Aircraft Observations of Liquid and Ice in Midlatitude Mixed-Phase Clouds

ABSTRACT This paper reports airborne measurements of midlatitude altostratus clouds observed over Zhengzhou, Henan Province, China on 3 March 2007. The case demonstrates mixed-phase conditions at altitudes from 3200 to 4600 m （0°C to -7.6°C）, with liquid water content ranging from 0.01 to 0.09 g m-3. In the observed mixed-phase cloud, liquid water content exhibited a bimodal distribution, whereas the maximum ice particle concentration was located in the middle part of the cloud. The liquid and ice particle data showed significant horizontal variability on the scale of a few hundred meters. The cloud droplet concentration varied greatly over the horizontal sampling area. There was an inverse relationship between the cloud droplet concentration and ice particle concentration. A gamma distribution provided the best description of the cloud droplet spectra. The liquid droplet distributions were found to increase in both size and concentration with altitude. It was inferred from the profile of the spectra parameters that the cloud droplet sizes tend to form a quasi-monodisperse distribution. Ice particle spectra in the cloud were fitted well by an exponential distribution. Finally, a remarkable power law relationship was found between the slope （λ） and intercept （No） parameters of the exponential size distribution.

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.

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.

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.

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.

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.

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.

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.

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.

基于CloudSim的电力系统计算云原生模型与仿真方法

分析基于云计算的电力系统计算过程是实现资源高效利用的基础。因此,提出一种基于CloudSim的电力系统计算的云原生建模与仿真方法。基于云原生技术阐述了电力系统计算架构的关键要素,提出基于CloudSim的电力系统计算架构的云原生模型,分析电力云任务调配时的资源供需平衡问题。建立基于微服务的电力云任务模型,分析微服务的处理时序逻辑及其层级关系并生成微服务队列。据此提出微服务的动态调配模型,采用微服务-容器映射策略与实体动态整合策略,实现微服务在实体间的动态映射。利用CloudSim进行仿真分析,结果证明所提方法可行且有效,可为电力云计算的资源配置、任务调度等提供基本的仿真工具与方法。

基于CloudSat-CALIPSO数据的黄土高原地区云特征分析

云是地气系统的重要组成部分,为深入分析黄土高原地区云特征,利用2007-2016年搭载首部云探测雷达云卫星(CloudSat)与云-气溶胶激光雷达和红外探测者观测卫星(The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation, CALIPSO)资料,选取黄土高原半湿润、半干旱、干旱和寒旱四个区域,对云的宏、微观物理特征进行了分析。结果表明:(1)黄土高原各区域云出现频率年均值达到了55%以上,其中,春、夏季云出现频率最高,秋冬两季相对较低;半湿润区云出现频率高于其他区域,但其他三个区域云出现频率最高的月份均早于半湿润区。(2)各区域中单层云出现频率最高,占到总云量的60%以上,多层云中主要是双层云,约占总云量的25%。云层高度在不同区域表现为春、夏季节大于秋、冬季节,半湿润区的云层高度在四季均大于其他区域。各区域云几何厚度季节变化不显著,均在1~4 km之间,主要以薄云为主,且78.13%的云几何厚度不超过2 km。(3)各区域的云液态水含量年均值均达到了220.5 mg·m^(-3),约为冰水含量年均值的6.5倍,主要分布在8.5 km以下的高度层。随着高度的减小,液态水含量逐渐增多,其中半湿润区云液态水含量大于其他区域。各区域全年冰水含量占比较小,主要分布在16.5 km以下的高度层。(4)液滴有效半径在各区域的值主要集中在12~16μm,在半干旱区的春季出现了最大值,约为24μm;冰粒子有效半径最大值出现在半湿润区的夏季。液滴数浓度在各区域的值集中在60~80 cm^(-3),均小于冰粒子数浓度平均值,其峰值出现在各区域的夏季,冰粒子数浓度的峰值出现在半湿润和半干旱区的春季。该研究结果有助于深入认识黄土高原云的特征,为区域气候模式对黄土高原地区云特征的模拟提供一定的参考依据。

Comparison of Cloud Properties between Cloud Sat Retrievals and Airplane Measurements in Mixed-Phase Cloud Layers of Weak Convective and Stratus Clouds

Cloud microphysical properties including liquid and ice particle number concentration （NC）, liquid water content （LWC）, ice water content （IWC） and effective radius （RE） were retrieved from CloudSat data for a weakly convective and a widespread stratus cloud. Within the mixed-phase cloud layers, liquid-phase fractions needed to be assumed in the data retrieval process, and one existing linear （Pl） and two exponential （P2 and P3） functions, which estimate the liquid-phase fraction as a function of subfreezing temperature （from -20℃ to 0℃）, were tested. The retrieved NC, LWC, IWC and RE using Pl were on average larger than airplane measurements in the same cloud layer, Function P2 performed better than p1 or P3 in retrieving the NCs of cloud droplets in the convective cloud, while function Pl performed better in the stratus cloud. Function P3 performed better in LWC estimation in both convective and stratus clouds. The REs of cloud droplets calculated using the retrieved cloud droplet NC and LWC were closer to the values of in situ observations than those retrieved directly using the Pl function. The retrieved NCs of ice particles in both convective and stratus clouds, on the assumption of liquid-phase fraction during the retrieval of liquid droplet NCs, were closer to those of airplane observations than on the assumption of function P1.

基于CloudSat-CALIPSO数据的大兴安岭地区云宏微观物理量的垂直结构特征分析

大兴安岭是我国重要生态资源保护区,深入分析该区域云物理特性参量分布特征,对了解复杂地形区域气候变化及人工影响天气等具有重要意义。基于CloudSat-CALIPSO(CloudSat-Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations)卫星观测资料,分析了大兴安岭地区云层的宏、微观物理特征,结果表明:大兴安岭地区年平均云出现频率为59.5%,主要以高层云、卷云和层积云为主,春夏季云发生频率高于秋冬季。云层主要以薄云为主,61.41%的云厚度不超过2 km,云顶高度、云底高度分别呈现双峰型和单峰型分布形式。云垂直结构特征为单层云的出现频率最高,占到总云量的69.19%,随着云层数的增加,云的发生频率逐渐降低。大兴安岭地区云中液态水含量丰富,年平均值达244.41 mg·m^(-3),约为冰水含量年平均值的4倍,有83.2%的云水含量集中在低空5 km以下的区域。水滴粒子有效粒径和数浓度的年平均值分别为15.86μm和34.47个·cm^(-3),均小于冰晶粒子平均值。云中含水量和有效粒径随高度呈现单峰型分布形式,而云滴粒子数浓度则在低空呈现为双峰型分布形式。

CT-CloudDetect:用于遥感卫星云检测的混合模型

云检测是在遥感卫星云图中检测云的任务。近年来,人们提出了基于深度学习的云检测方法,并取得了良好的性能。然而,现有的基于深度学习的云检测模型大多还是基于卷积神经网络(convolutional neural network,CNN),由于卷积运算的固有局部性,难以捕获长距离依赖关系。针对上述问题,文章提出一个基于CNN和ViT(Vision Transformer)的混合型云检测模型,并提出一种基于CNN和ViT的编码器,使网络具备捕捉局部和全局信息的能力。为了更好地融合语义和尺度不一致的特征,提出了一个双尺度注意力融合模块,通过注意力机制有选择地融合特征。此外,提出了轻量级路由解码器,该解码器通过路由结构降低模型复杂度。在3个公开云检测数据集上对模型进行了评估。大量实验表明,所提出的模型具有比现有模型更好的性能。

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.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

基于Spring Cloud的分布式医疗数据平台建设

为解决互联网时代医疗大数据的可靠存储问题,探索异构数据的有效处理方案,加强医疗数据检索能力。提出一种基于Spring Cloud的分布式医疗数据平台建设方案,该方案通过Spring Cloud技术构建分布式架构,选取MongoDB非关系型数据库作为分布式存储,同时对数据进行标准化、分词等操作,从而实现一个异构数据可存储、系统高可用、规模易扩展、检索高效率的医疗数据平台。

基于POC CLOUD智能管理云平台的即时检验教学探索

目的探讨使用POC CLOUD智能管理云平台培养住培学员掌握即时检验(point-of-care testing,POCT)各设备的工作原理、操作技能,使其具备一定的质量控制管理能力,保证结果的准确性。方法结合《住培学员规范化培训内容与标准(2022年版)》要求及科室POCT教学开展情况,利用POC CLOUD智能管理云平台,信息化、规范化地培养住培学员。结果通过POC CLOUD智能管理云平台管理住培学员操作POCT设备的资格准入,帮助住培学员快速了解轮转科室POCT设备的使用现状,提高住培学员的学习能力及质量控制能力,帮助住培学员分析与审核检验结果。结论基于POC CLOUD智能管理云平台的即时检验教学,有助于系统化培养住培学员,提升其在POCT操作和质量控制方面的能力,确保检验结果的准确性和可靠性,提高住培学员的综合素质。

A Blockchain and CP-ABE Based Access Control Scheme with Fine-Grained Revocation of Attributes in Cloud Health

The Access control scheme is an effective method to protect user data privacy.The access control scheme based on blockchain and ciphertext policy attribute encryption(CP–ABE)can solve the problems of single—point of failure and lack of trust in the centralized system.However,it also brings new problems to the health information in the cloud storage environment,such as attribute leakage,low consensus efficiency,complex permission updates,and so on.This paper proposes an access control scheme with fine-grained attribute revocation,keyword search,and traceability of the attribute private key distribution process.Blockchain technology tracks the authorization of attribute private keys.The credit scoring method improves the Raft protocol in consensus efficiency.Besides,the interplanetary file system(IPFS)addresses the capacity deficit of blockchain.Under the premise of hiding policy,the research proposes a fine-grained access control method based on users,user attributes,and file structure.It optimizes the data-sharing mode.At the same time,Proxy Re-Encryption(PRE)technology is used to update the access rights.The proposed scheme proved to be secure.Comparative analysis and experimental results show that the proposed scheme has higher efficiency and more functions.It can meet the needs of medical institutions.