Observations of Atmospheric Aerosol and Cloud Using a Polarized Micropulse Lidar in Xi’An, China

Article Observations of Atmospheric Aerosol and Cloud Using a Polarized Micropulse Lidar in Xi’an, China

Chao Chen 1,2,3,4, Xiaoquan Song 1,* , Zhangjun Wang 1,2,3,4,*, Wenyan Wang 5, Xiufen Wang 2,3,4, Quanfeng Zhuang 2,3,4, Xiaoyan Liu 1,2,3,4 , Hui Li 2,3,4, Kuntai Ma 1, Xianxin Li 2,3,4, Xin Pan 2,3,4, Feng Zhang 2,3,4, Boyang Xue 2,3,4 and Yang Yu 2,3,4

1 College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China; [email protected] (C.C.); [email protected] (X.L.); [email protected] (K.M.) 2 Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266100, China; [email protected] (X.W.); [email protected] (Q.Z.); [email protected] (H.L.); [email protected] (X.L.); [email protected] (X.P.); [email protected] (F.Z.); [email protected] (B.X.); [email protected] (Y.Y.) 3 Shandong Provincial Key Laboratory of Marine Monitoring Instrument Equipment Technology, Qingdao 266100, China 4 National Engineering and Technological Research Center of Marine Monitoring Equipment, Qingdao 266100, China 5 Xi’an Meteorological Bureau of Shanxi Province, Xi’an 710016, China; [email protected] * Correspondence: [email protected] (X.S.); [email protected] (Z.W.)

Abstract: A polarized micropulse lidar (P-MPL) employing a pulsed laser at 532 nm was developed by the Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy Citation: Chen, C.; Song, X.; Wang, of Sciences). The optomechanical structure, technical parameters, detection principle, overlap factor Z.; Wang, W.; Wang, X.; Zhuang, Q.; calculation method, and inversion methods of the atmospheric boundary layer (ABL) depth and Liu, X.; Li, H.; Ma, K.; Li, X.; et al. depolarization ratio (DR) were introduced. Continuous observations using the P-MPL were carried Observations of Atmospheric Aerosol out at Xi’an Meteorological Bureau, and the observation data were analyzed. In this study, we and Cloud Using a Polarized Micropulse Lidar in Xi’an, China. gleaned much information on aerosols and clouds, including the temporal and spatial variation of Atmosphere 2021, 12, 796. https:// aerosols and clouds, aerosol extinction coefficient, DR, and the structure of ABL were obtained by doi.org/10.3390/atmos12060796 the P-MPL. The variation of aerosols and clouds before and after a short rainfall was analyzed by combining time-height-indication (THI) of range corrected signal (RCS) and DR was obtained by Academic Editors: Damao Zhang, the P-MPL with profiles of potential temperature (PT) and relative humidity (RH) detected by GTS1 Adeyemi Adebiyi and Digital Radiosonde. Then, the characteristics of tropopause cirrus cloud were discussed using the Jean-Christophe Raut data of DR, PT, and RH. Finally, a haze process from January 1st to January 5th was studied by using aerosol extinction coefficients obtained by the P-MPL, PT, and RH profiles measured by GTS1 Digital Received: 18 May 2021 Radiosonde and the time-varying of PM2.5 and PM10 observed by ambient air quality monitor. The Accepted: 15 June 2021 source of the haze was simulated by using the NOAA HYSPLIT Trajectory Model. Published: 21 June 2021

Keywords: micropulse lidar; depolarization ratio; aerosol; cloud; atmospheric boundary layer; haze Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. 1. Introduction Atmospheric aerosols are microscopically small particles and mainly suspended in the lower troposphere [1,2]. They play an important role in the energy balance of the Earth system by directly scattering and absorbing solar radiation, and indirectly playing Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. the role of cloud condensation nuclei (CCN) in cloud-forming processes. Accordingly, This article is an open access article aerosol particles serving as CCN have a direct impact on cloud optical properties and distributed under the terms and rainfall probability [3–5]. In addition, the concentration and distribution of aerosols affect conditions of the Creative Commons atmospheric visibility and public health by changing air quality [6,7]. With the development Attribution (CC BY) license (https:// of urbanization and industrialization, air pollution is becoming more and more serious, creativecommons.org/licenses/by/ and people pay more attention to air quality [8,9]. Therefore, it is important to study the 4.0/). optical properties and dynamics of aerosols.

Atmosphere 2021, 12, 796. https://doi.org/10.3390/atmos12060796 https://www.mdpi.com/journal/atmosphere Atmosphere 2021, 12, 796 2 of 17

Lidar (Light Detection and Ranging) is an active optical remote sensing device that has been proven to be a reliable tool for high temporal and spatial resolution remote sensing of atmospheric aerosols in real-time [10,11]. Knowledge of aerosol optical properties and distribution characteristics will help us to understand their role in atmospheric processes, as well as their effects on human health and the atmospheric environment. Using polarized lidar to observe the depolarization ratios (DR) and optical properties of atmospheric aerosols will make it possible to distinguish between types of aerosols and provide effective methods for the study of optical and microphysical properties of aerosols [12,13]. As early as 1971, Schotland et al., of New York University used polarized lidar to study the DR of cloud particles [14]. In 2006, the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite was launched by the National Aeronautics and Space Administration (NASA) carrying a dual-wavelength (532 nm and 1064 nm) lidar for the study of global clouds and atmospheric aerosols. By using the 532 nm polarization channel to observe the polarization of global clouds and aerosols, satellite polarized lidar observation was realized [15]. Polarization measurement has become one of the standard configurations of atmospheric lidar, such as the micropulse lidar developed by Sigma Space Corporation of the United States, which has been used to observe volcanic ash [16]. A polarized micropulse lidar (P-MPL), designed and developed by the Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences) for measurements of aerosols and clouds in an automatic and continuous mode, was deployed at Xi’an Meteorological Bureau (34◦180 N, 108◦560 E) for operational observation. The purpose of the observation campaign is mainly to study the optical and polarization characteristics of urban aerosols and clouds. In this paper, the observation campaign is introduced firstly, and then the functions and specifications of the observation instruments, including the P-MPL, GTS1 Digital Radiosonde, and ambient air quality monitor are outlined. The detection principle of the P-MPL is discussed, while a geometric overlap factor calculation method, a DR calculation method, and an atmospheric boundary layer (ABL) depth inversion algorithm are proposed and tested in Section3. Finally, some observation results in different weather conditions are presented and discussed.

2. Observation Campaign and Instruments 2.1. Observation Campaign in Xi’an Xi’an is an important central city in Western China with a warm, temperate, semi- humid continental monsoon climate and with different features for each season. There are more rainy days in summer, while in winter, haze events occur frequently due to the inﬂuence factors of geographical location, meteorological conditions, and highway transportation. From June 2019 to April 2020, the P-MPL was operated automatically and continuously at Xi’an Meteorological Bureau (Figure1), which is located in the urban area of Xi’an. The temporal and spatial distribution characteristics, optical characteristic parameters, and DR of atmospheric aerosols and clouds were obtained. In order to better understand the characteristics of aerosols and clouds in different weather conditions, the observational data of the P-MPL, GTS1 Digital Radiosonde and air quality monitor were used together for analysis. Atmosphere 2021, 12, x FOR PEER REVIEW 3 of 18

Atmosphere 20212021,, 1212,, 796x FOR PEER REVIEW 3 3of of 18 17

(a) ( b) Figure 1. (a) Geographical(a) location of Xi’an Meteorological Bureau;(b) (b) The observation figure of P-MPL (polarized micropulse lidar) at Xi’an Meteorological Bureau. Figure 1. ((aa)) Geographical Geographical location location of of Xi Xi’an’an Meteorological Meteorological Bureau; Bureau; (b ()b The) The observation observation figure ﬁgure of of PP-MPL-MPL (polarized micropulse lidar) at Xi’anXi’an Meteorological Bureau. 2.2. The P-MPL Setup 2.2. The TheThe P P-MPL- MPLP-MPL Setup Setup with a depolarization-sensitive channel based on Mie scattering is devel- opedThe for P P-MPL automatic-MPL with observations a a depolarization depolarization-sensitive of tropospheric-sensitive channel channel aerosols based based and onclouds. on Mie Mie scattering scatteringThe structure is is devel- devel- of the opedP-MPL for isautomatic shown in observations Figure 2 and of the tropospheric biaxial structure aerosols is adopted. andand clouds. A single The structure wavelength of the(532 PP-MPLnm)-MPL pulsed is is shown shown laser in in emittingFigure Figure 22 anda and Gaussian the the biaxial biaxial laser structure structurebeam with is adopted. is single adopted. laser A single A energy single wavelength of wavelength 60 μJ, (532pulse nm)(532length pulsed nm) of pulsed 5 laserns, and laseremitting repetition emitting a Gaussian frequency a Gaussian laser of beam laser7 kHz beamwith as the single with transmitter singlelaser energy laser is employed energy of 60 μJ of in, 60pu thelµse J,P - lengthpulseMPL. length ofThe 5 nsbackscattering of, and 5 ns, repetition and repetition signals frequency frequencyare acquired of 7 kHz of 7 by kHzas anthe as 8 transmitter the-inch transmitter diameter is employed isaperture employed inSchmidt the in the P-– MPL.P-MPL.Cassegrain The The backscattering backscatteringtelescope. The signals signals signals are collected are acquired acquired by bythe by an telescope an 8 8-inch-inch diameterare diameter further aperture aperture separated Schmidt Schmidt– by a po-– Cassegrainlarizing beam telescope. telescope. splitter The The (PBS) signals signals into collected collected parallel by and by the the perpendiculartelescope telescope are are further polarization further separated separated components. by a by po- a larizingpolarizingEach component beam beam splitter splitter is measured (PBS) (PBS) into intoseparately parallel parallel by and and the perpendicular perpendicularparallel channel polarization polarization and perpendicular components. chan- Eachnel using component photomultiplier isis measuredmeasured tubes separatelyseparately (PMTs). by by Fi the thenally, parallel parallel the data channel channel are andrecorded and perpendicular perpendicular by a dual channel-channel chan- nelusingphoton using photomultiplier counting photomultiplier card tubes with tubes (PMTs).a resolution (PMTs). Finally, Fiofnally, 250 the ns datathe, to data arewhich recordedare the recorded corresponding by a by dual-channel a dual range-channel pho-reso- photontonlution counting countingof P-MPL card card is with 37.5 with a m. resolution a resolution of 250 of ns,250 to ns which, to which the corresponding the corresponding range range resolution reso- lutionof P-MPL of P is-MPL 37.5 is m. 37.5 m.

M1 M2

M: Mirror Telescope PH: Pinhole Beamexpander M:L Mirror: Lens

Telescope PHIF: :Pinhole Interference filter Beamexpander L:PBS Lens: Polarizing beam splitter

Laser IF:PMT Interference: Photomultiplier filter tube PBS: Polarizing beam splitter

Laser PMT: Photomultiplier tube PH L1 PH IF L2 PBS L1 PMT Photon counting IF L2 PBS card Trigger L2 PMT Photon counting card Trigger L2 PC

Optical system PMT PC

Optical system PMT

FigureFigure 2. 2.The The schematic schematic diagram diagram of of the the P-MPL P-MPL (polarized (polarized micropulse micropulse lidar). lidar). Figure 2. The schematic diagram of the P-MPL (polarized micropulse lidar). TheThe speciﬁcations specifications of of the the P-MPL P-MPL are are shown shown in in Table Table1. 1. The specifications of the P-MPL are shown in Table 1.

Atmosphere 2021, 12, 796 4 of 17

Table 1. The speciﬁcations of the P-MPL.

Parameters Speciﬁcation Transmitter Type Diode-pumped solid state Laser wavelength 532 nm Pulse energy 60 µJ Pulse duration 5 ns Pulse repetition frequency 7 kHz Beam divergence 200 µrad Receiver Telescope type Schmidt–Cassegrain Telescope diameter 8-inch Field of view 500 µrad Filter bandwidth 0.5 nm Detector type PMT Detection mode Photon counting Range resolution 37.5 m Detection range 0.1~20 km

2.3. GTS1 Digital Radiosonde The GTS1 Digital Radiosonde is a relatively advanced digital high-altitude detector at present with the advantages of high precision, small volume, and high sensitivity. The strong anti-interference ability can ensure that it is not affected by the weather and the same frequency interference, and further ensure the stability and reliability of data acquisition and transmission. It is carried by hydrogen balloon, which is usually released at 7:15 and 19:15 local time every day. In the process of balloon rising, the meteorological elements such as temperature, humidity, wind, and pressure at different altitudes are measured. The main speciﬁcations of the GTS1 Digital Radiosonde are shown in Table2. The range resolution of the digital radiosonde data used in this paper is 15 m.

Table 2. The speciﬁcations of the GTS1 Digital Radiosonde.

Parameters Speciﬁcation Temperature −90 ◦C~50 ◦C Relative humidity 0%~100% Pressure 5 hPa~1060 hPa Maximum detection altitude 36 km

2.4. Ambient Air Quality Monitor Based on the principle of β-ray absorption, the ambient air quality monitor adopts a dual-channel structure and can simultaneously collect two kinds of pollution particles (PM2.5 and PM10) in the atmosphere, which can effectively reﬂect the variation of air pollution particle concentration. The speciﬁcations can be seen form Table3.

Table 3. The speciﬁcations of the ambient air quality monitor.

Parameters Speciﬁcation Measuring range 0~1000 µg/m3 Minimum detection value ≤5 µg/m3 Minimum display unit 0.1 µg/m3 Sampling ﬂow deviation 16.7 L/min ± 3%

3. Detection Principle and Data Processing Methods of the P-MPL 3.1. The Lidar Equation The P-MPL emits a linearly polarized laser light and mainly uses the echo signals of parallel channel and perpendicular channel to study the atmospheric aerosols and clouds. Atmosphere 2021, 12, 796 5 of 17

For spherical aerosol particles, such as water droplets, the polarization planes of echo signals are oriented parallel to the outgoing beam. If the laser interacts with non-spherical aerosol particles (dust, ice crystals, etc.), the polarization planes of backscattering signals are no longer consistent with the polarization plane of the emitted laser beam; that is, depolarization occurs [17]. Generally, the lidar equations for the two polarization channels are shown as follows [18]:

Z r kpP0 Ar βp(r) 0 0 Pp(r) = exp −2 α r dr (1) r2 0

Z r ksP0 Ar βs(r) 0 0 Ps(r) = exp −2 α r dr (2) r2 0

where P(r) is the intensity of echo signal at the distance r, P0 is the intensity of laser emission, Ar is the aperture area, α(r) is extinction coefficient, β(r) is the backscattering coefficient, and k is the lidar correction constant, which is related to geometric overlap factor, total transmittance, and range resolution. The subscript P and S represent parallel polarization and perpendicular polarization components, respectively. The range corrected signal (RCS) is defined in Equation (3):

RCS(r) = P(r) · r2 (3)

Both extinction coefficient and backscattering coefficient reflect the optical properties of the aerosol. Here, the extinction coefficient and backscattering coefficient are retrieved using the Fernald method [19], and the lidar ratio of urban aerosol is assumed to be 50 sr [20].

3.2. Geometric Overlap Factor Calculation Method To better understand the near distance signal, the geometric overlap factor G(r) should be calculated. G(r) is deﬁned as the ratio of laser beam energy P(r) to total energy P0(r) in the ﬁeld of view at range r in biaxial lidar [21]. Assuming that the laser energy is uniformly distributed, G(r) can be described by Equation (4):

Soverlap(r) G(r) = (4) SL(r)

where Soverlap(r) is the overlap area of the ﬁeld of view and the laser beam area in range r, and SL(r) is the laser beam area in range r. The G(r) changes with r and is shown in Figure3 . The altitude of G(r) = 0 is the detection blind area, where atmospheric echo data will not be received. The altitude of 0< G(r) < 1 is referred to as the transition zone, where only part of the data will be received. After passing the transition zone, the echo data will be Atmosphere 2021, 12, x FOR PEER REVIEWcompletely received and G(r) = 1. Therefore, it is necessary to calculate G(r) to normalize6 of 18

the signal in the transition zone to the condition when G(r) = 1.

θFOV/2

θL/2

G(r)=0 0

The main parameters for calculating G(r) in biaxial lidar include output beam diameter DL, telescope diameter DR, laser beam divergence angle θL, field of view θFOV, the transmitter-receiver separation D, and angle of misaligned optical axes ω. The diameters of laser beam and the field of view of receiver changing with r are described in Equations (5) and (6):

퐷퐿(푟) = 푟 ⋅ 휃퐿 + 퐷퐿 (5)

퐷푅(푟) = 푟 ⋅ 휃퐹푂푉 + 퐷푅 (6) If the optical axes of transmitter and receiver are misaligned, the transmitter-receiver separation also varies with r: 퐷(푟) = 퐷 + 휔 ⋅ 푟 (7) When ω is positive, it means that the optical axes of transmitter and receiver are separated, and when ω is negative, it indicates that the optical axes of transmitter and receiver   are intersecting. If optics are slightly misaligned, for example  −FOV L , above the blind 22 area and transition area, the G(r) will be 1. If ω is larger, the long-distance beam plane will partially coincide with the field of view or completely separate from the field of view, and then affect the detection efficiency and signal accuracy of lidar. SL(r) and Soverlap(r) are described in Equations (8) and (9): 퐷 (푟) 2 푆 (푟) = 휋 ⋅ ( 퐿 ) 퐿 2 (8)

퐷 (푟) 2 퐷 (푟) 2 2 ( 퐿 ) +퐷(푟)2−( 푅 ) 2 퐷퐿(푟) 2 2 1 퐷퐿(푟) 푆overlap(푟) = ( ) ⋅ 푎푟푐푐표푠 ( ) − ⋅ ( ) ⋅ 푠푖푛 (2 ⋅ 2 퐷퐿(푟)⋅퐷(푟) 2 2 2 2 2 2 퐷퐿(푟) 2 퐷푅(푟) 퐷푅(푟) 2 퐷퐿(푟) ( ) +퐷(푟) −( ) 퐷 (푟) 2 ( ) +퐷(푟) −( ) 1 푎푟푐푐표푠 ( 2 2 )) + ( 푅 ) ⋅ 푎푟푐푐표푠 ( 2 2 ) − ⋅ (9) 퐷퐿(푟)⋅퐷(푟) 2 퐷푅(푟)⋅퐷(푟) 2 2 2 퐷푅(푟) 2 퐷퐿(푟) 퐷 (푟) 2 ( ) +퐷(푟) −( ) ( 푅 ) ⋅ 푠푖푛 (2 ⋅ 푎푟푐푐표푠 ( 2 2 )) 2 퐷푅(푟)⋅퐷(푟)

In our system, D is 0.15 m, DL is 0.03 m, DR is 0.2 m, θL is 200 µrad, θFOV is 500 µrad and ω is zero due to the good alignment. By putting these parameters into the equations above, G(r) can be obtained (Figure 4a). The data of 0:30 on 24 June and 10:30 on 30 De- cember 2019, are selected for geometric factor correction. The change in the near-field signal before and after correction can clearly be seen from Figure 4b,c.

