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The quantitative analysis of measurements with horizontally scanning aerosol lidar instruments faces two major challenges: the background correction can be affected by abnormal signal peaks, and the choice of a reference extinction coefficient αref is complicated if aerosols are ubiquitous in the sampled volume. Here, we present the newly developed multi-section method for the stable solution of extinction coefficient retrievals from horizontally scanning lidar measurements. The algorithm removes irregular peaks related to signal noise based on an experimentally derived fitting model. A representative value for αref is inferred from converging retrievals along different scan axes and over multiple scans of 10 to 15 min under the assumption that they are only related to ambient aerosols without distinct emission sources. Consequently, αref obtained through the multi-section method reflects typical atmospheric aerosols unaffected by emissions and noise. When comparing αref to the PM 2.5 mass concentrations at national monitoring stations near the measurement area, a significant correlation with an r2 value exceeding 0.74 was observed. The presented case studies show that the new method allows for the retrieval and visualization of spatio-temporal aerosol distributions and subsequent products such as PM 2.5 concentrations. [ABSTRACT FROM AUTHOR]

The effective radiative forcing (ERF) due to aerosol-cloud interactions (ACI) and rapid adjustments (ERFaci) still causes the largest uncertainty in the assessment of climate change. It is understood only with medium confidence and studied primarily for warm clouds. Here, we present a novel cloud-by-cloud (C×C) approach for studying ACI in satellite observations that combines the concentration of cloud condensation nuclei (n CCN) and ice nucleating particles (n INP) from polar-orbiting lidar measurements with the development of the properties of individual clouds from tracking them in geostationary observations. We present a step-by-step description for obtaining matched aerosol-cloud cases. The application to satellite observations over Central Europe and Northern Africa during 2014 together with rigorous quality assurance leads to 399 liquid-only clouds and 95 ice-containing clouds that can be matched to surrounding n CCN and n INP, respectively, at cloud level. We use this initial data set for assessing the impact of changes in cloud-relevant aerosol concentrations on the cloud droplet number concentration (N d) and effective radius (r eff) of liquid clouds and the phase of clouds in the regime of heterogeneous ice formation. We find a Δ ln N d/Δ ln n CCN of 0.13 to 0.30 which is at the lower end of commonly inferred values of 0.3 to 0.8. The Δ ln r eff/Δ ln n CCN between -0.09 and -0.21 suggests that r eff decreases by -0.81 to -3.78 nm per increase in n CCN of 1 cm-3. We also find a tendency towards more cloud ice and more fully glaciated clouds with increasing n INP that cannot be explained by the increasingly lower cloud-top temperature of super-cooled liquid, mixed-phase, and fully glaciated clouds alone. Applied to a larger amount of observations, the C×C approach has the potential to enable the systematic investigation of warm and cold clouds. This marks a step change in the quantification of ERFaci from space. [ABSTRACT FROM AUTHOR]

The quantitative analysis of measurements with horizontally scanning aerosol lidar instruments faces two major challenges: the background correction can be affected by abnormal signal peaks and the choice of a reference extinction coefficient α ref is complicated if aerosols are ubiquitous in the sampled volume. Here, we present the newly-developed multi-section method for the stable solution of extinction coefficient retrievals from horizontally scanning lidar measurements. The algorithm removes irregular peaks related to signal noise based on an experimentally derived fitting model. A representative value for α ref is inferred from converging retrievals along different scan axes and over multiple scans of 10 to 15 min under the assumption that they are related only to ambient aerosols without distinct emission sources. Consequently, α ref obtained through the multisection method reflects typical atmospheric aerosols unaffected by emissions and noise. When comparing α ref to the PM2.5 mass concentrations at national monitoring stations near the measurement area, a significant correlation with an r-squared value exceeding 0.74 was observed. The presented case studies show that the new method allows for the retrieval and visualization of spatio-temporal aerosol distributions and subsequent products such as PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

At the University of Hertfordshire, we have been developing a new remote sensing facility (LITES) to explore the feasibility of using Raman and/or fluorescence backscattering for chemical aerosol profiling. This paper provides an overview of the instruments of the facility and measurement examples. LITES includes a ultra-high-energy Nd:YAG/OPO setup, spectroscopic equipment with high spectral resolution, several imaging and single detectors that allow for time-resolved (lidar) signal detection, a Raman/fluorescence microscope, and a suite of gas and aerosol chambers. We present examples of elastic, rotational and vibrational spectroscopic lidar signals, as well as in-situ microscopic spectrums of dust and bio-aerosol compounds.

