cleanup

notebooks/renku_datasets_biweekly.ipynb

 %% Cell type:markdown id: tags:
 
 # Create biweekly renku datasets from `climetlab-s2s-ai-challenge`
 
 %% Cell type:markdown id: tags:
 
 Goal:
 
 - Create biweekly renku datasets from [`climatelab-s2s-ai-challenge`](https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge).
 - These renku datasets are then used in notebooks:
     - `ML_train_and_predict.ipynb` to train the ML model and do ML-based predictions
     - `RPSS_verification.ipynb` to calculate RPSS of the ML model
     - `mean_bias_reduction.ipynb` to remove the mean bias and
 
 Requirements:
 - [`climetlab`](https://github.com/ecmwf/climetlab)
 - [`climatelab-s2s-ai-challenge`](https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge)
 - S2S and CPC observations uploaded on [European Weather Cloud (EWC)](https://storage.ecmwf.europeanweather.cloud/s2s-ai-challenge/data/training-input/0.3.0/netcdf/index.html)
 
 Output: [renku dataset](https://renku-python.readthedocs.io/en/latest/commands.html#module-renku.cli.dataset) `s2s-ai-challenge`
 - observations
     - deterministic:
         - `hindcast-like-observations_2000-2019_biweekly_deterministic.zarr`
         - `forecast-like-observations_2020_biweekly_deterministic.zarr`
     - edges:
         - `hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc`
     - probabilistic:
         - `hindcast-like-observations_2000-2019_biweekly_terciled.zarr`
         - `forecast-like-observations_2020_biweekly_terciled.nc`
 - forecasts/hindcasts
     - deterministic:
         - `ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr`
         - `ecmwf_forecast-input_2020_biweekly_deterministic.zarr`
     - more models could be added
 - benchmark:
     - probabilistic:
         - `ecmwf_recalibrated_benchmark_2020_biweekly_terciled.nc`
 
 %% Cell type:code id: tags:
 
 ``` python
 import matplotlib.pyplot as plt
 import xarray as xr
 import xskillscore as xs
 import pandas as pd
 
 import climetlab_s2s_ai_challenge
 import climetlab as cml
 print(f'Climetlab version : {cml.__version__}')
 print(f'Climetlab-s2s-ai-challenge plugin version : {climetlab_s2s_ai_challenge.__version__}')
 
 xr.set_options(keep_attrs=True)
 xr.set_options(display_style='text')
 ```
 
 %%%% Output: stream
 
     Climetlab version : 0.8.0
     Climetlab-s2s-ai-challenge plugin version : 0.6.7
 
 %%%% Output: stream
 
     WARNING: ecmwflibs universal: cannot find a library called MagPlus
     /work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/climetlab/plotting/drivers/magics/actions.py:36: UserWarning: Magics library could not be found
       warnings.warn(str(e))
 
 %%%% Output: execute_result
 
-    <xarray.core.options.set_options at 0x2ae6e00b6310>
+    <xarray.core.options.set_options at 0x2ac3e9480050>
 
 %% Cell type:code id: tags:
 
 ``` python
 # caching path for climetlab
 cache_path = "/work/mh0727/m300524/S2S_AI/cache" # set your own path
 cml.settings.set("cache-directory", cache_path)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 cache_path = "../data"
 ```
 
 %% Cell type:markdown id: tags:
 
 # Download and cache
 
 Download all files for the observations, forecast and hindcast.
 
 %% Cell type:code id: tags:
 
 ``` python
 # shortcut
 from scripts import download
 #download()
 ```
 
 %% Cell type:markdown id: tags:
 
 ## hindcast and forecast `input`
 
 %% Cell type:code id: tags:
 
 ``` python
 # starting dates forecast_time in 2020
 dates = xr.cftime_range(start='20200102',freq='7D', periods=53).strftime('%Y%m%d').to_list()
 
 forecast_dataset_labels = ['training-input','test-input'] # ML community
 # equiv to
 forecast_dataset_labels = ['hindcast-input','forecast-input'] # NWP community
 
 varlist_forecast = ['tp','t2m'] # can add more
 
 center_list = ['ecmwf'] # 'ncep', 'eccc'
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 %%time
 # takes ~ 10-30 min to download for one model one variable depending on number of model realizations
 # and download settings https://climetlab.readthedocs.io/en/latest/guide/settings.html
 for center in center_list:
     for ds in forecast_dataset_labels:
         cml.load_dataset(f"s2s-ai-challenge-{ds}", origin=center, parameter=varlist_forecast, format='netcdf').to_xarray()
 ```
 
