm
- code :
- 218
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- SNMEL
- description :
- snow melt
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - DSNAC(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- snow depth change
- units :
- mm
- code :
- 221
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- DSNAC
- description :
- snow depth change
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - QVI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- vertically integrated specific humidity
- units :
- kg/m²
- code :
- 230
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- QVI
- description :
- vertically integrated specific humidity
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - ALWCVI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- vertically integrated liquid water cont.
- units :
- kg/m²
- code :
- 231
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- ALWCVI
- description :
- vertically integrated liquid water cont.
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - BFLHS(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- code :
- 456
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- BFLHS
- description :
- surface sensible heat flux (=146)
- units :
- W/m²
- layer :
- 1.0
- cf_name :
- hfss
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - BFLHSL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface sensible heat flux (land)
- units :
- W/m²
- code :
- 87
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- BFLHSL
- description :
- surface sensible heat flux (land) (= 69)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - BFLHSW(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface sensible heat flux (water)
- units :
- W/m²
- code :
- 88
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- BFLHSW
- description :
- surface sensible heat flux (water) (= 70)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - BFLHSI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface sensible heat flux (ice)
- units :
- W/m²
- code :
- 89
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- BFLHSI
- description :
- surface sensible heat flux (ice) (= 71)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - BFLQDS(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface latent heat flux
- units :
- W/m²
- code :
- 457
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- BFLQDS
- description :
- surface latent heat flux (=147)
- layer :
- 1.0
- cf_name :
- hfls
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - BFLQDSL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface latent heat flux (land)
- units :
- W/m²
- code :
- 90
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- BFLQDSL
- description :
- surface latent heat flux (land) (= 66)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - BFLQDSW(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface latent heat flux (water)
- units :
- W/m²
- code :
- 91
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- BFLQDSW
- description :
- surface latent heat flux (water) (= 67)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - BFLQDSI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface latent heat flux (ice)
- units :
- W/m²
- code :
- 92
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- BFLQDSI
- description :
- surface latent heat flux (ice) (= 68)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TLAMBDA(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- heat conductivity of dry soil
- units :
- W/(K*m)
- code :
- 101
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TLAMBDA
- description :
- heat conductivity of dry soil
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - DLAMBDA(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- parameter for increasing the heat conductivity of the soil
- code :
- 103
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- DLAMBDA
- description :
- parameter for increasing the heat conductivity of the soil due to soil moisture (TLAMBDA+DLAMBDA gives the heat conductivity of saturated soil)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - PORVOL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- pore volume
- code :
- 104
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- PORVOL
- description :
- pore volume
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - WI3(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- fraction of frozen soil
- code :
- 106
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- WI3
- description :
- fraction of frozen soil (layers analogous soil temp.)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - WI4(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- fraction of frozen soil
- code :
- 107
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- WI4
- description :
- fraction of frozen soil (layers analogous soil temp.)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - WI5(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- fraction of frozen soil
- code :
- 108
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- WI5
- description :
- fraction of frozen soil (layers analogous soil temp.)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - WI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- fraction of frozen soil
- code :
- 109
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- WI
- description :
- fraction of frozen soil (layers analogous soil temp.)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - WICL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- fraction of frozen soil
- code :
- 110
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- WICL
- description :
- fraction of frozen soil (layers analogous soil temp.)
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - U10ER(time, height10m, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- 10m u-velocity derotated
- units :
- m/s
- code :
- 265
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
- variable :
- U10ER
- description :
- 10m u-velocity
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - V10ER(time, height10m, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- 10m v-velocity derotated
- units :
- m/s
- code :
- 266
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
- variable :
- V10ER
- description :
- 10m v-velocity
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - VBM10M(time, height10m, rlat, rlon)float32dask.array<chunksize=(1, 1, 433, 433), meta=np.ndarray>
- long_name :
- maximum of the expected gust velocity near the surface
- units :
- m/s
- code :
- 275
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
- variable :
- VBM10M
- description :
- maximum of the expected gust velocity near the surface
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 1, 433, 433) (1, 1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - GHPBL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- code :
- 271
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- GHPBL
- description :
- height of the planetary boundary layer
- units :
- m
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - ETRANS(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- transpiration at the surface
- units :
- mm
- code :
- 12
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- ETRANS
- description :
- transpiration at the surface
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - EBSOIL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- bare soil evaporation at the surface
- units :
- mm
- code :
- 13
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- EBSOIL
- description :
- Bare soil evaporation at the surface
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - ESNOW(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- evaporation over snow at the surface
- units :
- mm
- code :
- 301
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- ESNOW
- description :
- evaporation over snow at the surface
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - ESKIN(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- evaporation from skin reservoir at the surface
- units :
- mm
- code :
- 302
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- ESKIN
- description :
- Evaporation from skin reservoir at the surface
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - QIVI(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- vertically integrated cloud ice
