pycontrails.models.cocip.wind_shear¶

Wind shear functions.

Functions

 wind_shear(u_wind_top, u_wind_btm, ...) Calculate the total wind shear. Calculate the multiplication factor to enhance the wind shear based on contrail depth. wind_shear_normal(u_wind_top, u_wind_btm, ...) Calculate the total wind shear normal to an axis.
pycontrails.models.cocip.wind_shear.wind_shear(u_wind_top, u_wind_btm, v_wind_top, v_wind_btm, dz)

Calculate the total wind shear.

The total wind shear is the vertical gradient of the horizontal velocity.

Parameters:
Returns:

ArrayScalarLike – Total wind shear, [$$s^{-1}$$]

pycontrails.models.cocip.wind_shear.wind_shear_enhancement_factor(contrail_depth, effective_vertical_resolution, wind_shear_enhancement_exponent)

Calculate the multiplication factor to enhance the wind shear based on contrail depth.

This factor accounts for any subgrid-scale that is not captured by the resolution of the meteorological datasets.

Parameters:
• contrail_depth (npt.NDArray[np.float64]) – Contrail depth , [$$m$$]. Expected to be positive and bounded away from 0.

• effective_vertical_resolution (float | npt.NDArray[np.float64]) – Vertical resolution of met data , [$$m$$]

• wind_shear_enhancement_exponent (float | npt.NDArray[np.float64]) – Exponent used in calculation. Expected to be nonnegative. Discussed in paragraphs following eq. (39) in Schumann 2012 and referenced as n. When this parameter is 0, no enhancement occurs.

Returns:

npt.NDArray[np.float64] – Wind shear enhancement factor

Notes

Implementation based on eq (39) in .

References

pycontrails.models.cocip.wind_shear.wind_shear_normal(u_wind_top, u_wind_btm, v_wind_top, v_wind_btm, cos_a, sin_a, dz)

Calculate the total wind shear normal to an axis.

The total wind shear is the vertical gradient of the horizontal velocity.

Parameters:
Returns:

ArrayScalarLike – Wind shear normal to axis, [$$s^{-1}$$]