 %% Cell type:markdown id: tags:
 
 ## observations `output-reference`
 
 %% Cell type:code id: tags:
 
 ``` python
 obs_dataset_labels = ['training-output-reference','test-output-reference'] # ML community
 # equiv to
 obs_dataset_labels = ['hindcast-like-observations','forecast-like-observations'] # NWP community
 
 varlist_obs = ['tp', 't2m']
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 %%time
 # takes 10min to download
 for ds in obs_dataset_labels:
     print(ds)
     # only netcdf, no format choice
     cml.load_dataset(f"s2s-ai-challenge-{ds}", date=dates, parameter=varlist_obs).to_xarray()
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # download obs_time for to create output-reference/observations for other models than ecmwf and eccc,
 # i.e. ncep or any S2S or Sub model
 obs_time = cml.load_dataset(f"s2s-ai-challenge-observations", parameter=['t2m', 'pr']).to_xarray()
 ```
 
 %% Cell type:markdown id: tags:
 
 # create bi-weekly aggregates
 
 %% Cell type:code id: tags:
 
 ``` python
 from scripts import aggregate_biweekly, ensure_attributes
 
 #aggregate_biweekly??
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 for c, center in enumerate(center_list):  # forecast centers (could also take models)
     for dsl in obs_dataset_labels + forecast_dataset_labels:  # climetlab dataset labels
         for p, parameter in enumerate(varlist_forecast):  # variables
             if c != 0 and 'observation' in dsl:  # only do once for observations
                 continue
             print(f"datasetlabel: {dsl}, center: {center}, parameter: {parameter}")
             if 'input' in dsl:
                 ds = cml.load_dataset(f"s2s-ai-challenge-{dsl}", origin=center, parameter=parameter, format='netcdf').to_xarray()
             elif 'observation' in dsl: # obs only netcdf, no choice
                 if parameter not in ['t2m', 'tp']:
                     continue
                 ds = cml.load_dataset(f"s2s-ai-challenge-{dsl}", parameter=parameter, date=dates).to_xarray()
 
             if p == 0:
                 ds_biweekly = ds.map(aggregate_biweekly)
             else:
                 ds_biweekly[parameter] = ds.map(aggregate_biweekly)[parameter]
 
             ds_biweekly = ds_biweekly.map(ensure_attributes, biweekly=True)
 
             ds_biweekly = ds_biweekly.sortby('forecast_time')
 
         if 'test' in dsl:
             ds_biweekly = ds_biweekly.chunk('auto')
         else:
             ds_biweekly = ds_biweekly.chunk({'forecast_time':'auto','lead_time':-1,'longitude':-1,'latitude':-1})
 
         if 'hindcast' in dsl:
             time = f'{int(ds_biweekly.forecast_time.dt.year.min())}-{int(ds_biweekly.forecast_time.dt.year.max())}'
             if 'input' in dsl:
                 name = f'{center}_{dsl}'
             elif 'observations':
                 name = dsl
 
         elif 'forecast' in dsl:
             time = '2020'
             if 'input' in dsl:
                 name = f'{center}_{dsl}'
             elif 'observations':
                 name = dsl
         else:
             assert False
 
         # pattern: {model_if_not_observations}{observations/forecast/hindcast}_{time}_biweekly_deterministic.zarr
         zp = f'{cache_path}/{name}_{time}_biweekly_deterministic.zarr'
         ds_biweekly.attrs.update({'postprocessed_by':'https://renkulab.io/gitlab/aaron.spring/s2s-ai-challenge-template/-/blob/master/notebooks/renku_datasets_biweekly.ipynb'})
         print(f'save to: {zp}')
         ds_biweekly.astype('float32').to_zarr(zp, consolidated=True, mode='w')
 ```
 
 %% Cell type:markdown id: tags:
 
 ## add to `renku` dataset `s2s-ai-challenge`
 
 %% Cell type:code id: tags:
 
 ``` python
 # observations as hindcast
 # run renku commands from projects root directory only
 # !renku dataset add s2s-ai-challenge data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # for further use retrieve from git lfs
 # !renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 obs_2000_2019 = xr.open_zarr(f"{cache_path}/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr", consolidated=True)
 print(obs_2000_2019.sizes,'\n',obs_2000_2019.coords,'\n', obs_2000_2019.nbytes/1e6,'MB')
 ```
 