- units :
- kg/m²
- code :
- 336
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- QIVI
- description :
- vertically integrated cloud ice
- layer :
- 1.0
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - AOD(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- code :
- 550
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - FLTMWAT(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- mean temperature of the water column [K]
- code :
- 470
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- FLTMWAT
- description :
- mean temperature of the water column
- units :
- K
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - FLTMIX(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- mixed-layer temperature [K]
- code :
- 471
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- FLTMIX
- description :
- mixed-layer temperature
- units :
- K
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - FLTWBSED(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- temperature at the water-bottom sediment interface [K]
- code :
- 472
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- FLTWBSED
- description :
- temperature at the water-bottom sediment interface
- units :
- K
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - FLTULSED(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- temperature at the bottom of the upper layer of the sediments [K]
- code :
- 473
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- FLTULSED
- description :
- temperature at the bottom of the upper layer of the sediments
- units :
- K
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - FLSHF(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- shape factor (thermocline)
- code :
- 474
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- FLSHF
- description :
- shape factor (thermocline)
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - FLHSN(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- snow thickness [m]
- code :
- 475
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- FLHSN
- description :
- snow thickness
- units :
- m
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - FLHICE(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- ice thickness [m]
- code :
- 476
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- FLHICE
- description :
- ice thickness
- units :
- m
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - FLHMIX(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- thickness of the mixed-layer [m]
- code :
- 477
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- FLHMIX
- description :
- thickness of the mixed-layer
- units :
- m
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - FLHULBS(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- thickness of the upper layer of bottom sediments [M]
- code :
- 478
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- FLHULBS
- description :
- thickness of the upper layer of bottom sediments
- units :
- m
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - FLTSN(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- temperature at the air-snow interface [K]
- code :
- 479
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- FLTSN
- description :
- temperature at the air-snow interface
- units :
- K
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - FLTICE(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- temperature at the snow-ice or air-ice interface [K]
- code :
- 480
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- FLTICE
- description :
- temperature at the snow-ice or air-ice interface
- units :
- K
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - QDBFL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- specific humidity surface (lake)
- code :
- 481
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- QDBFL
- description :
- surface specific humidity over lake fraction
- units :
- kg/kg
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TSFLECH(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface temperature (lake) [K]
- code :
- 482
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TSFLECH
- description :
- surface temperature over lakes
- units :
- K
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - BFLHSFL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface sensible heat flux (progexp) [W/m2]
- code :
- 483
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- BFLHSFL
- description :
- surface sensible heat flux on lake fraction (PROGEXP)
- units :
- W/m²
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - AHFSFL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface sensible heat flux (vdiff) [W/m2]
- code :
- 484
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- AHFSFL
- description :
- surface sensible heat flux on lake fraction (VDIFF)
- units :
- W/m²
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - BFLQDSFL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface latent heat flux (progexp) [W/m2]
- code :
- 485
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- BFLQDSFL
- description :
- surface latent heat flux on lake fraction (PROGEXP)
- units :
- W/m²
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - AHFLFL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface latent heat flux (vdiff) [W/m2]
- code :
- 486
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- AHFLFL
- description :
- surface latent heat flux on lake fraction (VDIFF)
- units :
- W/m²
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - AZ0FL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface roughness length [m]
- code :
- 487
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- AZ0FL
- description :
- surface roughness length of lake fraction
- units :
- m
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - ALSOFL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- VIS albedo (lake)
- code :
- 488
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- ALSOFL
- description :
- surface albedo of lake fraction
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - USTRFL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface u-stress [Pa]
- code :
- 489
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- USTRFL
- description :
- surface u-stress over lake fraction
- units :
- Pa
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - VSTRFL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface v-stress [Pa]
- code :
- 490
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- VSTRFL
- description :
- surface v-stress over lake fraction
- units :
- Pa
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - EVAPFL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- surface evaporation [mm]
- code :
- 491
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- EVAPFL
- description :
- surface evaporation on lake fraction
- units :
- mm
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - TMCHFL(time, rlat, rlon)float32dask.array<chunksize=(1, 433, 433), meta=np.ndarray>
- long_name :
- turb. trans. coef. of heat at the surf.
- code :
- 492
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
- variable :
- TMCHFL
- description :
- turbulent transfer coefficient of heat at the surface on lake fraction
- layer :
- 1.0
- time_cell_method :
- mean
Array Chunk Bytes 326.14 MiB 732.38 kiB Shape (456, 433, 433) (1, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - SNMLRHO(time, snlevs, rlat, rlon)float32dask.array<chunksize=(1, 3, 433, 433), meta=np.ndarray>
- long_name :
- snow density (multilayer) [kg/m3]
- code :
- 564
- leveltype :
- 105
- grid_mapping :
- rotated_latitude_longitude
Array Chunk Bytes 0.96 GiB 2.15 MiB Shape (456, 3, 433, 433) (1, 3, 433, 433) Count 1368 Tasks 456 Chunks Type float32 numpy.ndarray - BLA(rlat, rlon)float32...
- long_name :
- land sea mask
- units :
- fract.
- code :
- 172
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
[187489 values with dtype=float32]
- FIB(rlat, rlon)float32...
- long_name :
- surface geopotential (orography)
- units :
- m
- code :
- 129
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
[187489 values with dtype=float32]
- mask(rlat, rlon)int641 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
- long_name :
- land sea mask
- units :
- fract.