Atmosphere 2021, 12, 796 6 of 17

The main parameters for calculating G(r) in biaxial lidar include output beam diameter DL, telescope diameter DR, laser beam divergence angle θL, ﬁeld of view θFOV, the transmitter-receiver separation D, and angle of misaligned optical axes ω. The diameters of laser beam and the ﬁeld of view of receiver changing with r are described in Equations (5) and (6):

DL(r) = r · θL + DL (5)

DR(r) = r · θFOV + DR (6) If the optical axes of transmitter and receiver are misaligned, the transmitter-receiver separation also varies with r: D(r) = D + ω · r (7) When ω is positive, it means that the optical axes of transmitter and receiver are separated, and when ω is negative, it indicates that the optical axes of transmitter and θFOV θL receiver are intersecting. If optics are slightly misaligned, for example ω < 2 − 2 , above the blind area and transition area, the G(r) will be 1. If ω is larger, the long-distance beam plane will partially coincide with the field of view or completely separate from the field of view, and then affect the detection efficiency and signal accuracy of lidar. SL(r) and Soverlap(r) are described in Equations (8) and (9):

D (r) 2 S (r) = π · L (8) L 2

  D (r) 2 D (r) 2 2 L +D(r)2− R DL(r) 2 2 Soverlap(r) = · arccos − 2 DL(r)·D(r)      D (r) 2 D (r) 2 D (r) 2 D (r) 2 2 L + 2− R 2 R + 2− L D (r) 2 D(r) 2 D (r) 2 D(r) 2 1 · L ·sin2·arccos + R · arccos − (9) 2 2 DL(r)·D(r) 2 DR(r)·D(r)    D (r) 2 D (r) 2 2 R + 2− L D (r) 2 D(r) 2 1 · R · sin2 · arccos  2 2 DR(r)·D(r)

In our system, D is 0.15 m, DL is 0.03 m, DR is 0.2 m, θL is 200 µrad, θFOV is 500 µrad and ω is zero due to the good alignment. By putting these parameters into the equations above, G(r) can be obtained (Figure4a). The data of 0:30 on 24 June and 10:30 on 30 Atmosphere 2021, 12, x FOR PEER REVIEWDecember 2019, are selected for geometric factor correction. The change in the near-ﬁeld7 of 18

signal before and after correction can clearly be seen from Figure4b,c.

(a) (b) (c)

FigureFigure 4. (4.a )(a The) The proﬁle profile of of geometric geometric overlap overlap factorfactor changechange with range; range; ( (bb) )The The signal signal at at 0:30 0:30 on on 24 24June June 2019, 2019, corrected corrected by by geometric overlap factor; (c) The signal at 10:30 on 30 December 2019, corrected by geometric overlap factor. geometric overlap factor; (c) The signal at 10:30 on 30 December 2019, corrected by geometric overlap factor. 3.3. Calculation of DR The DR is directly related to the type of aerosol particles. The presence of large nonspherical particles in sufficient quantities will produce changes in optical properties and especially the DR of 532 nm. DR measurements make it possible to discriminate the type of aerosols and clouds (e.g., smoke, dust, water cloud, and ice cloud) [22–24]. Generally, the DR range of urban pollution aerosols is between 5% and 10%, the DR of water cloud is less than 15%, the DR of mixed cloud is from 15% to 50% and the DR of ice cloud is between 30% and 80% [25–27]. In cloudless weather or with less aerosol content, the atmospheric depolarization is basically caused by atmospheric molecules. For 532 nm, the DR of molecules is about 0.365% [28]. DR δDP(r) can be calculated by the ratio of parallel polarization signal PP(r) and perpendicular polarization signal PS(r) from Equation (10). K is a calibration factor.

푃푠(푟) 훿퐷푃(푟) = 퐾 ⋅ (10) 푃푝(푟) As can be seen from the RCSs of two channels with 1 min time resolution and 37.5 m range resolution shown in Figure 5a, the aerosols in the bottom layer mainly exist below 2.4 km and cirrus clouds are distributed between 10.4 km and 12.5 km. At the altitude of cirrus clouds, there is a good separation between parallel polarization signal and perpendicular polarization signal, indicating that the depolarization is relatively high. According to the DR profile in Figure 5b, the bottom aerosols are mainly composed of urban pollution aerosols and the DR values of cirrus clouds between 10.4 km and 12.5 km are more than 0.4, which is a typical feature of ice clouds.

Atmosphere 2021, 12, 796 7 of 17

3.3. Calculation of DR The DR is directly related to the type of aerosol particles. The presence of large nonspherical particles in sufﬁcient quantities will produce changes in optical properties and especially the DR of 532 nm. DR measurements make it possible to discriminate the type of aerosols and clouds (e.g., smoke, dust, water cloud, and ice cloud) [22–24]. Generally, the DR range of urban pollution aerosols is between 5% and 10%, the DR of water cloud is less than 15%, the DR of mixed cloud is from 15% to 50% and the DR of ice cloud is between 30% and 80% [25–27]. In cloudless weather or with less aerosol content, the atmospheric depolarization is basically caused by atmospheric molecules. For 532 nm, the DR of molecules is about 0.365% [28]. DR δDP(r) can be calculated by the ratio of parallel polarization signal PP(r) and perpendicular polarization signal PS(r) from Equation (10). K is a calibration factor.

Ps(r) δDP(r) = K · (10) Pp(r)

As can be seen from the RCSs of two channels with 1 min time resolution and 37.5 m range resolution shown in Figure5a, the aerosols in the bottom layer mainly exist below 2.4 km and cirrus clouds are distributed between 10.4 km and 12.5 km. At the altitude of cirrus clouds, there is a good separation between parallel polarization signal and perpendicular polarization signal, indicating that the depolarization is relatively high. According to the DR proﬁle in Figure5b, the bottom aerosols are mainly composed of urban pollution Atmosphere 2021, 12, x FOR PEER REVIEW 8 of 18 aerosols and the DR values of cirrus clouds between 10.4 km and 12.5 km are more than 0.4, which is a typical feature of ice clouds.

(a) (b)

Figure Figure5. (a) The 5. (profilesa) The proﬁles of parallel of parallel polarization polarization and perpendicular and perpendicular polarization polarization channels channels’’ RCSs at RCSs at 22:20 on 6 July 2020; (b) The profile of DR at 22:20 on 6 July 2020. 22:20 on 6 July 2020; (b) The proﬁle of DR at 22:20 on 6 July 2020.

3.4. ABL3.4. Depth ABL Determination Depth Determination The ABLThe has ABL the has closest the closest and most and direct most directrelationship relationship with human with human activities, activities, and air and air pollutionpollution mainly mainly occurs occurs in this in layer. this layer. Generally, Generally, there there is a very is a very strong strong temperature temperature inver- inversion sion atat the the top top of of the the ABL ABL,, limiting limiting the the upward upward diffusion diffusion of of pollutants pollutants and and water water vapor vapor [29]. [29]. Correspondingly,Correspondingly, there there is a veryvery strongstrong positive positive gradient gradient of of potential potential temperature temperature (PT) and (PT) andnegative negative gradient gradient of relativeof relative humidity humidity (RH) (RH) at the at topthe oftop the of boundarythe boundary layer. layer. Therefore, Therefore,the temperaturethe temperature and and humidity humidity proﬁles profiles of radiosonde of radiosonde data data can can be usedbe used to obtainto obtain the ABL the ABLdepth depth [30 [].30]. The high spatial and temporal resolution makes aerosol lidar one of the most suitable systems for continuously monitoring the structure of ABL. Generally, the aerosol concentration in the ABL is significantly higher than that in the free atmosphere above the ABL. Based on this principle, the gradient of the lidar signal profile exhibits a strong negative peak at the top of ABL. Gradient algorithms are widely used in ABL depth inversion, including the first gradient of raw signal (G-Raw), the first gradient of the RCS (G-RCS), and the first normalized gradient of RCS (NG-RCS) [31,32]. The ABL depth is defined at the altitude where the gradient reaches a minimum. The G-Raw can be obtained by Δ푃(푟) 퐷 (푟) = (11) G-Raw Δ푟 The G-RCS can be calculated by the equation: ΔRCS(푟) 퐷 (푟) = (12) G-RCS Δ푟 The calculation method of NG-RCS is shown below: ΔRCS(푟) 퐷 (푟) = (13) NG-RCS Δ푟 × RCS(푟) The three methods are compared in the process of ABL depth inversion. In Figure 6a, RCS of 532 nm at 19:15 on 20 July coincides with atmospheric molecular profile when the altitude is above 2.5 km. When the altitude is below 2.5 km, the lidar echo signal is significantly higher than the atmospheric molecular signal due to the existence of aerosols. Fig- ure 6b shows that the trend of the gradient signals obtained by the three inversion methods is basically similar, and the position of ABL depth at 1.23 km can be determined. From

Atmosphere 2021, 12, 796 8 of 17

The high spatial and temporal resolution makes aerosol lidar one of the most suitable systems for continuously monitoring the structure of ABL. Generally, the aerosol concentration in the ABL is significantly higher than that in the free atmosphere above the ABL. Based on this principle, the gradient of the lidar signal profile exhibits a strong negative peak at the top of ABL. Gradient algorithms are widely used in ABL depth inversion, including the first gradient of raw signal (G-Raw), the first gradient of the RCS (G-RCS), and the first normalized gradient of RCS (NG-RCS) [31,32]. The ABL depth is defined at the altitude where the gradient reaches a minimum. The G-Raw can be obtained by

∆P(r) D − (r) = (11) G Raw ∆r The G-RCS can be calculated by the equation:

∆RCS(r) D − (r) = (12) G RCS ∆r The calculation method of NG-RCS is shown below: ∆RCS(r) D − (r) = (13) NG RCS ∆r × RCS(r)

The three methods are compared in the process of ABL depth inversion. In Figure6a , RCS of 532 nm at 19:15 on 20 July coincides with atmospheric molecular proﬁle when the altitude is above 2.5 km. When the altitude is below 2.5 km, the lidar echo signal is Atmosphere 2021, 12, x FOR PEER REVIEWsigniﬁcantly higher than the atmospheric molecular signal due to the existence of aerosols.9 of 18

Figure6b shows that the trend of the gradient signals obtained by the three inversion methods is basically similar, and the position of ABL depth at 1.23 km can be determined. From the radiosonde data (Figure6c), the proﬁles of RH and PT have signiﬁcant gradient the radiosonde data (Figure 6c), the profiles of RH and PT have significant gradient changes at the altitude of 1.23 km. However, at the initial altitude, the gradient signals changes at the altitude of 1.23 km. However, at the initial altitude, the gradient signals obtained by G-Raw and G-RCS will cause misjudgment of the minimum value. obtained by G-Raw and G-RCS will cause misjudgment of the minimum value.

(a) (b) (c)

Figure 6. ((aa)) Profiles Profiles of of RCS RCS and and atmospheric atmospheric molecular molecular signal signal at at 19: 19:15,15, 20 20 July July 2019; 2019; (b) ( bGradient) Gradient profiles profiles obtained obtained by byG- Raw, NG-RCS, and G-RCS; (c) Profiles of PT and RH obtained by GTS1 Digital Radiosonde at 19:15, 20 July 2019. G-Raw, NG-RCS, and G-RCS; (c) Profiles of PT and RH obtained by GTS1 Digital Radiosonde at 19:15, 20 July 2019. Sometimes, the irregular distribution of aerosols in the ABL will lead to the jitter of the signal profile, which will affect the accurate inversion of the ABL depth. From the data of the P-MPL, and the PT and RH of the GTS1 Digital Radiosonde at 7:15 on 23 July (Figure 7), the aerosols mainly exist below 2.6 km. The irregular distribution of aerosols in the ABL causes the jitter of gradient signals, especially the gradient profile obtained by the G- RCS method. The gradient curves obtained by G-Raw and G-RCS still have a minimum value at low altitude, which will lead to misjudgment of ABL depth. Therefore, the NG- RCS method is used to invert ABL depth here.

(a) (b) (c)

Atmosphere 2021, 12, x FOR PEER REVIEW 9 of 18

the radiosonde data (Figure 6c), the profiles of RH and PT have significant gradient changes at the altitude of 1.23 km. However, at the initial altitude, the gradient signals obtained by G-Raw and G-RCS will cause misjudgment of the minimum value.

Atmosphere 2021, 12, 796 (a) (b) (c) 9 of 17 Figure 6. (a) Profiles of RCS and atmospheric molecular signal at 19:15, 20 July 2019; (b) Gradient profiles obtained by G- Raw, NG-RCS, and G-RCS; (c) Profiles of PT and RH obtained by GTS1 Digital Radiosonde at 19:15, 20 July 2019. Sometimes, the irregular distribution of aerosols in the ABL will lead to the jitter of the signal proﬁle,Sometimes, which will affectthe irregular the accurate distribut inversionion of ofaerosols the ABL in depth. the ABL From will the lead data to ofthe jitter of the P-MPL,the and signal the profile, PT and RHwhich of thewill GTS1 affect Digital the accurate Radiosonde inversion at 7:15 of onthe 23ABL July depth.(Figure From7) , the data the aerosolsof the mainly P-MPL exist, and below the PT 2.6 and km. RH The of irregularthe GTS1 distributionDigital Radiosonde of aerosols at 7:15 in theon 23 ABL July (Figure causes the7), jitter the ofaerosols gradient ma signals,inly exist especially below 2.6 the km. gradient The irregular proﬁle obtained distribution by the of G-RCS aerosols in the method. TheABL gradient causes the curves jitter obtainedof gradient by signals, G-Raw especially and G-RCS the still gradient have a profile minimum obtained value by the G- at low altitude,RCS method. which The will gradient lead to misjudgment curves obtained of ABL by G depth.-Raw and Therefore, G-RCS thestill NG-RCShave a minimum method isvalue used at to lo invertw altitude, ABL depth which here. will lead to misjudgment of ABL depth. Therefore, the NG- RCS method is used to invert ABL depth here.

(a) (b) (c)

Figure 7. (a) Profiles of RCS and atmospheric molecular signal at 7:15, 23 July 2019; (b) Gradient profiles obtained by G-Raw, NG-RCS, and G-RCS; (c) Profiles of PT and RH obtained by GTS1 Digital Radiosonde at 7:15, 23 July 2019. 4. Results and Discussion 4.1. Observation of Aerosols and Clouds during a Short Rainfall Process As shown in Figure8, from 12:00 on 16 June to 8:00 on 18 June 2019, the changes of aerosols and clouds can be clearly seen from the Time-Height-Indication (THI) figures of RCS and DR, while a short rainfall process is captured. From 12:00 to 24:00 on 16 June, the clouds are mainly distributed between 6 km and 9 km. From 1:00 to 10:00 on 17 June, the clouds mainly exist between 7 km and 10 km. Following this, the clouds drop rapidly. At 11:00, the base height of cloud is around 4 km, and a light rain follows, which ends at approximately 18:00. The lidar signal in Figure8a is less affected by the rainfall process, indicating that the rainfall is relatively small. In Figure9, the profiles of temperature, PT, and RH of the GTS1 Digital Radiosonde data and the extinction coefficient profiles retrieved from the P-MPL data at different times are shown. By combining the data of the GTS1 Digital Radiosonde and the lidar, the aerosols and clouds are further analyzed. Atmosphere 2021, 12, x FOR PEER REVIEW 10 of 18

Figure 7. (a) Profiles of RCS and atmospheric molecular signal at 7:15, 23 July 2019; (b) Gradient profiles obtained by G- Atmosphere 2021, 12, x FOR PEER REVIEW 10 of 18 Raw, NG-RCS, and G-RCS; (c) Profiles of PT and RH obtained by GTS1 Digital Radiosonde at 7:15, 23 July 2019.

4. Results and Discussion Figure 7. (a) Profiles of RCS 4.1.and Observationatmospheric ofmolecular Aerosols signal and Clouds at 7:15 d, uring23 July a 2019;Short ( bRainfall) Gradient Process profiles obtained by G- Raw, NG-RCS, and G-RCS; (c) Profiles of PT and RH obtained by GTS1 Digital Radiosonde at 7:15, 23 July 2019. As shown in Figure 8, from 12:00 on 16 June to 8:00 on 18 June 2019, the changes of 4.aerosols Results and and clouds Discussion can be clearly seen from the Time-Height-Indication (THI) figures of RCS and DR, while a short rainfall process is captured. From 12:00 to 24:00 on 16 June, the 4.1. Observation of Aerosols and Clouds during a Short Rainfall Process clouds are mainly distributed between 6 km and 9 km. From 1:00 to 10:00 on 17 June, the cloudAss mainlyshown inexist Figure between 8, from 7 km 12:00 and on 10 16km. June Following to 8:00 onthis, 18 the June cloud 2019,s drop the changes rapidly. ofAt aerosols11:00, the and base clouds height can of be cloud clearly is seenaround from 4 thekm ,Time and -aHeight light -rainIndication follows (THI), which figures ends of at RCSapproximately and DR, while 18:00. a short The rainfalllidar signal process in Figure is captured. 8a is less From affected 12:00 toby 24:00 the rainfallon 16 June process,, the cloudindicatings are mainly that the distribut rainfall eisd relativelybetween 6small. km and 9 km. From 1:00 to 10:00 on 17 June, the clouds mainly exist between 7 km and 10 km. Following this, the clouds drop rapidly. At Atmosphere 2021, 12, 796 11:00, the base height of cloud is around 4 km, and a light rain follows, which end10s of at 17 approximately 18:00. The lidar signal in Figure 8a is less affected by the rainfall process, indicating that the rainfall is relatively small.

(a) (b) Figure 8. (a)THI of RCS and (b) THI of DR from 12:00 on 16 June to 8:00 on 18 June 2019.

In Figure 9, the profiles of temperature, PT, and RH of the GTS1 Digital Radiosonde (a) (b) data and the extinction coefficient profiles retrieved from the P-MPL data at different FigureFigure 8. 8. (a(a)THItimes)THI of of areRCS RCS shown. and and ( (bb) By) THI THI combin of of DR DR froming from the 12:00 12:00 data on on of16 16 theJune June GTS1 to to 8:00 8:00 Digital on on 18 18 JuneRadiosonde June 2019. 2019. and the lidar, the aerosols and clouds are further analyzed. In Figure 9, the profiles of temperature, PT, and RH of the GTS1 Digital Radiosonde data and the extinction coefficient profiles retrieved from the P-MPL data at different times are shown. By combining the data of the GTS1 Digital Radiosonde and the lidar, the aerosols and clouds are further analyzed.

Atmosphere 2021, 12, x FOR PEER REVIEW 11 of 18

(a) (b)

Figure 9. (a))DataData profiles profiles of GTS1 Digital Radiosonde and lidar at 19:15 on 16 June 2019; ( b) Data profiles profiles of GTS1 Digital Radiosonde and lidar at 7:15 on 17 June 2019; ( c) Data profilesprofiles of GTS1 Digital Radiosonde and li lidardar at 19:15 on 17 June 2019; (d) Data profiles of GTS1 Digital Radiosonde and lidar at 7:15 on 18 June 2019. 2019; (d) Data profiles of GTS1 Digital Radiosonde and lidar at 7:15 on 18 June 2019.

As shown inin FigureFigure9 a,9a, from from the the extinction extinction coefﬁcient, coefficient, the aerosolsthe aerosols in the in bottom the bottom layer layermainly mainly exist belowexist below 3 km, 3 andkm, aand layer a layer of cloud of cloud is distributed is distribu fromted from 6 km 6 to km 6.5 to km. 6.5 Thekm. TheDR belowDR below 1 km 1 iskm less is less than than 0.1 and0.1 and decreases decrease withs with the increasingthe increasing altitude. altitude At. 3At km, 3 km, the theDR DR is close is close to that to that of atmospheric of atmospheric molecules. molecul Comparinges. Comparing it with it with the the RH RH proﬁle, profile, the DRthe DRbelow below 3 km 3 tendskm tend to decreases to decrease with with the increase the increase of RH. of AtRH the. At height the height of 6 km, of 6 because km, because of the of the existence of clouds, the extinction coefficient increases significantly, and the DR of cloud is about 0.45. From the RH profile, the RH at the height of cloud is relatively high. From Figure 9b, the lidar data at 7:15 on 17 June show that the bottom aerosols mainly exist below 3 km, and the DR is relatively small. There is a layer of cloud from 7.5 km to 9.5 km, and the DR of cloud at 8 km is about 0.3. Above 8 km, with the increase of altitude, the DR decreases, which is likely caused by the decline in signal-to-noise ratio (SNR) of the lidar signal. The profiles of PT and RH have obvious gradient changes at 3 km, which also reflects the depth of the ABL. The RH in the ABL is higher, and the DR in the ABL is relatively small. After the rainfall stops, according to the lidar data at 19:15 on 17 June (Figure 9c), the bottom layer aerosols are mainly distributed below 2 km, and the DR is relatively small. There are clouds at the altitude of 4.5 km to 6 km. The RH of the clouds is higher according to the profile of RH. The DR of clouds is lower than 0.15, which indicates that the clouds here are mainly water clouds. As seen from Figure 9d, the bottom aerosols mainly exist below 2 km with small DR and high RH at 7:15 on 18 June. Clouds are located between 9 km and 12.5 km, and the RH increases significantly at the altitude of clouds, and the temperature at 9 km is lower than −20 °C . The maximum value of clouds’ DR is close to 0.4, and the clouds are mainly composed of ice crystals. Through this observation, a short rainfall process is captured. From the data of lidar and digital radiosonde, it can be seen that the DR of aerosols in the ABL will be significantly smaller when the RH is higher, which might be due to the aerosols’ hygroscopic growth. The rainfall process is accompanied by the decrease in the altitude of clouds. Be- fore the decline of the clouds, the DR of the clouds has clear ice cloud characteristics. When the clouds drop to a certain height, the DR of the clouds decreases and rainfall occurs.

Atmosphere 2021, 12, 796 11 of 17

existence of clouds, the extinction coefficient increases significantly, and the DR of cloud is about 0.45. From the RH profile, the RH at the height of cloud is relatively high. From Figure9b, the lidar data at 7:15 on 17 June show that the bottom aerosols mainly exist below 3 km, and the DR is relatively small. There is a layer of cloud from 7.5 km to 9.5 km, and the DR of cloud at 8 km is about 0.3. Above 8 km, with the increase of altitude, the DR decreases, which is likely caused by the decline in signal-to-noise ratio (SNR) of the lidar signal. The profiles of PT and RH have obvious gradient changes at 3 km, which also reflects the depth of the ABL. The RH in the ABL is higher, and the DR in the ABL is relatively small. After the rainfall stops, according to the lidar data at 19:15 on 17 June (Figure9c), the bottom layer aerosols are mainly distributed below 2 km, and the DR is relatively small. There are clouds at the altitude of 4.5 km to 6 km. The RH of the clouds is higher according to the profile of RH. The DR of clouds is lower than 0.15, which indicates that the clouds here are mainly water clouds. As seen from Figure9d, the bottom aerosols mainly exist below 2 km with small DR and high RH at 7:15 on 18 June. Clouds are located between 9 km and 12.5 km, and the RH increases significantly at the altitude of clouds, and the temperature at 9 km is lower than −20 ◦C. The maximum value of clouds’ DR is close to 0.4, and the clouds are mainly composed of ice crystals. Through this observation, a short rainfall process is captured. From the data of lidar and digital radiosonde, it can be seen that the DR of aerosols in the ABL will be significantly smaller when the RH is higher, which might be due to the aerosols’ hygroscopic growth. The rainfall process is accompanied by the decrease in the altitude of clouds. Before the decline of the clouds, the DR of the clouds has clear ice cloud characteristics. When the clouds drop to a certain height, the DR of the clouds decreases and rainfall occurs.

4.2. Observation of Tropospheric Cirrus Clouds The formation and existence of cirrus clouds will affect the radiative transfer and water vapor in the upper troposphere. Tropospheric cirrus clouds are successfully observed at 20:00 on 28 July 2019. The THI ﬁgure of DR of the P-MPL in Figure 10b indicates that there are cirrus clouds above the altitude of 12 km and that the DR of the cirrus clouds is relatively high. Since 0:00 on 29 July, the cirrus cloud signals are not very continuous due to the attenuation of lidar signals caused by low-altitude clouds. As seen in Figure 10c, the DR of cirrus clouds reaches approximately 0.8, indicating that the cirrus clouds are composed of ice crystals. From the proﬁles of the radiosonde data at 7:15 on 28 July 2019 (Figure 10a), it can be seen that PT increases with the increase in altitude. However, from 12 km to 15 km, the variation trend of PT with altitude decreases, indicating that this altitude is the top of the tropopause, which is also the location of the cirrus clouds. At altitudes above 15 km, the trend of PT increasing with altitude is more obvious, which just shows that the base of the stratospheric is around 15 km. At the location of the cirrus clouds, the RH is about 10%, which indicates that the cirrus clouds have low RH. It also indirectly indicates that the ice crystal cirrus cloud has the characteristics of water vapor consumption and condensation growth. Atmosphere 2021, 12, x FOR PEER REVIEW 12 of 18

that there are cirrus clouds above the altitude of 12 km and that the DR of the cirrus clouds is relatively high. Since 0:00 on 29 July, the cirrus cloud signals are not very continuous due to the attenuation of lidar signals caused by low-altitude clouds. As seen in Figure 10c, the DR of cirrus clouds reaches approximately 0.8, indicating that the cirrus clouds are composed of ice crystals. From the profiles of the radiosonde data at 7:15 on 28 July 2019 (Figure 10a), it can be seen that PT increases with the increase in altitude. However, from 12 km to 15 km, the variation trend of PT with altitude decreases, indicating that this altitude is the top of the tropopause, which is also the location of the cirrus clouds. At altitudes above 15 km, the trend of PT increasing with altitude is more obvious, which just shows that the base of the stratospheric is around 15 km. At the location of the cirrus Atmosphere 2021, 12, 796 clouds, the RH is about 10%, which indicates that the cirrus clouds have low RH.12 It ofalso 17 indirectly indicates that the ice crystal cirrus cloud has the characteristics of water vapor consumption and condensation growth.

(a) (b) (c)

FigureFigure 10. 10. (a)( aThe) The RH RH and and PT PT profiles proﬁles of of GTS1 GTS1 Digital Digital Radiosonde atat 19:15 19:15 on on 28 28 June June 2019; 2019; (b )(b THI) THI of DRof DR from from 20:00 20:00 on 28on 28 JulyJuly toto 4:004:00 onon 1919 July July 2019; 2019; ( c()c) Proﬁles Profiles of of DR DR at at different different times. times.

4.3.4.3. Observation Observation of ofa aHaze Haze Process Process AA typical typical continuous continuous haze haze process process i iss observed observed from 1 January toto 55 JanuaryJanuary 2020. 2020. Due Due toto the the interference interference of ofhaze haze attenuation attenuation and and background background light light during during the the day, day, the the SNR SNR of lidarof lidar signal signal is reduced. is reduced. Therefore, Therefore, the observation the observation data of data lidar of at lidar night at during night during this period this areperiod selected are and selected compared and compared with the withradiosonde the radiosonde data to study data tothe study haze the proc hazeess. process.Aerosol extinctionAerosol extinctioncoefficient coefficient can directly can reflect directly the reflect aerosol the concentration aerosol concentration distribution, distribution, so the THI so figurethe THI of aerosol figure ofextinction aerosol extinction coefficient coefficient retrieved retrieved by the Fernald by the method Fernald can method show can the show tem- the temporal and spatial distribution of aerosols during this period. The deeper the color, poral and spatial distribution of aerosols during this period. The deeper the color, the the higher the aerosol concentration. At the same time, the NG-RCS method was used to higher the aerosol concentration. At the same time, the NG-RCS method was used to re- retrieve the haze thickness (shown by the “*” line in the THI figure of extinction coefficient). trieve the haze thickness (shown by the “*” line in the THI figure of extinction coefficient). As shown in Figure 11a, from 19:00 on 1 January to 7:00 on 2 January, the haze mainly concentratesAs shown below in Figure 1 km, 11 anda, from there 19:00 is a stableon 1 January thin cloud to 7:00 layer on at 2 around January, 2 km. the Ithaze is obvious mainly concentratfrom thees digital below radiosonde 1 km, and data there profiles is a stable of two thin approaching cloud layer momentsat around that 2 km. the It RHis obvious below from1 km theis digital basically radiosonde 50% to 60%. data At profiles the altitude of two of theapproaching thin cloud, moments the RH is that close the to 100%,RH below the 1 kmgradients is basically of RH 50% and to PT 60%. reach At the the maximum. altitude of the thin cloud, the RH is close to 100%, the gradientsShown of RH inthe and THI PT of reach RCS the from maximum. 19:00 on 2 January to 7:00 on 3 January 2020 (Figure 11b), theShown haze still in existsthe THI below of RCS 1 km, from and 19:00 the cloud on 2 at January 2 km always to 7:00 exists on 3 until January 6:00 on2020 3 January. (Figure 11b),The the characteristics haze still exists of the below PT profile 1 km, and and RH the profile cloud at at the 2 twokm adjacentalways exist momentss until are 6:00 similar on 3 to those of the previous day. There are obvious gradient changes at the thin cloud altitude. The stable thin cloud with low altitude leads to the existence of high humidity and a strong inversion layer, which limits the diffusion of haze. Therefore, the haze thickness is relatively stable from 1 January to 3 January. Atmosphere 2021, 12, x FOR PEER REVIEW 13 of 18

January. The characteristics of the PT profile and RH profile at the two adjacent moments are similar to those of the previous day. There are obvious gradient changes at the thin Atmosphere 2021, 12, 796 cloud altitude. The stable thin cloud with low altitude leads to the existence of high13 hu- of 17 midity and a strong inversion layer, which limits the diffusion of haze. Therefore, the haze thickness is relatively stable from 1 January to 3 January.

(a) (b)

FigureFigure 11. 11. (a(a) )THI THI of of aerosol aerosol extinction extinction coef coefficientficient and and profiles profiles of of radiosonde radiosonde data data from from 19:00 19:00 on on 1 1 January January to to 7:00 7:00 on on 2 2 JanuaryJanuary 2020; 2020; (b (b) )THI THI of of aerosol aerosol extinction extinction coefficient coefficient and and profiles profiles of of radiosonde radiosonde data data from from 19:00 19:00 on on 2 2 January January to to 7:00 7:00 on on 3 3 January 2020; (c) THI of aerosol extinction coefficient and profiles of radiosonde data from 19:00 on 3 January to 7:00 on 4 January 2020; (c) THI of aerosol extinction coefficient and profiles of radiosonde data from 19:00 on 3 January to 7:00 on 4 January 2020; (d) THI of aerosol extinction coefficient and profiles of radiosonde data from 19:00 on 4 January to 3:00 on 5 January 2020; (d) THI of aerosol extinction coefficient and profiles of radiosonde data from 19:00 on 4 January to 3:00 on 5 January 2020. January 2020. As seen in Figure 11c, the THI of aerosol extinction coefficient shows that the thick- As seen in Figure 11c, the THI of aerosol extinction coefficient shows that the thickness ness of haze is still lower than 1 km from 19:00 on 3 January to 7:00 on 4 January 2020, and of haze is still lower than 1 km from 19:00 on 3 January to 7:00 on 4 January 2020, and it itis is obvious obvious that that thethe aerosolsaerosols existexist belowbelow aboveabove 33 km.km. The ABL depth depth determined determined by by the the gradientgradient changes changes of of PT PT and and RH RH profil profileses at at 19:15 19:15 on on 3 3 January January is is around around 2.3 2.3 km, km, and and the the ABLABL depth depth at at 7:15 7:15 on on 4 January 4 January is about is about 1.8 1.8km. km. The TheRH RHin the in ABL the ABLis more is more than 50%. than The 50%. RHThe above RH above the ABL the decrease ABL decreasess rapidly rapidly to about to about20%. The 20%. radiosonde The radiosonde data at data7:15 aton7:15 4 Jan- on uary4 January show showthat the that RH the increase RH increasess gradually gradually above abovethe altitude the altitude of 3.3 km. of 3.3 Due km. to Duethe influ- to the enceinfluence of clouds of clouds,, the RH the is RHabout is about70% from 70% 5 from km to 5 km10 km to 10. The km. irregular The irregular clouds clouds from 5 from km to5 10 km km to 10can km also can be also seen be from seen the from THI the of THI aerosol of aerosol extinction extinction coefficient. coefficient. AfterAfter 3:00 3:00 on on 5 5 January, January, the the SNR SNR of of lid lidarar signal signal decrease decreasess due due to to the the influence influence of of rainfall.rainfall. Therefore, Therefore, Figure Figure 11d11d shows thethe THITHI ofofaerosol aerosol extinction extinction coefficient coefficient from from 19:00 19:00 on on4 January4 January to to 3:00 3:00 on on 5 January 5 January 2020. 2020. It canIt can be be found found that that the the altitude altitude of hazeof haze is below is below 1 km, 1 km,and and there there are are irregular irregular clouds clouds from from 2 km 2 km to 5to km. 5 km. From From the the radiosonde radiosonde data data profiles profile ats at19:15 19:15 on on 4 January,4 January, mainly mainly due due to to the the influence influence of cloudsof clouds at theat the altitude altitude of 2.6of 2.6 km, km, the the RH RHreaches reaches the the first first maximum maximum negative negative gradient gradient value, value and, and the PTthereaches PT reaches the firstthe first maximum max- imumpositive positive gradient grad valueient value at this at altitude. this altitude From. From the radiosonde the radiosonde data at data 7:15 at on 7:15 5 January, on 5 Janu- the RH is almost 100% below the altitude of 4.5 km, indicating that rainfall is occurring. Above 4.5 km, the RH gradually decreases, which is likely caused by the GTS1 Digital Radiosonde exceeding the altitude of rainfall clouds. The haze has seriously affected the air quality. From Figure 12, the changes of PM2.5 and PM10 concentrations with time recorded by ambient air quality monitor at Xi’an Meteorological Bureau from 19:00 on 1 January to 7:00 on 5 January 2020. It is obvious that the changes of PM2.5 and PM10 concentrations in this haze process show a consistent upward trend. From 0:00 to 10:00 on 4 January, the concentrations of PM2.5 and PM10 are Atmosphere 2021, 12, x FOR PEER REVIEW 14 of 18

ary, the RH is almost 100% below the altitude of 4.5 km, indicating that rainfall is occurring. Above 4.5 km, the RH gradually decreases, which is likely caused by the GTS1 Dig- ital Radiosonde exceeding the altitude of rainfall clouds. The haze has seriously affected the air quality. From Figure 12, the changes of PM2.5 and PM10 concentrations with time recorded by ambient air quality monitor at Xi’an Me- Atmosphere 2021, 12, 796 teorological Bureau from 19:00 on 1 January to 7:00 on 5 January 2020. It is obvious14 ofthat 17 the changes of PM2.5 and PM10 concentrations in this haze process show a consistent upward trend. From 0:00 to 10:00 on 4 January, the concentrations of PM2.5 and PM10 are higher than 200 μg/m3,3 and the concentrations of PM2.5 and PM10 are 227 μg/m3 and3 293 higher than 200 µg/m , and the concentrations of PM2.5 and PM10 are 227 µg/m and 3 293μg/mµg/m, respectively3, respectively at 9:00, at reaching 9:00, reaching the peak the of peak the day. of the At day. 1:00 Aton 1:005 January, on 5 January, the concen- the trations of PM2.5 and PM10 reach the highest value in this haze process, which were 289 concentrations of PM2.5 and PM10 reach the highest value in this haze process, which were 289μg/mµg/m3 and3 334and μg/m 334 µ3,g/m respectively3, respectively.. After 1:00 After on 1:00 5 January, on 5 January, aerosol aerosol particles particles begin beginto fall to fallthe ground to the ground with the with rain the and rain the and concentration the concentrationss of particulate of particulate matter beg matterin to begin decline. to decline.The haze The weather haze weatherlasting for lasting nearly for four nearly days four end dayss due ends to the due rainfall. to the rainfall. The red The dot red line dot in lineFigure in Figure12 is the 12 aerosolis the aerosol extinction extinction coefficient coefﬁcient at the at altitude the altitude of 200 of m 200 retrieved m retrieved from from the thenight night-time-time observation observation data dataof P- ofMPL P-MPL during during the haze the process. haze process. The variation The variation trend of trend aer- ofosol aerosol extinction extinction coefficient coefﬁcient at the at altitude the altitude of 200 of m 200 is basically m is basically consistent consistent with withthat thatof par- of particulateticulate matter matter concentration concentrations.s.

FigureFigure 12.12. ComparisonComparison of of aerosol aerosol extinction extinction coefﬁcient coefficient at theat the altitude altitude of 200of 200 m and m and particulate particulate matter matter concentration concentration recorded rec- byorded ambient by ambient air quality air quality monitor monitor in haze in event. haze event.

In order toto observeobserve the the source source of of the the haze haze more more clearly, clearly, the the NOAA NOAA HYSPLIT HYSPLIT Trajectory Trajec- Modeltory Model is used. is Takingused. Taking Xi’an Meteorological Xi’an Meteorological Bureau as Bureau the reference as the referencelocation, the location, backward the trajectorybackward oftrajectory aerosols of at aerosols different at altitudesdifferent couldaltitudes be tracked.could be Astracked. the haze As the formed haze atformed 19:00 onat 19:00 1 January on 1 2020January (Beijing 2020 time), (Beijing and time), the haze and depth the haze was depth less than was 1 km,less threethan 1 altitudes km, three of 200altitudes m, 500 of m,200 and m, 500 800 m m, and 24 h 800 before m 24 11:00 h before on 1 11:00 January on 1 2020 January (UTC) 2020 are (UTC) selected are selected to track theto track backward the backward trajectory. trajectory. It can be It seen can frombe seen Figure from 13 Figure that the13 that pollution the pol sourcelution mainlysource comesmainlyfrom comes the from south the of south Xi’an, of and Xi’an, the and pollutants the pollutants of the three of the reference three reference altitudes altitudes are all goingare all downgoing fromdown a from higher a higher altitude altitude during during the transmission the transmission process, process, while thewhile pollutants the pol- oflutants 500 m ofand 500 800m and m come800 mfrom come 1.2 from km. 1.2 Therefore, km. Therefore, it can it be can inferred be inferred from from the backward the back- trajectoryward trajectory of aerosols of aerosols that the that possible the possible aerosol aerosol type during type during the haze the event haze should event beshould mainly be urbanmainly pollution urban pollution aerosol. aerosol. The poor The atmospheric poor atmospheric diffusion diffusion conditions conditions are not conducive are not con- to theducive dilution to the and dilution diffusion and ofdiffusion pollutants. of pollutants.

Atmosphere 2021, 12, 796 15 of 17 Atmosphere 2021, 12, x FOR PEER REVIEW 15 of 18

Figure 13. The backward trajectories of aerosols from HYSPLIT at the measurement site. Figure 13. The backward trajectories of aerosols from HYSPLIT at the measurement site.

5. Conclusions The operational observation was carried out using P-MPL at Xi’an Meteorological Bureau. Using the data of the P-MPL combined with the data of GTS1 Digital Radiosonde and the ambient air quality monitor, the aerosols and clouds in different weather conditions are studied. In order to ensure the reliability of lidar data, the near-field signal of the P- MPL is corrected by using a theoretical calculation of lidar overlap. The signal separation between parallel polarization and perpendicular polarization signals of 532 nm at the altitude below the boundary layer and at the altitude of cirrus clouds is tested and the rationality of DR profile inversed by the algorithm of DR is further tested by the molecular DR. In the inversion of ABL depth, the gradient data of P-MPL obtained by three inversion algorithms are compared with the profiles of PT and RH obtained by a GTS1 Digital Radiosonde, NG-RCS method is selected as the inversion algorithm of ABL depth. Finally, several typical observation examples are introduced. Firstly, the evolution process of aerosols and clouds in a short rainfall process is analyzed. Through the comparison between the DR of aerosols obtained by the P-MPL and the profiles of PT and RH of GTS1 Digital Radiosonde, it can be found that the DR of aerosols in ABL changes inversely with the RH. Then, tropopause cirrus clouds are observed and analyzed. The tropopause cirrus clouds are mainly distributed in 12 km to 16 km and have typical characteristics of ice crystal clouds with low RH and high DR of 0.8. Finally, the data of the P-MPL combined with the data of GTS1 Digital Radiosonde and ambient air quality monitor, the observation and analysis of the continuous haze weather in early 2020 are carried out. The THI figures of extinction coefficient obtained by the P-MPL and the profiles of PT and RH show that the distribution of haze is basically below 1 km during the whole haze period, the strong inversion layer caused by low altitude thin clouds is not conducive to the diffusion of haze. The overall trend of PM2.5 and PM10 obtained by ambient air quality monitor is rising from 19:00 on 1 January to 0:00 on 5 January 2020. At 1:00 on 5 January, both the PM2.5 and PM10 reach the maximum value of this haze process, and the concentrations of PM2.5 and PM10 reach 289 µg/m3 and 334 µg/m3, respectively. Then, with the occurrence of rainfall, the particle concentrations gradually decrease. A comparison between the aerosol extinction coefficient at the altitude of 200 m retrieved from the P-MPL night-time observation during the haze process and the particle concentrations shows that the variation trend of an aerosol extinction coefficient of 200 m is consistent with that of particle concentrations. Through

Atmosphere 2021, 12, 796 16 of 17

the 24-h backward trajectories of aerosols at 200 m, 500 m, and 800 m simulated by NOAA HYSPLIT Trajectory Model, the transport trajectory of aerosols at different altitudes is revealed. We can clearly see that the pollution aerosols mainly come from the south of Xi’an.

Author Contributions: Conceptualization, C.C., X.S. and Z.W.; methodology, C.C. and X.S.; soft- ware, X.L. (Xiaoyan Liu) and Q.Z.; validation, W.W. and K.M.; formal analysis, C.C., X.S. and H.L.; investigation, X.W., X.L. (Xianxin Li) and Y.Y.; data curation, X.P. and F.Z.; writing—original draft preparation, C.C.; writing—review and editing, X.S.; and B.X.; project administration, X.S. and Z.W.; funding acqui-sition, X.S. and Z.W. All authors have read and agreed to the published version of the manuscript. Funding: This work is jointly supported by the following project grants: National Key and De- velopment Program of China (2018YFC0213101), International Cooperation Project of Shandong Academy of Sciences (2019GHZD02), Key Research and Development Plan of Shandong Province (2020CXGC010104), Development Fund Project of Institute of Oceanographic Instrumentation, Shan- dong Academy of Sciences (HYPY202106) and Key Projects of the Regional Joint Fund (2020B1515120056). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Public available datasets were analyzed. The data can be found from the link: ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1/ (accessed on UTC 1 January 2020). Acknowledgments: Special thanks to Xi’an Meteorological Bureau of Shanxi Province for providing observational support for P-MPL and data support for digital radiosonde and ambient air quality monitoring. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (https://www. ready.noaa.gov, accessed on UTC 1 January 2020) used in this publication. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Wu, X.; Vu, T.V.; Shi, Z.; Harrison, R.M.; Liu, D.; Cen, K. Characterization and Source Apportionment of Carbonaceous PM2.5 Particles in China—A Review. Atmos. Environ. 2018, 189, 187–212. [CrossRef] 2. Zhang, Y.; Zhang, Y.; Yu, C.; Yi, F. Evolution of Aerosols in the Atmospheric Boundary Layer and Elevated Layers during a Severe, Persistent Haze Episode in a Central China Megacity. Atmosphere 2021, 12, 152. [CrossRef] 3. Leonardi, A.; Ricker, H.M.; Gale, A.G.; Ball, B.T.; Odbadrakh, T.T.; Shields, G.C.; Navea, J.G. Particle Formation and Surface Processes on Atmospheric Aerosols: A Review of Applied Quantum Chemical Calculations. Int. J. Quantum Chem. 2020, 120, e26350. [CrossRef] 4. Kim, D.; Ramanathan, V. Solar Radiation Budget and Radiative Forcing due to Aerosols and Clouds. J. Geophys. Res. Atoms. 2008, 113, D02203. [CrossRef] 5. Solomos, S.; Bougiatioti, A.; Soupiona, O.; Papayannis, A.; Mylonaki, M.; Papanikolaou, C.; Argyrouli, A.; Nenes, A. Effects of Regional and Local Atmospheric Dynamics on the Aerosol and CCN Load Over Athens. Atmos. Environ. 2019, 197, 53–65. [CrossRef] 6. Wu, B.; Qin, L.; Wang, M.; Zhou, T.; Dong, Y.; Chai, T. The Composition of Microbial Aerosols, PM2.5, and PM10 in a Duck House in Shandong Province, China. Poult. Sci. 2019, 98, 5913–5924. [CrossRef] 7. Wu, J.; Zhang, Y.; Wang, T.; Qian, Y. Rapid Improvement in Air Quality due to Aerosol-pollution Control during 2012–2018: An Evidence Observed in Kunshan in the Yangtze River Delta, China. Atmos. Pollut. Res. 2020, 11, 693–701. [CrossRef] 8. Jin, Q.; Fang, X.; Wen, B.; Shan, A. Spatio-temporal Variations of PM2.5 Emission in China from 2005 to 2014. Chemosphere 2017, 183, 429–436. [CrossRef] 9. Salam, A.; Mamoon, H.A.; Ullah, M.B.; Ullah, S.M. Measurement of the Atmospheric Aerosol Particle Size Distribution in a Highly Polluted Mega-city in Southeast Asia (Dhaka-Bangladesh). Atmos. Environ. 2012, 59, 338–343. [CrossRef] 10. Rader, F.; Traversi, R.; Severi, M.; Becagli, S.; Müller, K.-J.; Nakoudi, K.; Ritter, C. Overview of Aerosol Properties in the European Arctic in Spring 2019 Based on In Situ Measurements and Lidar Data. Atmosphere 2021, 12, 271. [CrossRef] 11. Liu, D.; Yang, Y.; Cheng, Z.; Huang, H.; Zhang, B.; Ling, T.; Shen, Y. Retrieval and Analysis of a Polarized High-spectral-resolution Lidar for Proﬁling Aerosol Optical Properties. Opt. Express 2013, 21, 13084–13093. [CrossRef] 12. Yorks, J.E.; Selmer, P.A.; Kupchock, A.; Nowottnick, E.P.; Christian, K.E.; Rusinek, D.; Dacic, N.; McGill, M.J. Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data. Atmosphere 2021, 12, 606. [CrossRef] Atmosphere 2021, 12, 796 17 of 17

13. Wang, Z.; Mao, J.; Li, J.; Zhao, H.; Zhou, C.; Sheng, H. Six-channel Multi-wavelength Polarization Raman Lidar for Aerosol and Water Vapor Profiling. Appl. Opt. 2017, 56, 5620–5629. [CrossRef][PubMed] 14. Schotland, R.M.; Sassen, K.; Stone, R. Observations by Lidar of Linear Depolarization Ratios for Hydrometeors. J. Appl. Meteorol. 1971, 10, 1011–1017. [CrossRef] 15. Winker, D.M.; Vaughan, M.; Omar, A.; Hu, Y.; Powell, K.A.; Liu, Z.; Hunt, W.H.; Young, S.A. Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms. J. Atmos. Ocean. Tech. 2009, 26, 2310–2323. [CrossRef] 16. Pisani, G.; Boselli, A.; Coltelli, M.; Leto, G.; Pica, G.; Scollo, S.; Spinelli, N.; Wang, X. Lidar Depolarization Measurement of Fresh Volcanic Ash from Mt. Etna, Italy. Atmos. Environ. 2012, 62, 34–40. [CrossRef] 17. Wang, L.; Staniˇc,S.; Eichinger, W.E.; Moˇcnik, G.; Drinovec, L.; Gregoriˇc,A. Investigation of Aerosol Properties and Structures in Two Representative Meteorological Situations over the Vipava Valley Using Polarization Raman LiDAR. Atmosphere 2019, 10, 128. [CrossRef] 18. Xian, J.; Sun, D.; Xu, W.; Tian, C.; Tan, Q.; Han, Y.; Yang, S. Calibration and Calculation of Polarization Lidar. Earth Space Sci. 2019, 6, 1161–1170. [CrossRef] 19. Fernald, F.G. Analysis of Atmospheric Lidar Observations: Some Comments. Appl. Opt. 1984, 23, 652. [CrossRef] 20. Wang, N.; Shen, X.; Xiao, D.; Veselovskii, I.; Zhao, C.; Chen, F.; Liu, C.; Rong, Y.; Ke, J.; Wang, B.; et al. Development of ZJU High-spectral-resolution Lidar for Aerosol and Cloud: Feature Detection and Classification. J. Quant. Spectrosc. Radiat. Transf. 2021, 261, 07513. [CrossRef] 21. Smith, J.A.; Chu, X. High-efficiency Receiver Architecture for Resonance-Fluorescence and Doppler Lidars. Appl. Opt. 2015, 54, 3173–3184. [CrossRef] 22. Haarig, M.; Ansmann, A.; Baars, H.; Jimenez, C.; Veselovskii, I.; Engelmann, R.; Althausen, D. Depolarization and Lidar Ratios at 355, 532, and 1064 nm and Microphysical Properties of Aged Tropospheric and Stratospheric Canadian Wildfire Smoke. Atmos. Chem. Phys. 2018, 18, 11847–11861. [CrossRef] 23. Liu, L.; Mishchenko, M.I. Spectrally Dependent Linear Depolarization and Lidar Ratios for Nonspherical Smoke Aerosols. J. Quant. Spectrosc. Radiat. Transf. 2020, 248, 106953. [CrossRef] 24. Wang, L.; Staniˇc,S.; Eichinger, W.; Song, X.; Zavrtanik, M. Development of an Automatic Polarization Raman Lidar for Aerosol Monitoring over Complex Terrain. Sensors (Basel) 2019, 19, 3186. [CrossRef] 25. Chen, W.; Chiang, C.; Nee, J. Lidar Ratio and Depolarization Ratio for Cirrus Clouds. Appl. Opt. 2002, 41, 6470–6746. [CrossRef] [PubMed] 26. Delanoë, J.; Hogan, R.J. Combined CloudSat-CALIPSO-MODIS Retrievals of the Properties of Ice Clouds. J. Geophys. Res. Atoms. 2010, 115, D00H29. [CrossRef] 27. Wu, S.; Song, X.; Liu, B.; Dai, G.; Liu, J.; Zhang, K.; Qin, S.; Hua, D.; Gao, F.; Liu, L. Mobile Multi-wavelength Polarization Raman Lidar for Water Vapor, Cloud and Aerosol Measurement. Opt. Express 2015, 23, 33870–33892. [CrossRef][PubMed] 28. Alvarez, J.M.; Vaughan, M.A.; Hostetler, C.A.; Hung, W.H.; Winker, D.M. Calibration Technique for Polarization-Sensitive Lidars. J. Atmos. Ocean. Tech. 2006, 23, 683–699. [CrossRef] 29. Wang, H.; Li, Z.; Lv, Y.; Xu, H.; Li, K.; Li, D.; Hou, W.; Zheng, F.; Wei, Y.; Ge, B. Observational Study of Aerosol-induced Impact on Planetary Boundary Layer Based on Lidar and Sunphotometer in Beijing. Environ. Pollut. 2019, 252, 897–906. [CrossRef] 30. Miao, Y.; Liu, S.; Guo, J.; Huang, S.; Yan, Y.; Lou, M. Unraveling the Relationships between Boundary Layer Height and PM2.5 Pollution in China Based on Four-year Radiosonde Measurements. Environ. Pollut. 2018, 243, 1186–1195. [CrossRef] 31. He, Q.; Mao, J.; Chen, J.; Hu, Y. Observational and Modeling Studies of Urban Atmospheric Boundary-layer Height and Its Evolution Mechanisms. Atmos. Environ. 2006, 40, 1064–1077. [CrossRef] 32. Yang, T.; Wang, Z.; Zhang, W.; Gbaguidi, A.; Nobuo, S.; Wang, X.; Matsui, I.; Sun, Y. Technical note: Boundary Layer Height Determination from Lidar for Improving Air Pollution Episode Modeling: Development of New Algorithm and Evaluation. Atmos. Chem. Phys. 2017, 17, 6215–6225. [CrossRef]