Cloud Particles Research Articles

ExoLyn: A golden mean approach to multispecies cloud modeling in atmospheric retrieval

Clouds are ubiquitous in exoplanets' atmospheres and play an important role in setting the opacity and chemical inventory of the atmosphere. Understanding clouds is a critical step in interpreting exoplanets' spectroscopic data. The aim is to model the multispecies nature of clouds in atmospheric retrieval studies. To this end, we developed -- a 1D cloud model that balances physical consistency with computational efficiency. solves the transport equation of cloud particles and vapor under cloud condensation rates that are self-consistently calculated from thermodynamics. is a standalone, open source package capable of being combined with optool to calculate solid opacities and with petitRADTRANS to generate transmission or emission spectra. With we find that the compositional structure of clouds in hot Jupiter planets' atmospheres is layered with a cloud dominated by magnesiumsilicates on top of an iron cloud. This finding is consistent with more complex cloud formation models but can be obtained with in only a few seconds. The composition of the cloud particles can be constrained from the spectrum, for example MgSiO3 and Mg2SiO4 components give rise to an absorption feature at $8-10\ m $. We investigate the dependence of the cloud structure on the bulk elemental composition of the planet and find that SiO2 -dominated clouds form on metal-rich planets and Fe clouds with a strong extinction effect form on C-rich planets. Designed toward maximum flexibility can also be used in retrieval analysis of sub-Neptunes and self-luminous planets. The efficiency of opens the possibility of joint retrieval of exoplanets' gas and cloud components.

A Statistical Study of Polar Mesospheric Cloud Fronts in the Northern Hemisphere

AbstractComplex spatial structures in polar mesospheric cloud (PMC) images provide visual clues to the dynamics that occur in the summer mesosphere. In this study, we document one such structure, a PMC front, by analyzing PMC images in the northern hemisphere from the Cloud Imaging and Particle Size (CIPS) instrument onboard the aeronomy of ice in the mesosphere (AIM) satellite. A PMC front is defined as a sharp boundary that separates cloudy and mostly clear regions, and where the clouds at the front boundary are brighter than the clouds in the cloudy region. We explore the environment that supports the formation of PMC fronts using near‐coincident temperature and water vapor observations from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) satellite instrument. A comparison of PMC front locations to near‐coincident temperature profiles reveals the presence of inversion layers at PMC altitudes. The adiabatic and superadiabatic topside lapse rates of these temperature inversions indicate that some of the identified inversion layers may have been formed by gravity wave (GW) dissipation. The structure of the squared buoyancy frequency profiles indicates a stable layer or thermal duct that can be associated with large‐amplitude mesospheric inversion layers (MILs) that extend large distances. These inversion layers may be conducive to horizontal wave propagation. We hypothesize that ducted GWs may be a formation mechanism of PMC fronts.

Effects of the internal temperature on vertical mixing and on cloud structures in ultra-hot Jupiters

The vertical mixing in hot-Jupiter atmospheres plays a critical role in the formation and spacial distribution of cloud particles in their atmospheres. This affects the observed spectra of a planet through cloud opacity, which can be influenced by the degree of cold trapping of refractory species in the deep atmosphere. We aim to isolate the effects of the internal temperature on the mixing efficiency in the atmospheres of ultra-hot Jupiters (UHJs) and the spacial distribution of cloud particles across the planet. We combined a simplified tracer-based cloud model, a picket fence radiative-transfer scheme, and a mixing length theory to the Exo-FMS general circulation model. We ran the model for five different internal temperatures at typical UHJ atmosphere system parameters. Our results show the convective eddy diffusion coefficient remains low throughout the vast majority of the atmosphere, with mixing dominated by advective flows. However, some regions can show convective mixing in the upper atmosphere for colder interior temperatures. The vertical extent of the clouds is reduced as the internal temperature is increased. Additionally, a global cloud layer gets formed below the radiative--convective boundary (RCB) in the cooler cases. Convection is generally strongly inhibited in UHJ atmospheres above the RCB due to their strong irradiation. Convective mixing plays a minor role compared to advective mixing in keeping cloud particles aloft in UHJs with warm interiors. Higher vertical turbulent heat fluxes and the advection of potential temperature inhibit convection in warmer interiors. Our results suggest that isolated upper atmosphere regions above cold interiors may exhibit strong convective mixing in isolated regions around Rossby gyres, allowing aerosols to be better retained in these areas.