- code :
- 172
- leveltype :
- 1
- grid_mapping :
- rotated_latitude_longitude
array([[1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], [1, 1, 1, ..., 0, 0, 0], ..., [1, 1, 1, ..., 1, 1, 1], [1, 1, 1, ..., 1, 1, 1], [1, 1, 1, ..., 1, 1, 1]])
- CDI :
- Climate Data Interface version 1.9.6 (http://mpimet.mpg.de/cdi)
- Conventions :
- CF-1.6
- history :
- preprocessing with pyremo = 0.1.0
- institution :
- European Centre for Medium-Range Weather Forecasts
- CDO :
- Climate Data Operators version 1.9.6 (http://mpimet.mpg.de/cdo)
- _NCProperties :
- version=2,netcdf=4.7.4,hdf5=1.10.6
- forcing_file_format :
- NetCDF
- remo_version :
- 2.0.0
- git_branch :
- nc_meta
- git_hash :
- c4ee7f4
- system :
- Linux eddy3 2.6.32-754.33.1.el6.x86_64 #1 SMP Mon Aug 10 10:29:45 EDT 2020 x86_64
The obs
module provides the same easy access to some obervational datasets. Right now, available datasets contain CRU_TS4
, EOBS
and HYRAS
for Germany. Note, that the functions that accumulate those datasets right now rely on how that data is provided on the filesystem at DKRZ since there is no real easy cloud access to those observational datasets. Note that the variable names of the datasets are renamed to CF conventions so that we can agree on a common variable name for
comparison.
[9]:
%time cru_ds = obs.cru_ts4()
cru_ds
CPU times: user 5.39 s, sys: 4.19 s, total: 9.58 s
Wall time: 1min 2s
[9]:
<xarray.Dataset> Dimensions: (lon: 720, lat: 360, time: 1428) Coordinates: * lon (lon) float32 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8 * lat (lat) float32 -89.75 -89.25 -88.75 -88.25 ... 88.75 89.25 89.75 * time (time) datetime64[ns] 1901-01-16 1901-02-15 ... 2019-12-16 Data variables: tas (time, lat, lon) float32 dask.array<chunksize=(476, 120, 240), meta=np.ndarray> stn (time, lat, lon) float64 dask.array<chunksize=(476, 120, 240), meta=np.ndarray> pr (time, lat, lon) float32 dask.array<chunksize=(476, 120, 240), meta=np.ndarray> cld (time, lat, lon) float32 dask.array<chunksize=(476, 120, 240), meta=np.ndarray> dtr (time, lat, lon) float32 dask.array<chunksize=(476, 120, 240), meta=np.ndarray> frs (time, lat, lon) timedelta64[ns] dask.array<chunksize=(476, 120, 240), meta=np.ndarray> pet (time, lat, lon) float32 dask.array<chunksize=(476, 120, 240), meta=np.ndarray> orog (lat, lon) float32 dask.array<chunksize=(360, 720), meta=np.ndarray> mask (lat, lon) int64 dask.array<chunksize=(120, 240), meta=np.ndarray> Attributes: Conventions: CF-1.4 title: CRU TS4.04 Mean Temperature institution: Data held at British Atmospheric Data Centre, RAL, UK. source: Run ID = 2004151855. Data generated from:tmp.2004011744.dtb history: Wed 15 Apr 2020 19:58:33 BST : User ianharris : Program mak... references: Information on the data is available at http://badc.nerc.ac... comment: Access to these data is available to any registered CEDA user. contact: support@ceda.ac.uk
- lon: 720
- lat: 360
- time: 1428
- lon(lon)float32-179.8 -179.2 ... 179.2 179.8
- long_name :
- longitude
- units :
- degrees_east
array([-179.75, -179.25, -178.75, ..., 178.75, 179.25, 179.75], dtype=float32)
- lat(lat)float32-89.75 -89.25 ... 89.25 89.75
- long_name :
- latitude
- units :
- degrees_north
array([-89.75, -89.25, -88.75, ..., 88.75, 89.25, 89.75], dtype=float32)
- time(time)datetime64[ns]1901-01-16 ... 2019-12-16
- long_name :
- time
array(['1901-01-16T00:00:00.000000000', '1901-02-15T00:00:00.000000000', '1901-03-16T00:00:00.000000000', ..., '2019-10-16T00:00:00.000000000', '2019-11-16T00:00:00.000000000', '2019-12-16T00:00:00.000000000'], dtype='datetime64[ns]')
- tas(time, lat, lon)float32dask.array<chunksize=(476, 120, 240), meta=np.ndarray>
- long_name :
- near-surface temperature
- units :
- degrees Celsius
- correlation_decay_distance :
- 1200.0
Array Chunk Bytes 1.38 GiB 52.29 MiB Shape (1428, 360, 720) (476, 120, 240) Count 28 Tasks 27 Chunks Type float32 numpy.ndarray - stn(time, lat, lon)float64dask.array<chunksize=(476, 120, 240), meta=np.ndarray>
- description :
- number of stations contributing to each datum
