The telecover test: A quality assurance tool for the optical part of a lidar system

A test is described, which serves as a self-check of the optical part of a lidar system. Several lidar signals using different parts of the lidar telescope are compared. Differences in these signals indicate range dependent transmission changes of the optical receiver, resulting in signal distortion. A systematic approach including comparison with ray tracing simulations of the optical system can point out causes of the deviations

Optimized background suppression in near field lidar telescopes

It is shown that a tilted slit in the focal plane of the lidar telescope limits the telescopes field of view efficiently and suppresses the background in the lidar signal by up to an order of magnitude better than a circular diaphragm

Lidar Rayleigh-fit criteria

The talk explains how aerosol-free regions in a lidar signal can be determined by means of the so-called Rayleigh-fit. Criteria based on statistical analyses of the residuals are proposed and explained in detail, which allow to assess the quality of the Rayleigh-fit. Some of the methods can be used to develop quantitative criteria for the uncertainty in the retrieved reference value for the lidar signal inversion

Polarizing lidars and the instrument function

Although the determination of the linear depolarization ratio with lidar seems to be a simple task at first sight, systematic errors can skew the results a great deal. Different unknown systematic errors in different instruments make it difficult to compare the results and to compile a consistent picture of the aerosol properties from measurements around the globe. For the correction of known systematic errors and for the estimation of the uncertainty of the correction we first need a model of the lidars with a mathematical description of the measurements which contribute to the determination of the linear depolarization ratio. This lecture will introduce the model that has recently been published for this purpose [1]. An important part deals with the relative calibration of the signal channels which contribute to the linear depolarization ratio. A basic understanding of the Mueller-Stokes formalism [2,3] will facilitate to follow the lecture

About the effects of polarising optics on lidar signals and the Delta 90 calibration

This paper provides a model for assessing the effects of polarising optics on the signals of typical lidar systems, which is based on the description of the individual optical elements of the lidar and of the state of polarisation of the light by means of the Muller-Stokes formalism. General analytical equations are derived for the dependence of the lidar signals on polarisation parameters, for the linear depolarisation ratio, and for the signals of different polarisation calibration setups. The equations can also be used for the calculation of systematic errors caused by non-ideal optical elements, their rotational misalignment, and by non-ideal laser polarisation. We present the description of the lidar signals including the polarisation calibration in a closed form, which can be applied for a large variety of lidar systems

Polarizing lidars and the instrument function

Although the determination of the linear depolarization ratio with lidar seems to be a simple task at first sight, systematic errors can skew the results a great deal. Different unknown systematic errors in different instruments make it difficult to compare the results and to compile a consistent picture of the aerosol properties from measurements around the globe. For the correction of known systematic errors and for the estimation of the uncertainty of the correction we first need a model of the lidars with a mathematical description of the measurements which contribute to the determination of the linear depolarization ratio. This lecture will introduce the model that has recently been published for this purpose [1]. An important part deals with the relative calibration of the signal channels which contribute to the linear depolarization ratio. A basic understanding of the Mueller-Stokes formalism [2,3] will facilitate to follow the lecture

About the effects of polarising optics on lidar signals and the Delta 90 calibration

This paper provides a model for assessing the effects of polarising optics on the signals of typical lidar systems, which is based on the description of the individual optical elements of the lidar and of the state of polarisation of the light by means of the Muller-Stokes formalism. General analytical equations are derived for the dependence of the lidar signals on polarisation parameters, for the linear depolarisation ratio, and for the signals of different polarisation calibration setups. The equations can also be used for the calculation of systematic errors caused by non-ideal optical elements, their rotational misalignment, and by non-ideal laser polarisation. We present the description of the lidar signals including the polarisation calibration in a closed form, which can be applied for a large variety of lidar systems

Accuracy of linear depolarisation ratios in clean air ranges measured with POLIS-6 at 355 and 532 NM

Linear depolarization ratios in clean air ranges were measured with POLIS-6 at 355 and 532 nm. The mean deviation from the theoretical values, including the rotational Raman lines within the filter bandwidths, amounts to 0.0005 at 355 nm and to 0.0012 at 532 nm. The mean uncertainty of the measured linear depolarization ratio of clean air is about 0.0005 at 355 nm and about 0.0006 at 532 nm

EARLINET Single Calculus Chain – technical – Part 1: Pre-processing of raw lidar data

In this paper we describe an automatic tool for the pre-processing of aerosol lidar data called ELPP (EARLINET Lidar Pre-Processor). It is one of two calculus modules of the EARLINET Single Calculus Chain (SCC), the automatic tool for the analysis of EARLINET data. ELPP is an open source module that executes instrumental corrections and data handling of the raw lidar signals, making the lidar data ready to be processed by the optical retrieval algorithms. According to the specific lidar configuration, ELPP automatically performs dead-time correction, atmospheric and electronic background subtraction, gluing of lidar signals, and trigger-delay correction. Moreover, the signal-to-noise ratio of the pre-processed signals can be improved by means of configurable time integration of the raw signals and/or spatial smoothing. ELPP delivers the statistical uncertainties of the final products by means of error propagation or Monte Carlo simulations. During the development of ELPP, particular attention has been payed to make the tool flexible enough to handle all lidar configurations currently used within the EARLINET community. Moreover, it has been designed in a modular way to allow an easy extension to lidar configurations not yet implemented. The primary goal of ELPP is to enable the application of quality-assured procedures in the lidar data analysis starting from the raw lidar data. This provides the added value of full traceability of each delivered lidar product. Several tests have been performed to check the proper functioning of ELPP. The whole SCC has been tested with the same synthetic data sets, which were used for the EARLINET algorithm inter-comparison exercise. ELPP has been successfully employed for the automatic near-real-time pre-processing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns

EARLINET Single Calculus Chain - technical - Part 1: Pre-processing of raw lidar data

In this paper we describe an automatic tool for the pre-processing of aerosol lidar data called ELPP (EAR-LINET Lidar Pre-Processor). It is one of two calculus modules of the EARLINET Single Calculus Chain (SCC), the automatic tool for the analysis of EARLINET data. ELPP is an open source module that executes instrumental corrections and data handling of the raw lidar signals, making the lidar data ready to be processed by the optical retrieval algorithms. According to the specific lidar configuration, ELPP automatically performs dead-time correction, atmospheric and electronic background subtraction, gluing of lidar signals, and trigger-delay correction. Moreover, the signal-to-noise ratio of the pre-processed signals can be improved by means of configurable time integration of the raw signals and/or spatial smoothing. ELPP delivers the statistical uncertainties of the final products by means of error propagation or Monte Carlo simulations. During the development of ELPP, particular attention has been payed to make the tool flexible enough to handle all lidar configurations currently used within the EARLINET community. Moreover, it has been designed in a modular way to allow an easy extension to lidar configurations not yet implemented. The primary goal of ELPP is to enable the application of quality-assured procedures in the lidar data analysis starting from the raw lidar data. This provides the added value of full traceability of each delivered lidar product. Several tests have been performed to check the proper functioning of ELPP. The whole SCC has been tested with the same synthetic data sets, which were used for the EARLINET algorithm inter-comparison exercise. ELPP has been successfully employed for the automatic near-real-time preprocessing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns