Cloud Particles Research Articles

A journey through space and time in the Godbert-Greenwald furnace: The evolution of a dust cloud particle size distribution

Minimum Ignition Temperature (MIT) characterizes the ignition sensibility of a dust cloud. This work assesses the role of a standard Godbert-Greenwald furnace in the agglomeration/deagglomeration and fragmentation phenomena. Several organic powders and different dispersion pressures were tested. The average residence time varied from 120 to 330 ms according to the amount of powder injected and the pulse pressure. Entanglement was observed for coarser cellulose particles, while surface cohesion forces characterised the agglomerates of finer cellulose, glucose and ascorbic acid. The latter was characterised by fragmentation, especially at 0.5 and 1 bar(g). Biot, Pyrolysis and Damköhler numbers were used to analyse the effects of cellulose agglomeration and deagglomeration. At 600 K, the pyrolysis is the rate-limiting step, but from 700 K, the external heat transfer becomes the rate-limiting step, which would not happen without agglomeration. Due to the particle size distribution evolution, but also to turbulence and residence time variation, MIT can vary by 50 °C.

Day–Night Transport-induced Chemistry and Clouds on WASP-39b: Gas-phase Composition

JWST has recently detected the first robust photochemical product on an exoplanet: sulfur dioxide (SO2) on WASP-39b. The data from the NIRISS instrument also reveal signs of partial coverage of clouds. Most of the previous studies have focused on interpreting spectral data with 1D models. To explore how the chemical species and cloud particles are altered by global circulation, we applied a 2D photochemical model and a 2D microphysical cloud model separately to postprocess the thermal and dynamical structures simulated by a 3D general circulation model of WASP-39b. We found that SO2 produced by photochemistry on the dayside can be transported to the nightside owing to the efficient replenishment of horizontal transport. The morning–evening limb differences in methane (CH4) abundances predicted by the 1D models disappeared after horizontal transport is included. Similarly, the inclusion of horizontal transport also reduced the limb differences in SO2. Our modeling results suggest that the fast zonal wind results in minimal or negligible limb asymmetry in composition. Based on the synthetic spectra generated by our 2D atmosphere simulations, we propose that observing SO2 absorption in the emission spectra of WASP-39b at different phases may offer opportunities to probe the horizontal quenching process of photochemical products. We will focus on the gas-phase chemistry in this paper and leave the results regarding clouds in the subsequent paper as part of the series.

A study of Io’s sodium jets with the TRAPPIST telescopes

Io is the most volcanically active body in the Solar System. This volcanic activity results in the ejection of material into Io’s atmosphere, which may then escape from the atmosphere to form various structures in the Jovian magnetosphere, including the plasma torus and clouds of neutral particles. The physical processes involved in the escape of particles – for example, how the volcanoes of Io provide material to the plasma torus – are not yet fully understood. In particular, it is not clear to what extent the sodium jet, one of the sodium neutral clouds related to Io, is a proxy of processes that populate the various reservoirs of plasma in Jupiter’s magnetosphere. Here, we report on observations carried out over 17 nights in 2014–2015, 30 nights in 2021, and 23 nights in 2022–2023 with the TRAPPIST (TRAnsiting Planets and PlanetesImals Small Telescope) telescopes, in which particular attention was paid to the sodium jet and the quantification of their physical properties (length and brightness). It was found that these properties can vary greatly from one jet to another and independently of the position of Io in its orbit. No clear link was found between the presence of jets and global brightening of the plasma torus and extended sodium nebula, indicating that jets do not contribute straightforwardly to their population. This work also demonstrates the advantage of regular and long-term monitoring in understanding the variability of the sodium jet and presents a large corpus of jet detections against which work in related fields may compare.

