ppcpy.calibration.lidarconstant#

Functions

get_best_LC

Get lidar constant with the lowest standard deviation.

lc_for_cldFreeGrps

Estimate the lidar constant from the optical profiles.
ppcpy.calibration.lidarconstant.lc_for_cldFreeGrps(data_cube, retrieval: str) → list[source]#

Estimate the lidar constant from the optical profiles.

Parameters:

  • data_cube (object) – Main PicassoProc object.

  • retrieval (str) – Retrieval type. ‘klett’ or ‘raman’.

Returns:

LCs – Lidar constant for retrieval type per channel per cloud free period.

Return type:

list

Notes

  • For NR, done directly form the optical profiles, whereas in the matlab version, the LC*olAttri387.sigRatio is taken.

  • TODO: Change back to Picasso version to check if lidar calibration constatns get more similar.

  • TODO: Check if LC’s are normalized with respect to the mean of the profiles.

ppcpy.calibration.lidarconstant.get_best_LC(LCs: list) → dict[source]#

Get lidar constant with the lowest standard deviation.

Parameters:

LCs (list) – Lidar constant for each channel per cloud free period.

Returns:

  • LCused (dict) – Lidar constants with lowest standard deviation per channel.

  • History

  • ——-

  • - 2026-02-16 (Added additional checks to hinder negative LCs to be chosen.)

Notes

  • Since LC = LC_sable and LCStd = LC_stable * LC_Std so will any negative LC also have a negative LCStd, and thus be chosen as the best LC.