Abstract

Compared with ground-based lidar, airborne lidar has a wider observation area, which is useful for studying aerosol distribution and transportation. A dual-wavelength high spectral resolution Lidar (HSRL) was developed for the validation and calibration of an upcoming satellite payload. The HSRL was installed on an airplane, and field campaigns were conducted in Qinhuangdao, China. Meanwhile, four observation sites were established at different locations on the ground to verify the results of the airborne lidar. This article compares the HSRL measurements with those from ground-based micro-pulse lidar (MPL), Mie-scattering lidar, sun photometer, and spaceborne cloud-aerosol Lidar and infrared pathfinder satellite observations (CALIPSO), and Moderate Resolution Imaging Spectroradiometer (MODIS). The stability and reliability of the HSRL system were fully verified. The flight area covered several surface types, including ocean, town, mountain, and forest, which strongly affect the AOD above them. The boundary layer AOD was analyzed in different regions, based on the impact of human activities. The results demonstrated that the AOD in urban area was the largest, and smallest in marine areas, a result ascribed to the influence of industrial activities.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Aerosols play a key role in the Earth's radiation budget and can affect the radiation balance of the Earth–air system [15]. M. Gharibzad studied the radiative effects and optical properties of aerosol during two dust events, the high values of aerosol optical depth (AOD) and low values of Ångström exponent (AE) during dusty days illustrate that coarse mode particles like dust are dominant. The results show that dominant aerosol types are dust particles which have a cooling effect on the earth's surface during the study periods [67]. A. Bayat researched the relationship of polarized phase function, AE and single scattering albedo. The result show that polarized phase function has a strong positive correlation with AE, and polarized phase function has a negative correlation with single scattering albedo [8].

Lidar is considered one of the most powerful tools for detecting atmospheric aerosols [9]. The high spectral resolution lidar (HSRL) are increasingly being developed for atmospheric aerosol remote sensing. HSRL is independent to calculate the aerosol extinction and backscatter coefficient without reliance on assumptions about lidar ratio [1017]. In HSRL technique, spectral discrimination between scattering from molecules and aerosol particles is one of the most critical processes, which needs to be accomplished by means of a narrowband spectroscopic filter. Common narrowband filters include iodine molecular absorption cell filter, Fabry-Perot interferometer (FPI) and Mach-Zehnder interferometer (MZI), but these interferometric filters have the disadvantage of small acceptable field of view (FOV), thus the photon efficiency of the instrument is not so satisfactory [18]. The NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL-2) was the first dual-wavelength HSRL system using field widened Michelson interferometer (FWMI) developed by Liu [19], which verified that multi-wavelength HSRL is a powerful tool for obtaining comprehensive atmospheric characteristics. A new type spectral discriminator for multiple-wavelength lidar is tested with a series of work about FWMI, which demonstrated its feasibility and stability and contributed to the development of multi-wavelength HSRL [18,20]. D. Liu et al. [21] developed a polarized HSRL based on FWMI in Zhejiang University, China, which is intended to profile various atmospheric aerosol optical properties simultaneously, such as the backscatter coefficient, the extinction coefficient, depolarization ratio, lidar ratio, etc. The HSRL system developed by Zhejiang University is the first new generation of lidar which employs the FWMI spectroscopic filter in China, and great potential will be shown with the gradually improved engineering design in near future.

Ground-based lidar can detect the vertical distribution and time variation characteristics of atmospheric aerosols and clouds. However, ground-based lidar can only measure them over a fixed position. Furthermore, there are still many problems regarding the consistency and comparability of the processing methods applied to ground-based lidar data. Compared with ground-based lidar, airborne lidar has high temporal resolution, high vertical resolution, and high measurement accuracy. It can detect the optical and morphological characteristics of atmospheric aerosols quickly and continuously in the experimental area, and obtain information about aerosols, clouds, and boundary layers [22]. Airborne HSRL lidar can make direct measurements of aerosol intensive properties, including the lidar ratio, that provide information on aerosol type. From July 26 to August 14, 2006, McGil et al. conducted airborne verification experiments on the spatial characteristics of aerosols and clouds measured by the CALIPSO lidar. Their study demonstrated that the cloud top heights measured by the CALIPSO satellite and the heights measured by the airborne lidar CPL exhibited good consistency [23]. In 2008, Esselborn et al. carried out an airborne lidar experiment using a high spectral resolution lidar based on iodine molecules and studied the optical characteristics of dust aerosols in the Sahara Desert. They compared the HSRL measurements with those from the ground-based Raman lidar and solar photometer and demonstrated good agreement [24]. In 2009, Hair et al. conducted an airborne HSRL experiment during the MILAGRO campaign. In the experiment, the aerosol optical depth (AOD) retrieved by the HSRL was verified with the AOD measured by a ground-based solar photometer. The difference in measured data was less than 3% (0.01 km-1) at 532 nm [25,26]. Muller, D. [27] developed an automated, unsupervised inversion algorithm. They present measurements acquired by the world’s first airborne 3 backscatter (β) + 2 extinction (α) high dpectral resolution lidar (HSRL-2). HSRL-2 measures particle backscatter coefficients at 355, 532, and 1064 nm, and particle extinction coefficients at 355 and 532 nm. They observed pollution outflow from the northeastern coast of the US out over the western Atlantic Ocean. Lidar ratios were 50-60 sr at 355 nm and 60–70 sr at 532 nm. Extinction-related Ångström exponents were on average 1.2-1.7, indicating comparably small particles. Their novel automated, unsupervised data inversion algorithm retrieved particle effective radii of approximately 0.2 μm, which is in agreement with the large Ångström exponents. They find good agreement with particle size parameters obtained from coincident in situ measurements carried out with the DOE Gulfstream-1 aircraft. For spaceborne lidar, airborne observations can verify satellite inversion algorithms and provide a reference for the design of key parameters of spaceborne lidar. In addition, after the launch of the satellite, flight experiments with the same orbit can be conducted with airborne lidar to calibrate satellite data. Before and after the launch of the CALIPSO satellite, NASA Langley Research Center (NASA LaRC) conducted long-term airborne observation experiments with more than 240 flights, and the total flight time exceeded 800 h. Including day and night flight experiments, many flights were undertaken under different aerosol and cloud conditions [28]. In summary, carrying out airborne lidar observations is of great significance to the development of lidar research.

In this study, a dual-wavelength HSRL was installed on an airborne platform to conduct experiments over Qinhuangdao, China. A total of 7 flights were undertaken and the total flight time was approximately 28 h. The flight area included ocean, town, mountain, forest, and other land types. In this study, the flight area was divided into three regions that differed by land class. The distribution of aerosol optical properties in the boundary layer of the three regions was analyzed according to the influence of human activity across the three regions. During the campaign, four observation sites were installed at different locations on the ground to compare and verify the airborne lidar results. This article mainly introduces the results from a flight on March 16, 2019. Compared the results of the airborne HSRL with results from ground-based MPL, Mie scattering lidar, and sun photometer. At the same time, measurements of the airborne HSRL were compared with the results of spaceborne CALIPSO and MODIS. The comparison of results exhibited good agreement, which verified the stability and reliability of the airborne HSRL system.

2. Methods

The receiving system of our high spectral resolution lidar, which is based on an iodine molecular absorption cell, can be simplified into a three-detection channel structure, as shown in Fig. 1.

 figure: Fig. 1.

Fig. 1. Schematic diagram of the high spectral resolution lidar receiving system.

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The echo signal passes through a 532 nm band-pass filter and then passes through a narrow-band FP. The central wavelength of the FP is selected to be the same as that of the iodine absorption line wavelength. If the polarization direction of the backscattered signal is changed, there will be a vertical polarization component and a parallel polarization component. The vertical polarization component of the echo signal is reflected by the polarization beam splitter prism at 45° after passing through the first polarization beam splitter prism (PBS1), and then focused into a vertical channel detector. The depolarized echo signal or the parallel polarization component of the depolarized echo signal is transmitted through the first polarization beam splitting prism, and then passes through a 1/2 wave plate and the second polarization beam splitting prism (PBS2). Part of the echo signal light passes through the iodine molecular absorption cell and enters the hyperspectral molecular channel detector through the focusing lens. The remainder of the signal passes through the focusing lens and enters into the parallel reference channel detector. By changing the angle of the 1/2 wave plate, the spectral ratio of the hyperspectral molecular channel to the parallel reference channel can be changed.

The equation of the high spectral resolution polarized lidar based on the iodine molecular absorption cell can be described by the following three equations [29]:

$$P_C^ \bot \left( {{r_0}} \right) = \frac{{{P_0}{\eta _1}{A_r}L {\Psi }\left( {{r_0}} \right)}}{{r_0^2}}\left( {\beta _m^ \bot \left( {{r_0}} \right) + \beta _a^ \bot \left( {{r_0}} \right)} \right)\exp \left( { - 2\int_0^{{r_0}} {\left( {{\alpha _a}\left( {{r_0}} \right) + {\alpha _m}\left( {{r_0}} \right)} \right)dr} } \right)$$
$$P_C^\parallel ({{r_0}} )= \frac{{{P_0}{\eta _2}{A_r}L\Psi ({{r_0}} )}}{{r_0^2}}({\beta_m^\parallel ({{r_0}} )+ \beta_a^\parallel ({{r_0}} )} )\exp \left( { - 2\int_0^{{r_0}} {({{\alpha_a}({{r_0}} )+ {\alpha_m}({{r_0}} )} )dr} } \right)$$
$$P_M^\parallel ({{r_0}} )= \frac{{{P_0}{\eta _3}{A_r}L\Psi ({{r_0}} )}}{{r_0^2}}({{T_m}({{r_0}} )\beta_m^\parallel ({{r_0}} )+ {T_a}\beta_a^\parallel ({{r_0}} )} )\exp \left( { - 2\int_0^{{r_0}} {({{\alpha_a}({{r_0}} )+ {\alpha_m}({{r_0}} )} )dr} } \right)$$
where $P_C^ \bot $, $P_C^\parallel $ and $P_M^\parallel $ are the optical power of the echo signal of the vertical polarization channel, parallel reference channel, and hyperspectral molecular channel, respectively. βa(r0) and βm(r0) represent the atmospheric aerosol backscatter coefficient and the backscatter coefficient of the air molecules at the detection distance r0, respectively. The superscripts $\bot $ and ${\parallel} $ indicate vertical polarization and parallel polarization, αa(r0) and αm(r0) represent the atmospheric aerosol extinction coefficient and the extinction coefficient of the air molecules at the detection distance r0, respectively. P0 is the laser emission pulse power. Ar is the receiving area of the receiving telescope and $\Psi $(r0) is the lidar overlap factor with distance r0. ${\eta _i}$, i=1,2,3 represent the optical efficiencies of the vertical channel, reference parallel channel, and molecular channel, respectively. L is the half length of the pulse space (L = cΔt/2), where c=299792.458 km/s and represents the speed of light, and Δt represents the pulse time width. Tm(r0) represents the transmittance of the molecular signal through the iodine absorption cell at a distance of r0, and Ta represents the transmittance of the aerosol signal through the iodine absorption cell. Ta is a system constant [30], the value is 0.001 of our HSRL system. On combining these equations and the transmittance of atmospheric molecules and aerosols through the iodine pool, the expression of the atmospheric backscatter coefficient can be obtained:
$${\beta _a}(r )= {\beta _m}(r )\frac{{({1 + \delta (r )} )}}{{({1 + {\delta_m}} )}}\frac{{({{T_m}(r )- {T_a}} )K(r )}}{{({1 - {T_a}K(r )} )}} - {\beta _m}(r )$$
where δm is the depolarization ratio of atmospheric molecules, which can be obtained by accurate evaluation of the atmospheric model [31]. δ is the total depolarization coefficient of the atmosphere (including air molecules and aerosols), which is related to the non-spherical state of the target. The atmospheric optical depth is defined as:
$$\tau ({{r_0}} )= \int_0^{{r_0}} {({{\alpha_a}(r )+ {\alpha_m}(r )} )} dr ={-} \frac{1}{2}\ln \left[ {\frac{{(1 - K({r_0}){T_a})(1 + {\delta_m})B_M^\parallel }}{{({T_m}({r_0}) - {T_a})}}} \right]$$

According to Eq. (5), the extinction coefficient αa of the atmospheric aerosol can be obtained as follows:

$${\alpha _a}({r_0}) = \frac{{\partial \tau ({r_0})}}{{\partial \tau }} - {\alpha _m}({r_0})$$

The aerosol lidar ratio Sa is the ratio of the aerosol extinction coefficient αa to the backscatter coefficient βa. The airborne 1064 nm channel and ground lidar data inversion uses the Fernald forward integration algorithm [32].

3. Flight campaigns

The working wavelengths of the HSRL were 532 nm and 1064 nm. The 532 nm channel was used to detect the optical characteristics of aerosols and clouds, and the 1064 nm channel was used for ranging and Mie scattering inversion. The laser repetition frequency was 30 Hz, the horizontal resolution was 600 m (aircraft flight speed was 150 m/s), and the vertical distance resolution was 30 m. The experimental arrangement is shown in Table 1. The flight experiment accurately detected the AOD distribution and optical characteristics of the boundary layer in spring over Qinhuangdao, China.

Tables Icon

Table 1. Flight experiment arrangement.

This article mainly reports on analyses of the flight results obtained on March 16. Figure 2 shows a schematic diagram of the aircraft’s flight height over time. The horizontal flight altitude of the aircraft was 7.8 km on March 16. The aircraft made a downward spiral flight at 12:45. This flight path can accurately obtain the vertical profile distribution of atmospheric temperature, atmospheric pressure, and relative humidity, which were used to calculate atmospheric molecular optical parameters. Figure 3 shows the flight trajectory of the aircraft. The flight area contains a variety of land surfaces, including oceans, towns, mountains, and forests.

 figure: Fig. 2.

Fig. 2. Flight height of the aircraft. The aircraft took off at 10:16 am, circled and descended at 12:45, spiraled upward to a height of 2 km from the ground, landed at 13:49.

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 figure: Fig. 3.

Fig. 3. Aircraft flight trajectory. The red line is the flight trajectories of the aircraft, and the yellow points are ground stations.

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The layout of the ground stations is shown in Table 2. The comparison and verification equipment of the ground station included the MPL, Mie scattering lidar, and sun photometer.

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Table 2. Layout of the ground stations.

4. Results

4.1 Results of airborne lidar measurements

The airborne HSRL emits laser pulses with wavelengths of 532 nm and 1064 nm vertically. The following data products were obtained during the flight experiment: original backscatter signal, aerosol backscatter coefficient, aerosol extinction coefficient, optical depth of the cloud and boundary layer. Table 3 shows the atmospheric conditions of the Qinhuangdao area released by the Meteorological Station of Qinhuangdao City on March 16, and there was no pollution on that day.

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Table 3. Atmospheric conditions in Qinhuangdao on March 16, 2019.a

Figure 4 shows the results of comparisons of the aerosol backscatter coefficients detected by the airborne HSRL at 532 nm and 1064 nm channels. The results were both averaged for 10 min from 12: 35-12: 45. The two channel detection results exhibited good consistency. They can detect clouds at a height of 3 km, and even the fine vertical structure of the cloud.

 figure: Fig. 4.

Fig. 4. Profiles of aerosol backscatter coefficients measured by 532 nm and 1064 nm channels. The solid blue line is the backscatter coefficient of the aerosol at 1064 nm. The blue dotted line is the profile of the backscatter coefficient of atmospheric molecules at 1064 nm. The solid green line is the backscatter coefficient of the aerosol at 532 nm. The green dotted line is the profile of the backscatter coefficient of atmospheric molecules at 532 nm.

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4.2 Ground validation

The plane passed near the Funing surface station at 11:38 am on March 16, and the closest distance was approximately 35 km. The results of the airborne lidar were compared with those measured by the MPL and sun photometer at the Funing surface station. MPL profiles are 30-min averaged from 11:23-11:53 and the HSRL 532 nm channel profiles are 1-min averaged from 11:37:39-11:38:41 time period. The results of the comparison of the backscatter coefficients are shown in Fig. 5.

 figure: Fig. 5.

Fig. 5. Contrast profiles of the backscatter coefficients of the airborne lidar 532 nm channel and the MPL of the Funing ground station on March 16. The solid blue line is the backscatter coefficient after the 30-min average of the MPL. The solid green line is the profile of the backscatter coefficient of atmospheric molecules at 532 nm. The solid black line is the result of 1-minute averaging of the 532 nm channel of the airborne lidar. The shaded area represents the standard deviation of different detection heights.

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The results of the airborne lidar and the MPL were in good agreement. The correlation coefficient of the aerosol backscatter coefficient measured by the two devices was 0.8. In addition, we also compared the AOD measured by the airborne lidar with those measured by the sun photometer at Funing Station. The aircraft passed the vicinity of the Funing surface station twice at 11:30 and 12:14. At 12:14, the aircraft was closest to the ground station (approximately 12.8 km). The AOD measured by the 500 nm and 1020 nm bands of the sun photometer were compared with the results of the airborne lidar 532 nm and 1064 nm channels. Table 4 shows the measured values of the AOD and the results of the comparison are shown in Fig. 6. Values derived from the sun photometer always exceeded the values derived from the airborne HSRL. This was because of differences in their working principles.

 figure: Fig. 6.

Fig. 6. Aerosol optical depth detected by CE318 at the Funing Station and airborne HSRL on March 16, 2019. The blue asterisk is the value derived from the CE318 500 nm band. The solid blue dot is the value derived from the CE318 1020 nm band. The black asterisk is value derived from the HSRL 532 nm band. The black solid dots were measured by the HSRL 1064 nm band.

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Table 4. Aerosol optical depth measured by sun photometer (CE318) on Funing Station and airborne HSRL on March 16, 2019.

The results of the airborne lidar were also compared with those from the Mie scattering lidar located at the Beidaihe surface station. The comparison results are shown in Fig. 7.

 figure: Fig. 7.

Fig. 7. The profile of the backscatter coefficients measured by the airborne lidar 532 nm channel and the Mie scattering lidar at the Beidaihe ground station on March 16. The solid blue line is the backscatter coefficient of the Mie scattering lidar after 20-min average. The solid green line is the backscatter coefficient of atmospheric molecules at 532 nm. The solid black line is the result of 1-min averaging of the airborne lidar 532 nm channel. The shaded area represents the standard deviation of different detection heights.

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At 12:39 on March 16, the aircraft route was closest to the Beidaihe surface station, and the closest distance was 1.2 km. Mie scattering lidar profiles are a 20-min averaged from the 12: 30-12:50 time period. The Fernald method is used to inverse the data of the Mie scattering lidar, and the boundary value of the Mie scattering lidar in Fig. 7 is provided by the HSRL at 6 km. By comparing the backscatter coefficient results, the detection results of the Mie scattering lidar and the airborne HSRL were in good agreement. The correlation coefficient was 0.77. Because the aircraft route is far away from the ground station and the data inversion methods are different, the results are less relevant.

4.3 Satellite validation

In addition to comparing the data of the airborne HSRL with the ground stations, the results of the airborne HSRL were also compared with the spaceborne CALIPSO and MODIS. Since the CALIPSO satellite had no data on March 16, there were fewer valid data on March 15, the data at 11:41:20 on March 16, 2019 of the airborne HSRL 532 nm hyperspectral channel were selected for comparison with the data of three time periods of the CALIPSO satellite on March 15, 2019. The results of the comparison are shown in Fig. 8.

 figure: Fig. 8.

Fig. 8. Contrast profiles of the backscatter coefficients detected by the airborne lidar 532 nm channel and CALIPSO. The black dotted line is the measurement result of the airborne HSRL at 11:41:20, and the blue, green, and magenta dotted lines are the results of CALIPSO measured at three different times.

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The surface type of the airborne lidar at 11:41 was mountain, and the surface altitude was 90 m, so there were no data near the surface. The closest distance between the airborne lidar and CALIPSO orbit at this time was 148 km, and the time interval was approximately 22 h. Consequently, the data were partially different because of this time lag. The airborne HSRL can detect the aerosol layer at a height of 120 m from the surface. The weather was fine on March 15-16, 2019 and atmospheric conditions were stable. There was no significant transmission or diffusion of local aerosols. The maximum correlation coefficient between the detection results of the airborne HSRL and CALIPSO satellite was 0.76.

The AOD observed by the 532 nm channel of the airborne lidar was also compared with the 550 nm band of the MODIS (Aqua) satellite, and compared with the results of the 500 nm band of the sun photometer at Funing and Beidaihe ground stations. Table 5 shows the observation results of the aerosol optical depth. The comparison results are shown in Fig. 9. MODIS (Aqua) has no data on March 9, Funing ground station has no data on March 4, and Beidaihe ground station has no data on March 4/16/18/19.

 figure: Fig. 9.

Fig. 9. Comparison of aerosol optical depth. The black solid points were derived from the airborne lidar at 532 nm, the red solid points were derived from MODIS (Aqua). The solid blue points were derived from the solar photometer at Funing Ground Station. Green solid points were derived from the solar photometer at Beidaihe ground station.

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Table 5. Aerosol optical depth of the Airborne HSRL (the working wavelength is 532 nm), MODIS (Aqua) Satellite, Funing and Beidaihe ground stations.

The AOD detected by the airborne lidar were in good agreement with those obtained by the MODIS, Funing, and Beidaihe ground stations. The correlation coefficient of aerosol optical depth detected by airborne lidar and MODIS reaches 0.9926, and 0.936 with the sun photometer of Funing ground station. The observation results from Table 5 show that the AOD value was the smallest on March 14, when atmospheric conditions were good, the AQI index was 30, and the average value of the AOD observed by each device was 0.146. From March 14 to March 18, the concentration of pollutants gradually increased. On March 18, the AOD reached its maximum value because of the slight pollution on that day. On March 18, the AQI index was 103, and the average value of the AOD was 1.102. The HYSPLIT backward trajectory mode was used to analyze the first 36 hours of air mass trajectory at Funing station at 08:00 on March 18. The orbital results are shown in Fig. 10. Pollutants over Funing on March 18 mainly came from central China, located to the southwest of Funing ground station.

 figure: Fig. 10.

Fig. 10. 36-hour HYSPLIT backward trajectory over Funing Ground Station.

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4.4 Boundary layer aerosol optical depth analysis

The data products of the airborne HSRL during the horizontal flight on March 16, 2019, are shown in Fig. 11. The horizontal flight height of the aircraft was 7.8 km and there were many scattered clouds at a height of 3 to 4 km. During the flight we needed to adjust the instrument’s parameters so that some data are missing. The aircraft flew over ocean, towns and mountains, alternating between each land class, as indicated in Fig. 11. The entire flight was divided into seven stages, A-G, as a function of land class. A significant aerosol layer was apparent when flying over towns due to the influence of human activities. During the flight stages of C and G, the plane passed near a power plant, and this area contained obvious pollutants due to industrial production activities. In contrast, when the land class of the flight area was ocean (flight stages A and F) the aerosol content was minimal because of the absence of nearby industrial activity.

 figure: Fig. 11.

Fig. 11. Data products of the airborne HSRL on March 16, 2019. From top to bottom, the 532 nm channel distance corrected signal, the 532 nm channel aerosol backscatter coefficient, the 1064 nm channel distance corrected signal, the 1064 nm channel aerosol backscatter coefficient, and the 1064 nm channel aerosol extinction coefficient. Due to the low signal-to-noise ratio of the 1064 nm channel, an increased number of invalid values were generated.

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The boundary layer AOD was directly related to the land class and human activities below the flight path. To accurately analyze the distribution of AOD in the boundary layer of the flight path, the flight area was divided into three sectors according to land class and human activities, as shown in Fig. 12.

 figure: Fig. 12.

Fig. 12. Division map of the flight area. Sector 1 was mainly composed of mountains and forests, with many scattered human settlements in this area, and Sector 1 was less affected by human activities. Sector 2 was a coastal area where human settlements were concentrated and many industrial sites, including plants, were present. Consequently, sector 2 was most affected by human activities. The land class of sector 3 was ocean, and this area was least affected by human activities.

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Figure 13 shows the distribution of the boundary layer AOD measured by the 532 nm and 1064 nm channels of the airborne lidar during horizontal flight. Each point was averaged for 40 s, and cloud data were excluded. The atmospheric conditions were good and no pollution was apparent on March 16, so the AOD values were generally small on this day. The maximum value of the AOD detected by the 1064 nm channel was 0.07 and the 532 nm channel was 0.35. It can be seen from Fig. 13 that the values of the boundary layer AOD measured by 532 nm and 1064 nm channel are largest in sector 2 and smallest in sector 3. Sector 1 to sector 3 is defined in Fig. 12.

 figure: Fig. 13.

Fig. 13. AOD distribution from the 532 nm channel (a) and 1064 nm channel (b).

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In order to quantify the distribution of the boundary layer AOD in the three sectors defined in Fig. 12, (a) histogram was used to display the boundary layer AOD distribution of the two channels in Fig. 13. The AOD increment was set to 0.01 and results are shown in Figs. 14 and 15.

 figure: Fig. 14.

Fig. 14. Histogram of the boundary layer aerosol optical depth distribution in three sectors, derived from the 532 nm channel.

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 figure: Fig. 15.

Fig. 15. Histogram of the boundary layer aerosol optical depth distribution in three sectors, derived from the 1064 nm channel.

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The histogram distribution of the boundary layer AOD in the three sectors, derived from the 532 nm channel, is shown in Fig. 14. Most values of the boundary layer AOD measured by the 532 nm channel were distributed in interval of 0.21-0.22 in sector 1, interval 0.27-0.28 in sector 2, and interval 0.22-0.23 in sector 3. Figure 15 shows the histogram distribution of the boundary layer AOD in the three sectors derived from the 1064 nm channel. Compared with the distribution results of the 532 nm channel, the difference in the boundary layer AOD detected by the 1064 nm channel was more apparent. Most values of the boundary layer AOD in sector 1 were distributed in the interval 0.02-0.04, and interval 0.01-0.15 in sector 2, and most values of the boundary layer AOD in sector 3 were distributed in the interval 0.01- 0.02. The value of the boundary layer AOD in sector 3 was the smallest.

5. Conclusions

A dual-wavelength high spectral resolution lidar (HSRL) was developed for the validation and calibration of an upcoming satellite payload. The HSRL was installed on an airplane, and field campaigns were conducted in Qinhuangdao in March 2019. A total of 7 flights were undertaken, with a total flight time of 28h. Meanwhile, four cooperative observation sites were installed at different locations on the ground to verify the results of the airborne lidar. First, we compared the results of the airborne HSRL with those derived from the ground station equipment, including the MPL, Mie scattering lidar, and sun photometer at Funing and Beidaihe surface stations. The results of the airborne HSRL were also compared with the spaceborne CALIPSO and MODIS. The results of the comparison demonstrated good agreement. The correlation coefficients of AOD between the airborne lidar and MODIS, sun photometer of Funing ground station were 0.9926, and 0.936. The stability and reliability of the HSRL system were fully verified. These flights accurately detected the distribution of boundary layer AOD and aerosol optical characteristics in spring in the Qinhuangdao area. The flight path covered several land types, including ocean, town, mountain, and forest. Boundary layer AOD was directly related to the land class and human activities. To accurately analyze the distribution of AOD in the boundary layer of the flight path, the flight area was divided into three sectors according to land class and human activities. The results show that AOD was largest above the town and coastal areas. Most values of the boundary layer AOD detected by the 532nm channel were distributed in interval of 0.27-0.28, and 0.01-0.15 detected by the 1064nm channel. AOD in the mountainous area was the second largest and values were distributed in intervals of 0.21-0.22 detected by the 532nm channel, and 0.02-0.04 detected by the 1064nm channel. AOD in the marine area was the smallest, with most values detected by the 532nm channel distributed in interval of 0.22-0.23, and 0.01-0.02 detected by the 1064nm channel.

Funding

National Natural Science Foundation of China (41675133).

Acknowledgments

Thanks for the support of Shanghai Institute of Satellite Engineering for this experiment. Thanks to the Institute of Remote Sensing of the Chinese Academy of Sciences for providing the data of the Mie scattering lidar and the sun photometer at Beidaihe surface station. Thanks to Zhejiang University, Wuhan University and Ocean University of China for their collaborative experiments. We also would like to thank Editage for English language editing. Thanks to Farhan Mustafa for revising the wording and grammar of the article.

Disclosures

The authors declare no conflicts of interest.

References

1. S. Twomey, “The Influence of Pollution on the Shortwave Albedo of Clouds,” J. Atmos. Sci. 34(7), 1149–1152 (1977). [CrossRef]  

2. B. A. Albrecht, “Aerosols, Cloud Microphysics, and Fractional Cloudiness,” Science 245(4923), 1227–1230 (1989). [CrossRef]  

3. I. Tegen, A. A. Lacis, and I. Fung, “The influence on climate forcing of mineral aerosols from disturbed soils,” Nature 380(6573), 419–422 (1996). [CrossRef]  

4. R. L. Miller, “Surface radiative forcing by soil dust aerosols and the hydrologic cycle,” J. Geophys. Res. 109, 361–375 (2004). [CrossRef]  

5. J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006). [CrossRef]  

6. R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E. Hansen, and D. J. Hofmann, “Climate Forcing by Anthropogenic Aerosols,” Science 255(5043), 423–430 (1992). [CrossRef]  

7. Y. J. Kaufman, D. Tanré, O. Dubovik, A. Karnieli, and L. Remer, “Absorption of sunlight by dust as inferred from satellite and ground-based remote sensing,” Geophys. Res. Lett. 28(8), 1479–1482 (2001). [CrossRef]  

8. X. Wang, J. P. Huang, M. X. Ji, and K. Higuchi, “Variability of East Asia dust events and their long-term trend,” Atmos. Environ. 42(13), 3156–3165 (2008). [CrossRef]  

9. G. Fiocco and L. D. Smullin, “Detection of Scattering Layers in the Upper Atmosphere (60-140 km) by Optical Radar,” Nature 199(4900), 1275–1276 (1963). [CrossRef]  

10. Z. Huang, J. B. Nee, C. W. Chiang, S. Zhang, H. Jin, W. Wang, and T. Zhou, “Real-Time Observations of Dust–Cloud Interactions Based on Polarization and Raman Lidar Measurements,” Remote Sens. 10(7), 1017 (2018). [CrossRef]  

11. J. Martínez Sánchez, A. Váquez Álvarez, D. López Vilariño, F. Fernández Rivera, J. C. Cabaleiro Domínguez, and T. Fernández Pena, “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation,” Remote Sens. 11(19), 2256 (2019). [CrossRef]  

12. D. N. Whiteman, S. H. Melfi, and R. A. Ferrare, “Raman lidar system for the measurement of water vapor and aerosols in the Earth’s atmosphere,” Appl. Opt. 31(16), 3068–3082 (1992). [CrossRef]  

13. A. Behrendt, T. Nakamura, and T. Tsuda, “Combined Temperature Lidar for Measurements in the Troposphere, Stratosphere, and Mesosphere,” Appl. Opt. 43(14), 2930–2939 (2004). [CrossRef]  

14. A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005). [CrossRef]  

15. A. Ansmann, M. Riebesell, U. Wandinger, C. Weitkamp, E. Voss, W. Lahmann, and W. Michaelis, “Combined Raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio,” Appl. Phys. B 55(1), 18–28 (1992). [CrossRef]  

16. J. T. Sroga, E. W. Eloranta, S. T. Shipley, F. L. Roesler, and P. J. Tryon, “High spectral resolution lidar to measure optical scattering properties of atmospheric aerosols 2: Calibration and data analysis,” Appl. Opt. 22(23), 3725–3732 (1983). [CrossRef]  

17. D. M. Winker, W. H. Hunt, and M. J. Mcgill, “Initial performance assessment of CALIOP,” Geophys. Res. Lett. 34(19), L19803 (2007). [CrossRef]  

18. K. Sassen, “The Polarization Lidar Technique for Cloud Research: A Review and Current Assessment,” Bull. Am. Meteorol. Soc. 72(12), 1848–1866 (1991). [CrossRef]  

19. R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019). [CrossRef]  

20. W. Liu, F. Yamazaki, and Y. Maruyama, “Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data,” Remote Sens. 11(19), 2292 (2019). [CrossRef]  

21. M. J. Mao, Y. C. Zhang, H. T. Fang, F. D. Qi, S. S. Shao, H. L. Hu, and J. Zhou, “Detection of Aerosol Distribution by Atmospheric Environment Airborne Lidar over Qingdao and Adjacent Sea Area,” Chin. J. Geophys. 50(2), 358–364 (2007). [CrossRef]  

22. S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair, “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask,” Atmos. Meas. Tech. Discuss. 6(1), 1815–1858 (2013). [CrossRef]  

23. S. P. Burton, M. A. Vaughan, R. A. Ferrare, and C. A. Hostetler, “Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data,” Atmos. Meas. Tech. 7(2), 419–436 (2014). [CrossRef]  

24. M. J. Mcgill, M. A. Vaughan, C. R. Trepte, W. D. Hart, D. L. Hlavka, D. M. Winker, and R. Kuehn, “Airborne validation of spatial properties measured by the CALIPSO lidar,” J. Geophys. Res.: Atmos. 112(D20), D20201 (2007). [CrossRef]  

25. E. Michael, W. Martin, F. Andreas, T. Matthias, and E. Gerhard, “Airborne high spectral resolution lidar for measuring aerosol extinction and backscatter coefficients,” Appl. Opt. 47(3), 346–358 (2008). [CrossRef]  

26. J. W. Hair, L. M. Caldwell, D. A. Krueger, and C. Y. She, “High-spectral-resolution lidar with iodine-vapor filters: measurement of atmospheric-state and aerosol profiles,” Appl. Opt. 40(30), 5280–5294 (2001). [CrossRef]  

27. D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014). [CrossRef]  

28. J. W. Hair, C. A. Hostetler, A. L. Cook, D. B. Harper, R. A. Ferrare, T. L. Mack, W. Welch, L. R. Isquierdo, and F. E. Hovis, “Airborne high spectral resolution lidar for profiling aerosol optical properties,” Appl. Opt. 47(36), 6734–6752 (2008). [CrossRef]  

29. R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011). [CrossRef]  

30. Y. P. Zhang, D. Liu, Z. F. Zheng, Z. K. Liu, D. Y. Hu, B. Qi, C. Liu, L. Bi, K. J. Zhang, C. N. Wen, L. Y. Jiang, Y. L. Liu, J. Ke, and Z. Zang, “Effects of auxiliary atmospheric state parameters on the aerosol optical properties retrieval errors of high-spectral-resolution lidar,” Appl. Opt. 57(10), 2627–2637 (2018). [CrossRef]  

31. F. Cairo, G. D. Donfrancesco, A. Adriani, L. Pulvirenti, and F. Fierli, “Comparison of various linear depolarization parameters measured by lidar,” Appl. Opt. 38(21), 4425–4432 (1999). [CrossRef]  

32. F. G. Fernald, “Analysis of atmospheric LIDAR observations: Some comments,” Appl. Opt. 23(5), 652–653 (1984). [CrossRef]  

References

  • View by:

  1. S. Twomey, “The Influence of Pollution on the Shortwave Albedo of Clouds,” J. Atmos. Sci. 34(7), 1149–1152 (1977).
    [Crossref]
  2. B. A. Albrecht, “Aerosols, Cloud Microphysics, and Fractional Cloudiness,” Science 245(4923), 1227–1230 (1989).
    [Crossref]
  3. I. Tegen, A. A. Lacis, and I. Fung, “The influence on climate forcing of mineral aerosols from disturbed soils,” Nature 380(6573), 419–422 (1996).
    [Crossref]
  4. R. L. Miller, “Surface radiative forcing by soil dust aerosols and the hydrologic cycle,” J. Geophys. Res. 109, 361–375 (2004).
    [Crossref]
  5. J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006).
    [Crossref]
  6. R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E. Hansen, and D. J. Hofmann, “Climate Forcing by Anthropogenic Aerosols,” Science 255(5043), 423–430 (1992).
    [Crossref]
  7. Y. J. Kaufman, D. Tanré, O. Dubovik, A. Karnieli, and L. Remer, “Absorption of sunlight by dust as inferred from satellite and ground-based remote sensing,” Geophys. Res. Lett. 28(8), 1479–1482 (2001).
    [Crossref]
  8. X. Wang, J. P. Huang, M. X. Ji, and K. Higuchi, “Variability of East Asia dust events and their long-term trend,” Atmos. Environ. 42(13), 3156–3165 (2008).
    [Crossref]
  9. G. Fiocco and L. D. Smullin, “Detection of Scattering Layers in the Upper Atmosphere (60-140 km) by Optical Radar,” Nature 199(4900), 1275–1276 (1963).
    [Crossref]
  10. Z. Huang, J. B. Nee, C. W. Chiang, S. Zhang, H. Jin, W. Wang, and T. Zhou, “Real-Time Observations of Dust–Cloud Interactions Based on Polarization and Raman Lidar Measurements,” Remote Sens. 10(7), 1017 (2018).
    [Crossref]
  11. J. Martínez Sánchez, A. Váquez Álvarez, D. López Vilariño, F. Fernández Rivera, J. C. Cabaleiro Domínguez, and T. Fernández Pena, “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation,” Remote Sens. 11(19), 2256 (2019).
    [Crossref]
  12. D. N. Whiteman, S. H. Melfi, and R. A. Ferrare, “Raman lidar system for the measurement of water vapor and aerosols in the Earth’s atmosphere,” Appl. Opt. 31(16), 3068–3082 (1992).
    [Crossref]
  13. A. Behrendt, T. Nakamura, and T. Tsuda, “Combined Temperature Lidar for Measurements in the Troposphere, Stratosphere, and Mesosphere,” Appl. Opt. 43(14), 2930–2939 (2004).
    [Crossref]
  14. A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
    [Crossref]
  15. A. Ansmann, M. Riebesell, U. Wandinger, C. Weitkamp, E. Voss, W. Lahmann, and W. Michaelis, “Combined Raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio,” Appl. Phys. B 55(1), 18–28 (1992).
    [Crossref]
  16. J. T. Sroga, E. W. Eloranta, S. T. Shipley, F. L. Roesler, and P. J. Tryon, “High spectral resolution lidar to measure optical scattering properties of atmospheric aerosols 2: Calibration and data analysis,” Appl. Opt. 22(23), 3725–3732 (1983).
    [Crossref]
  17. D. M. Winker, W. H. Hunt, and M. J. Mcgill, “Initial performance assessment of CALIOP,” Geophys. Res. Lett. 34(19), L19803 (2007).
    [Crossref]
  18. K. Sassen, “The Polarization Lidar Technique for Cloud Research: A Review and Current Assessment,” Bull. Am. Meteorol. Soc. 72(12), 1848–1866 (1991).
    [Crossref]
  19. R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019).
    [Crossref]
  20. W. Liu, F. Yamazaki, and Y. Maruyama, “Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data,” Remote Sens. 11(19), 2292 (2019).
    [Crossref]
  21. M. J. Mao, Y. C. Zhang, H. T. Fang, F. D. Qi, S. S. Shao, H. L. Hu, and J. Zhou, “Detection of Aerosol Distribution by Atmospheric Environment Airborne Lidar over Qingdao and Adjacent Sea Area,” Chin. J. Geophys. 50(2), 358–364 (2007).
    [Crossref]
  22. S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair, “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask,” Atmos. Meas. Tech. Discuss. 6(1), 1815–1858 (2013).
    [Crossref]
  23. S. P. Burton, M. A. Vaughan, R. A. Ferrare, and C. A. Hostetler, “Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data,” Atmos. Meas. Tech. 7(2), 419–436 (2014).
    [Crossref]
  24. M. J. Mcgill, M. A. Vaughan, C. R. Trepte, W. D. Hart, D. L. Hlavka, D. M. Winker, and R. Kuehn, “Airborne validation of spatial properties measured by the CALIPSO lidar,” J. Geophys. Res.: Atmos. 112(D20), D20201 (2007).
    [Crossref]
  25. E. Michael, W. Martin, F. Andreas, T. Matthias, and E. Gerhard, “Airborne high spectral resolution lidar for measuring aerosol extinction and backscatter coefficients,” Appl. Opt. 47(3), 346–358 (2008).
    [Crossref]
  26. J. W. Hair, L. M. Caldwell, D. A. Krueger, and C. Y. She, “High-spectral-resolution lidar with iodine-vapor filters: measurement of atmospheric-state and aerosol profiles,” Appl. Opt. 40(30), 5280–5294 (2001).
    [Crossref]
  27. D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
    [Crossref]
  28. J. W. Hair, C. A. Hostetler, A. L. Cook, D. B. Harper, R. A. Ferrare, T. L. Mack, W. Welch, L. R. Isquierdo, and F. E. Hovis, “Airborne high spectral resolution lidar for profiling aerosol optical properties,” Appl. Opt. 47(36), 6734–6752 (2008).
    [Crossref]
  29. R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
    [Crossref]
  30. Y. P. Zhang, D. Liu, Z. F. Zheng, Z. K. Liu, D. Y. Hu, B. Qi, C. Liu, L. Bi, K. J. Zhang, C. N. Wen, L. Y. Jiang, Y. L. Liu, J. Ke, and Z. Zang, “Effects of auxiliary atmospheric state parameters on the aerosol optical properties retrieval errors of high-spectral-resolution lidar,” Appl. Opt. 57(10), 2627–2637 (2018).
    [Crossref]
  31. F. Cairo, G. D. Donfrancesco, A. Adriani, L. Pulvirenti, and F. Fierli, “Comparison of various linear depolarization parameters measured by lidar,” Appl. Opt. 38(21), 4425–4432 (1999).
    [Crossref]
  32. F. G. Fernald, “Analysis of atmospheric LIDAR observations: Some comments,” Appl. Opt. 23(5), 652–653 (1984).
    [Crossref]

2019 (3)

J. Martínez Sánchez, A. Váquez Álvarez, D. López Vilariño, F. Fernández Rivera, J. C. Cabaleiro Domínguez, and T. Fernández Pena, “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation,” Remote Sens. 11(19), 2256 (2019).
[Crossref]

R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019).
[Crossref]

W. Liu, F. Yamazaki, and Y. Maruyama, “Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data,” Remote Sens. 11(19), 2292 (2019).
[Crossref]

2018 (2)

2014 (2)

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

S. P. Burton, M. A. Vaughan, R. A. Ferrare, and C. A. Hostetler, “Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data,” Atmos. Meas. Tech. 7(2), 419–436 (2014).
[Crossref]

2013 (1)

S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair, “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask,” Atmos. Meas. Tech. Discuss. 6(1), 1815–1858 (2013).
[Crossref]

2011 (1)

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

2008 (3)

2007 (3)

D. M. Winker, W. H. Hunt, and M. J. Mcgill, “Initial performance assessment of CALIOP,” Geophys. Res. Lett. 34(19), L19803 (2007).
[Crossref]

M. J. Mcgill, M. A. Vaughan, C. R. Trepte, W. D. Hart, D. L. Hlavka, D. M. Winker, and R. Kuehn, “Airborne validation of spatial properties measured by the CALIPSO lidar,” J. Geophys. Res.: Atmos. 112(D20), D20201 (2007).
[Crossref]

M. J. Mao, Y. C. Zhang, H. T. Fang, F. D. Qi, S. S. Shao, H. L. Hu, and J. Zhou, “Detection of Aerosol Distribution by Atmospheric Environment Airborne Lidar over Qingdao and Adjacent Sea Area,” Chin. J. Geophys. 50(2), 358–364 (2007).
[Crossref]

2006 (1)

J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006).
[Crossref]

2005 (1)

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

2004 (2)

R. L. Miller, “Surface radiative forcing by soil dust aerosols and the hydrologic cycle,” J. Geophys. Res. 109, 361–375 (2004).
[Crossref]

A. Behrendt, T. Nakamura, and T. Tsuda, “Combined Temperature Lidar for Measurements in the Troposphere, Stratosphere, and Mesosphere,” Appl. Opt. 43(14), 2930–2939 (2004).
[Crossref]

2001 (2)

J. W. Hair, L. M. Caldwell, D. A. Krueger, and C. Y. She, “High-spectral-resolution lidar with iodine-vapor filters: measurement of atmospheric-state and aerosol profiles,” Appl. Opt. 40(30), 5280–5294 (2001).
[Crossref]

Y. J. Kaufman, D. Tanré, O. Dubovik, A. Karnieli, and L. Remer, “Absorption of sunlight by dust as inferred from satellite and ground-based remote sensing,” Geophys. Res. Lett. 28(8), 1479–1482 (2001).
[Crossref]

1999 (1)

1996 (1)

I. Tegen, A. A. Lacis, and I. Fung, “The influence on climate forcing of mineral aerosols from disturbed soils,” Nature 380(6573), 419–422 (1996).
[Crossref]

1992 (3)

R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E. Hansen, and D. J. Hofmann, “Climate Forcing by Anthropogenic Aerosols,” Science 255(5043), 423–430 (1992).
[Crossref]

D. N. Whiteman, S. H. Melfi, and R. A. Ferrare, “Raman lidar system for the measurement of water vapor and aerosols in the Earth’s atmosphere,” Appl. Opt. 31(16), 3068–3082 (1992).
[Crossref]

A. Ansmann, M. Riebesell, U. Wandinger, C. Weitkamp, E. Voss, W. Lahmann, and W. Michaelis, “Combined Raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio,” Appl. Phys. B 55(1), 18–28 (1992).
[Crossref]

1991 (1)

K. Sassen, “The Polarization Lidar Technique for Cloud Research: A Review and Current Assessment,” Bull. Am. Meteorol. Soc. 72(12), 1848–1866 (1991).
[Crossref]

1989 (1)

B. A. Albrecht, “Aerosols, Cloud Microphysics, and Fractional Cloudiness,” Science 245(4923), 1227–1230 (1989).
[Crossref]

1984 (1)

1983 (1)

1977 (1)

S. Twomey, “The Influence of Pollution on the Shortwave Albedo of Clouds,” J. Atmos. Sci. 34(7), 1149–1152 (1977).
[Crossref]

1963 (1)

G. Fiocco and L. D. Smullin, “Detection of Scattering Layers in the Upper Atmosphere (60-140 km) by Optical Radar,” Nature 199(4900), 1275–1276 (1963).
[Crossref]

Adriani, A.

Albrecht, B. A.

B. A. Albrecht, “Aerosols, Cloud Microphysics, and Fractional Cloudiness,” Science 245(4923), 1227–1230 (1989).
[Crossref]

Andersson, E.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Andreas, F.

Ansmann, A.

A. Ansmann, M. Riebesell, U. Wandinger, C. Weitkamp, E. Voss, W. Lahmann, and W. Michaelis, “Combined Raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio,” Appl. Phys. B 55(1), 18–28 (1992).
[Crossref]

Ayers, J. K.

J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006).
[Crossref]

Behrendt, A.

Berg, L. K.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

Bi, L.

Burton, S. P.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

S. P. Burton, M. A. Vaughan, R. A. Ferrare, and C. A. Hostetler, “Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data,” Atmos. Meas. Tech. 7(2), 419–436 (2014).
[Crossref]

S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair, “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask,” Atmos. Meas. Tech. Discuss. 6(1), 1815–1858 (2013).
[Crossref]

Cabaleiro Domínguez, J. C.

J. Martínez Sánchez, A. Váquez Álvarez, D. López Vilariño, F. Fernández Rivera, J. C. Cabaleiro Domínguez, and T. Fernández Pena, “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation,” Remote Sens. 11(19), 2256 (2019).
[Crossref]

Cairo, F.

Caldwell, L. M.

Cess, R. D.

R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E. Hansen, and D. J. Hofmann, “Climate Forcing by Anthropogenic Aerosols,” Science 255(5043), 423–430 (1992).
[Crossref]

Charlson, R. J.

R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E. Hansen, and D. J. Hofmann, “Climate Forcing by Anthropogenic Aerosols,” Science 255(5043), 423–430 (1992).
[Crossref]

Chemyakin, E.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

Chiang, C. W.

Z. Huang, J. B. Nee, C. W. Chiang, S. Zhang, H. Jin, W. Wang, and T. Zhou, “Real-Time Observations of Dust–Cloud Interactions Based on Polarization and Raman Lidar Measurements,” Remote Sens. 10(7), 1017 (2018).
[Crossref]

Cleckner, C. S.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

Coakley, J. A.

R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E. Hansen, and D. J. Hofmann, “Climate Forcing by Anthropogenic Aerosols,” Science 255(5043), 423–430 (1992).
[Crossref]

Cook, A. L.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

J. W. Hair, C. A. Hostetler, A. L. Cook, D. B. Harper, R. A. Ferrare, T. L. Mack, W. Welch, L. R. Isquierdo, and F. E. Hovis, “Airborne high spectral resolution lidar for profiling aerosol optical properties,” Appl. Opt. 47(36), 6734–6752 (2008).
[Crossref]

Culoma, A.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Dang, R.

R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019).
[Crossref]

Donfrancesco, G. D.

Dubovik, O.

Y. J. Kaufman, D. Tanré, O. Dubovik, A. Karnieli, and L. Remer, “Absorption of sunlight by dust as inferred from satellite and ground-based remote sensing,” Geophys. Res. Lett. 28(8), 1479–1482 (2001).
[Crossref]

Eloranta, E. W.

Endemann, M.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Fang, H. T.

M. J. Mao, Y. C. Zhang, H. T. Fang, F. D. Qi, S. S. Shao, H. L. Hu, and J. Zhou, “Detection of Aerosol Distribution by Atmospheric Environment Airborne Lidar over Qingdao and Adjacent Sea Area,” Chin. J. Geophys. 50(2), 358–364 (2007).
[Crossref]

Fernald, F. G.

Fernández Pena, T.

J. Martínez Sánchez, A. Váquez Álvarez, D. López Vilariño, F. Fernández Rivera, J. C. Cabaleiro Domínguez, and T. Fernández Pena, “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation,” Remote Sens. 11(19), 2256 (2019).
[Crossref]

Fernández Rivera, F.

J. Martínez Sánchez, A. Váquez Álvarez, D. López Vilariño, F. Fernández Rivera, J. C. Cabaleiro Domínguez, and T. Fernández Pena, “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation,” Remote Sens. 11(19), 2256 (2019).
[Crossref]

Ferrare, R. A.

S. P. Burton, M. A. Vaughan, R. A. Ferrare, and C. A. Hostetler, “Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data,” Atmos. Meas. Tech. 7(2), 419–436 (2014).
[Crossref]

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair, “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask,” Atmos. Meas. Tech. Discuss. 6(1), 1815–1858 (2013).
[Crossref]

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

J. W. Hair, C. A. Hostetler, A. L. Cook, D. B. Harper, R. A. Ferrare, T. L. Mack, W. Welch, L. R. Isquierdo, and F. E. Hovis, “Airborne high spectral resolution lidar for profiling aerosol optical properties,” Appl. Opt. 47(36), 6734–6752 (2008).
[Crossref]

D. N. Whiteman, S. H. Melfi, and R. A. Ferrare, “Raman lidar system for the measurement of water vapor and aerosols in the Earth’s atmosphere,” Appl. Opt. 31(16), 3068–3082 (1992).
[Crossref]

Fierli, F.

Fiocco, G.

G. Fiocco and L. D. Smullin, “Detection of Scattering Layers in the Upper Atmosphere (60-140 km) by Optical Radar,” Nature 199(4900), 1275–1276 (1963).
[Crossref]

Flamant, P.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Fung, I.

I. Tegen, A. A. Lacis, and I. Fung, “The influence on climate forcing of mineral aerosols from disturbed soils,” Nature 380(6573), 419–422 (1996).
[Crossref]

Gerhard, E.

Hair, J. W.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair, “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask,” Atmos. Meas. Tech. Discuss. 6(1), 1815–1858 (2013).
[Crossref]

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

J. W. Hair, C. A. Hostetler, A. L. Cook, D. B. Harper, R. A. Ferrare, T. L. Mack, W. Welch, L. R. Isquierdo, and F. E. Hovis, “Airborne high spectral resolution lidar for profiling aerosol optical properties,” Appl. Opt. 47(36), 6734–6752 (2008).
[Crossref]

J. W. Hair, L. M. Caldwell, D. A. Krueger, and C. Y. She, “High-spectral-resolution lidar with iodine-vapor filters: measurement of atmospheric-state and aerosol profiles,” Appl. Opt. 40(30), 5280–5294 (2001).
[Crossref]

Hales, J. M.

R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E. Hansen, and D. J. Hofmann, “Climate Forcing by Anthropogenic Aerosols,” Science 255(5043), 423–430 (1992).
[Crossref]

Hansen, J. E.

R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E. Hansen, and D. J. Hofmann, “Climate Forcing by Anthropogenic Aerosols,” Science 255(5043), 423–430 (1992).
[Crossref]

Hare, R. W.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

Harper, D. B.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

J. W. Hair, C. A. Hostetler, A. L. Cook, D. B. Harper, R. A. Ferrare, T. L. Mack, W. Welch, L. R. Isquierdo, and F. E. Hovis, “Airborne high spectral resolution lidar for profiling aerosol optical properties,” Appl. Opt. 47(36), 6734–6752 (2008).
[Crossref]

Hart, W. D.

M. J. Mcgill, M. A. Vaughan, C. R. Trepte, W. D. Hart, D. L. Hlavka, D. M. Winker, and R. Kuehn, “Airborne validation of spatial properties measured by the CALIPSO lidar,” J. Geophys. Res.: Atmos. 112(D20), D20201 (2007).
[Crossref]

Higuchi, K.

X. Wang, J. P. Huang, M. X. Ji, and K. Higuchi, “Variability of East Asia dust events and their long-term trend,” Atmos. Environ. 42(13), 3156–3165 (2008).
[Crossref]

Hlavka, D. L.

M. J. Mcgill, M. A. Vaughan, C. R. Trepte, W. D. Hart, D. L. Hlavka, D. M. Winker, and R. Kuehn, “Airborne validation of spatial properties measured by the CALIPSO lidar,” J. Geophys. Res.: Atmos. 112(D20), D20201 (2007).
[Crossref]

Hofmann, D. J.

R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E. Hansen, and D. J. Hofmann, “Climate Forcing by Anthropogenic Aerosols,” Science 255(5043), 423–430 (1992).
[Crossref]

Hostetler, C. A.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

S. P. Burton, M. A. Vaughan, R. A. Ferrare, and C. A. Hostetler, “Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data,” Atmos. Meas. Tech. 7(2), 419–436 (2014).
[Crossref]

S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair, “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask,” Atmos. Meas. Tech. Discuss. 6(1), 1815–1858 (2013).
[Crossref]

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

J. W. Hair, C. A. Hostetler, A. L. Cook, D. B. Harper, R. A. Ferrare, T. L. Mack, W. Welch, L. R. Isquierdo, and F. E. Hovis, “Airborne high spectral resolution lidar for profiling aerosol optical properties,” Appl. Opt. 47(36), 6734–6752 (2008).
[Crossref]

Hovis, F. E.

Hu, D. Y.

Hu, H. L.

M. J. Mao, Y. C. Zhang, H. T. Fang, F. D. Qi, S. S. Shao, H. L. Hu, and J. Zhou, “Detection of Aerosol Distribution by Atmospheric Environment Airborne Lidar over Qingdao and Adjacent Sea Area,” Chin. J. Geophys. 50(2), 358–364 (2007).
[Crossref]

Hu, X. M.

R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019).
[Crossref]

Hu, Y. G.

J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006).
[Crossref]

Huang, J. P.

X. Wang, J. P. Huang, M. X. Ji, and K. Higuchi, “Variability of East Asia dust events and their long-term trend,” Atmos. Environ. 42(13), 3156–3165 (2008).
[Crossref]

J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006).
[Crossref]

Huang, Z.

R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019).
[Crossref]

Z. Huang, J. B. Nee, C. W. Chiang, S. Zhang, H. Jin, W. Wang, and T. Zhou, “Real-Time Observations of Dust–Cloud Interactions Based on Polarization and Raman Lidar Measurements,” Remote Sens. 10(7), 1017 (2018).
[Crossref]

Hunt, W. H.

D. M. Winker, W. H. Hunt, and M. J. Mcgill, “Initial performance assessment of CALIOP,” Geophys. Res. Lett. 34(19), L19803 (2007).
[Crossref]

Ingmann, P.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Isaksen, L.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Isquierdo, L. R.

Ji, M. X.

X. Wang, J. P. Huang, M. X. Ji, and K. Higuchi, “Variability of East Asia dust events and their long-term trend,” Atmos. Environ. 42(13), 3156–3165 (2008).
[Crossref]

Jiang, L. Y.

Jin, H.

Z. Huang, J. B. Nee, C. W. Chiang, S. Zhang, H. Jin, W. Wang, and T. Zhou, “Real-Time Observations of Dust–Cloud Interactions Based on Polarization and Raman Lidar Measurements,” Remote Sens. 10(7), 1017 (2018).
[Crossref]

Källén, E.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Karnieli, A.

Y. J. Kaufman, D. Tanré, O. Dubovik, A. Karnieli, and L. Remer, “Absorption of sunlight by dust as inferred from satellite and ground-based remote sensing,” Geophys. Res. Lett. 28(8), 1479–1482 (2001).
[Crossref]

Kaufman, Y. J.

Y. J. Kaufman, D. Tanré, O. Dubovik, A. Karnieli, and L. Remer, “Absorption of sunlight by dust as inferred from satellite and ground-based remote sensing,” Geophys. Res. Lett. 28(8), 1479–1482 (2001).
[Crossref]

Ke, J.

Kolgotin, A.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

Krueger, D. A.

Kuehn, R.

M. J. Mcgill, M. A. Vaughan, C. R. Trepte, W. D. Hart, D. L. Hlavka, D. M. Winker, and R. Kuehn, “Airborne validation of spatial properties measured by the CALIPSO lidar,” J. Geophys. Res.: Atmos. 112(D20), D20201 (2007).
[Crossref]

Lacis, A. A.

I. Tegen, A. A. Lacis, and I. Fung, “The influence on climate forcing of mineral aerosols from disturbed soils,” Nature 380(6573), 419–422 (1996).
[Crossref]

Lahmann, W.

A. Ansmann, M. Riebesell, U. Wandinger, C. Weitkamp, E. Voss, W. Lahmann, and W. Michaelis, “Combined Raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio,” Appl. Phys. B 55(1), 18–28 (1992).
[Crossref]

Li, H.

R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019).
[Crossref]

Lin, B.

J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006).
[Crossref]

Liu, C.

Liu, D.

Liu, W.

W. Liu, F. Yamazaki, and Y. Maruyama, “Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data,” Remote Sens. 11(19), 2292 (2019).
[Crossref]

Liu, Y. L.

Liu, Z. K.

Liu, Z. Y.

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

López Vilariño, D.

J. Martínez Sánchez, A. Váquez Álvarez, D. López Vilariño, F. Fernández Rivera, J. C. Cabaleiro Domínguez, and T. Fernández Pena, “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation,” Remote Sens. 11(19), 2256 (2019).
[Crossref]

Mack, T. L.

Mao, M. J.

M. J. Mao, Y. C. Zhang, H. T. Fang, F. D. Qi, S. S. Shao, H. L. Hu, and J. Zhou, “Detection of Aerosol Distribution by Atmospheric Environment Airborne Lidar over Qingdao and Adjacent Sea Area,” Chin. J. Geophys. 50(2), 358–364 (2007).
[Crossref]

Martin, W.

Martínez Sánchez, J.

J. Martínez Sánchez, A. Váquez Álvarez, D. López Vilariño, F. Fernández Rivera, J. C. Cabaleiro Domínguez, and T. Fernández Pena, “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation,” Remote Sens. 11(19), 2256 (2019).
[Crossref]

Maruyama, Y.

W. Liu, F. Yamazaki, and Y. Maruyama, “Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data,” Remote Sens. 11(19), 2292 (2019).
[Crossref]

Matthias, T.

Maynart, R.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Mcgill, M. J.

M. J. Mcgill, M. A. Vaughan, C. R. Trepte, W. D. Hart, D. L. Hlavka, D. M. Winker, and R. Kuehn, “Airborne validation of spatial properties measured by the CALIPSO lidar,” J. Geophys. Res.: Atmos. 112(D20), D20201 (2007).
[Crossref]

D. M. Winker, W. H. Hunt, and M. J. Mcgill, “Initial performance assessment of CALIOP,” Geophys. Res. Lett. 34(19), L19803 (2007).
[Crossref]

Melfi, S. H.

Michael, E.

Michaelis, W.

A. Ansmann, M. Riebesell, U. Wandinger, C. Weitkamp, E. Voss, W. Lahmann, and W. Michaelis, “Combined Raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio,” Appl. Phys. B 55(1), 18–28 (1992).
[Crossref]

Miller, R. L.

R. L. Miller, “Surface radiative forcing by soil dust aerosols and the hydrologic cycle,” J. Geophys. Res. 109, 361–375 (2004).
[Crossref]

Minnis, P.

J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006).
[Crossref]

Muller, D.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

Nakamura, T.

Nee, J. B.

Z. Huang, J. B. Nee, C. W. Chiang, S. Zhang, H. Jin, W. Wang, and T. Zhou, “Real-Time Observations of Dust–Cloud Interactions Based on Polarization and Raman Lidar Measurements,” Remote Sens. 10(7), 1017 (2018).
[Crossref]

Obland, M. D.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

Omar, A. H.

S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair, “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask,” Atmos. Meas. Tech. Discuss. 6(1), 1815–1858 (2013).
[Crossref]

Pailleux, J.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Powell, K. A.

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

Pulvirenti, L.

Qi, B.

Qi, F. D.

M. J. Mao, Y. C. Zhang, H. T. Fang, F. D. Qi, S. S. Shao, H. L. Hu, and J. Zhou, “Detection of Aerosol Distribution by Atmospheric Environment Airborne Lidar over Qingdao and Adjacent Sea Area,” Chin. J. Geophys. 50(2), 358–364 (2007).
[Crossref]

Remer, L.

Y. J. Kaufman, D. Tanré, O. Dubovik, A. Karnieli, and L. Remer, “Absorption of sunlight by dust as inferred from satellite and ground-based remote sensing,” Geophys. Res. Lett. 28(8), 1479–1482 (2001).
[Crossref]

Riebesell, M.

A. Ansmann, M. Riebesell, U. Wandinger, C. Weitkamp, E. Voss, W. Lahmann, and W. Michaelis, “Combined Raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio,” Appl. Phys. B 55(1), 18–28 (1992).
[Crossref]

Roesler, F. L.

Rogers, R. R.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair, “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask,” Atmos. Meas. Tech. Discuss. 6(1), 1815–1858 (2013).
[Crossref]

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

Sassen, K.

K. Sassen, “The Polarization Lidar Technique for Cloud Research: A Review and Current Assessment,” Bull. Am. Meteorol. Soc. 72(12), 1848–1866 (1991).
[Crossref]

Schmid, B.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

Schwartz, S. E.

R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E. Hansen, and D. J. Hofmann, “Climate Forcing by Anthropogenic Aerosols,” Science 255(5043), 423–430 (1992).
[Crossref]

Schyberg, H.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Shao, S. S.

M. J. Mao, Y. C. Zhang, H. T. Fang, F. D. Qi, S. S. Shao, H. L. Hu, and J. Zhou, “Detection of Aerosol Distribution by Atmospheric Environment Airborne Lidar over Qingdao and Adjacent Sea Area,” Chin. J. Geophys. 50(2), 358–364 (2007).
[Crossref]

She, C. Y.

Shipley, S. T.

Smullin, L. D.

G. Fiocco and L. D. Smullin, “Detection of Scattering Layers in the Upper Atmosphere (60-140 km) by Optical Radar,” Nature 199(4900), 1275–1276 (1963).
[Crossref]

Sroga, J. T.

Stoffelen, A.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Tanré, D.

Y. J. Kaufman, D. Tanré, O. Dubovik, A. Karnieli, and L. Remer, “Absorption of sunlight by dust as inferred from satellite and ground-based remote sensing,” Geophys. Res. Lett. 28(8), 1479–1482 (2001).
[Crossref]

Tegen, I.

I. Tegen, A. A. Lacis, and I. Fung, “The influence on climate forcing of mineral aerosols from disturbed soils,” Nature 380(6573), 419–422 (1996).
[Crossref]

Tomlinson, J.

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

Trepte, C. R.

M. J. Mcgill, M. A. Vaughan, C. R. Trepte, W. D. Hart, D. L. Hlavka, D. M. Winker, and R. Kuehn, “Airborne validation of spatial properties measured by the CALIPSO lidar,” J. Geophys. Res.: Atmos. 112(D20), D20201 (2007).
[Crossref]

Tryon, P. J.

Tsuda, T.

Twomey, S.

S. Twomey, “The Influence of Pollution on the Shortwave Albedo of Clouds,” J. Atmos. Sci. 34(7), 1149–1152 (1977).
[Crossref]

Váquez Álvarez, A.

J. Martínez Sánchez, A. Váquez Álvarez, D. López Vilariño, F. Fernández Rivera, J. C. Cabaleiro Domínguez, and T. Fernández Pena, “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation,” Remote Sens. 11(19), 2256 (2019).
[Crossref]

Vaughan, J. M.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Vaughan, M.

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

Vaughan, M. A.

S. P. Burton, M. A. Vaughan, R. A. Ferrare, and C. A. Hostetler, “Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data,” Atmos. Meas. Tech. 7(2), 419–436 (2014).
[Crossref]

S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair, “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask,” Atmos. Meas. Tech. Discuss. 6(1), 1815–1858 (2013).
[Crossref]

M. J. Mcgill, M. A. Vaughan, C. R. Trepte, W. D. Hart, D. L. Hlavka, D. M. Winker, and R. Kuehn, “Airborne validation of spatial properties measured by the CALIPSO lidar,” J. Geophys. Res.: Atmos. 112(D20), D20201 (2007).
[Crossref]

Voss, E.

A. Ansmann, M. Riebesell, U. Wandinger, C. Weitkamp, E. Voss, W. Lahmann, and W. Michaelis, “Combined Raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio,” Appl. Phys. B 55(1), 18–28 (1992).
[Crossref]

Wandinger, U.

A. Ansmann, M. Riebesell, U. Wandinger, C. Weitkamp, E. Voss, W. Lahmann, and W. Michaelis, “Combined Raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio,” Appl. Phys. B 55(1), 18–28 (1992).
[Crossref]

Wang, T. H.

J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006).
[Crossref]

Wang, W.

Z. Huang, J. B. Nee, C. W. Chiang, S. Zhang, H. Jin, W. Wang, and T. Zhou, “Real-Time Observations of Dust–Cloud Interactions Based on Polarization and Raman Lidar Measurements,” Remote Sens. 10(7), 1017 (2018).
[Crossref]

Wang, X.

X. Wang, J. P. Huang, M. X. Ji, and K. Higuchi, “Variability of East Asia dust events and their long-term trend,” Atmos. Environ. 42(13), 3156–3165 (2008).
[Crossref]

J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006).
[Crossref]

Wang, Z.

R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019).
[Crossref]

Weitkamp, C.

A. Ansmann, M. Riebesell, U. Wandinger, C. Weitkamp, E. Voss, W. Lahmann, and W. Michaelis, “Combined Raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio,” Appl. Phys. B 55(1), 18–28 (1992).
[Crossref]

Welch, W.

Wen, C. N.

Wergen, W.

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Whiteman, D. N.

Winker, D. M.

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

D. M. Winker, W. H. Hunt, and M. J. Mcgill, “Initial performance assessment of CALIOP,” Geophys. Res. Lett. 34(19), L19803 (2007).
[Crossref]

M. J. Mcgill, M. A. Vaughan, C. R. Trepte, W. D. Hart, D. L. Hlavka, D. M. Winker, and R. Kuehn, “Airborne validation of spatial properties measured by the CALIPSO lidar,” J. Geophys. Res.: Atmos. 112(D20), D20201 (2007).
[Crossref]

Yamazaki, F.

W. Liu, F. Yamazaki, and Y. Maruyama, “Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data,” Remote Sens. 11(19), 2292 (2019).
[Crossref]

Yang, Y.

R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019).
[Crossref]

Yi, H. L.

J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006).
[Crossref]

Zang, Z.

Zhang, K. J.

Zhang, S.

Z. Huang, J. B. Nee, C. W. Chiang, S. Zhang, H. Jin, W. Wang, and T. Zhou, “Real-Time Observations of Dust–Cloud Interactions Based on Polarization and Raman Lidar Measurements,” Remote Sens. 10(7), 1017 (2018).
[Crossref]

Zhang, T.

R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019).
[Crossref]

Zhang, Y. C.

M. J. Mao, Y. C. Zhang, H. T. Fang, F. D. Qi, S. S. Shao, H. L. Hu, and J. Zhou, “Detection of Aerosol Distribution by Atmospheric Environment Airborne Lidar over Qingdao and Adjacent Sea Area,” Chin. J. Geophys. 50(2), 358–364 (2007).
[Crossref]

Zhang, Y. P.

Zheng, Z. F.

Zhou, J.

M. J. Mao, Y. C. Zhang, H. T. Fang, F. D. Qi, S. S. Shao, H. L. Hu, and J. Zhou, “Detection of Aerosol Distribution by Atmospheric Environment Airborne Lidar over Qingdao and Adjacent Sea Area,” Chin. J. Geophys. 50(2), 358–364 (2007).
[Crossref]

Zhou, T.

R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019).
[Crossref]

Z. Huang, J. B. Nee, C. W. Chiang, S. Zhang, H. Jin, W. Wang, and T. Zhou, “Real-Time Observations of Dust–Cloud Interactions Based on Polarization and Raman Lidar Measurements,” Remote Sens. 10(7), 1017 (2018).
[Crossref]

Appl. Opt. (9)

D. N. Whiteman, S. H. Melfi, and R. A. Ferrare, “Raman lidar system for the measurement of water vapor and aerosols in the Earth’s atmosphere,” Appl. Opt. 31(16), 3068–3082 (1992).
[Crossref]

A. Behrendt, T. Nakamura, and T. Tsuda, “Combined Temperature Lidar for Measurements in the Troposphere, Stratosphere, and Mesosphere,” Appl. Opt. 43(14), 2930–2939 (2004).
[Crossref]

J. T. Sroga, E. W. Eloranta, S. T. Shipley, F. L. Roesler, and P. J. Tryon, “High spectral resolution lidar to measure optical scattering properties of atmospheric aerosols 2: Calibration and data analysis,” Appl. Opt. 22(23), 3725–3732 (1983).
[Crossref]

E. Michael, W. Martin, F. Andreas, T. Matthias, and E. Gerhard, “Airborne high spectral resolution lidar for measuring aerosol extinction and backscatter coefficients,” Appl. Opt. 47(3), 346–358 (2008).
[Crossref]

J. W. Hair, L. M. Caldwell, D. A. Krueger, and C. Y. She, “High-spectral-resolution lidar with iodine-vapor filters: measurement of atmospheric-state and aerosol profiles,” Appl. Opt. 40(30), 5280–5294 (2001).
[Crossref]

J. W. Hair, C. A. Hostetler, A. L. Cook, D. B. Harper, R. A. Ferrare, T. L. Mack, W. Welch, L. R. Isquierdo, and F. E. Hovis, “Airborne high spectral resolution lidar for profiling aerosol optical properties,” Appl. Opt. 47(36), 6734–6752 (2008).
[Crossref]

Y. P. Zhang, D. Liu, Z. F. Zheng, Z. K. Liu, D. Y. Hu, B. Qi, C. Liu, L. Bi, K. J. Zhang, C. N. Wen, L. Y. Jiang, Y. L. Liu, J. Ke, and Z. Zang, “Effects of auxiliary atmospheric state parameters on the aerosol optical properties retrieval errors of high-spectral-resolution lidar,” Appl. Opt. 57(10), 2627–2637 (2018).
[Crossref]

F. Cairo, G. D. Donfrancesco, A. Adriani, L. Pulvirenti, and F. Fierli, “Comparison of various linear depolarization parameters measured by lidar,” Appl. Opt. 38(21), 4425–4432 (1999).
[Crossref]

F. G. Fernald, “Analysis of atmospheric LIDAR observations: Some comments,” Appl. Opt. 23(5), 652–653 (1984).
[Crossref]

Appl. Phys. B (1)

A. Ansmann, M. Riebesell, U. Wandinger, C. Weitkamp, E. Voss, W. Lahmann, and W. Michaelis, “Combined Raman elastic-backscatter LIDAR for vertical profiling of moisture, aerosol extinction, backscatter, and LIDAR ratio,” Appl. Phys. B 55(1), 18–28 (1992).
[Crossref]

Atmos. Chem. Phys. (1)

R. R. Rogers, C. A. Hostetler, J. W. Hair, R. A. Ferrare, Z. Y. Liu, M. D. Obland, D. B. Harper, A. L. Cook, K. A. Powell, M. Vaughan, and D. M. Winker, “Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne High Spectral Resolution Lidar,” Atmos. Chem. Phys. 11, 1295–1311 (2011).
[Crossref]

Atmos. Environ. (1)

X. Wang, J. P. Huang, M. X. Ji, and K. Higuchi, “Variability of East Asia dust events and their long-term trend,” Atmos. Environ. 42(13), 3156–3165 (2008).
[Crossref]

Atmos. Meas. Tech. (2)

D. Muller, C. A. Hostetler, R. A. Ferrare, S. P. Burton, E. Chemyakin, A. Kolgotin, J. W. Hair, A. L. Cook, D. B. Harper, R. R. Rogers, R. W. Hare, C. S. Cleckner, M. D. Obland, J. Tomlinson, L. K. Berg, and B. Schmid, “Airborne Multiwavelength High Spectral Resolution Lidar (HSRL-2) observations during TCAP 2012: vertical profiles of optical and microphysical properties of a smoke/urban haze plume over the northeastern coast of the US,” Atmos. Meas. Tech. 7(10), 3487–3496 (2014).
[Crossref]

S. P. Burton, M. A. Vaughan, R. A. Ferrare, and C. A. Hostetler, “Separating mixtures of aerosol types in airborne High Spectral Resolution Lidar data,” Atmos. Meas. Tech. 7(2), 419–436 (2014).
[Crossref]

Atmos. Meas. Tech. Discuss. (1)

