Cloud Size Research Articles

Life Cycle of Precipitating Cloud Systems from Synergistic Satellite Observations: Evolution of Macrophysical Properties and Precipitation Statistics from Geostationary Cloud Tracking and GPM Active and Passive Microwave Measurements

Abstract Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPM Core Observatory satellite are used in synergy with cloud tracking information derived from infrared imagery from the GOES-13 and Meteosat-7 geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds’ life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite observations.

Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold

In the intra-hour time scale, shielding of solar radiation by clouds is the main reason for the fluctuation of photovoltaic, so cloud parameters are important for intra-hour solar irradiance and photovoltaic power forecasting. Extracting cloud regions from cloud images provides a basis for quantifying cloud size and shape. It is difficult to detect clouds in these three cases, such as clouds in circumsolar region, clouds in haze weather and thin clouds at cloud edge. Aiming at such problems, this study firstly establishes a clear sky library based on pixel-level sun positions and haze conditions to deal with the complex changes of sky brightness. Then this study proposes a cloud detection method for ground-based images, which performs two segmentations on the cloud image. The initial segmentation process is a combination method that uses clear sky library method when the sun is visible and uses adaptive threshold method when the sun is occluded. The secondary segmentation process is based on superpixels and local threshold method, which restores some thin clouds that are easily ignored. Finally, for the influence of ghosts, this study summarizes the position and color features of ghosts, and uses different color channel information to deal with them.

Oort Cloud and sednoid formation in an embedded cluster, I: Populations and size distributions

In this paper we report on massive computer simulations aimed to clarify the formation of an Oort Cloud and the adjoining sednoid population during the earliest times of the solar system due to the perturbing effects of the Sun’s birth aggregate, for which we use different embedded cluster models. We define the sednoid population to have semi-major axes between 100 and 1600 astronomical units, and it can be seen as the innermost extension of the Oort Cloud. By also restricting the orbits to perihelion distances exceeding 60 astronomical units, the sednoid population can be regarded as dynamically inert with respect to both planetary and Galactic influences. It includes the objects (90377) Sedna, 2012 VP113 and (541132) Leleākūhonua. We start by creating 12 cluster models, using the NBODY6++GPU code, with three random realisations of four physically different models. In each of these we select six random solar template stars and equip those with a planetary system composed of Jupiter, Saturn and 4000 massless planetesimals representing a population of stray planetesimals in the Jupiter–Saturn zone with a total mass assumed to be about 20 Earth masses. From dynamical simulations of all these 72 variants of the early solar system including the cluster perturbations, we derive the total numbers and orbital characteristics of the sednoid and Oort Cloud populations, including the size distributions resulting from the limiting effects of solar nebula gas drag on extraction efficiency. We find that the predicted number of sednoids varies dramatically depending on the orbital evolution of the solar template star in the cluster. In general, numbers consistent with the existence of the observed sednoids is an expected outcome only for a birth aggregate with a strong central condensation and a relatively long lifetime of the residual gas, whereas other cluster models tend to fail to explain this. In the most successful, concentrated cluster models, we predict the existence of an as yet unseen population of at least 108 km-sized sednoids. However, we note that these cluster models produce an overheated inclination distribution at variance with the observed inclinations. Hence, we conclude that our study points to a possible, severe problem of the embedded cluster scenario in explaining the origin of the sednoids. The Oort Cloud predicted to originate in local, Jupiter–Saturn planetesimals by our simulations amounts to ∼10% of the estimated, current size of the entire Oort Cloud. This shows that such comets, likely characterised by Earth-like D/H ratios, may be a minority though a significant one. In an accompanying paper we discuss the extraction dynamics in some detail, based on the same simulations as here.

SkyPIE: A Fast & Accurate Oracle for Object Placement

Cloud object stores offer vastly different price points for object storage as a function of workload and geography. Poor object placement can thus lead to significant cost overheads. Prior cost-saving techniques attempt to optimize placement policies on the fly, deciding object placements for each object individually. In practice, these techniques do not scale to the size of the modern cloud. In this work, we leverage the static nature and pay-per-use pricing model of cloud environments to explore a different approach. Rather than computing object placements on the fly, we precompute a SkyPIE oracle---a lookup structure representing all possible placement policies and the workloads for which they are optimal. Internally, SkyPIE represents placement policies as a matrix of cost-hyperplanes, which we effectively precompute through pruning and convex optimization. By leveraging a fast geometric algorithm, online queries then are 1 to 8 orders of magnitude faster but as accurate as Integer-Linear-Programming. This makes exact optimization tractable for real workloads and we show >10x cost savings compared to state-of-the-art heuristic approaches.