Array Chunk Bytes 2.76 GiB 104.59 MiB Shape (1428, 360, 720) (476, 120, 240) Count 28 Tasks 27 Chunks Type float64 numpy.ndarray - pr(time, lat, lon)float32dask.array<chunksize=(476, 120, 240), meta=np.ndarray>
- long_name :
- precipitation
- units :
- mm/month
- correlation_decay_distance :
- 450.0
Array Chunk Bytes 1.38 GiB 52.29 MiB Shape (1428, 360, 720) (476, 120, 240) Count 28 Tasks 27 Chunks Type float32 numpy.ndarray - cld(time, lat, lon)float32dask.array<chunksize=(476, 120, 240), meta=np.ndarray>
- long_name :
- cloud cover
- units :
- percentage
- correlation_decay_distance :
- 600.0
Array Chunk Bytes 1.38 GiB 52.29 MiB Shape (1428, 360, 720) (476, 120, 240) Count 28 Tasks 27 Chunks Type float32 numpy.ndarray - dtr(time, lat, lon)float32dask.array<chunksize=(476, 120, 240), meta=np.ndarray>
- long_name :
- diurnal temperature range
- units :
- degrees Celsius
- correlation_decay_distance :
- 750.0
Array Chunk Bytes 1.38 GiB 52.29 MiB Shape (1428, 360, 720) (476, 120, 240) Count 28 Tasks 27 Chunks Type float32 numpy.ndarray - frs(time, lat, lon)timedelta64[ns]dask.array<chunksize=(476, 120, 240), meta=np.ndarray>
- long_name :
- ground frost frequency
- correlation_decay_distance :
- 750.0
Array Chunk Bytes 2.76 GiB 104.59 MiB Shape (1428, 360, 720) (476, 120, 240) Count 28 Tasks 27 Chunks Type timedelta64[ns] numpy.ndarray - pet(time, lat, lon)float32dask.array<chunksize=(476, 120, 240), meta=np.ndarray>
- long_name :
- potential evapotranspiration
- units :
- mm/day
- correlation_decay_distance :
- -999.0
Array Chunk Bytes 1.38 GiB 52.29 MiB Shape (1428, 360, 720) (476, 120, 240) Count 28 Tasks 27 Chunks Type float32 numpy.ndarray - orog(lat, lon)float32dask.array<chunksize=(360, 720), meta=np.ndarray>
- units :
- m
Array Chunk Bytes 0.99 MiB 0.99 MiB Shape (360, 720) (360, 720) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - mask(lat, lon)int64dask.array<chunksize=(120, 240), meta=np.ndarray>
- long_name :
- near-surface temperature
- units :
- degrees Celsius
- correlation_decay_distance :
- 1200.0
Array Chunk Bytes 1.98 MiB 225.00 kiB Shape (360, 720) (120, 240) Count 64 Tasks 9 Chunks Type int64 numpy.ndarray
- Conventions :
- CF-1.4
- title :
- CRU TS4.04 Mean Temperature
- institution :
- Data held at British Atmospheric Data Centre, RAL, UK.
- source :
- Run ID = 2004151855. Data generated from:tmp.2004011744.dtb
- history :
- Wed 15 Apr 2020 19:58:33 BST : User ianharris : Program makegridsauto.for called by update.for
- references :
- Information on the data is available at http://badc.nerc.ac.uk/data/cru/
- comment :
- Access to these data is available to any registered CEDA user.
- contact :
- support@ceda.ac.uk
[10]:
%time eobs_ds = obs.eobs()
eobs_ds
CPU times: user 236 ms, sys: 124 ms, total: 360 ms
Wall time: 1.24 s
[10]:
<xarray.Dataset> Dimensions: (longitude: 464, latitude: 201, time: 25749) Coordinates: * longitude (longitude) float64 -40.38 -40.12 -39.88 ... 74.88 75.12 75.38 * latitude (latitude) float64 25.38 25.62 25.88 26.12 ... 74.88 75.12 75.38 * time (time) datetime64[ns] 1950-01-01 1950-01-02 ... 2020-06-30 Data variables: tas (time, latitude, longitude) float32 dask.array<chunksize=(8583, 65, 58), meta=np.ndarray> tasmax (time, latitude, longitude) float32 dask.array<chunksize=(8583, 65, 58), meta=np.ndarray> tasmin (time, latitude, longitude) float32 dask.array<chunksize=(8583, 65, 58), meta=np.ndarray> pr (time, latitude, longitude) float32 dask.array<chunksize=(8583, 65, 58), meta=np.ndarray> rsds (time, latitude, longitude) float32 dask.array<chunksize=(8583, 65, 58), meta=np.ndarray> psl (time, latitude, longitude) float32 dask.array<chunksize=(8583, 65, 58), meta=np.ndarray> orog (latitude, longitude) float32 dask.array<chunksize=(201, 464), meta=np.ndarray> mask (latitude, longitude) int64 dask.array<chunksize=(65, 58), meta=np.ndarray> Attributes: E-OBS_version: 22.0e Conventions: CF-1.4 References: http://surfobs.climate.copernicus.eu/dataaccess/access_eo... history: Tue Dec 1 07:45:09 2020: ncks --no-abc -d time,0,25748 /... NCO: netCDF Operators version 4.7.5 (Homepage = http://nco.sf....
