Qlunc: Quantification of Lidar Uncertainties

In former releases Qlunc was capable of estimating the noise power in the lidar signal due to the insertion of different components in the lidar device.
What is new in this release?

• Estimation of the line-of-sight wind speed uncertainties due to pointing accuracy and focus distance errors when using a single lidar device
• Estimation of the horizontal wind speed uncertainty due to pointing accuracy and focus distance errors when using to lidars to estimate the horizontal wind speed

Qlunc: Quantification of lidar uncertainty

Qlunc, which stands for Quantification of lidar uncertainty, is an open-source, python-based tool to create a digital twin of the lidar device, and estimate the uncertainty of wind lidar wind speed measurements. Qlunc contains models of the uncertainty contributed by individual lidar components and modules, that are then combined to estimate the uncertainties in wind lidar measurements.

For now, Qlunc can compute wind lidar hardware uncertainties from the photonics module (including photodetector and optical amplifier components) and the optics module (including scanner pointing accuracy distance errors and optical circulator uncertainties). Shortly, uncertainties for other hardware components and lidar data processing methods will be implemented in the model. Qlunc generates several output plots. These show 1) the different signal noise contributors of the photodetector components and 2) estimates of the
distance error between theoretical and measured points. Other output plots can be created by the user from the output data.

The framework has been developed and tested using python 3.7. The programming environment required to use Qlunc is provided in the repository.

Contributions are very welcome!

Revised version (JOSS)

Acknowledgements: The author wants to acknowledge the following reviewers for their time and effort
@danielskatz
@antviro
@adi3

Qlunc

Qlunc, which stands for Quantification of lidar uncertainty, is an open-source, python-based tool to create a digital twin of the lidar device, and estimate the uncertainty of wind lidar wind speed measurements. Qlunc contains models of the uncertainty contributed by individual lidar components and modules, that are then combined to estimate the uncertainties in wind lidar measurements.

For now, Qlunc can compute wind lidar hardware uncertainties from the photonics module (including photodetector and optical amplifier components) and the optics module (including scanner pointing accuracy distance errors and optical circulator uncertainties). In the near
future, uncertainties for other hardware components and lidar data processing methods will be implemented in the model. Qlunc generates several output plots. These show 1) the different signal noise contributors of the photodetector components and 2) estimates of the
distance error between theoretical and measured points. Other output plots can be created by the user from the output data.

The framework has been developed and tested using python 3.7. The programming environment required to use Qlunc is provided in the repository.

Contributions are very welcome!

v0.9

First Qlunc release - v0.9.
Lidar hardware uncertainty characterization.