Signatures of Ultralight Bosons in the Orbital Eccentricity of Binary Black Holes.

We show that the existence of clouds of ultralight particles surrounding black holes during their cosmological history as members of a binary system can leave a measurable imprint on the distribution of masses and orbital eccentricities observable with future gravitational-wave detectors. Notably, we find that for nonprecessing binaries with chirp masses M≲10M_{⊙}, formed exclusively in isolation, larger-than-expected values of the eccentricity, i.e., e≳10^{-2} at gravitational-wave frequencies f_{GW}≃10^{-2} Hz, would provide tantalizing evidence for a new particle of mass between [0.5,2.5]×10^{-12} eV in nature. The predicted evolution of the eccentricity can also drastically affect the in-band phase evolution and peak frequency. These results constitute unique signatures of boson clouds of ultralight particles in the dynamics of binary black holes, which will be readily accessible with the Laser Interferometer Space Antenna, as well as future midband and decihertz detectors.

Why heterogeneous cloud particles matter. Iron-bearing species and cloud particle morphology affect exoplanet transmission spectra

The possibility of observing spectral features in exoplanet atmospheres with space missions like the James Webb Space Telescope (JWST) and Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) necessitates the accurate modelling of cloud particle opacities. In exoplanet atmospheres, cloud particles can be made from multiple materials and be considerably chemically heterogeneous. Therefore, assumptions on the morphology of cloud particles are required to calculate their opacities. The aim of this work is to analyse how different approaches to calculate the opacities of heterogeneous cloud particles affect the optical properties of cloud particles and how this may influence the interpretation of data observed by JWST and future missions. We calculated cloud particle optical properties using seven different mixing treatments: four effective medium theories (EMTs; Bruggeman, Landau-Lifshitz-Looyenga (LLL), Maxwell-Garnett, and Linear), core-shell, and two homogeneous cloud particle approximations. We conducted a parameter study using two-component materials to study the mixing behaviour of 21 commonly considered cloud particle materials for exoplanets. To analyse the impact on observations, we studied the transmission spectra of HATS-6b, WASP-39b, WASP-76b, and WASP-107b. Materials with large refractive indices, like iron-bearing species or carbon, can change the optical properties of cloud particles when they comprise less than 1<!PCT!> of the total particle volume. The mixing treatment of heterogeneous cloud particles also has an observable effect on transmission spectroscopy. Assuming core-shell or homogeneous cloud particles results in less muting of molecular features and retains the cloud spectral features of the individual cloud particle materials. The predicted transit depths for core-shell and homogeneous cloud particle materials are similar for all planets used in this work. If EMTs are used, cloud spectral features are broader and the cloud spectral features of the individual cloud particle materials are not retained. Using LLL leads to fewer molecular features in transmission spectra than when using Bruggeman.

Natural logarithm particle swarm optimization for loss reduction in an island power system

In an island power system, optimizing energy management is fundamental since there are renewable sources with their limitations. This management includes the allocation and capacity of energy sources to supply the loads. In this context, optimizing losses in the system contributes to improve the efficiency of this management. This paper proposes the losses optimization and energy management in the island power system. The authors propose the Natural Logarithm Particle Swarm Optimization to solve the problem and compare it with the Attractor Point Algorithm and Evolutionary Particle Swarm Optimization. And with that, we also propose a particle initialization for the studied particle-based algorithms to guarantee convergence in radial power systems. This is because the system configuration influences the response of the algorithm convergence. These techniques were applied to the IEEE-34 unbalanced radial island system.•Natural Logarithm Particle Swarm Optimization differs from classical PSO in that it does not calculate the velocity of the particles. Therefore, the method considers a cloud of particles with a natural logarithmic trajectory to solve the reduction of losses in a power system with a radial topology.•Natural Logarithmic Particle Swarm Optimization uses an initialization equation to minimize the initial estimation process, which is relevant to the convergence process.

