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Accurate identification and monitoring of aerosol properties is crucial for understanding their sources and impacts on human health and the environment. Therefore, we propose a feature extraction and attribute recognition method from in situ light-scattering measurements based on Bayesian Optimization, wavelet scattering transform, and long short-term memory neural network (BO-WST-LSTM), with empirical mode decomposition (EMD) and independent component analysis (ICA) algorithm for signal preprocessing. In this study, an experimental platform was utilized to gather light-scattering signals from particles with varying characteristics. The signals are then processed using the EMD-ICA noise reduction technique. Then, the wavelet scattering network is used to realize the adaptive extraction of the characteristics of the particle light-scattering signal, and the Bayesian Optimization model is used to optimize the hyperparameters of the LSTM neural network. The extracted scattering coefficient matrix is input into the LSTM for iterative training. Finally, the SoftMax layer's probability classification method is applied to the identification of particle attributes. The results show that the multi-angle particle light-scattering signal detection system designed and built in this study performs well and is capable of effectively collecting particle light-scattering signals. At the same time, the proposed new method for particle property recognition demonstrates good classification performance for six different types of particles with a particle size of 2 μm, achieving a classification accuracy of 98.83%. This proves its effectiveness in recognizing particle properties and provides a solid foundation for particle identification. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) are rapidly evolving experimental platforms that play an important role in remote sensing. In this study, we investigated a machine learning method for monitoring soil nutrient content using UAV hyperspectral remote sensing. We employed machine learning techniques for feature extraction and soil hyperspectral information modelling. In contrast to traditional mathematical transformation methods, we adopted a combination of random forest and differential evolution algorithms to rank the weights of individual hyperspectral data, thereby obtaining a series of spectral feature subsets for soil organic matter, total nitrogen and available phosphorus and potassium. Furthermore, the analytic hierarchy process was used for weight analysis, and the characteristic bands of the four soil nutrients were successfully extracted. Next, a quantitative inversion model based on a back-propagation (BP) neural network was established to estimate soil nutrient content, with determination coefficients higher than 0.7 and 0.6 for the modelling and verification sets, respectively. The relative percent difference values were greater than 2, among which the highest was for available potassium, with determination coefficients of 0.95 and 0.84 for the modelling and verification sets, respectively. In addition, visualization distribution maps of soil nutrients were obtained by combining the BP model and original reflectance hyperspectral images, and the comparisons of content histograms showed a relatively consistent distribution between the sampling and inversion points. The results verified the effectiveness of the combined machine learning method for large-scale and high-precision monitoring and visualization of soil nutrient contents. [ABSTRACT FROM AUTHOR]

With the rapid development of biotechnology, the importance of microfluidic bioseparation and bioassay in biomedicine, clinical diagnosis, and other fields has become increasingly prominent. Microfluidic technology, with its significant advantages of high throughput, automated operation, and low sample consumption, has brought new breakthroughs in the field of biological separation and bioassay. In this paper, the latest research progress in microfluidic technology in the field of bioseparation and bioassay is reviewed. Then, we focus on the methods of bioseparation including active separation, passive separation, and hybrid separation. At the same time, the latest research results of our group in particle separation are introduced. Finally, some application examples or methods for bioassay after particle separation are listed, and the current challenges and future prospects of bioseparation and bioassay are discussed. [ABSTRACT FROM AUTHOR]

This study proposed an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. The matching characteristics between aerosol and cloud particle size distributions and gamma distributions were studied using aircraft observation data. The feasibility of the retrieval of the particle effective radius from lidar ratios and backscatter ratios was simulated and studied. A method for inverting the effective radius and number concentration of atmospheric aerosols or small cloud droplets using the backscatter ratio was proposed, and the error sources and applicability of the algorithm were analyzed. This algorithm was suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmospheric particles and has good robustness. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 µm. For cloud droplets, it is 1.0–10 µm. An atmospheric-observation experiment was conducted using the multi-wavelength lidar developed by Xi'an University of Technology, and a thin cloud layer was captured. The microphysical parameters of aerosol and clouds during this process were retrieved. The results clearly demonstrate the growth of the effective radius and number concentration. [ABSTRACT FROM AUTHOR]

This study proposed an inversion method of atmosphere aerosol or cloud microphysical parameters based on dual wavelength lidar data. The matching characteristics between aerosol/cloud particle size distribution and Gamma distribution were studied using aircraft observation data. The feasibility of particle effective radius retrieval from lidar ratio and backscatter ratio was simulated and studied. A method for inverting the effective radius and number concentration of atmospheric aerosols or small cloud droplets using backscatter ratio was proposed, and the error sources and applicability of the algorithm were analyzed. This algorithm was suitable for the inversion of uniformly mixed and single property aerosol layers or small cloud droplets. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmosphere particles and has good robustness. For aerosol particles, the inversion range that this algorithm can achieve was 0.3–1.7 μm. For cloud droplets, it was 1.0–10 μm. An atmosphere observation experiment was conducted using the multi-wavelength lidar developed by Xi'an University of Technology, and a thin cloud formation process was captured. The microphysical parameters of aerosol and cloud during this process were retrieved. The results clearly demonstrate the growth of effective radius and number concentration. [ABSTRACT FROM AUTHOR]

The measurement of cloud microphysical parameters plays an important role in describing characteristics of liquid phase clouds and investigating mutual relationships between clouds and precipitation. In this paper, cloud microphysical parameters at Liupan Mountain Weather Station in Ningxia are measured with a high-resolution coaxial digital holographic imager and a fog monitor 120. There are differences in the measurement results between the two instruments. The number concentration measured by the digital holographic imager is about 1.5 times that of the fog monitor 120. However, their Pearson correlation coefficient is above 0.9. Through analysis, we found that the measurement results of the digital holographic imager and fog monitor 120 are differences in 2–4 µm and 7–50µm. For the droplets with the diameters of 4–7 µm, their measurement results have good consistency. By analyzing the influence of wind field and detection sensitivity on the measurement principle, the reasons which caused the difference are proposed. Advice is given to observe topographic clouds by using the above two instruments. In addition, the differences in liquid water content and visibility are analyzed due to the absence of small and large droplets. The study provides data support for improving the accuracy of instruments in measuring cloud droplets and is useful for research in the field of cloud microphysical processes. [ABSTRACT FROM AUTHOR]

The sky-radiometer/sun-photometer is the most widely used instrument for obtaining aerosol optical depth (AOD) or aerosol optical properties worldwide. Due to the existence of field of view (FOV, 1°), the radiation received by the sky-radiometer includes the forward scattering in addition to direct solar irradiance. This leads to more diffuse light errors of retrieved AODs, especially for shorter wavelength and heavily polluted weather conditions. Using simulation data of three typical aerosol particles (dust, soot, water-soluble), we first verified the accuracy of the Monte Carlo method for calculating the forward scattering effect. Based on the sky-radiometer data collected in Xi'an (2015–2020) where heavy pollution weather is common, the relative errors and correction factors of the AOD were obtained under different conditions, including various short wavelengths (≤400 nm), solar zenith angles (SZAs) and AODs. Our analysis indicates the close dependence of AOD correction factors on wavelength, SZA, AOD and the optical properties of aerosol particles. The mean relative error in Xi'an increases with the decrease of wavelength (~16.1% at 315 nm) and decreases first and then increases with the increase of the SZA. The relative errors caused by forward scattering can exceed 10% when the AOD is greater than 1 and 25% when the AOD is larger than 2 in the ultraviolet (UV) band. The errors with a wavelength greater than 400 nm and an AOD below 1.0 can be within 5%, which can be ignored. The correlation coefficients of AODs before and after a correction from 315 nm to 400 nm are greater than 0.96, which basically increase with the increase of the wavelength. This indicates that the significance of the forward scattering effect in the Xi'an area with heavy pollution cannot be ignored for short wavelengths. However, such effect is negligible at the longer wavelengths and lower AODs (<1.0) of a sky-radiometer. [ABSTRACT FROM AUTHOR]

Aerosol optical parameters detected by multiwavelength lidar can be used for the retrieval of the aerosol particle size distribution and aerosol microphysical properties (AMP). Based on the retrieval for AMPs using standard principal component analysis (PCA) algorithm, the influence of the number of optical parameters on the PCA retrieval results is studied. Four optical parameters (3β + α) or three optical parameters (3β) are selected for retrieval instead of five optical parameters (3β + 2α), respectively. The results show that three optical parameters and four optical parameters can be used for the retrieval of AMPs. For 3β + α, the accuracy of effective radius and volume concentration (VC) are 37% and 56%, respectively. For 3β, the accuracy of effective radius and VC are 37% and 74% respectively. The sensitivity analysis of the PCA algorithm with less than five optical parameters is also discussed, and this method is applied to the retrieval of cloud base particle size distribution. [ABSTRACT FROM AUTHOR]

Lidar at 1064 nm and Ka-band millimetre-wave cloud radar (MMCR) are powerful tools for detecting the height distribution of cloud boundaries and can monitor the entire life cycle of cloud layers. In this study, lidar and MMCR are employed to jointly detect cloud boundaries under different conditions. By enhancing the echo signal of lidar at 1064 nm and combining its signal-to-noise ratio (SNR), the cloud signal can be accurately extracted from the aerosol signals and background noise. The interference signal is eliminated from Doppler spectra of the MMCR by using the noise ratio of the smallest measurable cloud signal (SNR min⁡) and the spectral point continuous threshold (Nts). Moreover, the quality control of the reflectivity factor of MMCR obtained by the inversion is conducted, which improves the detection accuracy of the cloud signal. We analysed three typical cases studies; case one presents two interesting phenomena: (a) at 19:00–20:00 CST (China standard time), the ice crystal particles at the cloud top boundary are too small to be detected by MMCR, but they are well detected by lidar. (b) At 19:00–00:00 CST, the cirrus cloud changes to altostratus where the cloud particles eventually grow into large sizes, producing precipitation. Further, MMCR has more advantages than lidar in detection of the cloud top boundary within this period. Considering the advantages of the two devices, the change characteristics of the cloud boundary in Xi'an from December 2020 to November 2021 were analysed, with MMCR detection data as the main data and lidar data as the assistant data. The seasonal variation characteristics of clouds show that, in most cases, high clouds often occur in summer and autumn, and the low clouds are usually in winter. The normalized cloud cover shows that the maximum and minimum cloud cover occur in summer and winter, respectively. Furthermore, the cloud boundary frequency distribution results for the whole of the observation period show that the cloud bottom boundary below 1.5 km is more than 1 %, the frequency within the height range of 3.06–3.6 km is approximately 0.38 %, and the frequency above 8 km is less than 0.2 %. The cloud top boundary frequency distribution exhibits the characteristics of a bimodal distribution. The first narrow peak lies at approximately 1.0–3.1 km, and the second peak appears at 6.4–9.8 km. [ABSTRACT FROM AUTHOR]

To further exploit atmospheric cloud water resources (CWRs), it is necessary to correctly evaluate the number of CWRs in an area. The CWRs are hydrometeors that have not participated in precipitation formation at the surface and are suspended in the atmosphere to be exploited and maximise possible precipitation in the atmosphere (Zhou et al., 2020). Three items are included in CWRs: the existing hydrometeors at a certain time, the influx of atmospheric hydrometeors along the boundaries of the study area, and the mass of hydrometeors converted from water vapour through condensation or desublimation, defined as condensate. Condensate constitutes the most important part of CWRs. At present, there is a lack of effective observation methods for atmospheric column condensate evaluation, and direct observation data of CWRs are thus insufficient. A detection method for atmospheric column condensate is proposed and presented. The formation of condensate is closely related to atmospheric meteorological parameters (e.g. temperature and vertical airflow velocity). The amount of atmospheric column condensate can be calculated by the saturated water vapour density and the ascending velocity at the cloud base and top. Active and passive remote sensing technologies are applied to detect the mass of atmospheric column condensate. Combining millimetre cloud radar, lidar and microwave radiometers can suitably observe the vertical velocity and temperature at the cloud boundary. The saturated vapour density can be derived from the temperature, and then, water vapour flux and the maximum possible condensate can be deduced. A detailed detection scheme and data calculation method are presented, and the presented method can realise the determination of atmospheric column condensate. A case of cloud layer change before precipitation is considered, and atmospheric column condensate is deduced and obtained. This is the first application, to our knowledge, of observations for atmospheric column condensate evaluation, which is significant for research on the hydrologic cycle and the assessment of CWRs. [ABSTRACT FROM AUTHOR]

A macro-vertical structure is closely related to weather evolution and the energy budget balance of the atmospheric system of the Earth. In this study, radiosonde data were used to identify a cloud vertical structure (CVS) using the adjusted relative humidity threshold method. To evaluate the reliability and stability of this method, the results obtained based on the spatiotemporal matching criteria established in this study were compared with Ka-band millimetre-wave cloud radar (MMCR) observation data. This comparison showed that both devices exhibit high consistency in low-level cloud detection. With the increase in the cloud height, the frequency of the cloud appearance detection by the radiosonde became higher than that by the MMCR. In spring, the results of the CVS detection by the two devices were in good agreement. Specifically, the determination coefficients of the modified degrees of freedom (adjusted R-square) of the cloud base height (CBH) and cloud top height (CTH) detected by the two devices were 0.934 and 0.879, respectively. The horizontal drift of the radiosonde was the smallest in summer, and the adj. R-square values of the CBH and CTH were 0.814 and 0.852, respectively. The CVS observation results by the radiosonde and the MMCR were significantly different in autumn (the adj. R-Square values of the CBH and CTH were 0.715 and 0.629, respectively). In winter, the adj. R-Square values of the CBH and CTH observed by the radiosonde and the MMCR were 0.958 and 0.710, respectively. The statistics and analysis of the results of the distribution characteristics of the CVSs using radiosonde data from 2019 to 2021 from Xi'an showed that the average CTH and CBH were at 7–10 km and 3–5 km, respectively. The frequencies of the cloud absence, rainfall, and two- and three-layer clouds were the highest in the winter (34.36%), autumn (12.99%), and summer, respectively. [ABSTRACT FROM AUTHOR]

The multi-channel lidar has fast acquisition speed, large data volume, high dimension, and vital real-time storage, which makes it challenging to be met using the traditional lidar data storage methods. This paper presents a novel approach to storing the multi-channel lidar data based on the principle of the tree structure, the adjacency linked list, the binary data storage. In the proposed system, a tree structure is constructed by the four-dimensional structure of the multi-channel lidar data, and a data retrieval method of the multi-channel lidar data file is given. The results show that the proposed tree structure approach can save the storage capacity and improve the retrieval speed, which can meet the needs for efficient storage and retrieval of multi-channel lidar data, and improve the data storage utilization and the practicality of multi-channel lidar system. [ABSTRACT FROM AUTHOR]

Cloud droplets size distribution (DSD) is one of the significant characteristics for liquid clouds. It plays an important role for the aerosol–droplet–cloud mechanism and variation in cloud microphysics. However, the minuscule sampling space is insufficient for the observation of whole DSD when using high-magnification optical systems. In this paper, we propose an observation method for cloud droplets ranging from 2 to 16 μm, by which the balance relationship between sampling space and optical magnification is realized. The method combines an in-line digital holographic interferometer (DHI) with the optical magnification of 5.89× and spatial stitching technique. The minimum size in DSD is extended to 2 μm, which improves the integrity of size distribution. Simultaneously, the stability of DSD is enhanced by increasing the tenfold sampling volume of cloud droplets. The comparative experiment between the in-line DHI and fog monitor demonstrates that the DSD obtained by this method is reliable, which can be used for the analysis of microphysical parameters. In the Beijing Aerosol and Cloud Interaction Chamber (BACIC), the observation results show that the size of cloud droplets follows the Gamma distribution, which is consistent with the theoretical DSD. The results of cloud microphysical parameters indicate that each pair of parameters has a positive correlation, and then the validity of observation method is confirmed. Additionally, the high-concentration aerosol condition significantly mitigates the effect of random turbulence and enhances the robustness of the microphysical parameter data. [ABSTRACT FROM AUTHOR]

Quenching from sintering temperature enhances the depolarization temperature (Td) in Na1/2Bi1/2TiO3-based ceramics without significant deterioration of piezoelectric properties (d33). In this work, quenching effects in an ergodic relaxor 0.97(0.94Na0.5Bi0.5TiO3–0.06BaTiO3)–0.03AgNbO3 (NBT–6BT–3AN) were investigated based on structure, ferroelectric, and dielectric properties. The ergodicity to nonergodicity transition was obtained by quenching NBT–6BT–3AN above 1000 °C. The temperature stability of the quenching-induced nonergodicity was examined by annealing the quenched sample at 300 °C and 600 °C. The effect of oxygen vacancy on ergodicity to nonergodicity transition was investigated by comparing ferroelectric and electrostrain responses of the quenched and nitrogen-atmosphere-annealed samples. The influence of quenching on the structure including the average crystal structure, phase fraction and lattice distortion and the local structure including bond lengths and ordering of ions was analyzed. The ergodicity to nonergodicity transition upon quenching is ascribed to the contribution of the off-centered Bi3+ ions and ordered local structure. [ABSTRACT FROM AUTHOR]

Online identification and characterization of suspended aerosols can provide a scientific basis for understanding aerosol transformations, quantitatively evaluating the impacts on air quality, public health, and the source apportionment of different atmospheric particulate matters. In this study, we confirm the validity of our developed high-throughput multi-angle polarized scattering vector detection of aerosols and multidimensional polarization scattering index systems. By observation of the mean values, variance, and Wilk's Lambda of multidimensional polarization indices for different aerosol types, the polarization index shows unique characterization abilities for aerosol properties, and the optimal combination of polarization indices can always be found for a specific aerosol category with a high resolution and discrimination. Clearly, the multidimensional polarization indices of individual aerosols are more suitable for online and real-time aerosol identification and even help to explain the in situ microphysical characteristics of aerosols or evaluate the dynamic evolution of aerosols. [ABSTRACT FROM AUTHOR]

Poisson distributions have the characteristic of equality between their variance and mean values. By constructing a calculation model of the temporal variance and spatial variance, the relationship between the variance and mean values of lidar analog data and photon-counting data can be analyzed. The calculation results show that the photon-counting data from far field have the distribution property of equality between the variances and the corresponding mean values, while the analog data for the whole probing traces do not. In this paper, by analyzing the distribution properties of the spatial variance and temporal variance of lidar data, the dead time of photon-counting data was estimated, and the threshold voltage of the photon-counting system and the linear working range of photomultiplier tube were evaluated. The results show that the linear working range of the high voltage for the photomultiplier tube in the ultraviolet elastic scanning lidar is between −500 V and −1000 V, and the dead time and threshold voltage of the photon-counting system in the Licel transient recorder are 3.488 ns and 1.20 mV, respectively. Meanwhile, a novel gluing method between analog data and photon-counting data is presented, based on the calculation results of the variance distribution of lidar data. The linear transfer coefficients were determined by minimizing the differences between the variance and mean of the transformed photon-counting data in the near filed with high signal to noise ratio. The glued data were distributed to express the atmospheric conditions uniformly. [ABSTRACT FROM AUTHOR]

To realize the improvement of signal-to-noise ratio and rejection rate for elastic Mie-Rayleigh signals, a set of dichroic mirrors and narrow-band interference filters with high efficiency was proposed to constitute a new spectroscopy for atmospheric water vapor, aerosol, and cloud studies. Based on the curves of signal-tonoise ratio at three different channels, the actual rejection rates of elastic Mie-Rayleigh signals at the Raman channels were found to be higher than eight orders of magnitude with the cloudy conditions. Continuous nighttime observations showed that the statistical error of the water vapor mixing ratio was <10% at a height of 2.3 km with an aerosol backscatter ratio of 17. Temporal variations of water vapor and aerosols were obtained under the conditions of cloud and cloud-free, the change relevance between aerosol and water vapor was analyzed, and the growth characteristics of water vapor and aerosols showed a good agreement within the cloud layers. Obtained results indicate achievement of the continuous detection of water vapor, aerosol, and cloud with a high efficiency and stability by Raman lidar. [ABSTRACT FROM AUTHOR]

Owing to the second-order fiber Bragg grating possessing narrower reflectivity bandwidth than first-order, an all-fiber spectroscopic filter with several second-order fiber Bragg gratings and optical fiber couplers in the visible region is designed to extract interest rotational Raman spectra. Considering the inference fringe visibility of inscribing fiber Bragg grating and the fiber core index dispersion effect, numerical simulations are made by the improved mathematical model based on an exponential photorefractive dynamic, and its simulative results show better agreement with the experimental data. We propose an all-fiber spectroscopic configuration, which could balance lidar sensitivity against signal-to-noise ratio for optimizing remote sensing performance by fiber Bragg gratings serially. Simulative results show that the thumb principle for fabricating second-order fiber Bragg grating is the increasing gratings length, and that this all-fiber spectroscope can achieve >70 dB suppression to elastic scattering and efficiently extract the rotational Raman signal for profiling atmospheric temperature. [ABSTRACT FROM AUTHOR]

The use of Raman and high-spectral lidars enables measurements of a stratospheric aerosol extinction profile independent of backscatter, and a multi-wavelength (MW) lidar can obtain additional information that can aid in retrieving the microphysical characteristics of the sampled aerosol. The inversion method for retrieving aerosol particle size distributions and microphysical particle parameters from MW lidar data was studied. An inversion algorithm for retrieving aerosol particle size distributions based on the regularization method was established. Based on the inversion of regularization, the inversion method was optimized by choosing the base function closest to the aerosol distribution. The logarithmic normal distribution function was selected over the triangle function as the base function for the inversion. The averaging procedure was carried out for three main types of aerosol. The 1% averaging result near the minimum of the discrepancy gave the best estimate of the particle parameters. The accuracy and stabilization of the optimized algorithm for microphysical parameters were tested by scores of aerosol size distributions. The systematic effects and random errors impacting the inversion were also considered, and the algorithm was tested by the data, showing 10% systematic error and 15% random error. At the same time, the reliability of the proposed algorithm was also verified by using the aerosol particle size distribution data of the aircraft. The inversion results showed that the algorithm was reliable in retrieving the aerosol particle size distributions at vertical heights using lidar data. [ABSTRACT FROM AUTHOR]

To evaluate the potential of Raman lidar observations for measuring precipitable water vapor (PWV), PWV variations and distribution characteristics were investigated in Xi’an (34.233°N, 108.911°E), and its comparisons with meteorological parameters were also analysed. Comparisons of lidar PWV with radiosonde PWV verified the ability and accuracy of using Raman lidars for PWV measurements. The diurnal and monthly variation trends in PWV in different layers are first discussed via the statistical analysis of lidar data from November 2013 to July 2016; different proportions of PWV were found in different layers, and the PWV in each layer presented a slight diurnal change trend and consistent seasonal variation, which was relatively rich in summer, less so in spring and autumn, and relatively deficient in winter. Furthermore, correlation analyses between lidar PWV and meteorological parameters are explored. Water vapor pressure and surface temperature revealed the same inter-seasonal oscillation of PWV, with a correlation coefficient of ~0.90. However, incomplete synchronization was found between PWV and relative humidity and precipitation parameters. Higher humidity appeared in the late summer and the beginning of autumn of each year, which was also the case for precipitation and precipitation efficiency. In addition, atmospheric water vapor density profiles and the obtained PWV by Raman lidar are discussed employing a rainfall case, and a comprehensive analysis with meteorological parameters is undertaken. The intensifying characteristics of vertical change in water vapor and the accumulation of PWV in the lower troposphere can be captured by lidar before the onset of rainfall. In contrast to the obvious diurnal change trend, such meteorological parameters as relative humidity, water vapor pressure, and dew-point temperature difference are accompanied with stable trends with a change rate of close to 0 in the rainfall processes; they also show high correlated variations with lidar PWV. Thus, with the advantage of lidar detection, investigation of water vapor profiles and PWV by Raman lidar, and the comprehensive correlation analyses with synchronic meteorological parameters can prove to be good indications of rainfall. [ABSTRACT FROM AUTHOR]

All-day atmospheric water vapor content measurements determined by Raman lidar and a sunphotometer were combined to investigate the all-day variation characteristics in the water vapor distribution in Xi’an, China (34.233°N, 108.911°E). To enhance the daytime lidar performance, the wavelet threshold de-noising method is used to filter out the strong solar background light, and effective denoised results are demonstrated with the following optimization: wavelet sym6, the improved threshold function, and the improved threshold selection. The denoised system signal-to-noise ratio (SNR) for the water vapor daytime measurement is validated, with an enhancement of ~3.4 times up to a height of 3 km compared to that of the original signal. The time series of the atmospheric water vapor mixing ratio profiles and the obtained precipitable water vapor (PWV) measured by Raman lidar are used to reveal the temporal and spatial variations in water vapor, and the comparisons with the total column water vapor content (TCWV) measured by a sunphotometer validate the daytime variation trend of the water vapor. All-day continuous observations clearly present a consistent variation trend in the water vapor between the sunphotometer and Raman lidar measurements. The correlation analysis between TCWV and PWV at the layers below 850 hPa and below 700 hPa yields a good positive correlation coefficient (>0.75), indicating that PWV determination in the bottom layer by Raman lidar can directly reflect the variations in the total water vapor content. Moreover, different diurnal variation trends in water vapor are also observed, that is, a downward trend from the afternoon to the night, or a tendency of being high in the morning and afternoon and low at noon, demonstrating the high temporal-spatial variation characteristics of water vapor and close correlation with weather changes. The results reflected and validated that the diurnal variation in water vapor is complicated and can be an indicator of the weather to a certain extent. [ABSTRACT FROM AUTHOR]

A UV vibrational Raman lidar has been built and used to make quantitative measurements of water vapor and aerosol optical properties over Xi'an, China. Vertical profiles of the water vapor mixing ratio and aerosol extinction coefficient are retrieved. The water vapor mixing ratio is calibrated with radiosonde data. The diurnal variations of the water vapor mixing ratio, aerosol extinction coefficient, and aerosol optical depth are obtained. The results obtained in the form of a time-height indicator (THI) display clearly showed the relationship between water vapor and aerosols, in which the gradual enhancement of water vapor density results in aerosol accumulation in the early morning, in particular in the lower troposphere. The seasonal variations of the water vapor mixing ratio and aerosol optical depth over Xi'an were observed and analyzed using the average monthly distribution obtained by lidar for the first time, which will provide useful scientific data and real-time monitoring methods for studying local climate change. [ABSTRACT FROM AUTHOR]