SkewTDisplay.plot_enhanced_skewt(spd_name='wspd', dir_name='deg', temp_name='tdry', td_name='dp', p_name='pres', overwrite_data=None, add_data=None, color_field=None, component_range=80, uv_flag=False, dsname=None, figsize=(14, 10), layout='constrained')[source]

This will plot an enhanced Skew-T plot with a Hodograph on the top right and the stability parameters on the lower right. This will create a new figure so that one does not need to be defined through subplot_shape.

Requires Matplotlib v 3.7 and higher

  • spd_name (str) – The name of the field corresponding to the wind speed.

  • dir_name (str) – The name of the field corresponding to the wind direction in degrees from North.

  • temp_name (str) – The name of the temperature field.

  • td_name (str) – The name of the dewpoint field.

  • p_name (str) – The name of the pressure field.

  • overwrite_data (dict) – A disctionary of variables/values to write out instead of the ones calculated by MetPy. Needs to be of the form .. code-block:: python

    overwrite_data={‘LCL’: 234, ‘CAPE’: 25} …

  • add_data (dict) – A dictionary of variables and values to write out in addition to the MetPy calculated ones

  • color_field (str, optional) – The name of the field if wanting to shade by another variable

  • component_range (int) – Range of the hodograph. Default is 80

  • uv_flag (boolean) – If set to True, spd_field and dir_field will be treated as the U and V wind variable names

  • dsname (str) – Name of the datastream to plot if multiple in the plot object

  • figsize (tuple) – Figure size for the plot

  • layout (str) – String to pass to matplotlib.figure.Figure object layout keyword argument. Choice of ‘constrained,’ ‘compressed,’ ‘tight,’ or None. Default is ‘constrained’.


self.axes (matplotlib axes)