The Spatiotemporal Distribution Characteristics of Cloud Types and Phases in the Arctic Based on CloudSat and CALIPSO Cloud Classification Products

The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types(high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases(ice,mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows:(1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents.(2)The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer;altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer;stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn.(3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly.(4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas.(5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated.(6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.

Calculating the Excitation Temperature for H_2CO Absorption Lines in Molecular Clouds

The excitation temperature T_(ex)for molecular emission and absorption lines is an essential parameter for interpreting the molecular environment.This temperature can be obtained by observing multiple molecular transitions or hyperfine structures of a single transition,but it remains unknown for a single transition without hyperfine structure lines.Earlier H_(2)CO absorption experiments for a single transition without hyperfine structures adopted a constant value of T_(ex),which is not correct for molecular regions with active star formation and H II regions.For H_(2)CO,two equations with two unknowns may be used to determine the excitation temperature T_(ex)and the optical depthτ,if other parameters can be determined from measurements.Published observational data of the4.83 GHz(λ=6 cm)H_(2)CO(1_(10)-1_(11))absorption line for three star formation regions,W40,M17 and DR17,have been used to verify this method.The distributions of T_(ex)in these sources are in good agreement with the contours of the H110αemission of the H II regions in M17 and DR17 and with the H_(2)CO(1_(10)-1_(11))absorption in W40.The distributions of T_(ex)in the three sources indicate that there can be significant variation in the excitation temperature across star formation and H II regions and that the use of a fixed(low)value results in misinterpretation.

A LiDAR Point Clouds Dataset of Ships in a Maritime Environment

For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore,we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.

Optical Extinctions of Inter-Arm Molecular Clouds in M31:A Pilot Study for the Upcoming CSST Observations

Recent submillimeter dust thermal emission observations have unveiled a significant number of inter-arm massive molecular clouds in M31.However,the effectiveness of this technique is limited to its sensitivity,making it challenging to study more distant galaxies.This study introduces an alternative approach,utilizing optical extinctions derived from space-based telescopes,with a focus on the forthcoming China Space Station Telescope(CSST).We first demonstrate the capability of this method by constructing dust extinction maps for 17 inter-arm massive molecular clouds in M31 using the Panchromatic Hubble Andromeda Treasury data.Our analysis reveals that inter-arm massive molecular clouds with an optical extinction(A_(V)) greater than 1.6 mag exhibit a notable A_(V) excess,facilitating their identification.The majority of these inter-arm massive molecular clouds show an A_(V) around 1 mag,aligning with measurements from our JCMT data.Further validation using a mock CSST RGB star catalog confirms the method's effectiveness.We show that the derived A_(V)values using CSST z and y photometries align more closely with the input values.Molecular clouds with A_(V)> 1.6 mag can also be identified using the CSST mock data.We thus claim that future CSST observation clouds provide an effective way for the detection of inter-arm massive molecular clouds with significant optical extinction in nearby galaxies.

What is the Role of Gravity,Turbulence and Magnetic Fields in High-mass Star Formation Clouds?

To explore the potential role of gravity,turbulence and magnetic fields in high-mass star formation in molecular clouds,this study revisits the velocity dispersion–size(σ–L)and density–size(ρ–L)scalings and the associated turbulent energy spectrum using an extensive data sample.The sample includes various hierarchical density structures in high-mass star formation clouds,across scales of 0.01–100 pc.We observeσ∝L^(0.26)andρ∝L^(-1.54)scalings,converging toward a virial equilibrium state.A nearly flat virial parameter–mass(α_(vir)-M)distribution is seen across all density scales,withα_(vir)values centered around unity,suggesting a global equilibrium maintained by the interplay between gravity and turbulence across multiple scales.Our turbulent energy spectrum(E(k))analysis,based on theσ–L andρ–L scalings,yields a characteristic E(k)∝k^(-1.52).These findings indicate the potential significance of gravity,turbulence,and possibly magnetic fields in regulating dynamics of molecular clouds and high-mass star formation therein.

On Electron Clouds and Light

The wave equation of the electron, recently improved, allows physics to obtain all the quantum numbers and other results explaining the hydrogen spectrum. The Pauli exclusion principle then gives the description of electron clouds used in chemistry. The relativistic wave equation is associated with a Lagrangian density, thus also with an energy-momentum tensorial density. The wave of an electron cloud adds these energy-momentum densities, while photons in light are precisely those differences between such energy-momentum densities.

Building Facade Point Clouds Segmentation Based on Optimal Dual-Scale Feature Descriptors

To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.

Large Eddy Simulation of Vertical Structure and Size Density of Deep Layer Clouds

In a convective scheme featuring a discretized cloud size density, the assumed lateral mixing rate is inversely proportional to the exponential coefficient of plume size. This follows a typical assumption of-1, but it has unveiled inherent uncertainties, especially for deep layer clouds. Addressing this knowledge gap, we conducted comprehensive large eddy simulations and comparative analyses focused on terrestrial regions. Our investigation revealed that cloud formation adheres to the tenets of Bernoulli trials, illustrating power-law scaling that remains consistent regardless of the inherent deep layer cloud attributes existing between cloud size and the number of clouds. This scaling paradigm encompasses liquid, ice, and mixed phases in deep layer clouds. The exponent characterizing the interplay between cloud scale and number in the deep layer cloud, specifically for liquid, ice, or mixed-phase clouds, resembles that of shallow convection,but converges closely to zero. This convergence signifies a propensity for diminished cloud numbers and sizes within deep layer clouds. Notably, the infusion of abundant moisture and the release of latent heat by condensation within the lower atmospheric strata make substantial contributions. However, this role in ice phase formation is limited. The emergence of liquid and ice phases in deep layer clouds is facilitated by the latent heat and influenced by the wind shear inherent in the middle levels. These interrelationships hold potential applications in formulating parameterizations and post-processing model outcomes.

Estimating wood quality attributes from dense airborne LiDAR point clouds

Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories.We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees.Unlike object reconstruction methods,our approach is based on simple metrics computed on vertical slices that summarize information on point distances,angles,and geometric attributes of the space between and around the points.Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans.We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios.Our approach provides a simple,clear,and scalable solution that can be adapted to different situations both for research and more operational mapping.

Distribution Characteristics and Shielding Effects of Ion Clouds

The latest research shows that the ions generated by the corona discharge of lightning rod have dual functions of attracting and shielding lightning discharge. After the lightning rod is installed at a certain height on the ground,the lightning rod tip reaches the corona threshold to ionize the surrounding air and generate positive and negative ions under the action of the electric field at the end of the lightning downward leader. Constrained by Coulomb’s Law,its positive ions( opposite charges attract each other) form an upward leader( streamer),which moves towards the end of the lightning downward leader and is connected to the downward leader,establishing a discharge channel to attract lightning to the needle tip and discharge the current to the ground,and playing a role in attracting lightning. Its negative ions are repelled by the electric field at the end of the lightning downward leader( repelled by isotropic charges) and influenced by the wind,and diffuse in the downwind area to form an ion cloud,inhibiting the growth of corona at the tip of ground objects,and playing a role in shielding lightning. In this paper,Franklin’s understanding of the role of lightning rod and Yang Shaojie’s new definition of the working principle of lightning rod are briefly introduced. The formation mechanism,distribution characteristics,shielding effect,and impact on lightning strike point distribution of ion clouds are analyzed. Additionally,the important role of shielding effects of ion clouds in regional lightning protection is introduced,which provides a theoretical basis for the correct understanding and use of lightning rod.

In Search for Infall Gas in Molecular Clouds:A Catalogue of CO Blue-Profiles

We have started a systematic survey of molecular clumps with infall motions to study the very early phase of star formation.Our first step is to utilize the data products by MWISP to make an unbiased survey for blue asymmetric line profiles of CO isotopical molecules.Within a total area of~2400 square degrees nearby the Galactic plane,we have found 3533 candidates showing blue-profiles,in which 3329 are selected from the^(12)CO&^(13)CO pair and 204 are from the^(13)CO&C^(18)O pair.Exploration of the parametric spaces suggests our samples are in the cold phase with relatively high column densities ready for star formation.Analysis of the spatial distribution of our samples suggests that they exist virtually in all major components of the galaxy.The vertical distribution suggest that the sources are located mainly in the thick disk of~85 pc,but still a small part are located far beyond Galactic midplane.Our follow-up observation indicates that these candidates are a good sample to start a search for infall motions,and to study the condition of very early phase of star formation.

Building 3D CityGML models of mining industrial structures using integrated UAV and TLS point clouds

Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world.3D models of the real world have been increasingly popular with numerous applications,such as digital twins and smart factory management.In this study,3D models of mining engineering structures were built based on the CityGML standard.For collecting spatial data,the two most popular geospatial technologies,namely UAV-SfM and TLS were employed.The accuracy of the UAV survey was at the centimeter level,and it satisfied the absolute positional accuracy requirement of creat-ing all levels of detail(LoD)according to the CityGML standard.Therefore,the UAV-SfM point cloud dataset was used to build LoD 2 models.In addition,the comparison between the UAV-SfM and TLS sub-clouds of facades and roofs indicates that the UAV-SfM and TLS point clouds of these objects are highly consistent,therefore,point clouds with a higher level of detail and accuracy provided by the integration of UAV-SfM and TLS were used to build LoD 3 models.The resulting 3D CityGML models include 39 buildings at LoD 2,and two mine shafts with hoistrooms,headframes,and sheave wheels at LoD3.

