CloudSim Research Articles

Decaying turbulence in molecular clouds: how does it affect filament networks and star formation?

ABSTRACT The fragmentation of gas to form stars in molecular clouds is intrinsically linked to the turbulence within them. These internal motions are set at the birth of the cloud and may vary with galactic environment and as the cloud evolves. In this paper, we introduce a new suite of 15 high-resolution 3D molecular cloud simulations using the moving mesh code arepo to investigate the role of different decaying turbulent modes (mixed, compressive, and solenoidal) and virial ratios on the evolution of a $10^4\, \mathrm{M}_{\odot }$ molecular cloud. We find that diffuse regions maintain a strong relic of the initial turbulent mode, whereas the initial gravitational potential dominates dense regions. Solenoidal seeded models thus give rise to a diffuse cloud with filament-like morphology, and an excess of brown dwarf mass fragments. Compressive seeded models have an early onset of star-formation, centrally condensed morphologies and a higher accretion rate, along with overbound clouds. 3D filaments identified using disperse and analysed through a new python toolkit we develop and make publicly available with this work called fiesta, show no clear trend in lengths, masses and densities between initial turbulent modes. Overbound clouds, however, produce more filaments and thus have more mass in filaments. The hubs formed by converging filaments are found to favour star-formation, with surprisingly similar mass distributions independent of the number of filaments connecting the hub.

Above-cloud concentrations of cloud condensation nuclei help to sustain some Arctic low-level clouds

Abstract. Previous studies have found that low-level Arctic clouds often persist for long periods even in the face of very low surface cloud condensation nuclei (CCN) concentrations. Here, we investigate whether these conditions could occur due to continuous entrainment of aerosol particles from the free troposphere (FT). We use an idealized large eddy simulation (LES) modeling framework, where aerosol concentrations are low in the boundary layer (BL) but increased up to 50× in the free troposphere. We find that the tests with higher tropospheric aerosol concentrations simulated clouds, which persisted for longer and maintained higher liquid water paths (LWPs). This is due to direct entrainment of the tropospheric aerosol into the cloud layer, which results in a precipitation suppression from the increase in cloud droplet number and in stronger cloud-top radiative cooling, which causes stronger circulations maintaining the cloud in the absence of surface forcing. Together, these two responses result in a more well-mixed boundary layer with a top that remains in contact with the tropospheric aerosol reservoir and can maintain entrainment of those aerosol particles. The surface aerosol concentrations, however, remained low in all simulations. The free-tropospheric aerosol concentration necessary to maintain the clouds is consistent with concentrations that are frequently seen in observations.

Raindrop Size Distributions Simulated Using a Bin Microphysics Scheme: Different Biases in Stratiform and Convective Rain From an Extratropical Cyclone

AbstractBin microphysics schemes prognose the raindrop size distribution (RSD), which can be directly evaluated through comparison with disdrometer observations. This evaluation will provide implications on the reliability of simulated cloud microphysics by bin microphysics schemes. In this study, the RSDs of a precipitation event associated with an extratropical cyclone passing South Korea are simulated using a bin microphysics scheme and compared with those observed by a ground‐based disdrometer. The simulated mean RSD overall agrees with the observation. However, notable overestimations appear in the large‐ (3.3–4.3 mm) and small‐ (0.56–1.88 mm) diameter ranges, which respectively stem from the biases in two different time periods, one dominated by stratiform rain and the other largely involved with convective rain. In the stratiform‐rain‐dominated period, the melting of snow is the largest contributor to RSDs. The overestimation in the large‐diameter range in this period can be associated with overly active ice–ice collection at upper levels, which generates a local maximum in RSD at the diameter of 3.3 mm that is not seen in the observed RSDs. In the convective‐rain‐involved period, the warm‐rain collision–coalescence is the largest contributor to RSDs. The overestimation in the small‐diameter range and underestimation in the large‐diameter range imply that the collisional growth of raindrops is represented to be weaker than that in reality. The findings in this study suggest that the RSDs simulated using a bin microphysics scheme can have some systematic biases associated with misrepresentation of some microphysical processes.

A Machine Learning Parameterization of Clouds in a Coarse‐Resolution Climate Model for Unbiased Radiation

AbstractCoarse‐grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse‐grained cloud fields from a fine‐grid reference model are a natural target for a machine‐learned parameterization. We machine‐learn the coarsened‐fine cloud properties as a function of coarse‐grid model state in each grid cell of NOAA's FV3GFS global atmosphere model with 200 km grid spacing, trained using a 3 km fine‐grid reference simulation with a modified version of FV3GFS. The ML outputs are coarsened‐fine fractional cloud cover and liquid and ice cloud condensate mixing ratios, and the inputs are coarse model temperature, pressure, relative humidity, and ice cloud condensate. The predicted fields are skillful and unbiased, but somewhat under‐dispersed, resulting in too many partially cloudy model columns. When the predicted fields are applied diagnostically (offline) in FV3GFS's radiation scheme, they lead to small biases in global‐mean top‐of‐atmosphere (TOA) and surface radiative fluxes. An unbiased global‐mean TOA net radiative flux is obtained by setting to zero any predicted cloud with grid‐cell mean cloud fraction less than a threshold of 6.5%; this does not significantly degrade the ML prediction of cloud properties. The diagnostic, ML‐derived radiative fluxes are far more accurate than those obtained with the existing cloud parameterization in the nudged coarse‐grid model, as they leverage the accuracy of the fine‐grid reference simulation's cloud properties.

The moist halo region around shallow cumulus clouds in large eddy simulations

AbstractIn this study, the moist buffering halo region of shallow maritime cumulus clouds is systematically investigated using large eddy simulations with various grid resolutions and numerical choices. Autocorrelation analyses of cloud liquid water and relative humidity suggest a converged size of 200–300 m for moist patches outside clouds when the model resolution is below 50 m, but may overestimate this size due to noncloudy moist regions. Based on a composite analysis, the structure of the moist halo immediately outside individual clouds is examined. It is found that, regardless of model resolution, the distribution of relative humidity in the halo region does not depend on cloud size, but on the real distance away from the cloud boundary, indicating some size‐independent length scales are responsible for the halo formation. The relative humidity decays with distance more quickly with finer horizontal resolution, which is possibly related to the model resolution dependence of the cloud spectrum. The halo size near the cloud base is larger than that within the cloud layer and this feature is robust across all simulations. Further analyses of backward and forward Lagrangian trajectories originating from the moist halo region reveal the possible role for subcloud coherent structures in cloud‐base halo formation. Possible mechanisms explaining cloud halo sizes and associated length scales are discussed.

Inclusion of the radiative effect of deep convective clouds in the Eta model simulations

AbstractConvective clouds play an important role in the local energy budget by directly interacting with solar and terrestrial radiation. However, radiation parameterization schemes of atmospheric models generally consider clouds produced from microphysics schemes or some other grid saturation criteria. Deep convective parameterization schemes tend to rain out the convective cloud before the radiation scheme perceives its water load. This may be a source of the positive bias of the incoming solar radiation at the surface. The objective of this work is to include the effects of deep convective clouds in the radiation scheme of the regional Eta model and to evaluate the impacts on the net radiative energy and other meteorological variables. The radiation scheme is the Rapid Radiative Transfer Model. The work is developed in four stages. The positive bias in the incoming solar radiation was diagnosed in the first stage. In the second stage, the parameters of the convective parameterization scheme were modified to increase convective precipitation. In the third stage, the parameters of the microphysics scheme were modified to increase explicit clouds. In the fourth and last stage, in addition to the previous modifications, the condensates from the convective parameterization were input into the radiation scheme. The runs were performed for a period of one summer rainy month with intense convective activity over South America. Including deep convective cloud condensates into the radiation scheme improved the cloud cover, the diurnal cycle of the surface net radiation, and the 2‐m temperature. However, the reduction of the net radiation at the surface caused the reduction of the available energy for convective instability and, consequently, the precipitation reduction. The results show the importance of including cumulus cloud water load in the radiative scheme for bias reduction in the radiative energy components.

Integrating IoT with WSNs using fuzzy logic-based middleware

With the rapid adoption of cloud storage for business and personal use, data security has become a significant concern. This study investigates the effectiveness of advanced encryption algorithms to ensure the integrity, confidentiality, and availability of data stored in cloud environments. The research focuses on the implementation and evaluation of three encryption algorithms: AES-256, ChaCha20, and Threefish, comparing their performance in terms of computational complexity, key generation, and resistance to various attacks. The study utilizes a testbed consisting of a simulated cloud storage environment, where the encryption algorithms are deployed and assessed based on encryption/decryption time and throughput. Results indicate that the ChaCha20 algorithm outperforms both AES-256 and Threefish in terms of encryption/decryption speed while maintaining strong security. Moreover, the findings suggest that the combination of these encryption algorithms can enhance data security by providing a multi-layered defense mechanism against potential threats. The research contributes to the advancement of cloud storage security by identifying optimal encryption algorithms and proposing a robust solution for safeguarding sensitive information.

Effects of smoke on marine low clouds and radiation during 2020 western United States wildfires

Biomass burning aerosols (BBAs) influence the climate directly by scattering and absorbing sunlight and indirectly by changing the cloud properties through serving as cloud condensation nuclei (CCN) or ice-nucleating particles (INPs). Here we used the WRF-Chem model to simulate the transport of BBAs and their interactions with clouds and radiation during the western United States wildfires in September 2020. The simulated cloud and aerosol fields are comparable to the satellite observations from CALIPSO and MODIS. It is shown that smoke exerts negative radiative effects over both ocean and land. Over the nearly cloudless land, the negative radiative effect is caused by aerosols reflecting shortwave radiation. However, over the ocean covered by stratocumulus, aerosol-cloud interaction (ACI) induces a significant negative radiative effect (−25.6 W/m2 on average), because the reduction of droplet radius not only increases the brightness of clouds and prolongs their lifespan, but also cools their surroundings and increases relative humidity, which in turn increases cloud water content. On the contrary, aerosol-radiation interaction (ARI) leads to a positive radiative effect (+10.3 W/m2 on average) over the oceanic regions due to the absorption of sunlight reflected by low clouds and a slight decrease in cloud water. In our research, the counterclockwise vortex over the Northeast Pacific transports the smoke to remote regions and lifts the smoke to 4–8 km height. This results in a wide-ranging impact of the smoke on clouds and radiation, yet it diminishes the magnitude of ARI's effect on marine stratocumulus. Moreover, over ocean, ARI results in cooling at the cloud top and warming close to the sea surface, leading to a rise in marine stratocumulus height. These vertical atmospheric adjustments within the PBL differ from the reduced surface temperature with suppressed PBL over terrestrial regions.

A full‐probabilistic cloud analysis for structural seismic fragility via decoupled M‐PDEM

AbstractPerformance‐based earthquake engineering (PBEE) is essential for ensuring engineering safety. Conducting seismic fragility analysis within this framework is imperative. Existing methods for seismic fragility analysis often rely heavily on double loop reanalysis and empirical data fitting, leading to challenges in obtaining high‐precision results with a limited number of representative structural analysis instances. In this context, a new methodology for seismic fragility based on a full‐probabilistic cloud analysis is proposed via the decoupled multi‐probability density evolution method (M‐PDEM). In the proposed method, the assumption of a log‐normal distribution is not required. According to the random event description of the principle of preservation of probability, the transient probability density functions (PDFs) of intensity measure (IM) and engineering demand parameter (EDP), as key response quantities of the seismic‐structural system, are governed by one‐dimensional Li‐Chen equations, where the physics‐driven forces are determined by representative analysis data of the stochastic dynamic system. By generating ground motions based on representative points of basic random variables and performing structural dynamic analysis, the decoupled M‐PDEM is employed to solve the one‐dimensional Li‐Chen equations. This yields the joint PDF of IM and EDP, as well as the conditional PDF of EDP given IM, resulting in seismic fragility analysis outcomes. The numerical implementation procedure is elaborated in detail, and validation is performed using a six‐story nonlinear reinforced concrete (RC) frame subjected to non‐stationary stochastic ground motions. Comparative analysis against Monte Carlo simulation (MCS) and traditional cloud analysis based on least squares regression (LSR) reveals that the proposed method achieves higher computational precision at comparable structural analysis costs. By directly solving the physics‐driven Li‐Chen equations, the method provides the full‐probabilistic joint information of IM and EDP required for cloud analysis, surpassing the accuracy achieved by traditional methods based on statistical moment fitting and empirical distribution assumptions.

On the evolution of the observed mass-to-length relationship for star-forming filaments

ABSTRACT The interstellar medium is threaded by a hierarchy of filaments from large scales (∼100 pc) to small scales (∼0.1 pc). The masses and lengths of these nested structures may reveal important constraints for cloud formation and evolution, but it is difficult to investigate from an evolutionary perspective using single observations. In this work, we extract simulated molecular clouds from the ‘Cloud Factory’ galactic-scale ISM suite in combination with 3D Monte Carlo radiative transfer code polaris to investigate how filamentary structure evolves over time. We produce synthetic dust continuum observations in three regions with a series of snapshots and use the filfinder algorithm to identify filaments in the dust derived column density maps. When the synthetic filaments mass and length are plotted on an mass–length (M–L) plot, we see a scaling relation of L ∝ M0.45 similar to that seen in observations, and find that the filaments are thermally supercritical. Projection effects systematically affect the masses and lengths measured for the filaments, and are particularly severe in crowded regions. In the filament M–L diagram we identify three main evolutionary mechanisms: accretion, segmentation, and dispersal. In particular we find that the filaments typically evolve from smaller to larger masses in the observational M–L plane, indicating the dominant role of accretion in filament evolution. Moreover, we find a potential correlation between line mass and filament growth rate. Once filaments are actively star forming they then segment into smaller sections, or are dispersed by internal or external forces.

Dynamic Knowledge Management in an Agent-Based Extended Green Cloud Simulator

Cloud infrastructures operate in highly dynamic environments, and today, energy-focused optimization become crucial. Moreover, the concept of extended cloud infrastructure, which, among others, uses green energy, started to gain traction. This introduces a new level of dynamicity to the ecosystem, as “processing components” may “disappear” and “come back”, specifically in scenarios where the lack/return of green energy leads to shutting down/booting back servers at a given location. Considered use cases may involve introducing new types of resources (e.g., adding containers with server racks with “next-generation processors”). All such situations require the dynamic adaptation of “system knowledge”, i.e., runtime system adaptation. In this context, an agent-based digital twin of the extended green cloud infrastructure is proposed. Here, knowledge management is facilitated with an explainable Rule-Based Expert System, combined with Expression Languages. The tests were run using Extended Green Cloud Simulator, which allows the modelling of cloud infrastructures powered (partially) by renewable energy sources. Specifically, the work describes scenarios in which: (1) a new hardware resource is introduced in the system; (2) the system component changes its resource; and (3) system user changes energy-related preferences. The case study demonstrates how rules can facilitate control of energy efficiency with an example of an adaptable compromise between pricing and energy consumption.

Research on Ground Object Echo Simulation of Avian Lidar

The clutter suppression effect of ground objects significantly impacts the detection and tracking performance of avian lidar on low-altitude bird flock targets. It is imperative to simulate the point cloud data of ground objects in lidar to explore effective methods for suppressing clutter caused by ground objects in avian lidar. The traditional ray-tracing method is enhanced in this paper to efficiently obtain the point cloud simulation results of ground objects. By incorporating a beam constraint and a light-energy constraint, the screening efficiency of effective rays is improved, making them more suitable for simulating large scenes with narrow lidar beams. In this paper, a collision detection scheme is proposed based on beam constraints, aiming to significantly enhance the efficiency of ray-tracing collision detection. The simulation and experimental results demonstrate that, in comparison with other conventional simulation methods, the proposed method yields the point cloud results of ground objects that exhibit greater conformity to the actual lidar-collected point cloud results in terms of shape characteristics and intensity features. Additionally, the simulation speed is significantly enhanced.

Modeling collision–coalescence in particle microphysics: numerical convergence of mean and variance of precipitation in cloud simulations using the University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1

Modeling collision–coalescence in particle microphysics: numerical convergence of mean and variance of precipitation in cloud simulations using the University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1

The Impact of Radiative Transfer at Reduced Spectral Resolution in Large‐Eddy Simulations of Convective Clouds

AbstractMany radiative transfer schemes approximate the spectral integration over ∼105 to ∼106 wavelengths with correlated k‐distributions methods that typically require only 101–102 spectral integration points (g‐points). The exact number of g‐points is then chosen as an optimal balance between computational costs and accuracy, normally assessed in terms of a number of radiative quantities. How this radiative accuracy propagates to simulation accuracy, however, is not straightforward. In this study, we therefore explore the sensitivity of cloud properties in large‐eddy simulations (LES) to the accuracy of radiative fluxes and heating rates. We first generate smaller sets of g‐points from existing k‐distributions by repeatedly combining adjacent g‐points while maintaining the highest possible accuracy on a chosen set of radiative metrics. Next, we perform three sets of LES with varying cloud—radiation coupling pathways, and therefore different requirements for the accuracy of the radiative transfer computations, to investigate how these smaller and thus less accurate k‐distributions affect simulation characteristics. The decrease in radiative accuracy with 3–4 times smaller k‐distributions results in biases in cloud properties that are relative small compared to their temporal fluctuations. These results show potential for speeding up radiative transfer computations in cloud‐resolving models by reducing the resolved spectral detail. However, more statistically converged simulations and a wider set of case studies is required to fully assess the robustness of our results.

Impacts of a double-moment bulk cloud microphysics scheme (NDW6-G23) on aerosol fields in NICAM.19 with a global 14 km grid resolution

Abstract. In accordance with progression in current capabilities towards high-resolution approaches, applying a convective-permitting resolution to global aerosol models helps comprehend how complex cloud–precipitation systems interact with aerosols. This study investigates the impacts of a double-moment bulk cloud microphysics scheme, i.e., NICAM Double-moment bulk Water 6 developed in this study (NDW6-G23), on the spatiotemporal distribution of aerosols in the Nonhydrostatic ICosahedral Atmospheric Model as part of the version-19 series (NICAM.19) with 14 km grid spacing. The mass concentrations and optical thickness of the NICAM-simulated aerosols are generally comparable to those obtained from in situ measurements. However, for some aerosol species, especially dust and sulfate, the differences between experiments of NDW6 and of the NICAM single-moment bulk module with six water categories (NSW6) were larger than those between experiments with different horizontal resolutions (14 and 56 km grid spacing), as shown in a previous study. The simulated aerosol burdens using NDW6 are generally lower than those using NSW6; the net instantaneous radiative forcing due to aerosol–radiation interaction (IRFari) is estimated to be −1.36 W m−2 (NDW6) and −1.62 W m−2 (NSW6) in the global annual mean values at the top of the atmosphere (TOA). The net effective radiative forcing due to anthropogenic aerosol–radiation interaction (ERFari) is estimated to be −0.19 W m−2 (NDW6) and −0.23 W m−2 (NSW6) in the global annual mean values at the TOA. This difference among the experiments using different cloud microphysics modules, i.e., 0.26 W m−2 or 16 % difference in IRFari values and 0.04 W m−2 or 16 % difference in ERFari values, is attributed to a different ratio of column precipitation to the sum of the column precipitation and column liquid cloud water, which strongly determines the magnitude of wet deposition in the simulated aerosols. Since the simulated ratios in the NDW6 experiment are larger than those of the NSW6 result, the scavenging effect of the simulated aerosols in the NDW6 experiment is larger than that in the NSW6 experiment. A large difference between the experiments is also found in the aerosol indirect effect (AIE), i.e., the net effective radiative forcing due to aerosol–cloud interaction (ERFaci) from the present to preindustrial days, which is estimated to be −1.28 W m−2 (NDW6) and −0.73 W m−2 (NSW6) in global annual mean values. The magnitude of the ERFaci value in the NDW6 experiment is larger than that in the NSW6 result due to the differences in both the Twomey effect and the susceptibility of the simulated cloud water to the simulated aerosols between NDW6 and NSW6. Therefore, this study shows the importance of the impacts of the cloud microphysics module on aerosol distributions through both aerosol wet deposition and the AIE.

Distinct Mixing Regimes in Shallow Cumulus Clouds

AbstractUnderstanding the nature of mixing between cloudy air and its surroundings is an important and yet, open question. In this research, we use high‐resolution (10 m) bin‐microphysics Large Eddy Simulation of a cumulus cloud, together with a Lagrangian passive tracer tracking method, to study mixing. We analyze the passive tracers as a function of their trajectories and the thermodynamic conditions they undergo inside and outside the cloud. Three main mixing regimes (core, periphery, and skin) are identified, each determining a subset of tracers with similar trajectories. These mixing regimes can be observed throughout the cloud's lifetime and they provide evidence for the presence of an undiluted core in shallow cumulus clouds. At the dissipation stage, a fourth regime is identified: cloud‐top entrainment followed by downdrafts.

Low-level Arctic clouds: a blind zone in our knowledge of the radiation budget

Abstract. Quantifying the role of clouds in the earth's radiation budget is essential for improving our understanding of the drivers and feedback mechanisms of climate change. This holds in particular for the Arctic, the region currently undergoing the most rapid changes. This region, however, also poses significant challenges to remote-sensing retrievals of clouds and radiative fluxes, introducing large uncertainties in current climate data records. In particular, low-level stratiform clouds are common in the Arctic but are, due to their low altitude, challenging to observe and characterize with remote-sensing techniques. The availability of reliable ground-based observations as reference is thus of high importance. In the present study, radiative transfer simulations using state-of-the-art ground-based remote sensing of clouds are contrasted with surface radiative flux measurements to assess their ability to constrain the cloud radiative effect. Cloud radar, lidar, and microwave radiometer observations from the PS106 cruise in the Arctic marginal sea ice zone in summer 2017 were used to derive cloud micro- and macrophysical properties by means of the instrument synergy approach of Cloudnet. Closure of surface radiative fluxes can only be achieved by a realistic representation of the low-level liquid-containing clouds in the radiative transfer simulations. The original, most likely erroneous, representation of these low-level clouds in the radiative transfer simulations led to errors in the cloud radiative effect of 54 W m−2. In total, the proposed method could be applied to 11 % of the observations. For the data, where the proposed method was utilized, the average relative error decreased from 109 % to 37 % for the simulated solar and from 18 % to 2.5 % for the simulated terrestrial downward radiative fluxes at the surface. The present study highlights the importance of jointly improving retrievals for low-level liquid-containing clouds which are frequently encountered in the high Arctic, together with observational capabilities both in terms of cloud remote sensing and radiative flux observations. Concrete suggestions for achieving these goals are provided.

Understanding changes in cloud simulations from E3SM version 1 to version 2

Abstract. This study documents clouds simulated by the Energy Exascale Earth System Model (E3SM) version 2 (E3SMv2) and attempts to understand what causes the model behavior change in clouds relative to E3SMv1. This is done by analyzing the last 30-year (1985–2014) data from the 165-year historical simulations using E3SMv1 and v2 and four sensitivity tests to isolate the impact of changes in model parameter choices in its turbulence, shallow convection, and cloud macrophysics parameterization (Cloud Layers Unified By Binormals, CLUBB); microphysical parameterization (MG2); and deep-convection scheme (ZM), as well as model physics changes in convective triggering. It is shown that E3SMv2 significantly improves the simulation of subtropical coastal stratocumulus clouds and clouds with optical depth larger than 3.6 over the stratocumulus-to-cumulus transition regimes, where the shortwave cloud radiative effect (SWCRE) is also improved, and the Southern Ocean (SO) while seeing an overall slight degradation in low clouds over other tropical and subtropical oceans. The better performance in E3SMv1 over those regions is partially due to error compensation between its simulated optically thin and intermediate low clouds for which E3SMv2 actually improves simulation of optically intermediate low clouds. Sensitivity tests indicate that the changes in low clouds are primarily due to the tuning done in CLUBB. The impact of the ZM tuning is mainly on optically intermediate and thick high clouds, contributing to an improved SWCRE and longwave cloud radiative effect (LWCRE). The impact of the MG2 tuning and the new convective trigger is primarily on the high latitudes and the SO. They have a relatively smaller impact on clouds than CLUBB tuning and ZM tuning do. This study offers additional insights into clouds simulated in E3SMv2 by utilizing multiple data sets and the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) diagnostic tool as well as sensitivity tests. The improved understanding will benefit future E3SM developments.

XLPE cable joint defects measurement method based on point cloud remapping

For the problems of low detection accuracy caused by weak texture and strong reflection on the surface of Cross-linked polyethylene (XLPE) high-voltage cable joint, this paper proposed a joint point cloud remapping and image segmentation method for the cable joint defects measurement, which improved the defects measurement accuracy from the correlation of the disordered point cloud and the salience of defects. Firstly, the discrete noise in the joint point cloud is removed using a modified radius filtering algorithm. Secondly, for the problems of disorderliness and weak association of the cable joint point cloud, we utilized Rodrigues' rotation formula (RRF) to correct the position of the XLPE cable joint point cloud. Then, for the demand of improving feature expression of non-significant defects, a point cloud remapping method is proposed, which remapping insignificant defects point cloud into saliency image for more effective detection. Lastly, an image segmentation method based on 8-neighborhood growth is applied for explicit defects detection, and the detected defects are quantified by the proposed measurement approach. Extensive experiments are conducted on the collected cable joints' point cloud and simulated cable joints' point cloud. With the help of the proposed method, we can achieve the best effects in DE, FR quantitative comparison, and the measurement inaccuracies are less than 4.5%. Experimental results demonstrate that the proposed method enjoys state-of-the-art performance in cable joint defects measurement.

Leaf properties estimation enhancement over heterogeneous vegetation by correcting for terrestrial laser scanning beam divergence effect

The need for an accurate description of forest structure is increased as it is a key for monitoring carbon storage, energy, and mass exchange, and different ecosystem functions. The leaf area index (LAI), the leaf area density (ul), and the leaf inclination distribution function (LIDF) are regarded as important and critical keys allowing the characterization of the forest structure. The dependency between these properties and the heterogeneity of the forest makes their estimation challenging and delicate. In this context, despite the expanding body of works reporting forest structure properties retrieval using infinitesimal footprint size terrestrial LiDAR system (TLS) data, there seem to be few works evaluating the influence of the TLS beam divergence effect (i.e. its size increase with distance from the sensor), on the estimation accuracy, even though the influence is not negligible. In this study, we aim to jointly estimate LAI/ul and LIDF using non-infinitesimal footprint size TLS data based on forward/inverse radiative transfer (RT) modeling. Our approach assumes a uniform distribution of azimuth angles enabling us to attain valuable insights into inclination angles and leaf area density while simplifying our computational efforts. The forward modeling allows an understanding of the interaction between vegetation structure properties and TLS light beams, while the inversion allows retrieving the properties given the TLS data. The inversion technique is based on the maximum likelihood estimator (MLE). Our problem turns out to be a non-linear and high-dimensional cost function to be optimized. To tackle this complexity, the global optimization technique, the shuffled complex evolution (SCE-UA), is adapted to find the globally optimal solution. Our developed approach is validated with simulated and actual forest TLS point clouds acquired from Estonian Pine, Birch, and Spruce stands. Our findings using simulated data prove that our estimates are in agreement with the actual ul and LIDF values with (MAEul∈0.00120.0043) and (RMSEul∈0.01370.0224). In the actual case, RMSELAI for Pine, Birch and Spruce stands are 0.1066, 0.014, and 0.070, respectively. In all cases, our approach outperforms state-of-the-art techniques.