Spatiotemporal evolution of particle puffs in transitional channel flow

We study the shape evolution of puffs composed of tracers advected in a transitional channel flow. We perform a direct numerical simulation of a spatially evolving channel flow, where the inflow condition is given by a solution of the Orr–Sommerfeld equation and the flow evolves through all stages of transition up to fully developed turbulence. In this setting, we release spherical puffs of particles and track their evolution using measures derived from their approximation as ellipsoids. By varying the initial position of puffs, we characterize the spatial non-homogeneity of the flow, with respect to both the distance from the wall and, most importantly, the streamwise coordinate along which the flow evolves. Furthermore, we assess the influence of scale-dependent phenomena on puff shapes by varying the initial size of the clouds of particles. The present approach explores the interaction between flow features and advected Lagrangian structures. Additionally, it reveals the interplay between flow scales and how their balance changes during transition, where the intermittency causes large puffs to be much less elongated than smaller puffs independently of the distance from the walls.

Ice Cloud Formation Related to Oceanic Supply of Ice‐Nucleating Particles: A Case Study in the Southern Ocean Near an Atmospheric River in Late Summer

AbstractThis study investigated ice cloud formation associated with marine bioaerosols over the Southern Ocean (SO) using a combination of cloud particle sensor (CPS) sonde observations, satellite products, reanalysis data, and backward trajectory analysis. The CPS sonde detected ice clouds at temperatures higher than −10°C in the mid‐troposphere near an atmospheric river at high‐latitudes over the SO. Backward trajectory analyses indicated that a mid‐latitude air mass with a high concentration of atmospheric dimethylsulfide (DMS) in the atmospheric boundary layer (<1 km) arrived at the ice cloud formation layer over the high‐latitudes. The DMS in the boundary layer began to increase under high wave conditions, coincident with the highest chlorophyll‐a concentrations in the ocean. These results suggest that bioaerosols emitted from the ocean over the mid‐latitudes acted as ice‐nucleating particles for ice cloud formation over high‐latitudes.

Ephemeral Ice Clouds in the Upper Mesosphere of Venus

AbstractThe conditions in Venus' upper mesosphere at around 120 km have some similarities to the upper mesosphere of Earth and Mars where ice clouds form. Here we show, using published satellite products and numerical modeling, that the upper mesosphere of Venus can be sufficiently cold that both H2O and CO2 may condense to form particles. We show that amorphous solid water particles (ASW) are likely to nucleate both heterogeneously on meteoric smoke and also homogeneously, resulting in clouds of nano‐scaled particles at around 120 km that will occur globally. The temperatures may then become sufficiently low, below ∼90 K, that CO2 particles can nucleate on ASW particles. Given the uncertainty associated with retrievals of temperature in the upper mesosphere, it is unclear how frequently this occurs, but it could be >30% of the time poleward of 60°. Since the main component of Venus' tenuous atmosphere is CO2, any CO2 crystals that form will grow and sediment on a timescale of 10–20 min. Mie calculations show that these Venusian mesospheric clouds (VMCs) should be observable by contemporary satellite instruments, although their short lifetime means that the probability of detection is small. We suggest that VMCs are important for the redistribution of meteoric smoke and may serve as a cold‐trap, removing some water vapor from the very upper mesosphere of Venus through the growth and sedimentation of cloud particles, and possibly reducing the loss of water to space.

A Numerical Simulation Study of Secondary Ice Productions in a Squall Line Case

Secondary ice productions (SIPs) can produce ice crystals with a number concentration much higher than that of ice nucleating particles in mixed-phase clouds and therefore influence cloud glaciation and precipitation. For midlatitude continental mesoscale convective systems (MCSs), how SIPs affect the microphysical properties and precipitation is still not clear. There are few studies of SIPs in midlatitude continental MCSs. This study investigates the roles of three SIPs (rime splintering, freezing drop shattering, and ice-ice collisional breakup) on a squall line case in North China on 18 August 2020 using the WRF model with a modified Morrison double-moment bulk microphysical scheme. Including SIPs, especially ice-ice collisional breakup, in the model simulations markedly improves the simulated convective area and convective precipitation rate of the squall line, while slightly improving the area and precipitation of the stratiform region. Within the mixed-phase layer in both the convective and stratiform regions of the squall line, ice-ice collisional breakup is the dominant process to generate ice crystals. In contrast, rime splintering generates an order of magnitude fewer ice crystals than ice-ice collisional breakup, while freezing drop shattering plays a negligible role due to the lack of large drops. Ice multiplication through ice-ice collisional breakup and rime splintering produces numerous snowflakes and graupel. This leads to enhanced depositional growth and weaker riming, which in turn weakens rime splintering. It is recommended to add SIP parameterization to the model.

Computing differential operators of the particle velocity in moving particle clouds using tessellations

We propose finite-time measures to compute the divergence, the curl and the velocity gradient tensor of the point particle velocity for two- and three-dimensional moving particle clouds. For this purpose, a tessellation of the particle positions is performed to assign a volume to each particle. We introduce a modified Voronoi tessellation which overcomes some drawbacks of the classical construction. Instead of the circumcenter we use the center of gravity of the Delaunay cell for defining the vertices. Considering then two subsequent time instants, the dynamics of the volume can be assessed. Determining the volume change of tessellation cells yields the divergence of the particle velocity. Reorganizing the various velocity coefficients allows computing the curl and even the velocity gradient tensor. The helicity of particle velocity can be likewise computed and swirling motion of particle clouds can be quantified. First we assess the numerical accuracy for randomly distributed particles. We find a strong Pearson correlation between the divergence computed with the modified tessellation, and the exact value. Moreover, we show that the proposed method converges with first order in space and time in two and three dimensions. Then we consider particles advected with random velocity fields with imposed power-law energy spectra. We study the number of particles necessary to guarantee a given precision. Finally, applications to fluid and inertial particles advected in three-dimensional fully developed isotropic turbulence show the utility of the approach for real world applications to quantify self-organization in particle clouds and their vortical or even swirling motion.

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.

CLASS Observations of Atmospheric Cloud Polarization at millimeter Wavelengths

The dynamic atmosphere imposes challenges to ground-based cosmic microwave background observation, especially for measurements on large angular scales. The hydrometeors in the atmosphere, mostly in the form of clouds, scatter the ambient thermal radiation and are known to be the main linearly polarized source in the atmosphere. This scattering-induced polarization is significantly enhanced for ice clouds due to the alignment of ice crystals under gravity, which are also the most common clouds seen at the millimeter-astronomy sites at high altitudes. This work presents a multifrequency study of cloud polarization observed by the Cosmology Large Angular Scale Surveyor experiment on Cerro Toco in the Atacama Desert of northern Chile, from 2016–2022, at the frequency bands centered around 40, 90, 150, and 220 GHz. Using a machine-learning-assisted cloud classifier, we made connections between the transient polarized emission found in all four frequencies with the clouds imaged by monitoring cameras at the observing site. The polarization angles of the cloud events are found to be mostly 90° from the local meridian, which is consistent with the presence of horizontally aligned ice crystals. The 90 and 150 GHz polarization data are consistent with a power law with a spectral index of 3.90 ± 0.06, while an excess/deficit of polarization amplitude is found at 40/220 GHz compared with a Rayleigh scattering spectrum. These results are consistent with Rayleigh-scattering-dominated cloud polarization, with possible effects from supercooled water absorption and/or Mie scattering from a population of large cloud particles that contribute to the 220 GHz polarization.

Parameterization of optical properties for liquid cloud droplets containing black carbon based on neural network.

This paper introduces a novel back propagation (BP) neural network method to accurately characterize optical properties of liquid cloud droplets, including black carbon. The model establishes relationships between black carbon volume fraction, wavelength, cloud effective radius, and optical properties. Evaluated on a test set, the value of the root mean square error (RMSE) of the asymmetry factor, extinction coefficient, single-scattering albedo, and the first 4 moments of the Legendre expansion of the phase function are less than 0.003, with the maximum mean relative error (MRE) reaching 0.2%, which are all better than the traditional method that only uses polynomials to fit the relationship between the effective radius and optical properties. Notably, the BP neural network significantly compresses the optical property database size by 37,800 times. Radiative transfer simulations indicate that mixing black carbon particles in water clouds reduces the top-of-atmosphere (TOA) reflectance and heats the atmosphere. However, if the volume fraction of black carbon is less than 10-6, the black carbon mixed in the water cloud has a tiny effect on the simulated TOA reflectance.

CCN activation in homogeneous isotropic turbulence: Response to particle characteristics and environmental conditions

Direct Numerical Simulation (DNS) of turbulent cloud parcels are presented where particles evolve in response to the local values in supersaturation (s) led by turbulent fluctuations. A pseudo-spectral DNS is modified to incorporate aerosol particles and Cloud Condensation Nuclei (CCN) activation applying a droplet growth equation based on κ-Köhler theory that works for the whole range of warm cloud particles, from un-activated deliquesced aerosol particles to activated cloud droplets, that grow by condensation. The Lagrangian microphysics applied ensures that each particle experiences a supersaturation (fluctuations added to the mean) corresponding to its near-particle gas phase surroundings, which differs from the uniform parcel view where an average value of supersaturation, s¯, is applied to every particle in the domain. A non-turbulent Lagrangian parcel model with similar mean thermodynamics and CCN properties is compared with turbulent DNS parcels of varying fluctuation intensities to distinguish the impact of turbulence on CCN activation. It is shown that, in a given distribution, a subset of aerosols respond to fluctuations rather than the mean thermodynamics allowing particles of similar dry size to co-exist as un-activated haze particles as well as cloud droplets. The cloud microphysical properties are analysed in a series of DNS experiments with contrasting thermodynamic and aerosol properties. DNS results agrees with the general understanding of aerosol activation and cloud droplets evolution in response to various background conditions such as pristine and polluted aerosol distributions, composition of CCN with respect to organic - inorganic components and magnitude of vertical velocity. DNS considers turbulence-microphysics interactions in such problems providing additional knowledge on the role of turbulence in clouds. It is argued that incorporating turbulent fluctuations in simulating the CCN activation and droplet growth is well reasoned and a constructive way forward.

A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images

Abstract. In combination with observations from visible satellite channels, near-infrared channels can provide valuable additional cloud information, e.g. on cloud phase and particle sizes, which is also complementary to the information content of thermal infrared channels. Exploiting near-infrared channels for operational data assimilation and model evaluation requires a sufficiently fast and accurate forward operator. This study presents an extension to the method for fast satellite image synthesis (MFASIS) that allows for simulating reflectances of the 1.6 µm near-infrared channel based on a computationally efficient neural network with the same accuracy that has already been achieved for visible channels. For this purpose, it is important to better represent vertical variations in effective cloud particle radii, as well as mixed-phase clouds and molecular absorption in the idealized profiles used to train the neural network. A new approach employing a two-layer model of water, ice and mixed-phase clouds is described, and the relative importance of the different input parameters characterizing the idealized profiles is analysed. A comprehensive data set sampled from Integrated Forecasting System (IFS) forecasts together with different parameterizations of the effective water and ice particle radii is used for the development and evaluation of the method. Further evaluation uses a month of ICOsahedral Non-hydrostatic development based on version 2.6.1 (ICON-D2) hindcasts with effective radii directly determined by the two-moment microphysics scheme of the model. In all cases, the mean absolute reflectance error achieved is about 0.01 or smaller, which is an order of magnitude smaller than typical differences between reflectance observations and corresponding model values. The errors related to the imperfect training of the neural networks present only a small contribution to the total error, and evaluating the networks takes less than a microsecond per column on standard CPUs. The method is also applicable for many other visible and near-infrared channels with weak water vapour sensitivity.

Does doffing the FEES Box generate a significant cloud of particles after aerosol-generating procedures? A proof-of-concept study

Purpose The spread of airborne diseases, including coronaviruses, remains a widespread public health concern. Published studies outline the use of protective barriers to limit the spread of pathogenic particles and droplets resulting from coughing, sneezing, and talking. The findings suggest a role for these barriers during aerosol-generating procedures, such as flexible endoscopic evaluation of swallowing (FEES). However, the question remains of whether doffing a protective barrier will create a concentrated cloud of particles that will increase health professionals’ exposure. Method We simulated four clinical scenarios of coughing and sneezing, talking, eating and drinking, and delivering supplemental oxygen to test whether doffing the FEES Box protective barrier would result in a particle cloud. Result For all scenarios simulated, doffing the FEES Box did not result in a significant increase in mean particle count. Further, the manner of FEES Box removal did not significantly influence mean particle counts on a consistent basis. Conclusion These results suggest that doffing the FEES Box does not increase exposure to airborne particles. Although more research is needed to confirm these findings, FEES Box usage should be considered during aerosol-generating procedures, to protect and reassure healthcare professionals who work with patients with COVID-19 or other airborne diseases.

Modelling reflected polarized light from close-in giant exoplanet WASP-96b using PolHEx (Polarization of hot exoplanets)

ABSTRACT We present the Polarization of Hot Exoplanets (PolHEx) code for modelling the total flux (F) and degree of linear Polarization (P) of light spectra reflected by close-in, tidally locked exoplanets. We use the output from a global climate model (GCM) combined with a kinetic cloud model of hot Jupiter WASP-96b as a base to investigate effects of atmospheric longitudinal-latitudinal inhomogeneities on these spectra. We model F and P-spectra as functions of wavelength and planet orbital phase for various model atmospheres. We find different materials and sizes of cloud particles to impact the reflected flux F, and particularly the linear Polarization state P. A range of materials are used to form inhomogeneous mixed-material cloud particles (Al2O3, Fe2O3, Fe2SiO4, FeO, Fe, Mg2SiO4, MgO, MgSiO3, SiO2, SiO, TiO2), with Fe2O3, Fe, and FeO the most strongly absorbing species. The cloud particles near the relatively cool morning terminator are expected to have smaller average sizes and a narrower size distribution than those near the warmer evening terminator, which leads to different reflected spectra at the respective orbital phases. We also find differences in the spectra of F and P as functions of orbital phase for irregularly or spherically shaped cloud particles. This work highlights the importance of including Polarization in models and future observations of the reflection spectra of exoplanets.

YunMa: Enabling Spectral Retrievals of Exoplanetary Clouds

In this paper, we present YunMa, an exoplanet cloud simulation and retrieval package, which enables the study of cloud microphysics and radiative properties in exoplanetary atmospheres. YunMa simulates the vertical distribution and sizes of cloud particles and their corresponding scattering signature in transit spectra. We validated YunMa against results from the literature. When coupled to the TauREx 3 platform, an open Bayesian framework for spectral retrievals, YunMa enables the retrieval of the cloud properties and parameters from transit spectra of exoplanets. The sedimentation efficiency (f sed), which controls the cloud microphysics, is set as a free parameter in retrievals. We assess the retrieval performances of YunMa through 28 instances of a K2-18 b-like atmosphere with different fractions of H2/He and N2, and assuming water clouds. Our results show a substantial improvement in retrieval performances when using YunMa instead of a simple opaque cloud model and highlight the need to include cloud radiative transfer and microphysics to interpret the next-generation data for exoplanet atmospheres. This work also inspires instrumental development for future flagships by demonstrating retrieval performances with different data quality.

Zero-shot learning of aerosol optical properties with graph neural networks

Black carbon (BC), a strongly absorbing aerosol sourced from combustion, is an important short-lived climate forcer. BC’s complex morphology contributes to uncertainty in its direct climate radiative effects, as current methods to accurately calculate the optical properties of these aerosols are too computationally expensive to be used online in models or for observational retrievals. Here we demonstrate that a Graph Neural Network (GNN) trained to predict the optical properties of numerically-generated BC fractal aggregates can accurately generalize to arbitrarily shaped particles, including much larger (10times) aggregates than in the training dataset. This zero-shot learning approach could be used to estimate single particle optical properties of realistically-shaped aerosol and cloud particles for inclusion in radiative transfer codes for atmospheric models and remote sensing inversions. In addition, GNN’s can be used to gain physical intuition on the relationship between small-scale interactions (here of the spheres’ positions and interactions) and large-scale properties (here of the radiative properties of aerosols).

SPARSE–R: A point-cloud tracer with random forcing

A predictive, point-cloud tracer is presented that determines with a quantified uncertainty the Lagrangian motion of a group of point-particles within a finite region. The tracer assumes a random forcing within confidence intervals to account for the empiricism of data-driven force models and stochasticity related to the chaotic nature of the subcloud scale dynamics. It builds on the closed Subgrid Particle-Averaged Reynolds Stress-Equivalent (SPARSE) formulation presented in Domínguez-Vázquez et al. (2023) that assumes a deterministic forcing. SPARSE–R describes the first two moments of particle clouds with moment equations in closed-form, with a theoretical third-order convergence rate with respect to the standard deviations of the cloud variables. The cloud model alleviates computational cost and enhances the convergence rate as compared to Monte Carlo (MC) based point-particle methods. The randomness in the forcing model leads to virtual stresses that correlate random forcing and field fluctuations. These stresses strain and rotate the random cloud as compared to a deterministically forced cloud and thus determine to what extent the random forcing propagates into the confidence intervals of the dispersed solution. In symmetric flows the magnitude of the virtual stress is zero. Tests in analytical carrier fields and in a decaying homogeneous isotropic turbulence flow computed with a discontinuous Galerkin (DG) compressible DNS solver are performed to verify and validate the SPARSE–R method for randomly forced particles.

On the Consistency of Ice Cloud Optical Models for Spaceborne Remote Sensing Applications and Broadband Radiative Transfer Simulations

AbstractAqua satellite Moderate Resolution Imaging Spectroradiometer (MODIS) 1‐km observations are collocated with Clouds and the Earth's Radiant Energy System (CERES) fields of view taken during July 2008 afternoon satellite passes over the equatorial western Pacific Ocean. Radiation simulations are compared with collocated CERES observations to better understand the sensitivity of computed fluxes to two ice cloud broadband radiation parameterization schemes and inferred ice cloud characteristics. In particular, the radiation computational schemes and ice cloud property retrievals are based on two respective ice particle models, the MODIS Collection 6 (MC6) aggregate model and a more microphysically consistent two‐habit model (THM). The simulation results show that both MC6 and THM overestimate the shortwave (SW) and longwave (LW) cloud radiative effects at the top of the atmosphere, as compared to the CERES observations; the difference between the MC6 and THM‐based ice cloud retrievals is too small to compensate for the differences between the two model‐based radiation schemes. Therefore, the present finding suggests that broadband radiative simulations are more sensitive to the radiation parameterization scheme than to the input cloud properties retrieved using the corresponding ice cloud particle optical property model.

Proposed Missions to Collect Samples for Analyzing Evidence of Life in the Venusian Atmosphere.

The recent and still controversial claim of phosphine detection in the venusian atmosphere has reignited consideration of whether microbial life might reside in its cloud layers. If microbial life were to exist within Venus' cloud deck, these microorganisms would have to be multi-extremophiles enclosed within the cloud aerosol particles. The most straightforward approach for resolving the question of their existence is to obtain samples of the cloud particles and analyze their interior. While developing technology has made sophisticated in situ analysis possible, more detailed information could be obtained by examining samples with instrumentation in dedicated ground-based facilities. Ultimately, therefore, Venus Cloud-level Sample Return Missions will likely be required to resolve the question of whether living organisms exist in the clouds of Venus. Two multiphase mission concepts are currently under development for combining in situ analyses with a sample return component. The Venus Life Finder architecture proposes collection of cloud particles in a compartment suspended from a balloon that floats for weeks at the desired altitude, while the Novel solUtion for Venus explOration and Lunar Exploitation (NUVOLE) concept involves a glider that cruises within the cloud deck for 1200 km collecting cloud aerosol particles through the key regions of interest. Both architectures propose a rocket-driven ascent with the acquired samples transported to a high venusian orbit as a prelude to returning to Earth or the Moon. Both future conceptual missions with their combined phases will contribute valuable information relative to the habitability of the clouds at Venus, but their fulfillment is decades away. We suggest that, in the meantime, a simplification of a glider cloud-level sample collection scenario could be accomplished in a shorter development time at a lower cost. Even if the cloud particles are not organic and show no evidence of living organisms, they would reveal critical insights about the natural history and evolution of Venus.