The lidar development at the University of Hertfordshire explores the feasibility of using Raman backscattering for chemical aerosol profiling. This paper provides an overview of the new facility. A high-power Nd:YAG/OPO setup is used to excite Raman backscattering at a wide range of wavelengths. The receiver combines a spectrometer with a 32-channel detector or an ICCD camera to resolve Raman signals of various chemical compounds. The new facility will open new avenues for chemical profiling of aerosol pollution from measurements of Raman scattering by selected chemical compounds, provide data that allow to close the gap between optical and microphysical aerosol profiling with lidar and enables connecting lidar measurements to parameters used in atmospheric modelling.

Signals of many types of aerosol lidars can be affected with a significant systematic error, if depolarizing scatterers are present in the atmosphere. That error is caused by a polarization-dependent receiver transmission. In this contribution we present an estimation of the magnitude of this systematic error. We show that lidar signals can be biased by more than 20%, if linearly polarized laser light is emitted, if both polarization components of the backscattered light are measured with a single detection channel, and if the receiver transmissions for these two polarization components differ by more than 50%. This signal bias increases with increasing ratio between the two transmission values (transmission ratio) or with the volume depolarization ratio of the scatterers. The resulting error of the particle backscatter coefficient increases with decreasing backscatter ratio. If the particle backscatter coefficients are to have an accuracy better than 5%, the transmission ratio has to be in the range between 0.85 and 1.15. We present a method to correct the measured signals for this bias. We demonstrate an experimental method for the determination of the transmission ratio. We use collocated measurements of a lidar system strongly affected by this signal bias and an unbiased reference system to verify the applicability of the correction scheme. The errors in the case of no correction are illustrated with example measurements of fresh Saharan dust. OCIS codes: 280.3640, 280.1100.

An airborne high spectral resolution lidar (HSRL) based on an iodine absorption filter and a high-power frequency-doubled Nd:YAG laser has been developed to measure backscatter and extinction coefficients of aerosols and clouds. The instrument was operated aboard the Falcon 20 research aircraft of the German Aerospace Center (DLR) during the Saharan Mineral Dust Experiment in May--June 2006 to measure optical properties of Saharan dust. A detailed description of the lidar system, the analysis of its data products, and measurements of backscatter and extinction coefficients of Saharan dust are presented. The system errors are discussed and airborne HSRL results are compared to ground-based Raman lidar and sunphotometer measurements. OCIS codes: 010.0280, 010.1280, 280.1100, 280.3640, 290.2200, 290.5850.

Aerosol Raman lidar observations of profiles of the particle extinction and backscatter coefficients and the respective extinction-to-backscatter ratio (lidar ratio) were performed under highly polluted conditions in the Pearl River Delta (PRD) in southern China in October 2004 and at Beijing during a clear period with moderately polluted to background aerosol conditions in January 2005. The anthropogenic haze in the PRD is characterized by volume light-extinction coefficients of particles ranging from approximately 200 to 800 [Mm.sup.-1] and lidar ratios mostly between 40 and 55 sr (average of 47 [+ or -] 6 sr). Almost clean air masses were observed throughout the measurements of the Beijing campaign. These air masses originated from arid desert-steppe-like regions (greater Gobi area). Extinction values usually varied between 100 and 300 [Mm.sup.-1], and the lidar ratios were considerably lower (compared with PRD values) with values mostly from 30 to 45 sr (average of 38 [+ or -] 7 sr). Gobi dust partly influenced the observations. Unexpectedly low lidar ratios of approximately 25 sr were found for a case of background aerosol with a low optical depth of 0.05. The low lidar ratios are consistent with Mie-scattering calculations applied to ground-based observations of particle size distributions. OCIS codes: 010.0010, 010.3640, 120.0120, 120.0280, 280.0280, 280.3640.

Simulation studies were carried out with regard to the feasibility of using combined observations from sunphotometer (SPM) and lidar for microphysical characterization of aerosol particles, i.e., the retrieval of effective radius, volume, and surface-area concentrations. It was shown that for single, homogeneous aerosol layers, the aerosol parameters can be retrieved with an average accuracy of 30% for a wide range of particle size distributions. Based on the simulations, an instrument combination consisting of a lidar that measures particle backscattering at 355 and 1574 nm, and a SPM that measures at three to four channels in the range from 340 to 1020 nm is a promising tool for aerosol characterization. The inversion algorithm has been tested for a set of experimental data. The comparison with the particle size distribution parameters, measured with in situ instrumentation at the lidar site, showed good agreement. OCIS codes: 010.0010, 010.1100, 010.1110, 010.3640, 120.0280.