Perceptual Quality Assessment for Point Clouds:A Survey

A point cloud is considered a promising 3D representation that has achieved wide applications in several fields.However,quality degradation inevitably occurs during its acquisition and generation,communication and transmission,and rendering and display.Therefore,how to accurately perceive the visual quality of point clouds is a meaningful topic.In this survey,we first introduce the point cloud to emphasize the importance of point cloud quality assessment(PCQA).A review of subjective PCQA is followed,including common point cloud distortions,subjective experimental setups and subjective databases.Then we review and compare objective PCQA methods in terms of modelbased and projection-based.Finally,we provide evaluation criteria for objective PCQA methods and compare the performances of various methods across multiple databases.This survey provides an overview of classical methods and recent advances in PCQA.

Segmentation and Classification of Individual Clouds in Images Captured with Horizon-Aimed Cameras for Nowcasting of Solar Irradiance Absorption

One important aspect of solar energy generation especially in inter-tropical sites is the local variability of clouds. Satellite images do not have temporal resolution enough to nowcast its impacts on solar plants, this monitoring is made by local cameras. However, cloud detection and monitoring are not trivial due to cloud shape dynamics, the camera is a linear and self-adjusting device, with fish-eye lenses generating a flat image that distorts images near the horizon. The present work focuses on cloud identification to predict its effects on solar plants that are distinct for every site’s climatology and geography. We used RASPBERY-PI-based cameras pointed at the horizon to allow observation of clouds’ vertical distribution, not possible with a unique fish-eye lens. A large number of cloud image identification analyses led the researchers to use deep learning methods such as U-net, HRnet, and Detectron. We use transfer learning with weights trained over the “2012 ILSVRC ImageNet” data set and architecture configurations like Resnet, Efficient, and Detectron2. While cloud identification proved a difficult task, we achieved the best results by using Jaccard Coefficient as a validation metric, with the best model being a U-net with Resnet18 using 486 × 648 resolution. This model had an average IoU of 0.6, indicating a satisfactory performance in cloud segmentation. We also observed that the data imbalance affected the overall performance of all models, with the tree class creating a favorable bias. The HRNet model, which works with different resolutions, showed promising results with a more refined segmentation at the pixel level, but it was not necessary to detect the most predominant clouds in the sky. We are currently working on balancing the dataset and mapping out data augmentation transformations for our next experiments. Our ultimate goal is to use such models to predict cloud motion and forecast the impact it will have on solar power generation. The present work has contributed to a better understanding of what techniques work best for cloud identification and paves the way for future studies on the development of a better overall cloud classification model.

A New Mechanism Based on Similar Clouds Watermark for Database’s Information Security

This paper studies the digit watermark technology of numeric attributes in relational database for database's information security. It proposes a new mechanism based on similar clouds watermark and gives the concept of similar clouds. The algorithm SCWA that can insert the meaning watermark and detect it from the watermarked data is described. The mechanism can effectively and broadly scatter the watermark in the database; therefore the watermark is very robust. Key words copyright protection - digit watermark - similar clouds - clouds model CLC number TP 311. 52 Foundation item: Supported by the National Natural Science Foundation of China (60273072) and 863 Hi-technique Research (2002AA4Z3450)Biography: HUANG Min(1979-), female, Ph. D candidate, research direction: database's information security

Deep Convective Clouds over the Northern Pacific and Their Relationship with Oceanic Cyclones

Based on combined CloudSat/CALIPSO detections, the seasonal occurrence of deep convective clouds （DCCs） over the midlatitude North Pacific （NP） and cyclonic activity in winter were compared. In winter, DCCs are more frequent over the central NP, from approximately 30~N to 45~N, than over other regions. The high frequencies are roughly equal to those occurring in this region in summer. Most of these DCCs have cloud tops above a 12 km altitude, and the highest top is approximately 15 km. These wintertime marine DCCs commonly occur during surface circulation conditions of low pressure, high temperature, strong meridional wind, and high relative humidity. Further, the maximum probability of DCCs, according to the high correlation coefficient, was found in the region 10^-20~ east and 5^-10~ south of the center of the cyclones. The potential relationship between DCCs and cyclones regarding their relative locations and circulation conditions was also identified by a case study. Deep clouds were generated in the warm conveyor belt by strong updrafts from baroclinic flows. The updrafts intensified when latent heat was released during the adjustment of the cyclone circulation current. This indicates that the dynamics of cyclones are the primary energy source for DCCs over the NP in winter.