 %%%% Output: stream
 
     Frozen(SortedKeysDict({'forecast_time': 1060, 'latitude': 121, 'lead_time': 2, 'longitude': 240}))
      Coordinates:
       * forecast_time  (forecast_time) datetime64[ns] 2000-01-02 ... 2019-12-31
       * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
       * lead_time      (lead_time) timedelta64[ns] 14 days 28 days
       * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
         valid_time     (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 1060), meta=np.ndarray>
      492.546744 MB
 
 %% Cell type:code id: tags:
 
 ``` python
 # observations as forecast
 # run renku commands from projects root directory only
 # !renku dataset add s2s-ai-challenge data/forecast-like-observations_2020_biweekly_deterministic.zarr
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # for further use retrieve from git lfs
 # !renku storage pull ../data/forecast-like-observations_2020_biweekly_deterministic.zarr
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 obs_2020 = xr.open_zarr(f"{cache_path}/forecast-like-observations_2020_biweekly_deterministic.zarr", consolidated=True)
 print(obs_2020.sizes,'\n',obs_2020.coords,'\n', obs_2020.nbytes/1e6,'MB')
 ```
 
 %%%% Output: stream
 
     Frozen(SortedKeysDict({'forecast_time': 53, 'latitude': 121, 'lead_time': 2, 'longitude': 240}))
      Coordinates:
       * forecast_time  (forecast_time) datetime64[ns] 2020-01-02 ... 2020-12-31
       * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
       * lead_time      (lead_time) timedelta64[ns] 14 days 28 days
       * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
         valid_time     (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 53), meta=np.ndarray>
      24.630096 MB
 
 %% Cell type:code id: tags:
 
 ``` python
 # ecmwf hindcast-input
 # run renku commands from projects root directory only
 # !renku dataset add s2s-ai-challenge data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # for further use retrieve from git lfs
 # !renku storage pull ../data/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 hind_2000_2019 = xr.open_zarr(f"{cache_path}/ecmwf_hindcast-input_2000-2019_biweekly_deterministic.zarr", consolidated=True)
 print(hind_2000_2019.sizes,'\n',hind_2000_2019.coords,'\n', hind_2000_2019.nbytes/1e6,'MB')
 ```
 
 %%%% Output: stream
 
     Frozen(SortedKeysDict({'forecast_time': 1060, 'latitude': 121, 'lead_time': 2, 'longitude': 240, 'realization': 11}))
      Coordinates:
       * forecast_time  (forecast_time) datetime64[ns] 2000-01-02 ... 2019-12-31
       * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
       * lead_time      (lead_time) timedelta64[ns] 14 days 28 days
       * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
       * realization    (realization) int64 0 1 2 3 4 5 6 7 8 9 10
         valid_time     (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 1060), meta=np.ndarray>
      5417.730832 MB
 
 %% Cell type:code id: tags:
 
 ``` python
 # ecmwf forecast-input
 # run renku commands from projects root directory only
 # !renku dataset add s2s-ai-challenge data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # for further use retrieve from git lfs
 # !renku storage pull ../data/ecmwf_forecast-input_2020_biweekly_deterministic.zarr
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 fct_2020 = xr.open_zarr(f"{cache_path}/ecmwf_forecast-input_2020_biweekly_deterministic.zarr", consolidated=True)
 print(fct_2020.sizes,'\n',fct_2020.coords,'\n', fct_2020.nbytes/1e6,'MB')
 ```
 
 %%%% Output: stream
 
     Frozen(SortedKeysDict({'forecast_time': 53, 'latitude': 121, 'lead_time': 2, 'longitude': 240, 'realization': 51}))
      Coordinates:
       * forecast_time  (forecast_time) datetime64[ns] 2020-01-02 ... 2020-12-31
       * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
       * lead_time      (lead_time) timedelta64[ns] 14 days 28 days
       * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
       * realization    (realization) int64 0 1 2 3 4 5 6 7 ... 44 45 46 47 48 49 50
         valid_time     (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 53), meta=np.ndarray>
      1255.926504 MB
 
 %% Cell type:markdown id: tags:
 
 # tercile edges
 
 Create 2 tercile edges at 1/3 and 2/3 quantiles of the 2000-2019 biweekly distrbution for each week of the year
 
 %% Cell type:code id: tags:
 
 ``` python
 obs_2000_2019 = xr.open_zarr(f'{cache_path}/hindcast-like-observations_2000-2019_biweekly_deterministic.zarr', consolidated=True)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 from scripts import add_year_week_coords
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 obs_2000_2019 = add_year_week_coords(obs_2000_2019)
 ```
 
 %%%% Output: stream
 
     /work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.
       FutureWarning,
 
 %% Cell type:code id: tags:
 
 ``` python
 obs_2000_2019
 ```
 
 %%%% Output: execute_result
 
     <xarray.Dataset>
     Dimensions:        (forecast_time: 1060, latitude: 121, lead_time: 2, longitude: 240)
     Coordinates:
       * forecast_time  (forecast_time) datetime64[ns] 2000-01-02 ... 2019-12-31
       * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
       * lead_time      (lead_time) timedelta64[ns] 14 days 28 days
       * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
         valid_time     (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 1060), meta=np.ndarray>
         week           (forecast_time) int64 1 2 3 4 5 6 7 ... 47 48 49 50 51 52 53
         year           (forecast_time) int64 2000 2000 2000 2000 ... 2019 2019 2019
     Data variables:
         t2m            (lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(2, 530, 121, 240), meta=np.ndarray>
         tp             (lead_time, forecast_time, latitude, longitude) float32 dask.array<chunksize=(2, 530, 121, 240), meta=np.ndarray>
     Attributes:
         created_by_script:    tools/observations/makefile
         created_by_software:  climetlab-s2s-ai-challenge
         function:             climetlab_s2s_ai_challenge.extra.forecast_like_obse...
         postprocessed_by:     https://renkulab.io/gitlab/aaron.spring/s2s-ai-chal...
         source_dataset_name:  NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...
         source_hosting:       IRIDL
         source_url:           http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/...
 
 %% Cell type:code id: tags:
 
 ``` python
 tercile_file = f'{cache_path}/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc'
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 %%time
 obs_2000_2019.chunk({'forecast_time':-1,'longitude':'auto'}).groupby('week').quantile(q=[1./3.,2./3.], dim='forecast_time').rename({'quantile':'category_edge'}).astype('float32').to_netcdf(tercile_file)
 ```
 
 %%%% Output: stream
 
     /work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1390: RuntimeWarning: All-NaN slice encountered
       overwrite_input, interpolation)
 
 %%%% Output: stream
 
-    CPU times: user 20min 10s, sys: 8min 53s, total: 29min 3s
-    Wall time: 17min 15s
+    CPU times: user 20min 25s, sys: 8min 59s, total: 29min 25s
+    Wall time: 17min 33s
 
 %% Cell type:code id: tags:
 
 ``` python
 tercile_edges = xr.open_dataset(tercile_file)
 
 tercile_edges
 ```
 
 %%%% Output: execute_result
 
     <xarray.Dataset>
     Dimensions:        (category_edge: 2, latitude: 121, lead_time: 2, longitude: 240, week: 53)
     Coordinates:
       * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
       * lead_time      (lead_time) timedelta64[ns] 14 days 28 days
       * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
       * category_edge  (category_edge) float64 0.3333 0.6667
       * week           (week) int64 1 2 3 4 5 6 7 8 9 ... 45 46 47 48 49 50 51 52 53
     Data variables:
         t2m            (week, category_edge, lead_time, latitude, longitude) float32 ...
         tp             (week, category_edge, lead_time, latitude, longitude) float32 ...
     Attributes:
         created_by_script:    tools/observations/makefile
         created_by_software:  climetlab-s2s-ai-challenge
         function:             climetlab_s2s_ai_challenge.extra.forecast_like_obse...
         postprocessed_by:     https://renkulab.io/gitlab/aaron.spring/s2s-ai-chal...
         source_dataset_name:  NOAA NCEP CPC UNIFIED_PRCP GAUGE_BASED GLOBAL v1p0 ...
         source_hosting:       IRIDL
         source_url:           http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/...
 
