Stable Cloud Research Articles

Observation of self-excited oscillations in DC glow discharge dusty plasma

We present a detailed experimental study of discharge current oscillations in a planar cathode plasma with poly dispersed alumina dust particles. Our experimental measurements suggest that surface density of dust particles play an important role together with cathode spots, in deciding self excited oscillating regime or stable dust cloud regime. Experiments conducted with dust particles density below the critical limit shows self excited oscillations. Operating above the critical limit, a dust cloud levitated in the sheath region was observed.

Experimental study of discharge current oscillations with dust particles

We present a detailed experimental study of discharge current oscillations in a planar cathode plasma with poly-dispersed alumina dust particles. The dominant frequency of oscillation depends on the discharge voltage, operating pressure, and amount of dust particles placed on the cathode. The power-law variation in the dominant frequency with different external operating parameters is presented. Experimental observations suggest that the dominant mechanism behind the generation of these oscillations is the cathode spot injection of sub-micron-sized dust particles. The cathode spots also aid in the generation of fine dust particles. The threshold limit on dust particle density dispersed on the cathode suggests that below the threshold limit, the fine particles depleting the electrons play an important role and lead to the generation of self-excited oscillations. Operating above the threshold limit, a stable dust cloud was observed together with the suppression of self-excited oscillations.

CACM-Net: Daytime Cloud Mask for AGRI Onboard the FY-4A Satellite

Accurate cloud detection is a crucial initial stage in optical satellite remote sensing. In this study, a daytime cloud mask model is proposed for the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite based on a deep learning approach. The model, named “Convolutional and Attention-based Cloud Mask Net (CACM-Net)”, was trained using the 2021 dataset with CALIPSO data as the truth value. Two CACM-Net models were trained based on a satellite zenith angle (SZA) < 70° and >70°, respectively. The study evaluated the National Satellite Meteorological Center (NSMC) cloud mask product and compared it with the method established in this paper. The results indicate that CACM-Net outperforms the NSMC cloud mask product overall. Specifically, in the SZA < 70° subset, CACM-Net enhances accuracy, precision, and F1 score by 4.8%, 7.3%, and 3.6%, respectively, while reducing the false alarm rate (FAR) by approximately 7.3%. In the SZA > 70° section, improvements of 12.2%, 19.5%, and 8% in accuracy, precision, and F1 score, respectively, were observed, with a 19.5% reduction in FAR compared to NSMC. An independent validation dataset for January–June 2023 further validates the performance of CACM-Net. The results show improvements of 3.5%, 2.2%, and 2.8% in accuracy, precision, and F1 scores for SZA < 70° and 7.8%, 11.3%, and 4.8% for SZA > 70°, respectively, along with reductions in FAR. Cross-comparison with other satellite cloud mask products reveals high levels of agreement, with 88.6% and 86.3% matching results with the MODIS and Himawari-9 products, respectively. These results confirm the reliability of the CACM-Net cloud mask model, which can produce stable and high-quality FY-4A AGRI cloud mask results.

Study on Multi-Scale Cloud Growth Characteristics of Frustoconical Dispersal Devices

This study aims to understand cloud growth behavior and enhance cloud safety and reliability by investigating the design of cloud dispersal devices. Based on the experimental results and simulation results, this study analyzes the dispersion characteristics of cloud materials within a frustoconical device with a semi-cone angle ranging from 0° to 10° across multiple scales. The collision aggregation model for cloud particles and the multi-scale coupling mechanism for cloud growth are established. The research shows that the semi-cone angle of the device extends the effective cloud growth duration and enlarges the cloud macroscopic size. At the mesoscopic scale, vortex phenomena are observed, causing particles to converge within the cloud, resulting in collisions and aggregation. The vortices enhance the continuity of the cloud concentration. The magnitude of these vortices demonstrates a positive correlation with the magnitude of the semi-cone angle of the dispersal device. For a macroscopically stable cloud, the high-concentration area within the cloud moves outward radially with an increase in the semi-cone angle. This study provides a theoretical foundation for cloud morphology control technology, contributing to enhancing the safety and reliability of cloud systems.

Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map

In recent years, there has been growing interest in autonomous mobile robots equipped with Simultaneous Localization and Mapping (SLAM) technology as a solution to labour shortages in production and distribution settings. SLAM allows these robots to create maps of their environment using devices such as Lidar, radar, and sonar sensors, enabling them to navigate and track routes without prior knowledge of the environment. However, the manual operation of these robots for map construction can be labour-intensive. To address this issue, this research aims to develop a 3D SLAM autonomous mobile robot system that eliminates the need for manual map construction by utilizing existing layout maps. The system includes a PC for self-position estimation, 3DLidar, a camera for verification, a touch panel display, and the mobile robot itself. The proposed SLAM method extracts stable wall point cloud information from 3DLidar, matches it with the wall surface information in the layout map, and uses a particle filter to estimate the robot’s position. The system also includes features such as route creation, tracking, and obstacle detection for autonomous movement. Experiments were conducted to compare the proposed system with conventional 3D SLAM methods. The results showed that the proposed system significantly reduced errors in self-positioning and enabled accurate autonomous movement on specified routes, even in the presence of slight differences in layout maps and obstacles. Ultimately, this research demonstrates the effectiveness of a system that can transport goods without the need for manual environment mapping, addressing labour shortages in such environments.

Lightning Stroke Strength and Its Correlation with Cloud Macro- and Microphysics over the Tibetan Plateau

Lightning stroke strength, characterized by energy and peak currents, over the Tibetan Plateau (TP), is investigated by utilizing datasets from the World Wide Lightning Location Network and the Chinese Cloud-to-Ground Lightning Location System during 2016–2019. Focused on the south-central (SC) and southeast (SE) of the TP, it reveals that SE-TP experiences strokes with larger average energy and peak currents. Strong strokes (energy ≥ 100 kJ or peak currents ≥ |100| kA), exhibiting bimodal distribution in winter and summer, are more frequent and have larger average values over the SE-TP than the SC-TP, with diurnal distribution indicating peaks in energy and positive strokes in the middle of the night and negative strokes peaking in the morning. Utilizing the ECMWF/ERA-5 and MERRA-2 reanalysis, we find that stronger strokes correlate with thinner charge zone depths and larger CIWCFs but stable warm cloud depths and zero-degree levels over the SC-TP. Over the SE-TP, stronger strokes are associated with smaller CIWCFs and show turning points for warm cloud depths and zero-degree levels. Thicker charge zone depths correlate with stronger negative strokes but weaker positive strokes. Generating strokes of similar strength over the SC-TP requires larger CIWCFs, thinner warm cloud depths, and lower zero-degree levels than over the SE-TP.

Regional Cloud Forecast Verification Using Standard, Spatial, and Object-Oriented Methods

Abstract This study assesses the accuracy of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) forecasts for clouds within stable and unstable environments (thereafter refers as “stable” and “unstable” clouds). This evaluation is conducted by comparing these forecasts against satellite retrievals through a combination of traditional, spatial, and object-based methods. To facilitate this assessment, the Model Evaluation Tools (MET) community tool is employed. The findings underscore the significance of fine-tuning the MET parameters to achieve a more accurate representation of the features under scrutiny. The study’s results reveal that when employing traditional pointwise statistics (e.g., frequency bias and equitable threat score), there is consistency in the results whether calculated from Method for Object-Based Diagnostic Evaluation (MODE)-based objects or derived from the complete fields. Furthermore, the object-based statistics offer valuable insights, indicating that COAMPS generally predicts cloud object locations accurately, though the spread of these predicted locations tends to increase with time. It tends to overpredict the object area for unstable clouds while underpredicting it for stable clouds over time. These results are in alignment with the traditional pointwise bias scores for the entire grid. Overall, the spatial metrics provided by the object-based verification methods emerge as crucial and practical tools for the validation of cloud forecasts. Significance Statement As the general Navy meteorological and oceanographic (METOC) community engages in collaboration with the broader scientific community, our goal is to harness community tools like MET for the systematic evaluation of weather forecasts, with a specific focus on variables crucial to the Navy. Clouds, given their significant impact on visibility, hold particular importance in our investigations. Cloud forecasts pose unique challenges, primarily attributable to the intricate physics governing cloud development and the complexity of representing these processes within numerical models. Cloud observations are also constrained by limitations, arising from both top-down satellite measurements and bottom-up ground-based measurements. This study illustrates that, with a comprehensive understanding of community tools, cloud forecasts can be consistently verified. This verification encompasses traditional evaluation methods, measuring general qualities such as bias and root-mean-squared error, as well as newer techniques like spatial and object-based methods designed to account for displacement errors.

Cloud-Resolving ICON Simulations of Secondary Ice Production in Arctic Mixed-Phase Stratocumuli Observed during M-PACE

Abstract Field measurements and modeling studies suggest that secondary ice production (SIP) may close the gap between observed Arctic ice nucleating particle (INP) concentrations and ice crystal number concentrations ni. Here, we explore sensitivities with respect to the complexity of different INP parameterizations under the premise that ni is governed by SIP. Idealized, cloud-resolving simulations are performed for the marine cold air outbreak cloud deck sampled during the Mixed-Phase Arctic Cloud Experiment (M-PACE) with the Icosahedral Nonhydrostatic (ICON) model. The impact of the droplet shattering (DS) of raindrops and collisional breakup (BR) in addition to the existing Hallet–Mossop rime splintering mechanism were investigated. Overall, 12 different model experiments (12-h runs) were performed and analyzed. Despite the considerable amount of uncertainty remaining with regard to SIP mechanisms and their process representation in numerical models, we conclude from these experiments that (i) only simulations where DS dominates the SIP signal (potentially amplified by BR) capture observed ice-phase and liquid-phase cloud properties, and (ii) SIP events cluster around the convective outflow region and are structurally linked to mesoscale cloud organization. In addition, interactions with primary nucleation parameterizations of varied complexity were investigated. Here, our simulations show that (i) a stable long-lived mixed-phase cloud (MPC) can be maintained in the absence of primary nucleation once SIP is established, (ii) experiments using a computationally more efficient relaxation-based parameterization of primary nucleation are statistically invariant from simulations considering prognostic INP, and (iii) primary nucleation at cloud-top controls the areal extent of the mixed-phase cloud region, and reduces SIP efficacy via DS due to increased depletion of cloud liquid throughout the entire cloud column. Significance Statement Secondary ice production (SIP) remains a key challenge in our understanding of boundary layer mixed-phase clouds. Here, we use sensitivity experiments performed with the ICON model at the cloud-resolving scale to explore potential interactions between primary nucleation, SIP, and mesoscale cloud organization. We simulate an Arctic single-layer cold air outbreak stratocumulus deck that was sampled during the M-PACE campaign. We find that once established, SIP alone is sufficient to maintain the mixed-phase cloud state until the end of the simulation. Our sensitivity analysis also shows that numerically more efficient treatments of immersion freezing are statistically invariant from simulations with a full prognostic INP budget.

Blending of a novel all sky imager model with persistence and a satellite based model for high-resolution irradiance nowcasting

High shares of variable energy sources such as photovoltaics (PV) make balancing network load and generation increasingly challenging. Electricity grids with high PV-penetration benefit from the consideration of intra-minute and intra-hour variabilities via nowcasts (shortest-term forecasts).In this study we present a novel all-sky imager (ASI) nowcasting system which is benchmarked against an established ASI method, a satellite nowcasting system and persistence. Nowcasts of our novel ASI method, satellite method and persistence are subsequently combined to a hybrid model with improved accuracy.ASI systems exploit sky images from fisheye cameras to analyze sky conditions, predict upcoming cloud situations and derive irradiance nowcasts from these. Our ASI method uses a novel machine-learning (ML) based approach that makes direct use of pixel values and other image features. Measurements from a spatially distributed irradiance measuring network of eight stations within a radius of 10km around the camera position are used to train our ASI and our hybrid model and validate our nowcasting results. In the evaluation we put a special focus on irradiance variability conditions. The high share of stable cloud situations within our dataset (∼68 %) results in a relatively good performance of persistence nowcasts. In higher variability situations persistence is quickly outperformed by all other nowcasting methods.Our ASI method clearly outperforms the reference ASI method at all lead times (LTs). It moreover exhibits a significantly lower root mean square error (RMSE) than the satellite-based method up to LT≤11min ahead and a lower mean absolute error (MAE) throughout the entire interval. The hybrid model brings about an RMSE improvement score of 5-13% over the respective optimal individual method.

Application of Ultra Fine Bubbles for Deoxygenation of Produced Water and Tap Water via Nitrogen Purging Scheme

In this study, we present our findings from examining a gas lift tower for produced and tap water de-oxygenation, applying the nitrogen purging at an ultra-fine bubble scale (18 μm average size). The experiments were conducted for produced water samples grafted with polyacrylamide concentration 300 ppm with a measured bulk viscosity of 5 mPa.s. Upon applying a series of experimental sets, 0 ppm DO was attained in all examined operational schemes (semi batch and once through) within various time limits, depending on the water level in the column. Considering the zero DO level as an objective function, the results showed an improvement of 5.7–14 folds in reaching the 0 ppm DO upon experimenting with the ultra-fine bubble purging in different schemes, compared with of the results obtained from the ordinary bubble size (mm scale). The results show that DO reached < 10 ppb within 23 minutes with nitrogen flowrate 3 L/min while DO reaching < 10 ppb within 28 minutes with nitrogen flowrate 5 L/min. Furthermore, implementing the ultra-fine bubble nitrogen purging was successful in running the de-oxygenation tower in a full continuous mode at a balanced inlet/outlet water flow rate. This has been done after reaching stability in the column operation (lasting around 1 hour for 422 L of examined water sample). The stable fine bubbles cloud in the column was quite efficient in treating water influent stream to be exited directly at 0 ppm DO within the same effluent flow rate. The treatment efficiency has shown an increase with increasing water level in the column, resulting in a denser layer/cloud of fine bubbles. This result suggests a unique approach/solution for the complete removal of DO from produced water, which is accounted effective to be adopted industrially

Self-excited converging shock structure in a complex plasma medium.

We report the study of a self-excited converging shock structure observed in a complex plasma medium. A high-density dust cloud of melamine formaldehyde particles is created and horizontally confined by a circular ring in a dc glow discharge plasma at a particular discharge voltage and pressure. Later, as the discharge voltage is increased, a circular density crest is spontaneously generated around the outer boundary of the dust cloud. This nonlinear density structure is seen to propagate inward towards the center of the dust cloud. The properties of the excited structure are analyzed and found to follow the characteristics of a converging shock structure. A three-dimensional molecular dynamics simulation has also been performed in which a stable dust cloud is formed and levitated by the balance of forces due to gravity and an external electric field mimicking the cathode sheath electric field in the experiment. Particles are also horizontally confined by an external electric field, representing the sheath electric field of the circular ring present in the experiment. A circular shock structure has been excited by applying an external perturbation in the horizontal electric field around the outer boundary of the dust cloud. The characteristic properties of the shock are analyzed in the simulation and qualitatively compared with the experimental findings. This paper is not only of fundamental interest but has many implications concerning the study of converging shock waves excited in other media for various potential applications.

Star cluster progenitors are dynamically decoupled from their parent molecular clouds

ABSTRACT The formation of stellar clusters dictates the pace at which galaxies evolve, and solving the question of their formation will undoubtedly lead to a better understanding of the Universe as a whole. While it is well known that star clusters form within parsec-scale overdensities of interstellar molecular gas called clumps, it is, however, unclear whether these clumps represent the high-density tip of a continuous gaseous flow that gradually leads towards the formation of stars, or a transition within the gas physical properties. Here, we present a unique analysis of a sample of 27 infrared dark clouds embedded within 24 individual molecular clouds that combine a large set of observations, allowing us to compute the mass and velocity dispersion profiles of each, from the scale of tens of parsecs down to the scale of tenths of a parsec. These profiles reveal that the vast majority of the clouds, if not all, are consistent with being self-gravitating on all scales, and that the clumps, on parsec-scale, are often dynamically decoupled from their surrounding molecular clouds, exhibiting steeper density profiles (ρ∝r−2) and flat velocity dispersion profiles (σ∝r0), clearly departing from Larson’s relations. These findings suggest that the formation of star clusters correspond to a transition regime within the properties of the self-gravitating molecular gas. We propose that this transition regime is one that corresponds to the gravitational collapse of parsec-scale clumps within otherwise stable molecular clouds.

A novel cloud detection method based on segmentation prior and multiple features for Sentinel-2 images

ABSTRACT Clouds in Sentinel-2 images seriously affect its usage in various fields, such as agricultural production and environmental monitoring. Although cloud masks have been provided in Sentinel-2 products, the stability of cloud detection accuracy may be affected by different types of underlying surfaces. This study presents a novel cloud detection method for Sentinel-2 images based on segmentation prior and multiple features. In the presented method, spectral, texture, and exponential features are extracted to enhance the difference between clouds and underlying surfaces. Meanwhile, segmentation results are regarded as priors to constrain pre-classification results to improve the edge accuracy of cloud detection and to obtain detection results with low false alarm rate and low omission rate. Experiments on the Sentinel-2 image dataset show that the presented method achieves good and stable cloud detection results, and the accuracy of cloud detection for six underlying surface types (impervious areas, water, croplands, bare lands, snow & ice, and forest) are above 0.93. These findings demonstrate that the presented method has the potential to effectively improve the stability of cloud detection accuracy while reducing the requirement for the number of samples.

Convolutional Neural Network-Driven Improvements in Global Cloud Detection for Landsat 8 and Transfer Learning on Sentinel-2 Imagery

A stable and reliable cloud detection algorithm is an important step of optical satellite data preprocessing. Existing threshold methods are mostly based on classifying spectral features of isolated individual pixels and do not contain or incorporate the spatial information. This often leads to misclassifications of bright surfaces, such as human-made structures or snow/ice. Multi-temporal methods can alleviate this problem, but cloud-free images of the scene are difficult to obtain. To deal with this issue, we extended four deep-learning Convolutional Neural Network (CNN) models to improve the global cloud detection accuracy for Landsat imagery. The inputs are simplified as all discrete spectral channels from visible to short wave infrared wavelengths through radiometric calibration, and the United States Geological Survey (USGS) global Landsat 8 Biome cloud-cover assessment dataset is randomly divided for model training and validation independently. Experiments demonstrate that the cloud mask of the extended U-net model (i.e., UNmask) yields the best performance among all the models in estimating the cloud amounts (cloud amount difference, CAD = −0.35%) and capturing the cloud distributions (overall accuracy = 94.9%) for Landsat 8 imagery compared with the real validation masks; in particular, it runs fast and only takes about 41 ± 5.5 s for each scene. Our model can also actually detect broken and thin clouds over both dark and bright surfaces (e.g., urban and barren). Last, the UNmask model trained for Landsat 8 imagery is successfully applied in cloud detections for the Sentinel-2 imagery (overall accuracy = 90.1%) via transfer learning. These prove the great potential of our model in future applications such as remote sensing satellite data preprocessing.

On the Galactic Halos Rotation by Planck Data

As galactic halos are not directly visible, there are many ambiguities regarding their composition and rotational velocity. Though most of the dark matter is non-baryonic, some fraction is, and it can be used to trace the halo rotation. Asymmetries in the CMB towards M31 had been seen in the Planck data and ascribed to the rotational Doppler shift of the M31 halo. Subsequently, the same methods were used in the direction of five other galaxies belonging to the Local Group, namely M33, M81, M82, NGC 5128, and NGC 4594. It had been proved that there could be stable clouds of gas and dust in thermal equilibrium with the CMB at 2.7 K, which had been called “virial clouds”. In this paper, adopting this scenerio, an attempt is made to constrain the fraction of dust grains and gas molecules in the clouds.

Search for D-D Fusion initiated by Nuclear Hotspots in Boranes

In the group of boron hydrogen compounds, decaborane and aminoborane belong to those with a particularly high hydrogen density in the molecular volume. Almost 100% of the protons in the molecule can be replaced by deuterons. In the element boron, the isotope 10B is 19.9% naturally present. By capturing thermal neutrons in this isotope, the high-energy reaction products 4He and 7Li are formed. It thus forms a hotspot in a spatially stable cloud of deuterium nuclei, which can be triggered by external action and whose energy could be transferred to deuterium in a similar way to neutral beam injection, a successful method to heat a fusion plasma.

First-principles study on microstructure of CaO-Al2O3-B2O3 slag

The structure evolution of CaO-Al2O3-B2O3 slag at 1873 K with various CaO/Al2O3 ratio in 0.7 to 1.5 was studied by first-principles molecular dynamics. The bond stability was in the order of B-O > Al-O > Ca-O > O-O. In addition, the average coordination number of B and Al was observed as the increase in the CaO/Al2O3 ratio from 0.7 to 1.1, while decrease with further CaO/Al2O3 addition to 1.5. Combining the results of oxygen distribution and FTIR spectra, the slag polymerization degree was decreased with CaO/Al2O3 enhancement. Ca-O was ionic bond, while B-O and Al-O were charge-transfer bond, so that the structure evolutions of B-O and Al-O network were accompanied with charge transfer. Each O bonded with B formed a stable electron cloud connection, which was beneficial to the stability of B-O network structure, and there were few stable electron cloud connections between each O bonded to Al.

Driver's Head Pose and Gaze Zone Estimation Based on Multi-Zone Templates Registration and Multi-Frame Point Cloud Fusion.

Head pose and eye gaze are vital clues for analysing a driver’s visual attention. Previous approaches achieve promising results from point clouds in constrained conditions. However, these approaches face challenges in the complex naturalistic driving scene. One of the challenges is that the collected point cloud data under non-uniform illumination and large head rotation is prone to partial facial occlusion. It causes bad transformation during failed template matching or incorrect feature extraction. In this paper, a novel estimation method is proposed for predicting accurate driver head pose and gaze zone using an RGB-D camera, with an effective point cloud fusion and registration strategy. In the fusion step, to reduce bad transformation, continuous multi-frame point clouds are registered and fused to generate a stable point cloud. In the registration step, to reduce reliance on template registration, multiple point clouds in the nearest neighbor gaze zone are utilized as a template point cloud. A coarse transformation computed by the normal distributions transform is used as the initial transformation, and updated with particle filter. A gaze zone estimator is trained by combining the head pose and eye image features, in which the head pose is predicted by point cloud registration, and the eye image features are extracted via multi-scale spare coding. Extensive experiments demonstrate that the proposed strategy achieves better results on head pose tracking, and also has a low error on gaze zone classification.

Triggering star formation: Experimental compression of a foam ball induced by Taylor–Sedov blast waves

The interaction between a molecular cloud and an external agent (e.g., a supernova remnant, plasma jet, radiation, or another cloud) is a common phenomenon throughout the Universe and can significantly change the star formation rate within a galaxy. This process leads to fragmentation of the cloud and to its subsequent compression and can, eventually, initiate the gravitational collapse of a stable molecular cloud. It is, however, difficult to study such systems in detail using conventional techniques (numerical simulations and astronomical observations), since complex interactions of flows occur. In this paper, we experimentally investigate the compression of a foam ball by Taylor–Sedov blast waves, as an analog of supernova remnants interacting with a molecular cloud. The formation of a compression wave is observed in the foam ball, indicating the importance of such experiments for understanding how star formation is triggered by external agents.

Evaluation of robustness against external vibrations for long-range MEMS lidar with one-dimensional resonant micromirror

We describe the verification of a long-range one-dimensional (1D) scanning micro-electro-mechanical systems (MEMS) lidar specifically considering the robustness against external vibration influences. The 1D scanning MEMS lidar exploits a multichannel horizontal line laser to scan the scene vertically for a 10 deg × 11 deg horizontal and vertical field of view at a frame rate of up to 29 Hz. To evaluate the robustness against vibrations, a vibration evaluation setup is developed to apply a wideband vibration based on the automotive standard LV124. The vibration tests are performed in three conditions open loop without control and two phase-locked loops (PLLs) with default and high gain settings. The test results demonstrate that vibration can cause wobbly distortion along the scan angle in the open loop case and the PLLs can suppress effectively this influence in the mean and standard deviation of the standard point to surface error up to 69.3% and 90.0%, respectively. This verifies the benefits of the MEMS mirror control, ensuring stable point cloud measurements under vibrations in harsh automotive environments.