- longitude: 464
- latitude: 201
- time: 25749
- longitude(longitude)float64-40.38 -40.12 ... 75.12 75.38
- units :
- degrees_east
- long_name :
- Longitude values
- axis :
- X
- standard_name :
- longitude
array([-40.375, -40.125, -39.875, ..., 74.875, 75.125, 75.375])
- latitude(latitude)float6425.38 25.62 25.88 ... 75.12 75.38
- units :
- degrees_north
- long_name :
- Latitude values
- axis :
- Y
- standard_name :
- latitude
array([25.375, 25.625, 25.875, ..., 74.875, 75.125, 75.375])
- time(time)datetime64[ns]1950-01-01 ... 2020-06-30
- long_name :
- Time in days
- standard_name :
- time
array(['1950-01-01T00:00:00.000000000', '1950-01-02T00:00:00.000000000', '1950-01-03T00:00:00.000000000', ..., '2020-06-28T00:00:00.000000000', '2020-06-29T00:00:00.000000000', '2020-06-30T00:00:00.000000000'], dtype='datetime64[ns]')
- tas(time, latitude, longitude)float32dask.array<chunksize=(8583, 65, 58), meta=np.ndarray>
- units :
- Celsius
- long_name :
- mean temperature
- standard_name :
- air_temperature
Array Chunk Bytes 8.95 GiB 123.44 MiB Shape (25749, 201, 464) (8583, 65, 58) Count 97 Tasks 96 Chunks Type float32 numpy.ndarray - tasmax(time, latitude, longitude)float32dask.array<chunksize=(8583, 65, 58), meta=np.ndarray>
- units :
- Celsius
- long_name :
- maximum temperature
- standard_name :
- air_temperature
Array Chunk Bytes 8.95 GiB 123.44 MiB Shape (25749, 201, 464) (8583, 65, 58) Count 97 Tasks 96 Chunks Type float32 numpy.ndarray - tasmin(time, latitude, longitude)float32dask.array<chunksize=(8583, 65, 58), meta=np.ndarray>
- units :
- Celsius
- long_name :
- minimum temperature
- standard_name :
- air_temperature
Array Chunk Bytes 8.95 GiB 123.44 MiB Shape (25749, 201, 464) (8583, 65, 58) Count 97 Tasks 96 Chunks Type float32 numpy.ndarray - pr(time, latitude, longitude)float32dask.array<chunksize=(8583, 65, 58), meta=np.ndarray>
- units :
- mm
- long_name :
- rainfall
- standard_name :
- thickness_of_rainfall_amount
Array Chunk Bytes 8.95 GiB 123.44 MiB Shape (25749, 201, 464) (8583, 65, 58) Count 97 Tasks 96 Chunks Type float32 numpy.ndarray - rsds(time, latitude, longitude)float32dask.array<chunksize=(8583, 65, 58), meta=np.ndarray>
- standard_name :
- surface_downwelling_shortwave_flux_in_air
- long_name :
- surface downwelling shortwave flux in air
- units :
- W/m2
Array Chunk Bytes 8.95 GiB 123.44 MiB Shape (25749, 201, 464) (8583, 65, 58) Count 97 Tasks 96 Chunks Type float32 numpy.ndarray - psl(time, latitude, longitude)float32dask.array<chunksize=(8583, 65, 58), meta=np.ndarray>
- units :
- hPa
- long_name :
- sea level pressure
- standard_name :
- air_pressure_at_sea_level
Array Chunk Bytes 8.95 GiB 123.44 MiB Shape (25749, 201, 464) (8583, 65, 58) Count 97 Tasks 96 Chunks Type float32 numpy.ndarray - orog(latitude, longitude)float32dask.array<chunksize=(201, 464), meta=np.ndarray>
- units :
- metres
- long_name :
- Elevation
Array Chunk Bytes 364.31 kiB 364.31 kiB Shape (201, 464) (201, 464) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - mask(latitude, longitude)int64dask.array<chunksize=(65, 58), meta=np.ndarray>
- units :
- Celsius
- long_name :
- mean temperature
- standard_name :
- air_temperature
Array Chunk Bytes 728.62 kiB 29.45 kiB Shape (201, 464) (65, 58) Count 225 Tasks 32 Chunks Type int64 numpy.ndarray
- E-OBS_version :
- 22.0e
- Conventions :
- CF-1.4
- References :
- http://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php
- history :
- Tue Dec 1 07:45:09 2020: ncks --no-abc -d time,0,25748 /data2/Else/EOBSv22.0e/Grid_0.25deg/tg//tg_ensmean_master_rectime.nc /data2/Else/EOBSv22.0e/Grid_0.25deg/tg//tg_ens_mean_0.25deg_reg_v22.0e.nc Mon Nov 30 15:52:33 2020: ncks --no-abc --mk_rec_dmn time /data2/Else/EOBSv22.0e/Grid_0.25deg/tg//tg_ensmean_master.nc /data2/Else/EOBSv22.0e/Grid_0.25deg/tg//tg_ensmean_master_rectime.nc
- NCO :
- netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)
[12]:
%time hyras_ds = obs.hyras()
hyras_ds
CPU times: user 11 s, sys: 1.66 s, total: 12.7 s
Wall time: 1min 12s
[12]:
