Regularized algorithm for Raman lidar data processing

Development of ZJU High-Spectral-Resolution Lidar for Aerosol and Cloud: Extinction Retrieval

The retrieval of the extinction coefficients of aerosols and clouds without assumptions is the most important advantage of the high-spectral-resolution lidar (HSRL). The standard method to retrieve the extinction coefficient from HSRL signals depends heavily on the signal-to-noise ratio (SNR). In this work, an iterative image reconstruction (IIR) method is proposed for the retrieval of the aerosol extinction coefficient based on HSRL data, this proposed method manages to minimize the difference between the reconstructed and raw signals based on reasonable estimates of the lidar ratio. To avoid the ill-posed solution, a regularization method is adopted to reconstruct the lidar signals in the IIR method. The results from Monte-Carlo (MC) simulations applying both standard and IIR methods are compared and these comparisons demonstrate that the extinction coefficient and the lidar ratio retrieved by the IIR method have smaller root mean square error (RMSE) and relative bias values than the standard method. A case study of measurements made by Zhejiang University (ZJU) HSRL is presented, and their results show that the IIR method not only obtains a finer structure of the aerosol layer under the condition of low SNR, but it is also able to retrieve more reasonable values of the lidar ratio.

Optical properties of an industrial fire observed with a ground based N2-Raman lidar over the Paris area

This paper presents the first, to our knowledge, lidar measurement of an industrial fire plume, which covered the north of the Paris area on 17th April 2015. The fire started in a textile warehouse and rapidly spread by emitting large quantities of aerosols into the low troposphere. A ground based N2-Raman lidar performed continuous measurements during this event. Vertical profiles of the aerosol extinction coefficient, depolarization and lidar ratio are derived. A Monte Carlo algorithm was used to assess the uncertainties on the optical parameters, and to evaluate lidar inversion methods.

Expectation maximization and the retrieval of the atmospheric extinction coefficients by inversion of Raman lidar data

We consider the problem of retrieving the aerosol extinction coefficient from Raman lidar measurements. This is an ill-posed inverse problem that needs regularization, and we propose to use the Expectation-Maximization (EM) algorithm to provide stable solutions. Indeed, EM is an iterative algorithm that imposes a positivity constraint on the solution, and provides regularization if iterations are stopped early enough. We describe the algorithm and propose a stopping criterion inspired by a statistical principle. We then discuss its properties concerning the spatial resolution. Finally, we validate the proposed approach by using both synthetic data and experimental measurements; we compare the reconstructions obtained by EM with those obtained by the Tikhonov method, by the Levenberg-Marquardt method, as well as those obtained by combining data smoothing and numerical derivation.

LIDAR developments at Clermont-Ferrand--France for atmospheric observation

We present a Rayleigh-Mie-Raman LIDAR system in operation at Clermont-Ferrand (France) since 2008. The system provides continuous vertical tropospheric profiles of aerosols, cirrus optical properties and water vapour mixing ratio. Located in proximity to the high altitude Puy de Dôme station, labelled as the GAW global station PUY since August 2014, it is a useful tool to describe the boundary layer dynamics and hence interpret in situ measurements. This LIDAR has been upgraded with specific hardware/software developments and laboratory calibrations in order to improve the quality of the profiles, calibrate the depolarization ratio, and increase the automation of operation. As a result, we provide a climatological water vapour profile analysis for the 2009-2013 period, showing an annual cycle with a winter minimum and a summer maximum, consistent with in-situ observations at the PUY station. An overview of a preliminary climatology of cirrus clouds frequency shows that in 2014, more than 30% of days present cirrus events. Finally, the backscatter coefficient profile observed on 27 September 2014 shows the capacity of the system to detect cirrus clouds at 13 km altitude, in presence of aerosols below the 5 km altitude.

Retrieval of aerosol extinction coefficient profiles from Raman lidar data by inversion method

We regard the problem of differentiation occurring in the retrieval of aerosol extinction coefficient profiles from inelastic Raman lidar signals by searching for a stable solution of the resulting Volterra integral equation. An algorithm based on a projection method and iterative regularization together with the L-curve method has been performed on synthetic and measured lidar signals. A strategy to choose a suitable range for the integration within the framework of the retrieval of optical properties is proposed here for the first time to our knowledge. The Monte Carlo procedure has been adapted to treat the uncertainty in the retrieval of extinction coefficients.

Reconstruction of the aerosol optical parameters from the data of sensing with a multifrequency Raman lidar

A method of interpreting data of multifrequency Raman lidar sensing is developed. An algorithm for separating aerosol layers with different scattering properties and subsequently estimating the average value of the lidar ratio and Angström parameter within individual layers is suggested. The algorithm allows the error of reconstructing the backscattering coefficient from daytime observations to be at least halved. A well-posed numerical differentiation algorithm for determining the extinction coefficient is suggested for the interpretation of nighttime measurements based on the transformation of the range of allowable values that requires a solution of nonlinear equations. An iterative procedure envisaged for linearization improves the spatial resolution compared with the conventional methods. The methods can be successfully used to process routine lidar measurements under conditions of a priori uncertainty.