Atmospheric Lidar Data Storage Model Based on Ontology

Ontology is an effective method to solve the problem of heterogeneous data in lidar measurements. Due to complexity and diversity of data structure, traditional method of ontology storage cannot be directly applied to lidar data. In this work, we proposed a novel ontology storage model based on the object-oriented data model, in which the mapping mechanism was established from ontology of lidar data to the object-oriented data model. A new storage model of lidar data is then obtained by a combination of the characteristics of lidar data and the syntax of OWL DL. Compared to the traditional method of ontology storage, we believe that the new storage model can better serve the sharing of lidar data

Observation on the Droplet Ranging from 2 to 16 μm in Cloud Droplet Size Distribution Based on Digital Holography

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

Observation on the Droplet Ranging from 2 to 16 μm in Cloud Droplet Size Distribution Based on Digital Holography

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

Investigation of negative permeability metamaterials for wireless power transfer

In order to enhance the transmission efficiency of wireless power transfer (WPT), a negative permeability metamaterials (NPM) with a structure of honeycomb composed by units of hexagon-shaped spirals copper is proposed in this paper. The unit parameters of the NPM are optimized, to make sure the negative permeability at the special frequency. The S-parameters of the designed NPM are measured by a network analyzer and the permeability is extracted, it shows the honeycomb NPM has a negative permeability at 6.43 MHz. A two-coil WPT is setup and the transmission efficiency of WPT embedded with NPM at the different position and with different structure are investigated. The measured results show that the 2-slab honeycomb NPM have a good perform compared with the 1-slab NPM, and the efficiency can be increased up to 51%. The results show that honeycomb NPM embedded in the WPT help to improve the transmission efficiency remarkable

Aerosol Microphysical Particle Parameter Inversion and Error Analysis Based on Remote Sensing Data

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

Inverse Identification of Virtual Material Parameters Using Surface Response Methodology

The virtual material model is now widely applied for modeling the dynamical performance of assembled structures since it can effectively represent the complicated contact behavior of joint interfaces despite being relatively simple to create. In this study, a virtual material model is adopted for modeling the dominant physics of a bolted joint subject to a set of pretightening conditions. The unknown virtual material parameters are acquired by an inverse identification procedure that uses the surface response methodology. The greatest advantage of this approach is the ease with which it acquires the joint parameters without taking apart a built-up structure to do special measurements on each separated component. Intricate theoretical calculations can also be avoided when this method is used. This study addresses the responses of virtual material parameters under different pretightening considerations. Predictions based on the identified virtual material parameters are compared with the corresponding results obtained using the analytical method. The correlation between the two sets of results at all preload levels is promising, which indicates the successful identification of the virtual material parameters

Properties of tropospheric aerosols observed over southwest Slovenia

From August to October 2010 lidar measurements of aerosols in the troposphere were performed at Otlica observatory, Slovenia, using a vertical scanning elastic lidar. The lidar data sample, which contains 38 nighttime vertical profiles of the mean aerosol extinction, was combined with continuous ozone concen- tration (O3), particulate matter concentrations (PM) and daily radiosonde data. The obtained radiosonde- and lidar-derived heights of the atmospheric boundary layer (ABL), which varied considerably from day to day, were found to be in good agreement. The mean values of the aerosol optical depth (AOD) at 355 nm, were calculated separately for the ABL and for the free troposphere (FT). A ten-fold increase of the FT AOD was observed during the days with predicted presence of Saharan dust above the lidar site. To correlate AOD values with the type and origin of aerosols, backward trajectories of air-masses above Otlica were modeled using the HYSPLIT model and clustered. High ABL AOD values were found to be correlated with local circulations and slowly approaching air masses from the Balkans and low values with northwestern flows. The highest values correlated with southwestern flows originating in northern Africa

Confidence and Error Analyses of the Radiosonde and Ka-Wavelength Cloud Radar for Detecting the Cloud Vertical Structure

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