<xarray.Dataset> Dimensions: (Y: 220, X: 240, time: 23741) Coordinates: lon (Y, X) float32 dask.array<chunksize=(220, 240), meta=np.ndarray> lat (Y, X) float32 dask.array<chunksize=(220, 240), meta=np.ndarray> * time (time) object 1951-01-01 00:00:00 ... 2015-12-31 00:00:00 Dimensions without coordinates: Y, X Data variables: crs int32 ... tas (time, Y, X) float32 dask.array<chunksize=(365, 220, 240), meta=np.ndarray> pr (time, Y, X) float32 dask.array<chunksize=(365, 220, 240), meta=np.ndarray> tmax (time, Y, X) float32 dask.array<chunksize=(365, 220, 240), meta=np.ndarray> tmin (time, Y, X) float32 dask.array<chunksize=(365, 220, 240), meta=np.ndarray> hurs (time, Y, X) float32 dask.array<chunksize=(365, 220, 240), meta=np.ndarray> mask (Y, X) int64 dask.array<chunksize=(220, 240), meta=np.ndarray> Attributes: (12/15) CDI: Climate Data Interface version 1.8.2 (http://mpimet.mpg.... Conventions: CF-1.6 history: Mon Jul 09 12:20:58 2018: cdo -ifnotthen -eqc,11 /kp/kp0... source: surface observation institution: Deutscher Wetterdienst title: gridded_mean_temperature_dataset_(HYRAS-TAS) ... ... references: Datenlieferung2018_AP101b_HYRAS-TAS.pdf creation_date: 2018-02-16 13:09:04 conventions: CF-1.6 conventionsURL: http://cfconventions.org/cf-conventions/v1.6.0/cf-conven... tracking_id: aa16c4de-9a66-4a83-a191-d8779538c721 CDO: Climate Data Operators version 1.8.2 (http://mpimet.mpg....
- Y: 220
- X: 240
- time: 23741
- lon(Y, X)float32dask.array<chunksize=(220, 240), meta=np.ndarray>
- standard_name :
- longitude
- long_name :
- longitude
- units :
- degrees_east
- _CoordinateAxisType :
- Lon
Array Chunk Bytes 206.25 kiB 206.25 kiB Shape (220, 240) (220, 240) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - lat(Y, X)float32dask.array<chunksize=(220, 240), meta=np.ndarray>
- standard_name :
- latitude
- long_name :
- latitude
- units :
- degrees_north
- _CoordinateAxisType :
- Lat
Array Chunk Bytes 206.25 kiB 206.25 kiB Shape (220, 240) (220, 240) Count 2 Tasks 1 Chunks Type float32 numpy.ndarray - time(time)object1951-01-01 00:00:00 ... 2015-12-...
- standard_name :
- time
- long_name :
- time
- axis :
- T
array([cftime.DatetimeProlepticGregorian(1951, 1, 1, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeProlepticGregorian(1951, 1, 2, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeProlepticGregorian(1951, 1, 3, 0, 0, 0, 0, has_year_zero=True), ..., cftime.DatetimeProlepticGregorian(2015, 12, 29, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeProlepticGregorian(2015, 12, 30, 0, 0, 0, 0, has_year_zero=True), cftime.DatetimeProlepticGregorian(2015, 12, 31, 0, 0, 0, 0, has_year_zero=True)], dtype=object)
- crs()int32...
- grid_mapping_name :
- lambert_conformal_conic
- standard_parallel :
- [35. 65.]
- longitude_of_central_meridian :
- 10.0
- latitude_of_projection_origin :
- 52.0
- semi_major_axis :
- 6378137.0
- semi_minor_axis :
- 6356752.0
- inverse_flattening :
- 298.2572
- false_easting :
- 4000000.0
- false_northing :
- 2800000.0
- scale_factor_at_projection_origin :
- 0.017453292
- spatial_ref :
- ETRS_1989_LCC,DATUM:\D_ETRS_1989\,SPHEROID:\GRS_1980\,AUTHORITY:\EPSG 3034\,PRIMEM:\Greenwich 0.0\,PROJECTION:\Lambert_Conformal_Conic\
- first_standard_parallel :
- 35.0
- second_standard_parallel :
- 65.0
array(1, dtype=int32)
- tas(time, Y, X)float32dask.array<chunksize=(365, 220, 240), meta=np.ndarray>
- standard_name :
- mean_air_temperature
- long_name :
- mean air temperature
- units :
- Celsius
- grid_mapping :
- crs
- cell_methods :
- time: mean
Array Chunk Bytes 4.67 GiB 73.72 MiB Shape (23741, 220, 240) (366, 220, 240) Count 195 Tasks 65 Chunks Type float32 numpy.ndarray - pr(time, Y, X)float32dask.array<chunksize=(365, 220, 240), meta=np.ndarray>
- standard_name :
- thickness_of_rainfall_amount
- long_name :
- precipitation heigth
- units :
- mm
- grid_mapping :
- crs
- CoordinateSystems :
- LatLonCoordinateSystem ProjectionCoordinateSystem
- cell_methods :
- time: sum
Array Chunk Bytes 4.67 GiB 73.72 MiB Shape (23741, 220, 240) (366, 220, 240) Count 195 Tasks 65 Chunks Type float32 numpy.ndarray - tmax(time, Y, X)float32dask.array<chunksize=(365, 220, 240), meta=np.ndarray>
