Line Clouds Research Articles

Characterization of Line Ratio Variations in Orion A

Here we use a new method to study the correlation between different molecular emission lines in star-forming molecular dust clouds. This method is applied to maps from the Line Emission as a Tool for Galaxy Observations project (12CO, 13CO, C18O, N2H+, CCH, HCN, and CN) in Orion A. Our data shows the correlation properties change within dust clouds in Orion A, suggesting that the physical properties change as well. We are preparing our code and data to be publicly accessible in an online repository.

Sea breeze-driven effects on wind down-ramps: Implications for wind farms along the north-east coast of Brazil

This research focuses on understanding a meteorological phenomenon that is related to operational disruptions in a wind power plant on the North-East coast of Brazil. The study employed enhanced weather observations based on SODAR and LIDAR wind profilers, micrometeorological masts, ERA-5 reanalysis data, wind power production data from Brazilian power grid operators, and GOES-16 thermal and visible imagery. On August 15, 2023, Brazil experienced a widespread blackout, which was largely caused by down-ramp events that affected several wind farms at the same time. The factors leading to the phenomenon were categorized into three driven-dependent categories: i) a sharp fall in dew-point temperature, ii) the presence of a cumulus cloud line just above the coastline in the early morning, and/or iii) rainfall over the adjoining sea, providing the feasibility to nowcasting this severe wind ramp. The results derived from the conceptual model suggest that sea-breezes play a significant role in the net wind at the turbine hub-height, and often exceed the influence of large-scale flow. The net wind is considered to be caused by the interplay between trade winds and consistent breezes. It is hoped this research can provide a valuable insight into the complex interactions between local meteorological phenomena and wind energy production on the North-East coast of Brazil, and that its application has the potential to improve the operational forecasting of wind power plants.

A Deep-Learning-Based Method for Extracting an Arbitrary Number of Individual Power Lines from UAV-Mounted Laser Scanning Point Clouds

In recent years, laser scanners integrated with Unmanned Aerial Vehicles (UAVs) have exhibited great potential in conducting power line inspections in harsh environments. The point clouds collected for power line inspections have numerous advantages over remote image data. However, point cloud-based individual power line extraction, which is a crucial technology required for power line inspections, still poses several challenges such as massive 3D points, imbalanced category points, etc. Moreover, in various power line scenarios, previous studies often require manual setup and careful adjustment of different thresholds to separate different power lines, which is inefficient for practical applications. To handle these challenges, in this paper, we propose a multi-branch network to automatically extract an arbitrary number of individual power lines from point clouds collected by UAV-based laser scanners. Specifically, to handle the massive 3D point clouds in complex outdoor scenarios, we propose to leverage deep neural network for efficient and rapid feature extraction in large-scale point clouds. To mitigate imbalanced data quantities across different categories, we propose to design a weighted cross-entropy loss function to measure the varying importance of each category. To achieve the effective extraction of an arbitrary number of power lines, we propose leveraging a loss function to learn the discriminative features that can differentiate the points belonging to different power lines. Once the discriminative features are learned, the Mean Shift method can distinguish the individual power lines by clustering without supervision. The evaluations are executed on two datasets, which are acquired at different locations with UAV-mounted laser scanners. The proposed method has been thoroughly tested and evaluated, and the results and discussions confirm its outstanding ability to extract an arbitrary number of individual power lines in point clouds.

The snow‐shadow effect of Sado Island on Niigata City and the coastal plain

AbstractJapan's Hokuriku region, near the Sea of Japan, typically experiences heavy snowfall; however, Niigata City, the largest city on the Sea of Japan side, experiences lower levels of snowfall than neighbouring cities. This study investigates the snow‐shadow effect of Sado Island on snowfall in Niigata City, located 45 km away leeward. Statistical analysis of long‐term radar data for 10 winters showed that snow‐shadow effects in the Niigata plain occurred in 151 (80%) of the 188 cases, during which a strong approaching wind reached the island. The location of this snow‐shadow effect was always downwind of Sado Island and depended on the wind direction. Numerical experiments using the Weather Research and Forecasting model predicted that snowfall over the Niigata Plain would be lighter with the island than without it. Additionally, the snow‐shadow effect occurs in areas more than 150 km downwind. The experiments showed that Sado Island reduces heat fluxes from the sea surface by weakening leeward winds. At the same time, the horizontal wind convergence downwind is weakened. Meanwhile, the orographic snowfall over Sado Island reduces the amount of water vapour, cloud water and cloud ice over the leeward sea. Therefore, Sado Island prevents cloud lines from redeveloping over the leeward sea and can further reduce snowfall over the leeward plain, including in Niigata City.

Method for detecting pipeline spatial attitude using point cloud alignment

This paper introduces a new method for detecting the spatial attitude of pipeline welding based on point cloud registration. The method aligns the measured point cloud of the pipeline with the CAD model point cloud using point cloud registration and guides the pipeline adjustment accordingly. This automated measurement method has resolved the inefficiencies and potential for rework that can arise from manual instrument operation during pipeline pose detection, and has subsequently improved detection efficiency and accuracy. In addition, we propose a coarse registration algorithm based on the axial line of pipeline point clouds to help facilitate the alignment process. We conducted simulation experiments using 3D CAD models, which demonstrated that our coarse registration algorithm outperformed the other three compared methods in terms of accuracy. The simulation experiments also proved that the absolute value of the overall method's relative error was less than 0.1%.

A point cloud-based welding trajectory planning method for plane welds

Plane welds are a common type of weld in industrial sites. When the welding robot welds multiple types of plane welds at the same time, the traditional teaching and programming modes become relatively complicated. Therefore, in order to solve the problem of automatic robot welding of various types of plane welds, taking plane V-type butt, plane I-type butt, and plane lap welds as examples, this paper proposes a plane weld extraction method based on 3D vision. Firstly, in order to realize the line and plane segmentation of the workpiece point cloud, we establish the concept of plane point cloud density. Secondly, to segment workpiece edge lines, an iterative segmentation algorithm based on RANSAC is proposed. Then, based on the geometric features of the workpiece, a method for extracting weld feature points based on centroid positioning is proposed. Finally, the least squares method is used to fit the feature points of the welding seam to complete the welding trajectory planning. The experimental results show that the method can well solve the problem of automatic planning welding trajectory of plane V-type butt, plane I-type butt, and plane lap welds, to realize that the welding robot can simultaneously weld various plane welds without teaching and programming.

Causes and impact of extreme Low Level Wind Shear (LLWS) event at Soekarno-Hatta International Airport

The impact of low-level wind shear (LLWS) is still a critical issue in aviation. These days, as the aviation industry grows, it must follow safety improvement efforts and transportation management, one of which is through the accurate publishing of LLWS early warning. Therefore, it takes a high understanding related to phenomena, especially at airports with busy schedules. This study examined the causes and impacts of pilot-reported LLWS on April 5, 2018, from 13:15 to 16:00 UTC at Soekarno-Hatta International Airport which is the busiest airport in Indonesia. The data used are Doppler weather radar, automated weather observing system (AWOS), as well as synoptic and upper air observations. Based on the result of this research, linear convective clouds created from deep convection due to very unstable atmospheric circumstances are the cause of LLWS. A sizeable line of convective cloud with a 50-55 dBZ reflectivity accompanied by an outflow microburst (MBA) was detected crashing the airport. Heavy rains and strong winds up to 15-20 m/s speeds from the line of convective cloud triggered the formation of destructive LLWS. Subsequently, there was traffic build-up at 5 flight holding points before landing. Furthermore, 7 flights (Airbus, Boeing, and CRJX) underwent go-around, and 3 flights (Airbus and Boeing) were diverted.

Sea-Breeze Front Research Based on Remote Sensing Methods in Coastal Baltic Sea Climate: Case of Lithuania

Sea breezes, as one of the most important local varieties of daily wind dynamics, are responsible for the formation of the climate by coasts of large bodies of water. In recent decades, due to climate change, the air temperature is rising, causing larger temperature gradients to form and the dynamics of the atmosphere to change globally and locally. This research investigated the spread of sea breezes in the years 2018–2019 during the warm period of the year (June, July, and August) to the mainland territory of the southeastern Baltic and coastal Lithuania by applying in situ and remote methods. The results of the study showed that sea-breeze fronts are better identified by the formation line of convective clouds in the continental part seen in remote images. During the first half of the day (until noon), the effect of sea breezes extends on average about 20–30 km from the coast of the sea. However, maximum extension of the breeze fronts can penetrate the continent much further than previously thought. During the summer, when the westward movement of air masses prevails, the band of cumulus (Cu) clouds formed by the sea breeze marks the front of the sea breeze, and at the time of the most extended spread (around 5 pm) in the continental part of Lithuania, the sea-breeze front is an average of around 60 km away from the seacoast. Until noon, the area covered by sea breezes in the western part of Lithuania extends over 1886.2 km2. During the second half of the day, the spatial spread of the breeze impacts an average area of about 6445.2 km2 by around 5 pm. Hence, the sea breeze affects not only the coastal climate region of Lithuania, as previously recognized, but it also affects the climate of part of the region of the Samogitian (Žemaitijos) Uplands of Lithuania. Remote-sensing methods helped to identify sea-breeze fronts and evaluate the limits of marine climate expansion along the seashore. The methods used in this work can play a role in answering the question of how climate change can affect the coastal climate.

A Spatiotemporal Calibration Algorithm for IMU-LiDAR Navigation System Based on Similarity of Motion Trajectories.

The fusion of light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensing information can effectively improve the environment modeling and localization accuracy of navigation systems. To realize the spatiotemporal unification of data collected by the IMU and the LiDAR, a two-step spatiotemporal calibration method combining coarse and fine is proposed. The method mainly includes two aspects: (1) Modeling continuous-time trajectories of IMU attitude motion using B-spline basis functions; the motion of the LiDAR is estimated by using the normal distributions transform (NDT) point cloud registration algorithm, taking the Hausdorff distance between the local trajectories as the cost function and combining it with the hand–eye calibration method to solve the initial value of the spatiotemporal relationship between the two sensors’ coordinate systems, and then using the measurement data of the IMU to correct the LiDAR distortion. (2) According to the IMU preintegration, and the point, line, and plane features of the lidar point cloud, the corresponding nonlinear optimization objective function is constructed. Combined with the corrected LiDAR data and the initial value of the spatiotemporal calibration of the coordinate systems, the target is optimized under the nonlinear graph optimization framework. The rationality, accuracy, and robustness of the proposed algorithm are verified by simulation analysis and actual test experiments. The results show that the accuracy of the proposed algorithm in the spatial coordinate system relationship calibration was better than 0.08° (3δ) and 5 mm (3δ), respectively, and the time deviation calibration accuracy was better than 0.1 ms and had strong environmental adaptability. This can meet the high-precision calibration requirements of multisensor spatiotemporal parameters of field robot navigation systems.

IDENTIFICATION OF TROPICAL SQUALL LINE USING INFRARED CHANNEL HIMAWARI-8 SATELLITE IMAGERY (CASE STUDY OF 6-7 DECEMBER 2020 IN THE INDIAN OCEAN)

<p>Tropical squall line is a linear type of Mesoscale Convective Systems (MCS) phenomenon. On December 6-7, 2020, the Infrared (IR1) Himawari-8 satellite image in the Indian Ocean of Indonesian region, shows a cloud line identified as the tropical squall line. This study aims to identify the characteristics of the tropical squall line phenomenon that occurs in the Indian Ocean south of West Java using Himawari-8 Infrared (IR1) satellite imagery. Satellite image data is processed using an algorithm adapted to the MCC Maddox 1980 criteria. Furthermore, an objective analysis is carried out on the data based on the criteria from previous studies. The result shows that the tropical squall occurred for 19 hours with the initial type of tropical squall formation as intersecting convective band. In the mature stage, the trailing stratiform region and convective line develops an asymmetric pattern and shows a vortex (Mesoscale Convective Vortices) that forms inside the stratiform region. The result of rainfall distribution using the GSMaP model shows a category of heavy rain with rainfall in tropical squall areas exceeding 10 mm per hour.</p>

LEGO: A 3 mm molecular line study covering 100 pc of one of the most actively star-forming portions in the Milky Way

The current generation of high-sensitivity (sub)mm-telescopes such as the IRAM-30m and NOEMA have allowed the emission from molecular lines to become a major tool for studying the physical, kinematic and chemical properties of extragalactic systems. However, we need to develop a detailed understanding of emission lines in Milky Way molecular clouds before we can exploit extragalactic line emission data to its full potential. With this in mind, the IRAM-30m large project “Line Emission to assess Galaxy Observations” (LEGO) aims at developing a comprehensive picture of how 3mm-band emission lines in MilkyWay molecular clouds depend on a variety of environmental factors. In this proceeding, I will focus on our recent detailed investigation of the W49 massive star-forming region published in Barnes et al. 2020 [1].

A Point Cloud Vector Tracing Algorithm for Automatic Drawing of Interior Plan

Objectives According to the spatial attitude characteristics of the indoor walls, a point cloud vector tracking algorithm for automatically drawing the indoor plan was proposed. Methods Firstly, the indoor wall point cloud with a certain thickness was intercepted and projected onto the horizontal plane, and square grids were established on the projection plane. The plane points after projection were segmented and managed, and the point cloud in every grid was clustered by the eight neighborhood algorithm. Then the grid center of gravity method was used to generate sparse points, which were tracked and sorted according to the distance between points in the neighborhood and the angle of connection vector. The vector sequence of tracking direction was established, and the point cloud segmentation of different walls was completed combined with mutation of angle between adjacent values in the vector sequence. Finally, the least squares algorithm was used to fit the line of the same wall point cloud, and the intersection point of the adjacent wall line was obtained. Results The plane projection line segment of the house wall and its spatial connection relationship were established. The wall projection line segment was output in sequence. The drawing of the house plan was completed and the DXF format data exchange file was exported. Conclusions The proposed algorithm was verified by the analysis of indoor scanning data of a residential building. The results show that the proposed algorithm can accurately and quickly analyzed and processed the indoor laser scanning point cloud, and completed the drawing of interior plan.

Convergence line in the English Channel

How did a narrow line of convergence cloud form in the English Channel on 9 January 2021? Occasionally, in a northeasterly flow of limited instability, a line of cloud may be seen extending from the Strait of Dover along the English Channel. This short paper describes how one of these cloud lines formed on 9 January 2021.

Mechanism for the Formation of Arc-Shaped Cloud Lines over the Tropical Oceans

AbstractSatellite images frequently show mesoscale arc-shaped cloud lines with a spacing of several tens of kilometers. These clouds form in a shallow mixed boundary layer in locations where the near-surface horizontal wind speed exceeds ~7 m s−1. Unlike other mesoscale cloud line phenomena, such as horizontal convective rolls, these cloud lines do not align with the wind direction but form at large oblique angles to the near-surface wind. A particularly distinct event of this pattern developed on 31 January 2020 over the western tropical Atlantic Ocean. Radiosonde soundings are available for this time and location, allowing a detailed analysis. By comparing observations with theoretical predictions that are based on Jeffreys’s drag-instability mechanism, it is shown that drag-instability waves may contribute to the formation of this cloud pattern. The theory is formulated in only two dimensions and predicts that wavelike horizontal wind perturbations of this wavelength can grow, because they modulate the surface friction in a way that reinforces the perturbations. The theoretical horizontal wavelengths of 40–80 km agree with the observations. Streamlines from the ERA5 reanalysis show that the directional change of the near-surface wind is likely to contribute to the arc shape but that a radial propagation of an initial instability is also required to explain the strong curvature. Moreover, ERA5 winds suggest that other known explanations for the formation of cloud lines are unlikely to apply in the case studied here.

A well‐developed line of cumulonimbus clouds

A well‐developed line of cumulonimbus clouds

Synthetic line and continuum observations of simulated turbulent clouds: the apparent widths of filaments

ABSTRACT Filamentary structures are ubiquitous in observations of real molecular clouds and also in simulations of turbulent, self-gravitating gas. However, making comparisons between observations and simulations is complicated by the difficulty of estimating volume densities observationally. Here, we have post-processed hydrodynamical simulations of a turbulent isothermal molecular cloud, using a full time-dependent chemical network. We have then run radiative transfer models to obtain synthetic line and continuum intensities that can be compared directly with those observed. We find that filaments have a characteristic width of ${\sim }0.1 \, {\rm pc}$, both on maps of their true surface density and on maps of their $850\, {\rm \mu m}$ dust continuum emission in agreement with previous work. On maps of line emission from CO isotopologues, the apparent widths of filaments are typically several times larger because the line intensities are poorly correlated with the surface density. On maps of line emission from dense gas tracers such as N2H+ and HCN, the apparent widths of filaments are ${\la}0.1\, {\rm pc}$. Thus, current observations of molecular-line emission are compatible with the universal $0.1 \, {\rm pc}$ filament width inferred from Herschel observations, provided proper account is taken of abundance, optical depth, and excitation considerations. We find evidence for ${\sim}0.4 \, {\rm km \, s^{-1}}$ radial velocity differences across filaments. These radial velocity differences might be a useful indicator of the mechanism by which a filament has formed or is forming, for example the turbulent cloud scenario modelled here, as against other mechanisms such as cloud–cloud collisions.

Using Megha‐Tropiques satellite data to constrain humidity in regional convective simulations: A northern Australian test case

AbstractConvective initiation and growth are sensitive to atmospheric conditions, especially humidity, in ways that need to be understood and quantified. It is not clear, however, how well current observations and modelling systems can serve to test this understanding. We simulate multiple cases of convergent cloud lines observed during January 2016 over northeastern Australia using the Weather Research and Forecasting (WRF) model (version 3.7.1), initialized by and nudged to reanalysis data. Overall, WRF appears to skilfully simulate the convergence lines. However, the associated convective clouds as seen by satellite were not properly captured by the model. The model humidity was tested against the current version (V3‐00) of the Megha‐Tropiques satellite, which although up to 30% too moist in the lowest retrieved layer over the dry continental interior compared to radiosondes, is usually accurate to within ±10% near the cloud lines. Unperturbed WRF simulations were found to be 10–20% too dry in this region. Compensating for this humidity bias via the model initialization produced more realistic simulations of clouds and convection, and this had a comparable or larger impact than changing model physics. This result highlights the importance of observations of water vapour that are accurate and have good spatial coverage, especially in the lower troposphere, for properly constraining simulations of convective environments and for model evaluation of moist processes.

Concerning the asymmetric top rotational partition function in astronomical spectroscopy

The partition function plays an important role in determining column densities from emission lines in molecular clouds, since under typical conditions the two are linearly related. Here, we focus on successively applying quantum and centrifugal distortion correction terms to the classical rotational partition function for asymmetric top molecules, in an effort to uncover the relative importance of each term. We do this for example asymmetric top molecules that contain from one to four heavy atoms (water, methanimine, vinyl alcohol, and glycolaldehyde), at temperatures relevant to interstellar molecular clouds. Particular attention is paid to methanimine, for which it was recently attested that the value of the classical partition function significantly differs from the numerically summed (exact) value at several hundred Kelvin [Sharma et al., Astron. Nachrichten. 338, 125 (2017)]. We show that the classical values are in fact within 1% of the exact values from ~50 K to well above 400 K; the addition of three simple correction terms reduces the error to ≲0.001% (down to 10 K).

A Model for the Onset of Self-gravitation and Star Formation in Molecular Gas Governed by Galactic Forces. II. The Bottleneck to Collapse Set by Cloud–Environment Decoupling

Abstract In Meidt et al., we showed that gas kinematics on the scale of individual molecular clouds are not entirely dominated by self-gravity but also track a component that originates with orbital motion in the potential of the host galaxy. This agrees with observed cloud line widths, which show systematic variations from virial motions with environment, pointing at the influence of the galaxy potential. In this paper, we hypothesize that these motions act to slow down the collapse of gas and so help regulate star formation. Extending the results of Meidt et al., we derive a dynamical collapse timescale that approaches the free-fall time only once the gas has fully decoupled from the galactic potential. Using this timescale, we make predictions for how the fraction of free-falling, strongly self-gravitating gas varies throughout the disks of star-forming galaxies. We also use this collapse timescale to predict variations in the molecular gas star formation efficiency, which is lowered from a maximum, feedback-regulated level in the presence of strong coupling to the galactic potential. Our model implies that gas can only decouple from the galaxy to collapse and efficiently form stars deep within clouds. We show that this naturally explains the observed drop in star formation rate per unit gas mass in the Milky Way’s Central Molecular Zone and other galaxy centers. The model for a galactic bottleneck to star formation also agrees well with resolved observations of dense gas and star formation in galaxy disks and the properties of local clouds.

使用显著性划分的机载激光雷达点云滤波

Point cloud filtering is one of the key steps of airborne LiDAR point data processing. Most traditional methods obtain satisfactory effects just for several specific terrain types. However, ground filtering on the point cloud of complex or mixed terrain types faces a huge challenge. Therefore, a new point cloud filtering method based on grid ground saliency division was proposed. As the point clouds were organized with virtual grid, a ground saliency division based on elevation was performed on the scanning line of point clouds. For different types of grids, different filtering processes were employed to segment point clouds into ground points and non-ground points according to the ground saliency value. Compared with other classical methods, the proposed method avoids the iterative encryption process, and the curved surface was used to fit local terrain in those undulating area, which has better adaptability in complex and mixed terrains with limited increase of computational cost, and generates a set of ground points with high reliability.