Search

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 (SNRmin⁡) 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.

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.

Na0.5Bi0.5TiO3-based solid solutions are one of the most promising lead-free piezoelectric candidates since they can be easily tailored to exhibit large electrostrain. However, the large hysteresis and temperature-dependence of the electrostrain response are longstanding obstacles for their practical applications. In the present study, 0–3 type composites were developed with 0.97(0.94Na0.5Bi0.5TiO3-0.06BaTiO3)-0.03AgNbO3 (NBT-6BT-3AN) matrix phase and ZnO inclusions. The optimum addition of 5 wt.% ZnO in the composites leads to reduction in hysteresis of electrostrain by 35% in comparison with pure NBT-6BT-3AN at room temperature. Meanwhile, electrostrain of the composites maintains superior temperature stability, with a variance of less than ±10% in the temperature range between 25 and 125 °C. The reason for the improvement of electrostrain is proposed to be attributed to the ergodic/nonergodic mixture phases induced by residual stress between the inclusion and matrix, as well as the phase evolution caused by the incorporation of Zn2+ into the matrix. Therefore, this work provides a new strategy to improve the electromechanical properties of Na0.5Bi0.5TiO3-based ceramics.

A visible, near-infrared (VIS-NIR) dual-polarization lidar technique employing laser diodes and polarization cameras has been designed and implemented for all-day unattended field measurements of atmospheric aerosols. The linear volume depolarization ratios (LVDR) and the offset angles can be retrieved from four-directional polarized backscattering signals at wavelengths of 458 nm and 808 nm without additional optical components and sophisticated system adjustments. Evaluations on the polarization crosstalk of the polarization camera and the offset angle have been performed in detail. A rotating linear polarizer (RLP) method based on the Stokes-Mueller formalism has been proposed and demonstrated for measuring extinction ratios of the polarization camera, which can be used to eliminate the polarization crosstalk between different polarization signals. The offset angles can be online measured with a precision of 0.1°, leading to negligible measurement errors on the LVDR. One-month statistical analysis revealed a small temporal variation of the offset angles, namely -0.13°±0.07° at 458 nm and 0.33°±0.09° at 808 nm, indicating good system stability for long-term measurement. Atmospheric measurements have been carried out to verify the system performance and investigate aerosol optical properties. The spectral characteristics of the aerosol extinction coefficient, the color ratio, the linear particle polarization ratio (LPDR) and the ratio of LPDR were retrieved and evaluated based on one-month continuous atmospheric measurements, from which different types of aerosols can be classified. The promising results showed great potential of employing the VIS-NIR dual-polarization lidar in characterizing aerosol optical properties, discriminating aerosol types and analyzing long-range aerosol transportation.

The velocity of cloud droplets has a significant effect on the investigation of the turbulence-cloud microphysics interaction mechanism. The paper proposes an in-line digital holographic interferometry (DHI) technique based on depth expansion and self-fusion algorithm to simultaneously extract particle velocity from eight holograms. In comparison to the two-frame exposure method, the extraction efficiency of velocity is raised by threefold, and the number of reference particles used for particle registration is increased to eight. The experimental results obtained in the cloud chamber show that the velocity of cloud droplets increases fourfold from the stabilization phase to the dissipation phase. The measurement deviations of two phases are 1.138 and 1.153 mm/s, respectively. Additionally, this method provides a rapid solution for three-dimensional particle velocimetry investigation of turbulent field stacking and cloud droplets collisions.

Lidar and Ka-band Millimeter-Wave Cloud Radar (MMCR) are powerful equipment to detect the height distribution of cloud boundaries, which can monitor the whole life cycle of cloud layers. In this paper, we employ lidar and MMCR to jointly detect cloud boundaries under different conditions (e.g., single-layer clouds, multilayer clouds, and precipitating clouds). By enhancing the echo signal of lidar @1064 nm and combining its SNR, he cloud signal can be accurately extracted from the aerosol signals and background noise. The interference signal is eliminated from the power spectrum of the MMCR by using SNRmin and the spectral point continuous threshold, and the quality control of the meteorological signal (echo reflectivity factor) obtained by the inversion is carried out, which improves the detection accuracy of the cloud signal. Based on the advantages and disadvantages of the two devices in detecting cloud boundaries under different conditions, cloud boundary statistical rules are established to analyze the characteristics of cloud boundary changes in Xi'an in 2021. The seasonal variation characteristics of clouds show that the frequency distribution of cloud boundaries in vertical height in spring and summer has a similar variation trend. The normalized cloud amount is the lowest in spring (0.65) and the highest in summer (2.46). The frequency distribution of high-level clouds (at 11~12 km) is the highest in autumn, and the clouds in winter are mainly distributed below 8 km. Furthermore, the cloud boundary frequency distribution results for the whole year of 2021 show that the cloud bottom boundary below 1.5 km is more than 10 %, the frequency within the height range of 3.06 km~3.6 km is approximately 3.24 %, and the frequency above 8 km is less than 2 %. The cloud top boundary frequency distribution has the characteristics of a bimodal distribution. The first narrow peak lies at approximately 1.5~3.1 km, and the second peak appears at 7.5~10.5 km.