- standard_name :
- maximum_air_temperature
- long_name :
- maximum air temperature
- units :
- Celsius
- grid_mapping :
- crs
- cell_methods :
- time: mean
Array Chunk Bytes 4.67 GiB 73.72 MiB Shape (23741, 220, 240) (366, 220, 240) Count 195 Tasks 65 Chunks Type float32 numpy.ndarray - tmin(time, Y, X)float32dask.array<chunksize=(365, 220, 240), meta=np.ndarray>
- standard_name :
- minimum_air_temperature
- long_name :
- minimum air temperature
- units :
- Celsius
- grid_mapping :
- crs
- cell_methods :
- time: mean
Array Chunk Bytes 4.67 GiB 73.72 MiB Shape (23741, 220, 240) (366, 220, 240) Count 195 Tasks 65 Chunks Type float32 numpy.ndarray - hurs(time, Y, X)float32dask.array<chunksize=(365, 220, 240), meta=np.ndarray>
- standard_name :
- relative_humidity
- long_name :
- relative humidity
- units :
- Percent
- grid_mapping :
- crs
- cell_methods :
- time: mean
Array Chunk Bytes 4.67 GiB 73.72 MiB Shape (23741, 220, 240) (366, 220, 240) Count 195 Tasks 65 Chunks Type float32 numpy.ndarray - mask(Y, X)int64dask.array<chunksize=(220, 240), meta=np.ndarray>
- standard_name :
- mean_air_temperature
- long_name :
- mean air temperature
- units :
- Celsius
- grid_mapping :
- crs
- cell_methods :
- time: mean
Array Chunk Bytes 412.50 kiB 412.50 kiB Shape (220, 240) (220, 240) Count 199 Tasks 1 Chunks Type int64 numpy.ndarray
- CDI :
- Climate Data Interface version 1.8.2 (http://mpimet.mpg.de/cdi)
- Conventions :
- CF-1.6
- history :
- Mon Jul 09 12:20:58 2018: cdo -ifnotthen -eqc,11 /kp/kp01/hyras/regnie/source/Laendermaske_5km_hyras.nc /kp/kp01/hyras/output/tas/v3.0/05_abgabe/tmp/tmp2.nc /kp/kp01/hyras/output/tas/v3.0/05_abgabe/tmp/tas_hyras_5_1951_v3.0_ocz.nc Mon Jul 09 12:20:57 2018: cdo -ifnotthen -eqc,4 /kp/kp01/hyras/regnie/source/Laendermaske_5km_hyras.nc /kp/kp01/hyras/output/tas/v3.0/05_abgabe/tmp/tmp1.nc /kp/kp01/hyras/output/tas/v3.0/05_abgabe/tmp/tmp2.nc Mon Jul 09 12:20:56 2018: cdo -ifnotthen -eqc,8 /kp/kp01/hyras/regnie/source/Laendermaske_5km_hyras.nc /kp/kp01/hyras/output/tas/v3.0/05_abgabe/tas_hyras_5_1951_v3.0.nc /kp/kp01/hyras/output/tas/v3.0/05_abgabe/tmp/tmp1.nc
- source :
- surface observation
- institution :
- Deutscher Wetterdienst
- title :
- gridded_mean_temperature_dataset_(HYRAS-TAS)
- project_id :
- Expertennetzwerk
- realization :
- v3.0
- contact :
- Stefan Kraehenmann, stefan.kraehenmann@dwd.de; Simona Hoepp, Simona-Andrea.Hoepp@dwd.de
- references :
- Datenlieferung2018_AP101b_HYRAS-TAS.pdf
- creation_date :
- 2018-02-16 13:09:04
- conventions :
- CF-1.6
- conventionsURL :
- http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html
- tracking_id :
- aa16c4de-9a66-4a83-a191-d8779538c721
- CDO :
- Climate Data Operators version 1.8.2 (http://mpimet.mpg.de/cdo)
Once we have access to the data, it’s easy to use them for comparison with REMO output. We choose a climatological reasonable time scale of 30 years to compute a sesonal mean for both the REMO output and the observational datasets. For comparison, we will need to regrid the data to a common grid. We will always choose the coarser grid (which is mostly the grid of the observational dataset, except for HYRAS). For the regridding we use
xesmf and its capabilities of regridding with masks. For that purpose, the datasets contain a mask
variable that defines the validity of the datasets. For example, the EOBS mask looks like this:
[17]:
eobs_ds.mask.plot(cmap="binary_r")
[17]:
<matplotlib.collections.QuadMesh at 0x2b6217d9cb80>
and the REMO mask looks like this:
[19]:
remo_ds.mask.plot(cmap="binary_r")
[19]:
<matplotlib.collections.QuadMesh at 0x2b62188ff310>
During the regridding, those masks are used to avoid regridding NaNs that might produce artifacts in the data. For more information, please have a look at the xesmf documentation.
Now, we will choose the surface temperature tas
for comparison in seasonal means. We merge the variable with its mask and compare the REMO results to all three observational datasets.
[21]:
time_range = slice("1980", "2010")
[22]:
remo_tas = xr.merge([remo_ds.TEMP2.sel(time=time_range) - 273.5, remo_ds.mask]).rename(
{"TEMP2": "tas"}
)
remo_orog = remo_ds.FIB
eobs_tas = xr.merge([eobs_ds.tas.sel(time=time_range), eobs_ds.mask])
eobs_orog = eobs_ds.orog
cru_tas = xr.merge([cru_ds.tas.sel(time=time_range), cru_ds.mask])
cru_orog = cru_ds.orog
hyras_tas = xr.merge([hyras_ds.tas.sel(time=time_range), hyras_ds.mask])
HYRAS
[24]:
compare_hyras = analysis.compare_seasons(hyras_tas, remo_tas).compute()
xESMF Regridder
Regridding algorithm: bilinear
Weight filename: bilinear_220x240_433x433.nc
Reuse pre-computed weights? False
Input grid shape: (220, 240)
Output grid shape: (433, 433)
Periodic in longitude? False
For HYRAS
, we have regridded the HYRAS data (about 5km) to the more coarse REMO grid (about 12km). Consequently for the plotting, we define the grid transformation as rotated pole:
[29]:
import cartopy.crs as ccrs
pole = (
remo_ds.rotated_latitude_longitude.grid_north_pole_longitude,
remo_ds.rotated_latitude_longitude.grid_north_pole_latitude,
)
transform = ccrs.RotatedPole(*pole)
We define the extend of the plot to that of the HYRAS dataset and plot the data in the default Plate Carree projection.
[58]:
extent = {
"extents": [
hyras_ds.lon.min(),
hyras_ds.lon.max(),
hyras_ds.lat.min(),
hyras_ds.lat.max(),
]
}
plot.plot_seasons(
compare_hyras.tas,
transform=transform,
extent=extent,
borders=True,
xlocs=range(-180, 180, 5),
ylocs=range(-90, 90, 5),
figsize=(14, 10),
aspect="auto",
)
[58]:
<module 'matplotlib.pyplot' from '/work/ch0636/g300046/conda_envs/cmip6-processing/lib/python3.9/site-packages/matplotlib/pyplot.py'>
CRU_TS4
[34]:
compare_cru = analysis.compare_seasons(remo_tas, cru_tas).compute()
xESMF Regridder
Regridding algorithm: bilinear
Weight filename: bilinear_433x433_360x720.nc
Reuse pre-computed weights? False
Input grid shape: (433, 433)
Output grid shape: (360, 720)
Periodic in longitude? False
[59]:
extent = [
remo_ds.rlon.min(),
remo_ds.rlon.max(),
remo_ds.rlat.min(),
remo_ds.rlat.max(),
]
extent = {"extents": extent, "crs": transform}
plot.plot_seasons(
compare_cru.tas,
extent=extent,
projection=transform,
borders=True,
xlocs=range(-180, 180, 10),
ylocs=range(-90, 90, 10),
figsize=(14, 10),
)
[59]:
<module 'matplotlib.pyplot' from '/work/ch0636/g300046/conda_envs/cmip6-processing/lib/python3.9/site-packages/matplotlib/pyplot.py'>
/work/ch0636/g300046/conda_envs/cmip6-processing/lib/python3.9/site-packages/cartopy/crs.py:245: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.
if len(multi_line_string) > 1:
/work/ch0636/g300046/conda_envs/cmip6-processing/lib/python3.9/site-packages/cartopy/crs.py:297: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
for line in multi_line_string:
/work/ch0636/g300046/conda_envs/cmip6-processing/lib/python3.9/site-packages/cartopy/crs.py:364: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.
if len(p_mline) > 0:
EOBS
[44]:
compare_eobs = analysis.compare_seasons(remo_tas, eobs_tas).compute()
xESMF Regridder
Regridding algorithm: bilinear
Weight filename: bilinear_433x433_201x464.nc
Reuse pre-computed weights? False
Input grid shape: (433, 433)
Output grid shape: (201, 464)
Periodic in longitude? False
[60]:
extent = [
remo_ds.rlon.min(),
remo_ds.rlon.max(),
remo_ds.rlat.min(),
remo_ds.rlat.max(),
]
extent = {"extents": extent, "crs": transform}
plot.plot_seasons(
compare_eobs.tas,
extent=extent,
projection=transform,
borders=True,
xlocs=range(-180, 180, 10),
ylocs=range(-90, 90, 10),
figsize=(14, 10),
)
[60]:
<module 'matplotlib.pyplot' from '/work/ch0636/g300046/conda_envs/cmip6-processing/lib/python3.9/site-packages/matplotlib/pyplot.py'>
/work/ch0636/g300046/conda_envs/cmip6-processing/lib/python3.9/site-packages/cartopy/crs.py:245: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.
if len(multi_line_string) > 1:
/work/ch0636/g300046/conda_envs/cmip6-processing/lib/python3.9/site-packages/cartopy/crs.py:297: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
for line in multi_line_string:
/work/ch0636/g300046/conda_envs/cmip6-processing/lib/python3.9/site-packages/cartopy/crs.py:364: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.
if len(p_mline) > 0:
[ ]: