You can run this notebook in Binder, in Colab, in Deepnote or in Kaggle.

[1]:

!pip install --quiet climetlab matplotlib

WeatherBench

This is an attempt to reproduce this research: https://arxiv.org/abs/2002.00469. There is a notebook available at: https://binder.pangeo.io/v2/gh/pangeo-data/WeatherBench/master?filepath=quickstart.ipynb

[2]:

import matplotlib.pyplot as plt

[3]:

import climetlab as cml

[ ]:

ds = cml.load_dataset("weather-bench")

[5]:

ds

[5]:

weather-bench

Home pagehttps://raspstephan.github.io/blog/weatherbench/
Documentation-
Citation
@article{rasp2020weatherbench,
  title={WeatherBench: A benchmark dataset for data-driven weather forecasting},
  author={Rasp, Stephan and Dueben, Peter D and Scher, Sebastian and Weyn,
  Jonathan A and Mouatadid, Soukayna and Thuerey, Nils},
  journal={arXiv preprint arXiv:2002.00469},
  year={2020}
}
Licence-
[6]:

print(ds.citation)

@article{rasp2020weatherbench,
  title={WeatherBench: A benchmark dataset for data-driven weather forecasting},
  author={Rasp, Stephan and Dueben, Peter D and Scher, Sebastian and Weyn,
  Jonathan A and Mouatadid, Soukayna and Thuerey, Nils},
  journal={arXiv preprint arXiv:2002.00469},
  year={2020}
}


[7]:

z500 = ds.to_xarray()

[8]:

z500

[8]:

<xarray.Dataset>
Dimensions:  (lat: 32, lon: 64, time: 350640)
Coordinates:
    level    int32 500
  * lon      (lon) float64 0.0 5.625 11.25 16.88 ... 337.5 343.1 348.8 354.4
  * lat      (lat) float64 -87.19 -81.56 -75.94 -70.31 ... 75.94 81.56 87.19
  * time     (time) datetime64[ns] 1979-01-01 ... 2018-12-31T23:00:00
Data variables:
    z        (time, lat, lon) float32 dask.array<chunksize=(8760, 32, 64), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.6
    history:      2019-11-10 20:33:23 GMT by grib_to_netcdf-2.14.0: /opt/ecmw...
[9]:

cml.plot_map(z500)
../_images/examples_11-weatherbench_10_0.png 
[10]:

z500.z.isel(time=0).plot()

[10]:

<matplotlib.collections.QuadMesh at 0x7f62146b2cd0>
../_images/examples_11-weatherbench_11_1.png 
[11]:

cml.plot_map(z500.z.isel(time=0))
../_images/examples_11-weatherbench_12_0.png 
[12]:

climatology = z500.sel(time=slice("2016", "2016")).mean("time").load()

[13]:

climatology.z.plot()

[13]:

<matplotlib.collections.QuadMesh at 0x7f62145059a0>
../_images/examples_11-weatherbench_14_1.png 
[14]:

cml.plot_map(climatology.z)
../_images/examples_11-weatherbench_15_0.png 
[15]:

climatology.z

[15]:

<xarray.DataArray 'z' (lat: 32, lon: 64)>
array([[48765.18 , 48774.066, 48782.83 , ..., 48745.145, 48750.582,
        48757.645],
       [48755.164, 48798.348, 48845.02 , ..., 48690.23 , 48700.72 ,
        48721.727],
       [48890.742, 48941.703, 48997.42 , ..., 48815.234, 48822.87 ,
        48850.105],
       ...,
       [52378.613, 52398.484, 52420.074, ..., 52361.254, 52363.426,
        52366.344],
       [51937.207, 51943.78 , 51950.414, ..., 51906.508, 51919.223,
        51928.72 ],
       [51565.38 , 51571.426, 51578.15 , ..., 51543.97 , 51551.11 ,
        51558.305]], dtype=float32)
Coordinates:
    level    int32 500
  * lon      (lon) float64 0.0 5.625 11.25 16.88 ... 337.5 343.1 348.8 354.4
  * lat      (lat) float64 -87.19 -81.56 -75.94 -70.31 ... 75.94 81.56 87.19