Atmospheric scattered radiance is an important factor affecting slant visibility measurement in the daytime. This paper explores atmospheric scattered radiance errors and their influence on slant visibility measurements. Considering the difficulty in error synthesis of the radiative transfer equation, an error simulation scheme based on the Monte Carlo method is proposed. An error simulation and error analysis for atmospheric scattered radiance was carried out based on the Santa Barbara DISTORT atmospheric radiative transfer (SBDART) model and the Monte Carlo method. The error in aerosol parameters including the single-scattering albedo (SSA), the asymmetry factor, and the aerosol optical depth (AOD), was simulated by a random number and random error under different normal distributions, and the error influence of aerosol parameters on the error in the solar irradiance and 33-layer atmosphere scattered radiance is discussed in detail. The maximum relative deviations of the output scattered radiance at a certain slant direction are 5.98%, 1.47%, and 2.35%, when SSA, the asymmetry factor, and the AOD obey the normal distribution of (0, 5). The error sensitivity analysis also confirms that the SSA is the most sensitive factor affecting atmospheric scattered radiance and the total solar irradiance. Then, according to the error synthesis theory, we investigated the error transfer effect of three error sources related to the atmosphere based on the contrast ratio between the object and the background. The simulation results show that the error in the contrast ratio caused by solar irradiance and scattered radiance is lower than 6.2% and 2.84%, indicating the main role in contributing to the error transfer of slant visibility. Further, the comprehensive process of the error transfer in slant visibility measurements was demonstrated by a set of lidar experiments and the SBDART model. The results provide a reliable theoretical basis for the measurement of atmospheric scattered radiance and slant visibility, which is of great significance to improve the measurement accuracy of slant visibility.

A portable polarization lidar system based on the division-of-focal-plane scheme has been proposed for all-day accurate retrieval of the atmospheric depolarization ratio. The polarization lidar system has been designed as a T-shaped architecture consisting of a closed transmitter and a detachable large focal receiver, which is capable of outdoor unmanned measurements. The lidar system features low cost, low maintenance and short blind range (∼100 m) by utilizing a 450 nm multimode laser diode as the light source and a polarization image sensor with four polarized channels as the detector. Validation measurements have been carried out on a near horizontal path in ten consecutive days. The linear volume depolarization ratio (LVDR) as well as its measurement uncertainty has been theoretically and experimentally evaluated without employing additional optical components and sophisticated online calibrations. The offset angle can also be accurately retrieved (i.e., −0.06°) from the four-directional polarized lidar profiles with a standard deviation of ±0.02° during the whole measurement period, which contributes negligible influence on the retrieval of the LVDR. It has been found out that the uncertainty of the LVDR was mainly originated from the random noise, which was below 0.004 at nighttime and may reach up to 0.008 during daytime owing to the increasing sunlight background. The performance of the polarization lidar system has been further examined through atmospheric vertical measurements. The low-cost low-maintenance portable polarization lidar system, capable of detecting four-directional polarized lidar signals simultaneously, opens up many possibilities for all-day field measurements of dust, cloud, urban aerosol, oriented particles, etc.

Pure rotational Raman lidar is often used for atmospheric temperature profile measurements. However, high elastic scattering suppression ratios (>107) are required for temperature measurement in clouds and haze, which imposes stringent requirements on spectral separation techniques. To solve this problem, a lidar measurement technique based on vibrational and rotational Raman spectra is proposed. Using nitrogen vibrational and rotational Raman scattering to obtain temperature profiles under strong elastic scattering, combined with the dual-rotational Raman temperature measurements under weak elastic scattering, a vertical distribution of atmospheric temperature including cloud and haze layers, can be obtained. The feasibility of the method was verified by numerical simulation. The Raman lidar for temperature measurements was established in Xi'an University of Technology, and the obtained temperature results show good agreement with the radiosonde measurements. The proposed method combines the high sensitivity of the dual-rotational Raman method and the high Mie-scattering suppression of the vibrational Raman method, thus further improving the adaptability of Raman lidar to cloudy and hazy air conditions and supporting atmospheric and cloud physics research.

Understanding and characterization of the planetary boundary layer (PBL) are of great importance in terms of air pollution management, weather forecasting, modelling of climate change, etc. Although many lidar-based approaches have been proposed for the retrieval of the PBL height (PBLH) in case studies, development of a robust lidar-based algorithm without human intervention is still of great challenging. In this work, we have demonstrated a novel deep-learning method based on the wavelet covariance transform (WCT) for the PBLH evaluation from atmospheric lidar measurements. Lidar profiles are evaluated according to the WCT with a series of dilation values from 200 m to 505 m to generate 2-dimensional wavelet images. A large number of wavelet images and the corresponding PBLH-labelled images are created as the training set for a convolutional neural network (CNN), which is implemented based on a modified VGG16 (VGG – Visual Geometry Group) convolutional neural network. Wavelet images obtained from lidar profiles have also been prepared as the test set to investigate the performance of the CNN. The PBLH is finally retrieved by evaluating the predicted PBLH-labelled image and the wavelet coefficients. Comparison studies with radiosonde data and the Micro-Pulse-Lidar Network (MPLNET) PBLH product have successfully validated the promising performance of the deep-learning method for the PBLH retrieval in practical atmospheric sensing.

Measuring and predicting atmospheric visibility is important scientific research that has practical significance for urban air pollution control and public transport safety. We propose a deep learning model that uses principal component analysis and a deep belief network (DBN) to effectively predict atmospheric visibility in short- and long-term sequences. First, using a visibility meter, particle spectrometer, and ground meteorological station data from 2016 to 2019, the principal component analysis method was adopted to determine the influence of atmospheric meteorological and environmental parameters on atmospheric visibility, and an input dataset applicable to atmospheric visibility prediction was constructed. On the basis of deep belief network theory, network structure parameters, including data preprocessing, the number of hidden layers, the number of nodes, and activation and weight functions, are simulated and analyzed. A deep belief network model suitable for atmospheric visibility prediction is established, where a double hidden layer is adopted with the node numbers 70 and 50, and the Z-score method is used for normalization processing with the tanh activation function and Adam optimizer. The average accuracy of atmospheric visibility prediction by the deep belief network reached 0.84, and the coefficient of determination reached 0.96; these results are significantly superior to those of the back propagation (BP) neural network and convolutional neural network (CNN), thus verifying the feasibility and effectiveness of the established deep belief network for predicting atmospheric visibility. Finally, a deep belief network model based on time series is used to predict the short- and long-term trends of atmospheric visibility. The results show that the model has good visibility prediction results within 3 days and has an accuracy rate of 0.79. Covering the visibility change evaluations of different weather conditions, the model demonstrates good practicability. The established deep learning network model provides an effective and feasible technical solution for the prediction of atmospheric meteorology and environmental parameters, which enjoys a wide range of application prospects in highway transportation, navigation, sea and air, meteorology, and environmental research.

To satisfy the demand for active remote sensing of ocean salinity, this paper proposes a Raman spectra, salinity, and temperature model for seawater. Seawater is a solution containing a composite salt solute, changes in the solute, temperature, and salinity of seawater can affect the intensity of Raman spectra. It is difficult to directly analyze the influence of various factors on the Raman spectra of seawater. Therefore, the Raman spectra of solutions containing a single solute and mixed solutions were detected, and the effect of solutions containing different solutes on the spectra was analyzed. The experimental results revealed the variation in the low- and high-frequency spectral intensities of the Raman spectra with salinity and temperature. The Raman spectra of seawater were modeled as a function of temperature and salinity using the low- and high-frequency area ratios, and the spectra of seawater at different salinities were obtained; the model calculation results are consistent with the experimental results within the entire range of seawater temperature and salinity. Because the Raman spectra were a function of temperature and salinity. To achieve high precision remote sensing of ocean salinity, it is necessary to use Brillouin scattering for remote sensing of ocean temperature.

In order to achieve a high signal-to-noise ratio by using small laser energy and telescope aperture, we present a detection method based on Rayleigh-Brillouin scattering (RBS) for the measurement of atmospheric temperature without response functions and calibration procedures by using high spectral resolution lidar (HSRL). Different from the traditional HSRL, a Fabry-Pérot interferometer (FPI) with a continuous tunable cavity and polarization optical scheme are employed in a high spectral resolution filter. In order to continuously change the resonant frequency of the FPI, an electro-optical crystal of potassium dideuterium phosphate (DKDP) with two ring electrodes is used as a continuous tunable cavity in the FPI. At each scanned frequency point corresponded with the resonant frequency of the FPI, the received signals of four discrete points on RBS are obtained. Atmospheric temperature is inverted by using a RBS model. The polarization optical scheme is used to suppress the solar background light, and improve the utilization of return signals. In detection experiment of atmospheric temperature, the detection height is 2 km at night and 1.5 km during the day by using a pulsed energy of 30 mJ and telescope diameter of 250 mm. The results are in good agreement with the data detected by radiosonde.