The Physical Nature of H_2CO Clouds in Dark Clouds, B5 and L1535

By fitting the data of the H 2CO 2 mm (140 GHz and 150 GHz), 2cm (1 45GHz) and, 6 cm (4 3GHz) lines we observed in the dark clouds, B5 and L1535, to the values calculated by the hydrostatic equitlibrium polytropic model developed by B.E. Turner, we derived the total density, the fractional abundance of H 2CO, the column densities of H 2 and CO, the line temperature arising from various transitions, the line temperature ratio between different transitions, and visual extinction at the cores of the clouds. We also examined the effect of external UV field intensity on the line temperatures and their ratios.

Fundamentals on Thermodynamic Processes behind Clouds’ and Rainfalls’ Formation

The prevailing idea so far about why the rainfall occurs was that after agglutination of water droplets with condensation nuclei, the size of the particle formed by the condensation nuclei connected with droplets of water increased considerably and caused its fall. This idea has led to numerous scientific publications in which empirical distribution functions of clouds’ water droplets sizes were proposed. Estimates values provided by these empirical distribution functions, in most cases, were validated by comparison with UHF Radar measurements. The condensation nuclei concept has not been sufficiently exploited and this has led meteorologists to error, in their attempt to describe the clouds, thinking that clouds were formed by liquid water droplets. Indeed, MBANE BIOUELE paradox (2005) confirms this embarrassing situation. In fact, when applying Archimedes theorem to a liquid water droplet suspended in the atmosphere, we obtain a meaningless inequality ?which makes believe that the densities of pure water in liquid and solid phases are much lower than that of the atmosphere considered at the sea level. This meaningless inequality is easy to contradict: of course, if you empty a bottle of pure liquid water in the ocean (where z is equal to 0), this water will not remain suspended in the air, i.e., application of Archimedes’ theorem allows realizing that there is no liquid (or solid) water droplet, suspended in the clouds. Indeed, all liquid (or solid) water droplets which are formed in clouds, fall under the effect of gravity and produce rains. This means that our current description of the clouds is totally wrong. In this study, we describe the clouds as a gas composed of dry air and saturated water vapor whose optical properties depend on temperature, i.e., when the temperature of a cloud decreases, the color of this gaseous system tends towards white.

Energy-Optimal and Delay-Bounded Computation Offloading in Mobile Edge Computing with Heterogeneous Clouds

By Mobile Edge Computing(MEC), computation-intensive tasks are offloaded from mobile devices to cloud servers, and thus the energy consumption of mobile devices can be notably reduced. In this paper, we study task offloading in multi-user MEC systems with heterogeneous clouds, including edge clouds and remote clouds. Tasks are forwarded from mobile devices to edge clouds via wireless channels, and they can be further forwarded to remote clouds via the Internet. Our objective is to minimize the total energy consumption of multiple mobile devices, subject to bounded-delay requirements of tasks. Based on dynamic programming, we propose an algorithm that minimizes the energy consumption, by jointly allocating bandwidth and computational resources to mobile devices. The algorithm is of pseudo-polynomial complexity. To further reduce the complexity, we propose an approximation algorithm with energy discretization, and its total energy consumption is proved to be within a bounded gap from the optimum. Simulation results show that, nearly 82.7% energy of mobile devices can be saved by task offloading compared with mobile device execution.

On the Radiative Properties of Ice Clouds:Light Scattering, Remote Sensing,and Radiation Parameterization

Presented is a review of the radiative properties of ice clouds from three perspectives： light scattering simulations, remote sensing applications, and broadband radiation parameterizations appropriate for numerical models. On the subject of light scattering simulations, several classical computational approaches are reviewed, including the conventional geometric-optics method and its improved forms, the finite-difference time domain technique, the pseudo-spectral time domain technique, the discrete dipole approximation method, and the T-matrix method, with specific applications to the computation of the singlescattering properties of individual ice crystals. The strengths and weaknesses associated with each approach are discussed.With reference to remote sensing, operational retrieval algorithms are reviewed for retrieving cloud optical depth and effective particle size based on solar or thermal infrared（IR） bands. To illustrate the performance of the current solar- and IR-based retrievals, two case studies are presented based on spaceborne observations. The need for a more realistic ice cloud optical model to obtain spectrally consistent retrievals is demonstrated. Furthermore, to complement ice cloud property studies based on passive radiometric measurements, the advantage of incorporating lidar and/or polarimetric measurements is discussed.The performance of ice cloud models based on the use of different ice habits to represent ice particles is illustrated by comparing model results with satellite observations. A summary is provided of a number of parameterization schemes for ice cloud radiative properties that were developed for application to broadband radiative transfer submodels within general circulation models（GCMs）. The availability of the single-scattering properties of complex ice habits has led to more accurate radiation parameterizations. In conclusion, the importance of using nonspherical ice particle models in GCM simulations for climate studies is proven.