Cloud Types Research Articles

Comparison of reflective band (Vis, NIR, SWIR, eSWIR) performance in daytime reduced illumination conditions

Daytime low-light conditions such as overcast, dawn, and dusk pose a challenge for object discrimination in the reflective bands, where the majority of illumination comes from reflected solar light. In reduced-illumination conditions, the sensor signal-to-noise ratio can suffer, inhibiting range performance for detecting, recognizing, and identifying objects of interest. This performance reduction is more apparent in the longer wavelengths where there is less solar light. Range performance models show a strong dependence on cloud type and thickness, as well as time of day across the reflective wavebands. Through an experimental and theoretical analysis of a passive sensitivity- and resolution-matched testbed, we compare Vis (0.4–0.7 µm), NIR (0.7–1 µm), SWIR (1–1.7 µm), and eSWIR (2–2.5 µm) to assess the limiting cases in which reduced illumination inhibits range performance. The time during dawn and dusk is brief yet does show significant range performance reduction for SWIR and eSWIR. Under heavy cloud cover, eSWIR suffers the most at range due to a low signal-to-noise ratio. In cases of severe reduction in illumination, we propose utilizing active illumination or the emissive component of eSWIR to improve the signal-to-noise ratio for various discrimination tasks.

Nonlinear instability and scalar clouds of spherical exotic compact objects in scalar-Gauss-Bonnet theory

In this work, we present a new type of scalar clouds supported by spherically symmetric horizonless compact objects in the scalar-Gauss–Bonnet theory. Unlike the previous spontaneous scalarization that is triggered by the tachyonic instability, our scalarization arises from a nonlinear instability that is non-spontaneous. We explore two types of boundary conditions for the scalar field at the surface of the compact objects and find an infinite countable set of scalar clouds characterized by the number of nodes for both cases. Our study demonstrates that boundary conditions have a significant impact on the formation of scalar clouds. Specifically, for the Dirichlet boundary condition, scalarization is more likely to occur for compact objects with medium radii and becomes harder for ultra-compact and large ones. Conversely, for the Robin boundary condition, scalarization is easier for more compact objects.

Evaluation of cloud-type impact on performance of the GL model version 1.2 for estimation of solar irradiance

ABSTRACT This work evaluates the quality of the satellite-based GLobal radiation model version 1.2 (GL1.2) estimates for four cloud classes. The GL1.2 calculates the global solar irradiance at ground level using images from a single visible band channel (VIS) of the Geostationary Operational Environmental Satellite – GOES (GOES-East). The model´s performance was assessed by comparing hourly mean GL1.2 values with ground-based hourly measurements from the Brazilian National Institute of Meteorology (INMET, 354 automatic weather stations), for the entire year 2017. A satellite-based cloud classifier was adopted to discriminate the datasets according to prevailing cloud conditions (cumulus, stratus, cirrus and multilayer), but the clear sky behaviour was presented. The results were analysed for the five Brazilian regions. In the first analysis, we selected days with a predominance for five stations. It was found that the diurnal cycle was well reproduced. Then the regional investigation for the cloud types reveals that the best results are found for the Center West region over multilayer cloud (mean bias error, MBEannual = -2 ± 91 Wm−2 and root mean squared error, RMSE = 91 Wm−2), while the worst ones are in the North over cumulus fields (MBEannual = 101.5 ± 136.4 Wm−2). When considering all cloud types, the MBEannual is lower than 5 Wm−2 for the Northeast, Southeast and South regions, but it reaches 101 Wm−2 in the North. It is noteworthy that winter has the highest MBE in all classes analysed in the North, as well as cirrus situations in other regions. Although the inhomogeneities of cumulus and the semitransparent cirrus clouds tend to propagate errors to the model, the quality of GL1.2 data has a high degree of agreement with the observations. Improvements including an updated monthly minimum reflectance (R min) and water vapour column (H2Ovapour), and a better spatial resolution of the Advanced Baseline Imager (ABI) of GOES-16 (ABI/GOES-16) VIS imagery will allow refinement, especially for cumulus clouds.

Sentimental Approach to Airline Service Quality Evaluation

This paper focuses on the analysis of traditional methods of service quality evaluation and represents a new sentimental approach to airline service quality evaluation employing user-generated content. It identifies aspects of airline service that passengers react to positively or negatively using the word cloud method—a basic straightforward exploratory analysis tool. The aim is to introduce an approach that can be implemented using freely available analytical software tools and freely available data. As a case study, authors evaluated selected airlines’ service quality using sentimental analysis of user-generated content. The research relied on sentiment analysis of Twitter posts related to selected airlines’ service quality. The paper describes how Twitter can be used for data mining, sentimental analysis, and airline service quality evaluation. The authors analysed over 30,000 posts related to the service quality of Ryanair, Southwest Airlines, American Airlines and KLM and proposed two types of word clouds (for each individual airline) which allow more informed decisions about enhancing the service quality of selected airlines. Compared to rather expensive traditional methods of airline service quality evaluation, such as onboard surveys of airline passengers or on-site surveys of passengers at airport departure gates, the key advantages of this new approach are the availability of free data and free analytical software tools. Moreover, this approach allows analysis of the service quality of competing airlines and, thus, provides internal opportunities for comparison. The results contribute to the literature by clarifying how both positive and negative passenger feedback impacts airline service quality and airline product planning.

Fast Radius Outlier Filter Variant for Large Point Clouds

Currently, several devices (such as laser scanners, Kinect, time of flight cameras, medical imaging equipment (CT, MRI, intraoral scanners)), and technologies (e.g., photogrammetry) are capable of generating 3D point clouds. Each point cloud type has its unique structure or characteristics, but they have a common point: they may be loaded with errors. Before further data processing, these unwanted portions of the data must be removed with filtering and outlier detection. There are several algorithms for detecting outliers, but their performances decrease when the size of the point cloud increases. The industry has a high demand for efficient algorithms to deal with large point clouds. The most commonly used algorithm is the radius outlier filter (ROL or ROR), which has several improvements (e.g., statistical outlier removal, SOR). Unfortunately, this algorithm is also limited since it is slow on a large number of points. This paper introduces a novel algorithm, based on the idea of the ROL filter, that finds outliers in huge point clouds while its time complexity is not exponential. As a result of the linear complexity, the algorithm can handle extra large point clouds, and the effectiveness of this is demonstrated in several tests.

Microphysical Characteristics of Cyclonic Rainfall: A GPM‐DPR Based Study Over the Arabian Sea

AbstractThe precipitation characteristics of tropical cyclone (TC) formed over the Arabian Sea during the onset phase of the Indian summer monsoon (i.e., late‐May and early‐June months) and in the post‐monsoon season were investigated during the period from 2014 to 2021 using the Global Precipitation Measurement (GPM) satellite level 2 V07 data. For the cyclonic precipitation, 2‐D frequency distribution between liquid water content (LWC) and non‐liquid water content, that is, ice water content (IWC) shows significant variation with the rain type and season. A large proportion of rain droplets are within the LWC (IWC) range of 0–800 (0–350) g/m2 for the stratiform precipitation of TCs in both seasons. The average value of the mass‐weighted mean diameter (Dm), for the stratiform and convective precipitation at 2 km above the mean sea level in the monsoon (post‐monsoon) season is 1.29 (1.27) mm and 1.47 (1.31) mm, respectively. A gradual decrease in the normalized intercept parameters (Nw) is found to occur with an increase in Dm, irrespective of cloud type and season. There is an enormously high concentration of supercooled liquid and ice particles observed above the melting layer during the convection precipitation in both seasons. In the stratiform cyclonic cloud, the contribution of the collision‐coalescence and breaking‐up process is about 36.62% (47.58%) and 52.52% (41.52%) during the monsoon (post‐monsoon) season. However, the collision‐coalescence process (63.45% in monsoon and 70.2% in post‐monsoon) is acting as dominating microphysical process in the convective precipitation as compared to the break‐up process.

CLOUD TECHNOLOGIES: USE IN THE EDUCATIONAL PROCESS AS A WAY TO HIGH MANAGEMENT IN BUSINESS

The article is devoted to a comparative analysis of the types of clouds and cloud technologies; the use of cloud services in the educational process, in particular, in the organization of students' independent work. The authors analyze the latest research in the field of cloud services. A comparative analysis of the most famous and most frequently used cloud services for data storage is carried out. The tools and services of Microsoft and Google used in educational work are demonstrated. The advantages and disadvantages of using modern cloud services are identified.

Dissociative Recombination of Rotationally Cold OH+ and Its Implications for the Cosmic Ray Ionization Rate in Diffuse Clouds

Observations of OH+ are used to infer the interstellar cosmic ray ionization rate in diffuse atomic clouds, thereby constraining the propagation of cosmic rays through and the shielding by interstellar clouds, as well as the low energy cosmic ray spectrum. In regions where the H2-to-H number density ratio is low, dissociative recombination (DR) is the dominant destruction process for OH+ and the DR rate coefficient is important for predicting the OH+ abundance and inferring the cosmic ray ionization rate. We have experimentally studied DR of electronically and vibrationally relaxed OH+ in its lowest rotational levels, using an electron–ion merged-beams setup at the Cryogenic Storage Ring. From these measurements, we have derived a kinetic temperature rate coefficient applicable to diffuse cloud chemical models, i.e., for OH+ in its electronic, vibrational, and rotational ground level. At typical diffuse cloud temperatures, our kinetic temperature rate coefficient is a factor of ∼5 times larger than the previous experimentally derived value and a factor of ∼33 times larger than the value calculated by theory. Our combined experimental and modeling results point to a significant increase for the cosmic ray ionization rate inferred from observations of OH+ and H2O+, corresponding to a geometric mean of (6.6 ± 1.0) × 10−16 s−1, which is more than a factor of 2 larger than the previously inferred values of the cosmic ray ionization rate in diffuse atomic clouds. Combined with observations of diffuse and dense molecular clouds, these findings indicate a greater degree of cosmic ray shielding in interstellar clouds than has been previously inferred.

Observed Cloud Type‐Sorted Cloud Property and Radiative Flux Changes With the Degree of Convective Aggregation From CERES Data

AbstractCloud‐radiation interactions are a critical mechanism for convective self‐aggregation, especially the longwave radiative cooling of low clouds and environments. In this study, two data products from CERES observations combined with MERRA‐2 reanalysis are used to understand the changes of cloud properties and radiative fluxes by cloud type with the degree of convective aggregation at the 1000‐km scale, which is represented by the number of cloud objects (N), simple convective aggregation index (SCAI), modified SCAI (MCAI) or convective organization potential (COP). The changes with SCAI are similar to those with N as an index, agreeing with previous studies using grid‐averaged properties. For changes from weak to strong degrees of aggregation using N and SCAI, area fractions of middle‐ and high‐level cloud types decrease by up to 4% but those of low‐level cloud types increase by up to 2%, and more infrared radiation is emitted to space (2–8 W m−2) from optically thin cloud types but more solar radiation is reflected (2–4 W m−2) from optically‐thick cloud types. However, using COP (MCAI to lesser extent), area fractions of optically‐thick cloud types increase, which emit less infrared radiation and reflect more solar radiation, whereas the area fractions of low‐level clouds decrease. These results can be explained by greater expansion of cloud object sizes for COP than MCAI/SCAI as the degree of convective aggregation increases, which also explains the difference between SCAI and MCAI pertaining to the opposite changes of optically‐thick high‐level clouds. These results can have implications for understanding convective self‐aggregation.

All-Day Cloud Classification via a Random Forest Algorithm Based on Satellite Data from CloudSat and Himawari-8

It remains challenging to accurately classify complicated clouds owing to the various types of clouds and their distribution on multiple layers. In this paper, multi-band radiation information from the geostationary satellite Himawari-8 and the cloud classification product of the polar orbit satellite CloudSat from June to September 2018 are investigated. Based on sample sets matched by two types of satellite data, a random forest (RF) algorithm was applied to train a model, and a retrieval method was developed for cloud classification. With the use of this method, the sample sets were inverted and classified as clear sky, low clouds, middle clouds, thin cirrus, thick cirrus, multi-layer clouds and deep convection (cumulonimbus) clouds. The results indicate that the average accuracy for all cloud types during the day is 88.4%, and misclassifications mainly occur between low and middle clouds, thick cirrus clouds and cumulonimbus clouds. The average accuracy is 79.1% at night, with more misclassifications occurring between middle clouds, multi-layer clouds and cumulonimbus clouds. Moreover, Typhoon Muifa from 2022 was selected as a sample case, and the cloud type (CLT) product of an FY-4A satellite was used to examine the classification method. In the cloud system of Typhoon Muifa, a cumulonimbus area classified using the method corresponded well with a mesoscale convective system (MCS). Compared to the FY-4A CLT product, the classifications of ice-type (thick cirrus) and multi-layer clouds are effective, and the location, shape and size of these two varieties of cloud are similar.

CloudY-Net: A Deep Convolutional Neural Network Architecture for Joint Segmentation and Classification of Ground-Based Cloud Images

Ground-based cloud images contain a wealth of cloud information and are an important part of meteorological research. However, in practice, ground cloud images must be segmented and classified to obtain the cloud volume, cloud type and cloud coverage. Existing methods ignore the relationship between cloud segmentation and classification, and usually only one of these is studied. Accordingly, our paper proposes a novel method for the joint classification and segmentation of cloud images, called CloudY-Net. Compared to the basic Y-Net framework, which extracts feature maps from the central layer, we extract feature maps from four different layers to obtain more useful information to improve the classification accuracy. These feature maps are combined to produce a feature vector to train the classifier. Additionally, the multi-head self-attention mechanism is implemented during the fusion process to enhance the information interaction among features further. A new module called Cloud Mixture-of-Experts (C-MoE) is proposed to enable the weights of each feature layer to be automatically learned by the model, thus improving the quality of the fused feature representation. Correspondingly, experiments are conducted on the open multi-modal ground-based cloud dataset (MGCD). The results demonstrate that the proposed model significantly improves the classification accuracy compared to classical networks and state-of-the-art algorithms, with classification accuracy of 88.58%. In addition, we annotate 4000 images in the MGCD for cloud segmentation and produce a cloud segmentation dataset called MGCD-Seg. Then, we obtain a 96.55 mIoU on MGCD-Seg, validating the efficacy of our method in ground-based cloud imagery segmentation and classification.

Determination of the Dynamics of Thunderstorms Through the Dry Adiabatic Lapse Rate and Environmental Lapse Rate

This research aims to determine the types of thunderstorms formed in the thickness of the cloud (determine the Dry adiabatic lapse rate (DALR) and Environmental lapse rate (ELR)) in the case of precipitation during the day. Data were taken by Temperature, Dew point, Atmospheric Pressure, and Height from re-analysis by the (ECMWF) for the heights (0-18000) m, the levels of pressure (1000-100) mbar, low cloud cover data, and the characteristic days ((18, 24, 27) February, 28 April, and 25 November) of the year 2018 for Baghdad station were chosen to obtain the largest possible number of clouds and their diversity to use them in calculating the cloud cover and weather stability in terms of calculating the daily change, temperature, dew point in addition to calculating the low cloud cover with altitude and atmospheric instability. The Sigma Plot program was used in this research to determine the base of clouds and thunderstorms. The change in temperature, Dew point, clouds base, and altitude was determined, then the cloud thickness, types, and classification were calculated. The clouds found are strong thunderstorm clouds characterized by thickness and height, such as the clouds of Nimbostratus (Ns) and Cumulonimbus (Cb).

A Multicloud Model for Coastal Convection

Coastal convection is often organized into multiple mesoscale systems that propagate in either direction across the coastline (i.e., landward and oceanward). These systems interact non-trivially with synoptic and intraseasonal disturbances such as convectively coupled waves and the Madden–Julian oscillation. Despite numerous theoretical and observational efforts to understand coastal convection, global climate models still fail to represent it adequately, mainly because of limitations in spatial resolution and shortcomings in the underlying cumulus parameterization schemes. Here, we use a simplified climate model of intermediate complexity to simulate coastal convection under the influence of the diurnal cycle of solar heating. Convection is parameterized via a stochastic multicloud model (SMCM), which mimics the subgrid dynamics of organized convection due to interactions (through the environment) between the cloud types that characterize organized tropical convection. Numerical results demonstrate that the model is able to capture the key modes of coastal convection variability, such as the diurnal cycle of convection and the accompanying sea and land breeze reversals, the slowly propagating mesoscale convective systems that move from land to ocean and vice-versa, and numerous moisture-coupled gravity wave modes. The physical features of the simulated modes, such as their propagation speeds, the timing of rainfall peaks, the penetration of the sea and land breezes, and how they are affected by the latitudinal variation in the Coriolis force, are generally consistent with existing theoretical and observational studies.

Spatial Distributions of Cloud Occurrences in Terms of Volume Fraction as Inferred from CloudSat and CALIPSO

The cloud amount, referred to as the frequency of cloud occurrences, is of great importance for the Earth–atmosphere system. It was conventionally quantified as the area fraction of clouds in a given region, discarding the three-dimensional nature of both cloud entities and their spatial distribution. Although the area fraction is explicit, it is the volume fraction that fully depicts cloud occurrences, and the area fraction is just related to a projection of the volume fraction. In this study, by using spaceborne radar measurements, the spatial distribution of cloud volume fraction throughout the troposphere was investigated, and the contributions of various cloud types at each location were clarified. Overall, the volume fraction of total clouds in the whole troposphere is 15.9%, while the corresponding area fraction relative to the global surface is 73.6%. The peak volume fraction occurs at 1 km altitude, mainly contributed by stratocumulus and cumulus. For a single cloud type, the maximum fraction is 48.8%, which is from stratocumulus and occurs at 1 km altitude above the Greenland Sea. Half of the eight cloud types, altostratus, cirrus, nimbostratus, and deep convective clouds, reach the nominal tropopause. In particular, the vertical distribution difference among multiple cloud types in each category (low-level, middle-level, and vertically extending) was clarified, and it was found that the dominant cloud type in a category varies notably with the location in the atmosphere.

Przestrzenna i czasowa zmienność rodzajów chmur nad Polską w latach 1996-2020

The subject of the study is a comprehensive qualitative and quantitative analysis of the type of cloud cover over Poland in 1996–2020. The analysis was based on the characteristics of particular types of clouds at three levels, based on hourly measurement data from 36 meteorological stations (IMWM-NRI). The variability of the share of cloud types was analysed on three levels in the multi-annual, annual and daily course. Maps of spatial distribution for selected clouds were plotted. In the study of the multi-year period, clouds of the low-level were observed most frequently, and the most numerous cloud type was Stratocumulus. Altocumulus clouds were found to have the highest frequency on the mid-level, while Cirrus clouds of various species were found on the high-level. On the low-level, the highest increase in the share in the analysed multi-year period was recorded for Stratocumulus clouds, and the highest decrease for moderately and strongly uplifted Cumulus clouds. On the mid-level, the largest increase in the share was observed for Altocumulus clouds, and the largest decrease for Nimbostratus clouds. Among the clouds of the high-level, the greatest increase in the proportion of Cirrostratus clouds not stretching the sky was observed, while the greatest decrease in the share was for the Cirrus uncinus clouds. The annual and daily courses on all the levels showed cyclic occurrence, meaning that the different types of clouds have characteristic periods when they are observed more or less frequently, both on an annual and daily basis.

Chmury piętra wysokiego, średniego i niskiego: analiza zachmurzenia w Polsce

The subject of the paper is an analysis of the type of clouds in Poland in 1996–2020 on the low, middle and high levels. Daily data on cloud types and the cloudiness of the low-level from 36 IMWM-NRI stations were used. In the analysed multi-year period, the highest share of low-level clouds was observed (56%), followed by mid-level clouds (23%), and the lowest share of high-level clouds (21%). There was a slight decrease in the share of low-level clouds, and an increase in the share of middle and high levels clouds in the analysed period. The share of low-level clouds was highest in the cool season and during the afternoon and night hours; it was lowest in the summer, and early morning and evening hours. The middle and high levels had the highest share in the summer and during the early evening and early morning hours. These levels were least frequent in the winter, and around noon for the mid-level, and at night for the high-level. The Cumulus humilis and Stratocumulus cugen clouds were most frequently observed on the low-level during low cloudiness. Stratus fractus and Cumulonimbus capillatus were most frequently observed during total and high cloudiness on the low-level. In the warm season and the daytime, clouds are more isolated; in the cool season, they form more dense cloud layers.

Direct Observational Evidence of the Multi-scale, Dynamical Mass Accretion Toward a High-mass Star-forming Hub-filament System

There is growing evidence that high-mass star formation and hub-filament systems (HFS) are intricately linked. The gas kinematics along the filaments and the forming high-mass star(s) in the central hub are in excellent agreement with the new generation of global hierarchical high-mass star formation models. In this paper, we present an observational investigation of a typical HFS cloud, G310.142+0.758 (G310 hereafter), which reveals unambiguous evidence of mass inflow from the cloud scale via the filaments onto the forming protostar(s) at the hub conforming with the model predictions. Continuum and molecular line data from the ATOMS and MALT90 surveys that cover different spatial scales are used. Three filaments (with a total mass of 5.7 ± 1.1 × 103 M ⊙) are identified converging toward the central hub region where several signposts of high-mass star formation have been observed. The hub region contains a massive clump (1280 ± 260 M ⊙) harboring a central massive core. Additionally, five outflow lobes are associated with the central massive core implying a forming cluster. The observed large-scale, smooth, and coherent velocity gradients from the cloud down to the core scale, and the signatures of infall motion seen in the central massive clump and core, clearly unveil a nearly continuous, multi-scale mass accretion/transfer process at a similar mass infall rate of ∼10−3 M ⊙ yr−1 over all scales, feeding the central forming high-mass protostar(s) in the G310 HFS cloud.

Effects from Time Dependence of Ice Nucleus Activity for Contrasting Cloud Types

Abstract The role of time-dependent freezing of ice nucleating particles (INPs) is evaluated with the “Aerosol–Cloud” (AC) model in 1) deep convection observed over Oklahoma during the Midlatitude Continental Convective Cloud Experiment (MC3E), 2) orographic clouds observed over North California during the Atmospheric Radiation Measurement (ARM) Cloud Aerosol Precipitation Experiment (ACAPEX), and 3) supercooled, stratiform clouds over the United Kingdom, observed during the Aerosol Properties, Processes And Influences on the Earth’s climate (APPRAISE) campaign. AC uses the dynamical core of the WRF Model and has hybrid bin–bulk microphysics and a 3D mesoscale domain. AC is validated against coincident aircraft, ground-based, and satellite observations for all three cases. Filtered concentrations of ice (>0.1–0.2 mm) agree with those observed at all sampled levels. AC predicts the INP activity of various types of aerosol particles with an empirical parameterization (EP), which follows a singular approach (no time dependence). Here, the EP is modified to represent time-dependent INP activity by a purely empirical approach, using our published laboratory observations of time-dependent INP activity. In all simulated clouds, the inclusion of time dependence increases the predicted INP activity of mineral dust particles by 0.5–1 order of magnitude. However, there is little impact on the cloud glaciation because the total ice is mostly (80%–90%) from secondary ice production (SIP) at levels warmer than about −36°C. The Hallett–Mossop process and fragmentation in ice–ice collisions together initiate about 70% of the total ice, whereas fragmentation during both raindrop freezing and sublimation contributes <10%. Overall, total ice concentrations and SIP are unaffected by time-dependent INP activity. In the simulated APPRAISE case, the main causes of persistence of long-lived clouds and precipitation are predicted to be SIP in weak embedded convection and reactivation following recirculation of dust particles in supercooled layer cloud.

Quantitative Prediction of Sling Events in Turbulence at High Reynolds Numbers.

Collisional growth of droplets, such as occurring in warm clouds, is known to be significantly enhanced by turbulence. Whether particles collide depends on their flow history, in particular on their encounters with highly intermittent small-scale turbulent structures, which despite their rarity can dominate the overall collision rate. Here, we develop a quantitative criterion for sling events based on the velocity gradient history along particle paths. We show by a combination of theory and simulations that the problem reduces to a one-dimensional localization problem as encountered in condensed matter physics. The reduction demonstrates that the creation of slings is controlled by the minimal real eigenvalue of the velocity gradient tensor. We use fully resolved turbulence simulations to confirm our predictions and study their Stokes and Reynolds number dependence. We also discuss extrapolations to the parameter range relevant for typical cloud droplets, showing that sling events at high Reynolds numbers are enhanced by an order of magnitude for small Stokes numbers. Thus, intermittency could be a significant ingredient in the collisional growth of rain droplets.

Dimensional measurement of anti-stress cone of HV cable joints based on 3D point cloud

To solve the problem of automatic measurement of anti-stress cone of high voltage cable joints, a measurement algorithm based on 3D point cloud is proposed. Firstly, denoising and coordinate adjustment of point cloud of a cable joint are realized. Then the characteristic curve model of fitting circle radius indicating structure characteristic of cable joints is constructed by fitting circles with random sample consensus algorithm. After that, the discrete points on the characteristic curve are extracted according to the characteristics such as the fluctuation range of the characteristic curve for straight line fitting and the intersection of the fitted lines is calculated to complete the segmentation of the anti-stress cone area of the cable joint. Finally, the parameter measurement of the anti-stress cone was completed according to the structure of the anti-stress cone, and the error correction post-processing was performed. Two types of cable joint point clouds with standard dimensions and surface defects were used for measurement experiments, the absolute errors of the measurement results are less than 0.2 mm and 1.0 mm respectively, and the relative errors are less than 0.25% and 2% respectively, indicating that the proposed method has high robustness and measurement accuracy, which meet the measurement requirements in engineering practice.