S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair, “Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask,” Atmos. Meas. Tech. Discuss. 6(1), 1815–1858 (2013).
[Crossref]

Bull. Am. Meteorol. Soc. (2)

K. Sassen, “The Polarization Lidar Technique for Cloud Research: A Review and Current Assessment,” Bull. Am. Meteorol. Soc. 72(12), 1848–1866 (1991).
[Crossref]

A. Stoffelen, J. Pailleux, E. Källén, J. M. Vaughan, L. Isaksen, P. Flamant, W. Wergen, E. Andersson, H. Schyberg, A. Culoma, R. Maynart, M. Endemann, and P. Ingmann, “The atmospheric dynamics mission for global wind field measurement,” Bull. Am. Meteorol. Soc. 86(1), 73–88 (2005).
[Crossref]

Chin. J. Geophys. (1)

M. J. Mao, Y. C. Zhang, H. T. Fang, F. D. Qi, S. S. Shao, H. L. Hu, and J. Zhou, “Detection of Aerosol Distribution by Atmospheric Environment Airborne Lidar over Qingdao and Adjacent Sea Area,” Chin. J. Geophys. 50(2), 358–364 (2007).
[Crossref]

Geophys. Res. Lett. (3)

J. P. Huang, B. Lin, P. Minnis, T. H. Wang, X. Wang, Y. G. Hu, H. L. Yi, and J. K. Ayers, “Satellite-based assessment of possible dust aerosols semi-direct effect on cloud water path over East Asia,” Geophys. Res. Lett. 33(19), L19802 (2006).
[Crossref]

Y. J. Kaufman, D. Tanré, O. Dubovik, A. Karnieli, and L. Remer, “Absorption of sunlight by dust as inferred from satellite and ground-based remote sensing,” Geophys. Res. Lett. 28(8), 1479–1482 (2001).
[Crossref]

D. M. Winker, W. H. Hunt, and M. J. Mcgill, “Initial performance assessment of CALIOP,” Geophys. Res. Lett. 34(19), L19803 (2007).
[Crossref]

J. Atmos. Sci. (1)

S. Twomey, “The Influence of Pollution on the Shortwave Albedo of Clouds,” J. Atmos. Sci. 34(7), 1149–1152 (1977).
[Crossref]

J. Geophys. Res. (1)

R. L. Miller, “Surface radiative forcing by soil dust aerosols and the hydrologic cycle,” J. Geophys. Res. 109, 361–375 (2004).
[Crossref]

J. Geophys. Res.: Atmos. (1)

M. J. Mcgill, M. A. Vaughan, C. R. Trepte, W. D. Hart, D. L. Hlavka, D. M. Winker, and R. Kuehn, “Airborne validation of spatial properties measured by the CALIPSO lidar,” J. Geophys. Res.: Atmos. 112(D20), D20201 (2007).
[Crossref]

Nature (2)

I. Tegen, A. A. Lacis, and I. Fung, “The influence on climate forcing of mineral aerosols from disturbed soils,” Nature 380(6573), 419–422 (1996).
[Crossref]

G. Fiocco and L. D. Smullin, “Detection of Scattering Layers in the Upper Atmosphere (60-140 km) by Optical Radar,” Nature 199(4900), 1275–1276 (1963).
[Crossref]

Remote Sens. (4)

Z. Huang, J. B. Nee, C. W. Chiang, S. Zhang, H. Jin, W. Wang, and T. Zhou, “Real-Time Observations of Dust–Cloud Interactions Based on Polarization and Raman Lidar Measurements,” Remote Sens. 10(7), 1017 (2018).
[Crossref]

J. Martínez Sánchez, A. Váquez Álvarez, D. López Vilariño, F. Fernández Rivera, J. C. Cabaleiro Domínguez, and T. Fernández Pena, “Fast Ground Filtering of Airborne LiDAR Data Based on Iterative Scan-Line Spline Interpolation,” Remote Sens. 11(19), 2256 (2019).
[Crossref]

R. Dang, Y. Yang, H. Li, X. M. Hu, Z. Wang, Z. Huang, T. Zhou, and T. Zhang, “Atmosphere Boundary Layer Height (ABLH) Determination under Multiple-Layer Conditions Using Micro-Pulse Lidar,” Remote Sens. 11(3), 263 (2019).
[Crossref]

W. Liu, F. Yamazaki, and Y. Maruyama, “Detection of Earthquake-Induced Landslides during the 2018 Kumamoto Earthquake Using Multitemporal Airborne Lidar Data,” Remote Sens. 11(19), 2292 (2019).
[Crossref]

Science (2)

B. A. Albrecht, “Aerosols, Cloud Microphysics, and Fractional Cloudiness,” Science 245(4923), 1227–1230 (1989).
[Crossref]

R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E. Hansen, and D. J. Hofmann, “Climate Forcing by Anthropogenic Aerosols,” Science 255(5043), 423–430 (1992).
[Crossref]

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Figures (15)

Fig. 1.
Fig. 1. Schematic diagram of the high spectral resolution lidar receiving system.
Fig. 2.
Fig. 2. Flight height of the aircraft. The aircraft took off at 10:16 am, circled and descended at 12:45, spiraled upward to a height of 2 km from the ground, landed at 13:49.
Fig. 3.
Fig. 3. Aircraft flight trajectory. The red line is the flight trajectories of the aircraft, and the yellow points are ground stations.
Fig. 4.
Fig. 4. Profiles of aerosol backscatter coefficients measured by 532 nm and 1064 nm channels. The solid blue line is the backscatter coefficient of the aerosol at 1064 nm. The blue dotted line is the profile of the backscatter coefficient of atmospheric molecules at 1064 nm. The solid green line is the backscatter coefficient of the aerosol at 532 nm. The green dotted line is the profile of the backscatter coefficient of atmospheric molecules at 532 nm.
Fig. 5.
Fig. 5. Contrast profiles of the backscatter coefficients of the airborne lidar 532 nm channel and the MPL of the Funing ground station on March 16. The solid blue line is the backscatter coefficient after the 30-min average of the MPL. The solid green line is the profile of the backscatter coefficient of atmospheric molecules at 532 nm. The solid black line is the result of 1-minute averaging of the 532 nm channel of the airborne lidar. The shaded area represents the standard deviation of different detection heights.
Fig. 6.
Fig. 6. Aerosol optical depth detected by CE318 at the Funing Station and airborne HSRL on March 16, 2019. The blue asterisk is the value derived from the CE318 500 nm band. The solid blue dot is the value derived from the CE318 1020 nm band. The black asterisk is value derived from the HSRL 532 nm band. The black solid dots were measured by the HSRL 1064 nm band.
Fig. 7.
Fig. 7. The profile of the backscatter coefficients measured by the airborne lidar 532 nm channel and the Mie scattering lidar at the Beidaihe ground station on March 16. The solid blue line is the backscatter coefficient of the Mie scattering lidar after 20-min average. The solid green line is the backscatter coefficient of atmospheric molecules at 532 nm. The solid black line is the result of 1-min averaging of the airborne lidar 532 nm channel. The shaded area represents the standard deviation of different detection heights.
Fig. 8.
Fig. 8. Contrast profiles of the backscatter coefficients detected by the airborne lidar 532 nm channel and CALIPSO. The black dotted line is the measurement result of the airborne HSRL at 11:41:20, and the blue, green, and magenta dotted lines are the results of CALIPSO measured at three different times.
Fig. 9.
Fig. 9. Comparison of aerosol optical depth. The black solid points were derived from the airborne lidar at 532 nm, the red solid points were derived from MODIS (Aqua). The solid blue points were derived from the solar photometer at Funing Ground Station. Green solid points were derived from the solar photometer at Beidaihe ground station.
Fig. 10.
Fig. 10. 36-hour HYSPLIT backward trajectory over Funing Ground Station.
Fig. 11.
Fig. 11. Data products of the airborne HSRL on March 16, 2019. From top to bottom, the 532 nm channel distance corrected signal, the 532 nm channel aerosol backscatter coefficient, the 1064 nm channel distance corrected signal, the 1064 nm channel aerosol backscatter coefficient, and the 1064 nm channel aerosol extinction coefficient. Due to the low signal-to-noise ratio of the 1064 nm channel, an increased number of invalid values were generated.
Fig. 12.
Fig. 12. Division map of the flight area. Sector 1 was mainly composed of mountains and forests, with many scattered human settlements in this area, and Sector 1 was less affected by human activities. Sector 2 was a coastal area where human settlements were concentrated and many industrial sites, including plants, were present. Consequently, sector 2 was most affected by human activities. The land class of sector 3 was ocean, and this area was least affected by human activities.
Fig. 13.
Fig. 13. AOD distribution from the 532 nm channel (a) and 1064 nm channel (b).
Fig. 14.
Fig. 14. Histogram of the boundary layer aerosol optical depth distribution in three sectors, derived from the 532 nm channel.
Fig. 15.
Fig. 15. Histogram of the boundary layer aerosol optical depth distribution in three sectors, derived from the 1064 nm channel.

Tables (5)

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Table 1. Flight experiment arrangement.

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Table 2. Layout of the ground stations.

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Table 3. Atmospheric conditions in Qinhuangdao on March 16, 2019.a

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Table 4. Aerosol optical depth measured by sun photometer (CE318) on Funing Station and airborne HSRL on March 16, 2019.

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Table 5. Aerosol optical depth of the Airborne HSRL (the working wavelength is 532 nm), MODIS (Aqua) Satellite, Funing and Beidaihe ground stations.

Equations (6)

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P C ( r 0 ) = P 0 η 1 A r L Ψ ( r 0 ) r 0 2 ( β m ( r 0 ) + β a ( r 0 ) ) exp ( 2 0 r 0 ( α a ( r 0 ) + α m ( r 0 ) ) d r )
P C ( r 0 ) = P 0 η 2 A r L Ψ ( r 0 ) r 0 2 ( β m ( r 0 ) + β a ( r 0 ) ) exp ( 2 0 r 0 ( α a ( r 0 ) + α m ( r 0 ) ) d r )
P M ( r 0 ) = P 0 η 3 A r L Ψ ( r 0 ) r 0 2 ( T m ( r 0 ) β m ( r 0 ) + T a β a ( r 0 ) ) exp ( 2 0 r 0 ( α a ( r 0 ) + α m ( r 0 ) ) d r )
β a ( r ) = β m ( r ) ( 1 + δ ( r ) ) ( 1 + δ m ) ( T m ( r ) T a ) K ( r ) ( 1 T a K ( r ) ) β m ( r )
τ ( r 0 ) = 0 r 0 ( α a ( r ) + α m ( r ) ) d r = 1 2 ln [ ( 1 K ( r 0 ) T a ) ( 1 + δ m ) B M ( T m ( r 0 ) T a ) ]
α a ( r 0 ) = τ ( r 0 ) τ α m ( r 0 )

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