Variations in Cloud Concentration Nuclei Related to Continental Air Pollution Control and Maritime Fuel Regulation over the Northwest Pacific Ocean

Here, we compared the concentrations of cloud condensation nuclei (CCN) and particle number size distributions (PNSDs) measured during the transient period from the winter to the summer East Asian monsoon in 2021 with those in 2014 to explore possible responses to how CCN responds to upwind continental air pollutant mitigation and marine traffic fuel sulfur content (FSC) regulation over the northwest Pacific Ocean (NWPO). We also employed the Positive Matrix Factorization (PMF) analysis to apportion concentrations of CCN (Nccn) to different sources in order to quantify its source-specified responses to mitigation of air pollution during the transient period. Our results showed that (1) upwind continental mitigation likely reduced Nccn by approximately 200 cm−3 and 400 cm−3 at 0.2% and 0.4% supersaturation (SS), respectively, in the marine background atmosphere over the NWPO; (2) FSC regulation resulted in a decrease in Nccn at 0.4% SS by about 50 cm−3 and was nearly negligible at 0.2% SS over the NWPO. Additionally, a PMF-resolved factor, characterized by a dominant nucleation mode, was present only in 2014 and disappeared in 2021, likely due to the reduction. This estimation, however, suffered from uncertainties since seasonal changes were hard to be deducted accurately. PMF-resolved factors accurately represented Nccn in 80–90% of cases, but this accuracy was not observed in the remaining cases. Finally, an integrated analysis of satellite-derived cloud parameters and ship-based measurements indicated that the reduced Nccn over the NWPO might be co-limited with meteorological factors in forming cloud droplets during the transient period.

Design and experiment of a lightweight cloud particle imager

Abstract Accurate in-situ measurement of cloud microphysical parameters such as particle diameter, number concentration and droplet spectrum distribution are of great significance in cloud physics, climate change, numerical weather forecasting and weather modification. This work describes the design and characterization of a newly developed in situ measurement instrument for cloud and fog named lightweight cloud particle imager (LCPI). The basic measurement principle of LCPI is based on scattering light imaging of particles in a dark field. A ring LED lighting source was designed to improve the image quality. Field measurements were carried out at a high-altitude research station on Lushan Mountain. The measured size distributions showed good agreement with parallel measurements of a cloud droplet spectrometer (fog monitor-120). The field data also showed that LCPI was able to measure the size distribution of cloud droplets from 5 to 300 μm with a high spatial resolution. In addition, another observation experiment carried out in the Tuli River weather station showed that LCPI also had the ability to acquire clear images of ice crystal particles larger than 10 μm. Thus, LCPI provides an opportunity to simultaneously quantify the microphysical structure of different cloud types.

Aggregate Cloud Particle Effects in Exoplanet Atmospheres

Aerosol opacity has emerged as a critical factor controlling transmission and emission spectra. We provide a simple guideline for the effects of aerosol morphology on opacity and residence time in the atmosphere, as it pertains to transit observations, particularly those with flat spectra due to high altitude aerosols. This framework can be used for understanding complex cloud and haze particle properties before getting into detailed microphysical modeling. We consider high altitude aerosols to be composed of large fluffy particles that can have large residence times in the atmosphere and influence the deposition of stellar flux and/or the emergence of thermal emission in a different way than compact droplet particles, as generally modeled to date for extrasolar planetary atmospheres. We demonstrate the important influence of aggregate particle porosity and composition on the extent of the wavelength independent regime. We also consider how such fluffy particles reach such high altitudes and conclude that the most likely scenario is their local production at high altitudes via UV bombardment and subsequent blanketing of the atmosphere, rather than some mechanism of lofting or transport from the lower atmosphere.

