pycontrails.models.tau_cirrus

Calculate tau cirrus on Met data.

Functions

cirrus_effective_extinction_coef(ciwc, T, p)

Calculate the effective extinction coefficient for spectral range 0.2-0.69 um.

tau_cirrus(met)

Calculate the optical depth of NWP cirrus around each pressure level.
pycontrails.models.tau_cirrus.cirrus_effective_extinction_coef[A: (ndarray, DataArray)](ciwc, T, p)

Calculate the effective extinction coefficient for spectral range 0.2-0.69 um.

Parameters:

  • ciwc (A) – Cloud ice water content, [\(kg_{ice} kg_{dry \ air}^{-1}\)]. Note that ECMWF provides specific ice water content per mass moist air.

  • T (A) – Air temperature, [\(K\)]

  • p (A) – Air pressure, [\(Pa\)]

Returns:

A – Effective extinction coefficient for spectral range 0.2-0.69 um, [\(m^{-1}\)]

References

Notes

References as noted in [Schumann, 2012]:

  • Sun and Rikus QJRMS (1999), 125, 3037-3055

  • Sun QJRMS (2001), 127, 267-271

  • McFarquhar QJRMS (2001), 127, 261-265

pycontrails.models.tau_cirrus.tau_cirrus(met)

Calculate the optical depth of NWP cirrus around each pressure level.

Parameters:

met (MetDataset) – A MetDataset with the following variables:

  • “air_temperature”

  • “mass_fraction_of_cloud_ice_in_air”, “specific_cloud_ice_water_content”, or “ice_water_mixing_ratio”

Returns:

xarray.DataArray – Array of tau cirrus values. Has the same dimensions as the input data.

Notes

Implementation differs from original Fortran implementation in computing the vertical derivative of geopotential height. In particular, the finite difference at the top-most and bottom-most layers different from original calculation by a factor of two. The implementation here is consistent with a numerical approximation of the derivative.