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A dual-wavelength high-spectral-resolution lidar (HSRL) based on an iodine absorption filter and a field-widened Michelson interferometer (FWMI) has been developed to profile backscatter and extinction coefficients of aerosols and clouds accurately. This instrument was tested and calibrated on multiple observations in Hangzhou and Zhoushan, respectively, from August 2018 to April 2019. This paper discusses the design and the internal calibration method of the lidar system in detail, with several typical cases of observations and the analysis of these data products. The optical properties of urban aerosols in Hangzhou and the evolvement of clouds in Zhoushan are presented, respectively.

This paper presents two approaches to calibrate the overlap factor under inhomogeneous atmospheric condition without critical assumption and delivers detailed analysis about the retrieval errors of overlap profile in High-Spectral-Resolution-Lidar (HSRL). The first method employs an additional optical subsystem with different field-of-view, that is dual field-of-view HSRL, for the retrieval of overlap profile. The second method takes advantage of the difference of the result between the HSRL and Klett method, that is about the retrieval of backscatter coefficient for uncorrected lidar signal, to correct overlap profile. Surprisingly, two methods show very high-level consistency and stability of the result. It is potential that this technique would be an excellent solution for experimental determination of lidar overlap in ground-based HSRL.

Determination of overlap function is a key step in the calibration of a high-spectral-resolution lidar (HSRL). In this work, an iterative-based general determination (IGD) method for overlap function in HSRL is proposed. Error analysis with Monte-Carlo (MC) simulation is performed and illustrates the algorithm shows high accuracy and has high stability with an accurate lidar ratio assumption. A comparison experiment shows the overlap function from the proposed algorithm agrees well with that from a dual-field-of-view (dual-FOV) HSRL system. Since no modified optical system in HSRL is needed, this method shows great potential in real-time recording of overlap function in the HSRL system.

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.

The planetary boundary layer height (PBLH) is a vital parameter to characterize the surface convection, which determines the diffusion of air pollutants. The accurate inversion of PBLH is extremely important for the study of aerosol concentrations, in order to predict air quality and provide weather forecast. Aerosol lidar, a powerful remote sensing instrument for detecting the characteristics of atmospheric temporal and spatial evolution, can continuously retrieve the planetary boundary layer (PBL) and obtain high resolution measurements. However, multi-layer conditions, including one or more layers of aerosol, or cloud above the PBL, can seriously interfere the accuracy of PBLH determined by lidar. A new technique of maximum limited height initialization and range restriction (MLHI-RR) is proposed to eliminate the impact of multi-layer conditions on PBLH determination. Four widely used methods for deriving PBLH are utilized, in addition to the MLHI-RR constraint. Comparisons demonstrate that the proposed technique can determine the PBLH in multi-layer conditions with higher accuracy. The proposed technique requires no affiliate information besides lidar signals, which provide a convenient method for PBLH determination under complicated conditions.

The detection of clouds and aerosols is important for climate research. Lidar has been widely used in atmospheric remote sensing research because of its high spatial and temporal resolution and ability to detect profiles. High spectral resolution lidar (HSRL) accurately calculates the optical properties of aerosols and clouds without relying on any assumptions. Based on the 532nm iodine HSRL system, the lidar ratio of the urban aerosol in Hangzhou is 40-50sr, and the average lidar ratio of the cirrus is 24.79sr, demonstrating that the HSRL system and retrieval algorithms accurately obtain the optical properties of clouds and aerosols.