 %% Cell type:code id: tags:
 
 ``` python
 tercile_edges.nbytes*1e-6,'MB'
 ```
 
 %%%% Output: execute_result
 
     (49.255184, 'MB')
 
 %% Cell type:code id: tags:
 
 ``` python
 # run renku commands from projects root directory only
 # tercile edges
 #!renku dataset add s2s-ai-challenge data/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # to use retrieve from git lfs
 #!renku storage pull ../data/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc
 #xr.open_dataset("../data/hindcast-like-observations_2000-2019_biweekly_tercile-edges.nc")
 ```
 
 %% Cell type:markdown id: tags:
 
 # observations in categories
 
 - counting how many deterministic forecasts realizations fall into each category, like counting rps
 - categorize forecast-like-observations 2020 into categories
 
 %% Cell type:code id: tags:
 
 ``` python
 obs_2020 = xr.open_zarr(f'{cache_path}/forecast-like-observations_2020_biweekly_deterministic.zarr', consolidated=True)
 obs_2020.sizes
 ```
 
 %%%% Output: execute_result
 
     Frozen(SortedKeysDict({'forecast_time': 53, 'latitude': 121, 'lead_time': 2, 'longitude': 240}))
 
 %% Cell type:code id: tags:
 
 ``` python
 # create a mask for land grid
 mask = obs_2020.std(['lead_time','forecast_time']).notnull()
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # mask.to_array().plot(col='variable')
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # total precipitation in arid regions are masked
 # Frederic Vitart suggested by email: "Based on your map we could mask all the areas where the lower tercile boundary is lower than 0.1 mm"
 # we are using a dry mask as in https://doi.org/10.1175/MWR-D-17-0092.1
 th = 0.01
 tp_arid_mask = tercile_edges.tp.isel(category_edge=0, lead_time=0, drop=True) > th
 #tp_arid_mask.where(mask.tp).plot(col='forecast_time', col_wrap=4)
 #plt.suptitle(f'dry mask: week 3-4 tp 1/3 category_edge > {th} kg m-2',y=1., x=.4)
 #plt.savefig('dry_mask.png')
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # look into tercile edges
 
 # tercile_edges.isel(forecast_time=0)['tp'].plot(col='lead_time',row='category_edge', robust=True)
 # tercile_edges.isel(forecast_time=[0,20],category_edge=1)['tp'].plot(col='lead_time', row='forecast_time', robust=True)
 # tercile_edges.tp.mean(['forecast_time']).plot(col='lead_time',row='category_edge',vmax=.5)
 ```
 
 %% Cell type:markdown id: tags:
 
 ## categorize observations
 
 %% Cell type:markdown id: tags:
 
 ### forecast 2020
 
 %% Cell type:code id: tags:
 
 ``` python
 from scripts import make_probabilistic
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # tp_arid_mask.isel(week=[0,10,20,30,40]).plot(col='week')
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 obs_2020_p = make_probabilistic(obs_2020, tercile_edges, mask=mask)
 ```
 
+%%%% Output: stream
+
+    /work/mh0727/m300524/conda-envs/s2s-ai/lib/python3.7/site-packages/xarray/core/accessor_dt.py:381: FutureWarning: dt.weekofyear and dt.week have been deprecated. Please use dt.isocalendar().week instead.
+      FutureWarning,
+
 %% Cell type:code id: tags:
 
 ``` python
 obs_2020_p.nbytes/1e6, 'MB'
 ```
 
 %%%% Output: execute_result
 
-    (147.760264, 'MB')
+    (147.75984, 'MB')
 
 %% Cell type:code id: tags:
 
 ``` python
 obs_2020_p
 ```
 
 %%%% Output: execute_result
 
     <xarray.Dataset>
     Dimensions:        (category: 3, forecast_time: 53, latitude: 121, lead_time: 2, longitude: 240)
     Coordinates:
       * forecast_time  (forecast_time) datetime64[ns] 2020-01-02 ... 2020-12-31
       * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
       * lead_time      (lead_time) timedelta64[ns] 14 days 28 days
       * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
         valid_time     (lead_time, forecast_time) datetime64[ns] dask.array<chunksize=(2, 53), meta=np.ndarray>
-        year           (forecast_time) int64 2020 2020 2020 2020 ... 2020 2020 2020
       * category       (category) <U12 'below normal' 'near normal' 'above normal'
     Data variables:
         t2m            (category, lead_time, forecast_time, latitude, longitude) float64 dask.array<chunksize=(1, 2, 53, 121, 240), meta=np.ndarray>
         tp             (category, lead_time, forecast_time, latitude, longitude) float64 dask.array<chunksize=(1, 2, 53, 121, 240), meta=np.ndarray>
 
 %% Cell type:code id: tags: