<jupyter_start><jupyter_text>Let's look at one of those parameters --- `t2m`, the temperature at 2 meters above ground. We can see all the information on the dimensions, and the coordinate system below:<jupyter_code>ds['t2m']<jupyter_output><empty_output><jupyter_text>With CliMetLab, we can also draw this information on a map:<jupyter_code>cml.plot_map(ds['t2m'][0, 0, 1, 0])<jupyter_output><empty_output><jupyter_text>Due to the structure of the dataset, not every date has its own file. Loading a nonexistent date will trigger an error:<jupyter_code>plds = cml.load_dataset('maelstrom-ens10', date='0105')<jupyter_output><empty_output><jupyter_text>To find all available dates, call `all_datelist`:<jupyter_code>cmlds.all_datelist[0:10]<jupyter_output><empty_output><jupyter_text>Let's load January 4 in order to get the date that we want:<jupyter_code>plds = cml.load_dataset('maelstrom-ens10', date='0104')
pldsx = plds.to_xarray()
pldsx<jupyter_output><empty_output><jupyter_text>Similarly to surface-level data, let's draw the geopotential (parameter `z`) of the 1st ensemble member in January 5th, 2001 (year `3`, step `1` from 0104) on pressure level 500 hPa (index `4`, see dataset description with `pldsx['z']`):<jupyter_code>cml.plot_map(pldsx['z'][0, 3, 1, 4])<jupyter_output><empty_output>
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