Settling dynamics and thresholds for breakup and separation of bi-disperse particle clouds

Settling dynamics and thresholds for breakup and separation of bi-disperse particle clouds

Correlation of Escherichia coli inactivation with histotripsy bubble cloud size

Bacterial loads can be effectively reduced with cavitation-mediated focused ultrasound, or histotripsy. Our previous in vitro work with Escherichia coli (E. coli) established strong trends of bactericide with increasing peak negative pressure amplitude, pulse length, PRF, and treatment time. The current study correlates bactericide as a function of the histotripsy bubble cloud size produced for these treatment parameters at several frequencies. Histotripsy was applied to E. coli suspensions in 10-ml sample vials at 810 kHz, 1.2 MHz, or 3.25 MHz for 40 min. Separately, cavitation was recorded using a Photron Fastrax high-speed camera for acoustic parameters equivalent to those used in the E. coli studies. The images were used to quantify the maximum size of each bubble cloud, with the assumption that the cloud was axially symmetric around the propagation direction. A strong linear relationship exists between log kill versus cloud size (R2 = 0.96). Remarkably, across all variables studied, the log-reduction in viable bacteria exhibited a direct proportional relationship with the dimensions of the bubble cloud. This strong correlation between bacterial reduction and cavitation bubble cloud size could have significance for clinical applications where bacteria cannot easily be sampled. [Work supported by NIH R01AR080120 and R01EB023910.]

How Long-lived Grains Dominate the Shape of the Zodiacal Cloud

Grain–grain collisions shape the three-dimensional size and velocity distribution of the inner Zodiacal Cloud and the impact rates of dust on inner planets, yet they remain the least understood sink of zodiacal dust grains. For the first time, we combine the collisional grooming method combined with a dynamical meteoroid model of Jupiter-family comets (JFCs) that covers 4 orders of magnitude in particle diameter to investigate the consequences of grain–grain collisions in the inner Zodiacal Cloud. We compare this model to a suite of observational constraints from meteor radars, the Infrared Astronomical Satellite, mass fluxes at Earth, and inner solar probes, and use it to derive the population and collisional strength parameters for the JFC dust cloud. We derive a critical specific energy of QD*=5×105±4×105Rmet−0.24 J kg−1 for particles from JFC particles, making them 2–3 orders of magnitude more resistant to collisions than previously assumed. We find that the differential power-law size index −4.2 ± 0.1 for particles generated by JFCs provides a good match to observed data. Our model provides a good match to the mass-production rates derived from the Parker Solar Probe observations and their scaling with the heliocentric distance. The higher resistance to collisions of dust particles might have strong implications to models of collisions in solar and exosolar dust clouds. The migration via Poynting–Robertson drag might be more important for denser clouds, the mass-production rates of astrophysical debris disks might be overestimated, and the mass of the source populations might be underestimated. Our models and code are freely available online.

Nonlinear imaging-based bubble cloud size estimates compared with histotripsy treatment zone: An in vitro study

Volterra filtering combined with subharmonic imaging was previously reported to enhance the visualization of the histotripsy bubble cloud. In this study, we compared the bubble cloud size obtained by imaging to the histotripsy ablation region using receiver operating characteristic (ROC) analysis. Histotripsy was performed in a red blood cell (RBC)-doped agarose phantom using a 1 MHz transducer. Ultrasound imaging data was acquired using a C5-2V probe and chirp-coded excitation (bandwidth: 2–5 MHz). These ultrasound acquisitions were then processed by subharmonic (SH) matched filtering alone, SH with quadratic Volterra filtering, and SH with cubic Volterra filtering. The therapy pulse and imaging sequence were interleaved (N = 100 frames), and a video camera was used to visualize the damaged region. A mean bubble cloud image was generated across all treatment sequences and co-registered with the camera image. A ROC curve was formed using binary classification of the mean bubble cloud versus the ablation zone, and the area under the ROC curve was determined. Filtering by quadratic and cubic Volterra filters reduced artifacts significantly [over 20 and 30 dB contrast-to-tissue enhancement (p < 0.01), respectively], along with achieving a high area under the ROC curve (0.97). These findings highlight the potential of Volterra filtering for histotripsy guidance.

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.

Nanoparticles synthesis in microwave plasmas: peculiarities and comprehensive insight

Low-pressure plasma processes are routinely used to grow, functionalize or etch materials, and thanks to some of its unique attributes, plasma has become a major player for some applications such as microelectronics. Plasma processes are however still at a research level when it comes to the synthesis and functionalization of nanoparticles. Yet plasma processes can offer a particularly suitable solution to produce nanoparticles having very peculiar features since they enable to: (i) reach particle with a variety of chemical compositions, (ii) tune the size and density of the particle cloud by acting on the transport dynamics of neutral or charged particles through a convenient setting of the thermal gradients or the electric field topology in the reactor chamber and (iii) manipulate nanoparticles and deposit them directly onto a substrate, or codeposit them along with a continuous film to produce nanocomposites or (iv) use them as a template to produce 1D materials. In this article, we present an experimental investigation of nanoparticles synthesis and dynamics in low-pressure microwave plasmas by combining time-resolved and in-situ laser extinction and scattering diagnostics, QCL absorption spectroscopy, mass spectrometry, optical emission spectroscopy and SEM along with a particle transport model. We showed for the first time the thermophoresis-driven dynamic of particle cloud in electrodless microwave plasmas. We showed that this effect is linked to particular fluctuations in the plasma composition and results in the formation of a void region in the bulk of the plasma surrounded by a particle cloud in the peripherical post-discharge. We also reveals and analyze the kinetics of precursor dissociation and molecular growth that result in the observed nanoparticle nucleation.

DIMENSIONAL DISCOVERIES: UNVEILING THE POTENTIAL OF 3D HERITAGE POINT CLOUDS WITH A ROBUST ONTOLOGY FRAMEWORK

Abstract. 3D point clouds feature valuable geometric and, often, radiometric and semantic information to support studies, analyses and understanding of the surveyed scene. Due to their generally large size, the use and interpretation of point clouds could be problematic. User-friendly and quick approaches for querying these valuable datasets and retrieving information could surely support end-users, in particular in the heritage sector. This work presents an ontology-based approach to facilitate the query and use of 3D heritage point clouds by means of sets of rules in order to infer properties and characteristics of the surveyed scene. Our approach is focused on linking together 3D spatial data and expert knowledge, in a way that the ontology can elaborate, represent, enrich and query a given point cloud. Results show how different queries can be set-up and how the procedure can be replicated to various queries and datasets.

A Parsec-scale Catalog of Molecular Clouds in the Solar Neighborhood Based on 3D Dust Mapping: Implications for the Mass–Size Relation

We dendrogram the Leike et al. 3D dust map, leveraging its ∼1 pc spatial resolution to produce a uniform catalog of molecular clouds in the solar neighborhood. Using accurate distances, we measure the properties of 65 clouds in true 3D space, eliminating much of the uncertainty in mass, size, and density. Clouds in the catalog contain a total of 1.1 × 105 M ☉, span distances of 116−440 pc, and include a dozen well-studied clouds in the literature. In addition to deriving cloud properties in 3D volume density space, we create 2D dust extinction maps from the 3D data by projecting the 3D clouds onto a 2D “Sky” view. We measure the properties of the 2D clouds separately from the 3D clouds. We compare the scaling relation between the masses and sizes of clouds following Larson. We find that our 2D projected mass–size relation, M ∝ r 2.1, agrees with Larson's Third Relation, but our 3D derived properties lead to a scaling relation of about one order larger: M ∝ r 2.9. Validating predictions from theory and numerical simulations, our results indicate that the mass–size relation is sensitive to whether column or volume density is used to define clouds, since mass scales with area in 2D (M ∝ r 2) and with volume in 3D (M ∝ r 3). Our results imply a roughly constant column and volume density in 2D and 3D, respectively, for molecular clouds, as would be expected for clouds where the lower density, larger volume-filling gas dominates the cloud mass budget.

Liquid inertia versus bubble cloud buoyancy in circular plunging jet experiments

When a liquid jet plunges into a pool, it can generate a bubble-laden jet flow underneath the surface. This common and simple phenomenon is investigated experimentally for circular jets to illustrate and quantify the role played by the net gas/liquid void fraction on the maximum bubble penetration depth. It is first shown that an increase in either the impact diameter or the jet fall height to diameter ratio at constant impact momentum leads to a reduction in the bubble cloud size. By measuring systematically the local void fraction using optical probes in the biphasic jet, it is then demonstrated that this effect is a direct consequence of the increase in air content within the cloud. A simple momentum balance model, including only inertia and the buoyancy force, is shown to predict the bubble cloud depth without any fitting parameters. Finally, a Froude number based on the bubble terminal velocity, the cloud depth and also the net void fraction is introduced to propose a simple criterion for the threshold between the inertia-dominated and buoyancy-dominated regimes.

Histotripsy-induced bactericidal activity correlates to size of cavitation cloud in vitro.

Large abscesses are walled-off collections of pus and bacteria that often do not respond to antibiotic therapy. Standard of care involves percutaneous placement of indwelling catheter(s) for drainage, a long and uncomfortable process with high rehospitalization rates. The long-term goal of this work is to develop therapeutic ultrasound approaches to eradicate bacteria within abscesses as a noninvasive therapeutic alternative. Inertial cavitation induced by short pulses of focused ultrasound (histotripsy) is known to generate lethal mechanical damage in bacteria. Prior studies with Escherichia coli (E. coli) in suspension demonstrated that bactericidal effects increase with increasing peak negative amplitude, treatment time and duty cycle. The current study investigated correlates of bactericidal activity with histotripsy cavitation cloud size. Histotripsy was applied to E. coli suspensions in 10-mL sample vials at 810 kHz, 1.2 MHz, or 3.25 MHz for 40 minutes. The cavitation activity in the sample vials was separately observed with high-speed photography. The cavitation cloud area was quantified from those images. A linear relationship was observed between bacterial inactivation and cavitation cloud size (R2 = 0.96), regardless of the acoustic parameters (specifically frequency, pulse duration and power) used to produce the cloud.

Kondo screening cloud scaling: impurity entanglement and magnetization

The screening of an impurity spin in the Kondo model occurs over a characteristic length scale ξ K , that defines the size of the Kondo screening cloud or ‘mist’. The presence of such a length scale in experimental and numerical results is rather subtle. A consistent way to show the presence of the screening cloud is to demonstrate scaling in the spatial correlations in terms of the single variable, r/ξK instead of the correlations depending on r and ξ K separately. Here we study the paradigmatic one channel Kondo model using a spin chain representation, with an impurity spin at one end of the chain coupled with a strength JK′ . Using Fermi liquid theory combined with numerical results, we obtain new high precision estimates of the non-universal terms in the entanglement entropy which leads to a verification of the expected non-integer ground-state degeneracy, g. This then allows us to study the impurity contribution to the entanglement in detail. If the impurity coupling JK′ is varied, a precise determination of ξ K can then be obtained. The length scale, ξ K , is then shown to characterize the scaling of both the uniform and alternating part of a measure of the magnetization of part of an odd length chain.

Zwitterionic nanoparticles for thermally activated drug delivery in hyperthermia cancer treatment.

Hyperthermia is considered a promising strategy to boost the curative outcome of traditional chemotherapeutic treatments. However, this thermally mediated drug delivery is still affected by important limitations. First, the poor accumulation of the conventional anticancer formulations in the target site limits the bioavailability of the active ingredient and induces off-site effects. In addition, some tumoral scenarios, such as ovarian carcinoma, are characterized by cell thermotolerance, which induces tumoral cells to activate self-protecting mechanisms against high temperatures. To overcome these constraints, we developed thermoresponsive nanoparticles (NPs) with an upper critical solution temperature (UCST) to intracellularly deliver a therapeutic payload and release it on demand through hyperthermia stimulation. These NPs were synthesized via reversible addition-fragmentation chain transfer (RAFT) emulsion polymerization and combine polyzwitterionic stabilizing segments and an oligoester-based biodegradable core. By leveraging the pseudo-living nature of RAFT polymerization, important physicochemical properties of the NPs were controlled and optimized, including their cloud point (Tcp) and size. We have tuned the Tcp of NPs to match the therapeutic needs of hyperthermia treatments at 43 °C and tested the nanocarriers in the controlled delivery of paclitaxel, a common anticancer drug. The NPs released almost entirely the encapsulated drug only following 1 h incubation at 43 °C, whereas they retained more than 95% of the payload in the physiological environment (37 °C), thus demonstrating their efficacy as on-demand drug delivery systems. The administration of drug-loaded NPs to ovarian cancer cells led to therapeutic effects outperforming the conventional administration of non-encapsulated paclitaxel, which highlights the potential of the zwitterionic UCST-type NPs as an innovative hyperthermia-responsive drug delivery system.

Ion parking in native mass spectrometry.

A forced, damped harmonic oscillator model for gas-phase ion parking using single-frequency resonance excitation is described and applied to high-mass ions of relevance to native mass spectrometry. Experimental data are provided to illustrate key findings revealed by the modelling. These include: (i) ion secular frequency spacings between adjacent charge states of a given protein are essentially constant and decrease with the mass of the protein (ii) the mechanism for ion parking of high mass ions is the separation of the ion clouds of the oppositely-charged ions with much less influence from an increase in the relative ion velocity due to resonance excitation, (iii) the size of the parked ion cloud ultimately limits ion parking at high m/z ratio, and (iv) the extent of ion parking of off-target ions is highly sensitive to the bath gas pressure in the ion trap. The model is applied to ions of 17 kDa, 467 kDa, and 2 MDa while experimental data are also provided for ions of horse skeletal muscle myoglobin (≈17 kDa) and β-galactosidase (≈467 kDa). The model predicts and data show that it is possible to effect ion parking on a 17 kDa protein to the 1+ charge state under trapping conditions that are readily accessible with commercially available ion traps. It is also possible to park β-galactosidase efficiently to a roughly equivalent m/z ratio (i.e., the 26+ charge state) under the same trapping conditions. However, as charge states decrease, analyte ion cloud sizes become too large to allow for efficient ion trapping. The model allows for a semi-quantitative prediction of ion trapping performance as a function of ion trapping, resonance excitation, and pressure conditions.

Three-Dimensional Point Cloud Segmentation Algorithm Based on Depth Camera for Large Size Model Point Cloud Unsupervised Class Segmentation.

This paper proposes a 3D point cloud segmentation algorithm based on a depth camera for large-scale model point cloud unsupervised class segmentation. The algorithm utilizes depth information obtained from a depth camera and a voxelization technique to reduce the size of the point cloud, and then uses clustering methods to segment the voxels based on their density and distance to the camera. Experimental results show that the proposed algorithm achieves high segmentation accuracy and fast segmentation speed on various large-scale model point clouds. Compared with recent similar works, the algorithm demonstrates superior performance in terms of accuracy metrics, with an average Intersection over Union (IoU) of 90.2% on our own benchmark dataset.

Influence of a Secondary Plasma Cloud on the Ablation of Pellets in Magnetic Confinement Devices

The analysis of experimental data on the structure of hydrocarbon pellet clouds on the LHD stellarator has allowed one to estimate the relative contributions of neutral and plasma shielding at the ablation of macroparticles (pellets) in a high-temperature magnetized toroidal plasma. A method for the self-consistent calculation of the pellet ablation rate, the characteristic size of the pellet cloud, and the electron density in its singly ionized part including neutral gas and plasma shielding is described. This calculation for polystyrene pellets injected into the LHD plasma gives the results that agree with the experimental data obtained during the early ablation phase, when the ablation rate is determined by thermal electrons and the contribution of the superthermal component of the hot plasma to ablation can be neglected.

Design and evaluation of Swift routing for payment channel network

Payment Channel Networks (PCNs) are a promising alternative to improve the scalability of a blockchain network. A PCN employs off-chain micropayment channels that do not need a global block confirmation procedure, thereby sacrificing the ability to confirm transactions instantaneously. PCN uses a routing algorithm to identify a path between two users who do not have a direct channel between them to settle a transaction. The performance of most of the existing centralized path-finding algorithms does not scale with network size. The rapid growth of Bitcoin PCN necessitates considering distributed algorithms. However, the existing decentralized algorithms suffer from resource underutilization. We present a decentralized routing algorithm, Swift, focusing on fee optimization. The concept of a secret path is used to reduce the path length between a sender and a receiver to optimize the fees. Furthermore, we reduce a network structure into combinations of cycles to theoretically study fee optimization with changes in cloud size. The secret path also helps in edge load sharing, which results in an improvement of throughput. Swift routing achieves up to 21% and 63% in fee and throughput optimization, respectively. The results from the simulations follow the trends identified in the theoretical analysis.