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Gluing for Raman lidar systems using the lamp mapping technique.

Monique Walker, Demetrius Venable, David N Whiteman

|January 22, 2015

Summary

A new lamp mapping technique (LMT) accurately determines Lidar glue coefficients without atmospheric profiles. This method simplifies calibration for Lidar systems, improving data analysis.

Area of Science:

Atmospheric Science
Optical Remote Sensing
Lidar Technology

Background:

Glue coefficients are crucial for converting analog to photon counting signals in Lidar systems.
Traditional methods for determining glue coefficients rely on clear, nighttime atmospheric profiles, which are often unavailable.
Challenges exist in obtaining accurate coefficients due to clouds or high solar background.

Purpose of the Study:

To introduce and validate a novel laboratory-based method, the lamp mapping technique (LMT), for determining Lidar glue coefficients.
To demonstrate that LMT can accurately calculate glue coefficients without requiring atmospheric profile data.
To assess the applicability of LMT across different Raman Lidar systems.

Main Methods:

The lamp mapping technique (LMT) involves scanning a halogen lamp across the Lidar receiver telescope aperture.

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This process characterizes the optical efficiency of the entire detection system.

The method was tested on two Raman Lidar systems from Howard University and NASA/Goddard Space Flight Center.

Main Results:

Glue coefficients determined by LMT showed excellent agreement with the traditional Lidar backscattered method.
Agreement was within 1.2% for the water vapor channel and 2.5% for the nitrogen channel.
The study confirmed LMT's effectiveness for both tested Lidar systems.

Conclusions:

The lamp mapping technique (LMT) provides an accurate and efficient alternative for determining Lidar glue coefficients.
LMT eliminates the need for atmospheric profile data, enabling calibration even when such data is scarce.
This represents the first documented use of laboratory techniques for Lidar glue coefficient determination, advancing Lidar data analysis.