Enhancement of aerosol characterization using synergy of lidar and sun – photometer coincident observations: the GARRLiC algorithm

Abstract

Currently most of experiments pursuing comprehensive characterization of atmosphere include coordinated observations by both lidar and radiometers in order to obtain important complimentary information about aerosol properties. The passive observations by radiometers from ground are mostly sensitive to the properties of aerosol in total atmospheric column and have very limited sensitivity to vertical structure of the atmosphere. Such observations are commonly used for measuring aerosol optical thickness and deriving the information about aerosol microphysics including aerosol particles shape, size distribution, and complex refractive index. In a contrast, lidar observations of atmospheric responses from different altitudes to laser pulses emitted from ground are designed to provide accurate profiling of the atmospheric properties. The interpretation of the lidar observation generally relies on some assumptions about aerosol type and loading. Here we present the GARRLiC algorithm (Generalized Aerosol Retrieval from Radiometer and Lidar Combined data) that simultaneously inverts co-incident lidar and radiometer observations and derives a united set of aerosol parameters. Such synergetic retrieval is expected to result in additional enhancements in derived aerosol properties because the backscattering observations by lidar add some sensitivity to the columnar properties of aerosol, while radiometric observations provide sufficient constraints on aerosol type and loading that generally are missing in lidar signals. <br><br> GARRLiC is based on AERONET algorithm for inverting combined observations by radiometer and multi-wavelength elastic lidar observations. It is expected that spectral changes of backscattering signal obtained by multi-wavelength lidar at different altitudes provide some sensitivity to the vertical variability of aerosol particle sizes. In order to benefit from this sensitivity the algorithm is set to derive not only the vertical profile of total aerosol concentration but it also differentiates between the contributions of fine and coarse modes of aerosol. The detailed microphysical properties are assumed height independent and different for each mode and expected to be derived as a part of the retrieval. Thus, the GARRLiC inversion algorithm retrieves vertical distribution of both fine and coarse aerosol concentrations as well as the size distribution, complex refractive index and single scattering albedo for each mode. <br><br> The potential and limitations of the method are demonstrated by the series of sensitivity tests. The practical outcome of the approach is illustrated by applications of the algorithm to the real lidar and radiometer observations obtained over selected AERONET site

Advanced characterisation of aerosol size properties from measurements of spectral optical depth using the GRASP algorithm

“…The present study focuses on the potential of a single set of aerosol optical depth measurements, so that the multi-pixel inversion will not be used in this work. Nevertheless, in a number of recent promising applications the multi-pixel approach has been used for synergy retrievals when τ a observations were combined with other co-located but not coincident measurements ( Lopatin et al, 2013 , 2016 ). Moreover, it appears to be a powerful capability for the interpretation of τ a -only measurements in the future.…”

Evaluation of the Lidar/Radiometer Inversion Code (LIRIC) to determine microphysical properties of volcanic and desert dust

“…The method has been developed in a cooperation between the Institute of Physics Minsk (Belarus) and the Laboratoire d'Optique Atmosphérique Lille (France) (Chaikovsky et al, 2008(Chaikovsky et al, , 2012. The recently introduced Generalized Aerosol Retrieval from Radiometer and Lidar Combined Data (GARRLiC) (Lopatin et al, 2013) can be regarded as an extended version of LIRIC. The GARRLiC concept pursues an even deeper synergy of lidar and radiometer data in the retrievals, e.g., by using the lidar profile information to improve the AERONET retrievals.…”

Separation of aerosol fine‐ and coarse‐mode radiative properties: Effect on the mineral dust longwave, direct radiative forcing

“…): LIRIC (Lidar-Radiometer Inversion Code) [Chaikovsky et al, 2012] and Generalized Aerosol Retrieval from Radiometer and Lidar Combined Data [Lopatin et al, 2013]. Those codes are simpler than the ill-posed retrievals developed since the end of the 1990s [Müller et al, 1999;Veselovskii et al, 2002;Böckmann et al, 2005] and work during daytime only.…”

“…Since relatively little, the EARLINET (European Aerosol Research Lidar Network)/ACTRIS (Aerosols, Clouds, and Trace Gases Research Infrastructure Network-www.actris.net) community has developed codes that combine Sun photometer and lidar data to retrieve a set of variables vertically resolved related to the size distribution (fine-and coarse-mode extinction coefficients, fineand coarse-mode volumetric concentrations, etc. ): LIRIC (Lidar-Radiometer Inversion Code) [Chaikovsky et al, 2012] and Generalized Aerosol Retrieval from Radiometer and Lidar Combined Data [Lopatin et al, 2013]. Those codes are simpler than the ill-posed retrievals developed since the end of the 1990s [Müller et al, 1999;Veselovskii et al, 2002;Böckmann et al, 2005] and work during daytime only.…”