Light Scattering Measurements of KCl Particles as an Exoplanet Cloud Analog

Salt clouds are predicted to be common on warm exoplanets, but their optical properties are uncertain. The Exoplanet Cloud Ensemble Scattering System (ExCESS), a new apparatus to measure the scattering intensity and degree of linear polarization for an ensemble of particles, is introduced here and used to study the light scattering properties of KCl cloud analogs. ExCESS illuminates particles with a polarized laser beam (532 nm) and uses a photomultiplier tube detector to sweep the plane of illumination. Scattering measurements for KCl particles were collected for three size distributions representative of modeled clouds for the warm exoplanet GJ 1214b. Our measurements show that Lorenz–Mie calculations, commonly used to estimate the light scattering properties of assumedly spherical cloud particles, offer an inaccurate depiction of cubic and cuboid KCl particles. All of our measurements indicate that Lorenz–Mie scattering overestimates the backscattering intensity of our cloud analogs and incorrectly predicts the scattering at mid-phase angles (∼90°) and the preferential polarization state of KCl scattered light. Our results align with the general scattering properties of nonspherical particles and underscore the importance of further understanding the effects that such particles will have on radiative transfer models of exoplanet atmospheres and reflected light observations of exoplanets by the upcoming Nancy Grace Roman Space Telescope and Habitable Worlds Observatory.

Vortex formation driven by the particle flow at the interface of a phase-separated binary complex plasma under microgravity condition

Microparticles of two sizes are confined in a dc discharge in a glass tube with polarity switch in the PK-4 laboratory on board the International Space Station. Small and big particles separate from each other presumably due to the unbalance of the force under microgravity condition, forming an ellipsoidal interface. Particles close to the symmetric axis of the cylindrical glass tube are driven by a manipulation laser and a particle flow is generated. The flow velocity depends not only on the laser current but also on the configuration and location of the particle cloud. Counterintuitively, it is observed that a vortex can be formed at the interface, only if the flow velocity is below a certain critical value. Our experiments provide a great opportunity to study the new facets of vortex formation at particle-resolved level.

New experimental method for the simultaneous determination of concentration and size profiles of condensed combustion products around a burning aluminum droplet

When an aluminum particle burns in the diffusive regime, a cloud of nanometric particles of its primary oxide, alumina, is formed around the droplet. Characterizing this oxide smoke surrounding a burning aluminum droplet is essential. The original contribution of this paper is to introduce an experimental approach that allows for simultaneous size and concentration measurement of the particles composing the oxide smoke. This paper employs an electrodynamic levitator to facilitate the self-sustained combustion of an isolated aluminum particle, with a radius of 35μm, in atmospheric air. This levitation apparatus is paired with an optical setup that allows for a multi-spectral light extinction method. This technique is specifically designed to measure the size and concentration of alumina particles below the micrometric scale, which is scarcely documented in existing literature. Consequently, spatially resolved concentration and size profiles of the nanometric alumina particles are measured and compared to simulated data from the literature. The radii of alumina particles in the cloud are found to evolve closely around 80 nm. Volume fractions are evaluated from 2.8⋅10−4 near the particle surface to 3⋅10−5 further in the cloud. Therefore, significant concentrations of alumina particles are revealed during the short (15 to 20 ms) combustion process at distances of the order of magnitude of a few initial particle diameters. Such characterization could then lead to a better assessment of the interaction distance among aluminum particles as they burn collectively.Novelty and significance statementThis article introduces the first experimentally determined profiles of alumina concentration and size distribution surrounding a single burning aluminum droplet. The uniqueness of our experimental setup lies in its ability to study of the most fundamental case of the autonomous combustion of a single levitating particle, without any convective effects. A new non-intrusive method specifically designed provides unprecedented results in the field of metallic particle combustion. Data provided in the paper are crucial as they serve as the first reference case for future simulation and experiments. The results introduced in this paper highlight the inability of current simulation methods to accurately account for nucleation and condensation processes, and emphasize the need for a non-invasive characterization method.

Precipitation and cloud Parameters's characteristics of stratus in summer in Ningxia, eastern of northwest China

Stratus is the main artificial seeding target for weather modification in Ningxia. Meteorological satellites can detect macro and microcosmic physical parameters of large-scale stratus. Researching precipitation and cloud parameter characteristics of stratus can provide scientific and technological support for understanding the development and evolution of stratus to identify seeding conditions and effects. Eight typical stratus precipitation events of the summers of 2016–2021 of Ningxia were selected, using the nine point average method, that matched characteristic cloud parameters retrieved by FY-2G satellite with precipitation classification data of 25 national meteorological observation stations in Ningxia. These stratus precipitation characteristics were correlated with cloud parameters. The results indicate that precipitation decreased rapidly from the southeast to the northwest of Ningxia. In the synoptic background of precipitation, the cloud top height, super-cooled layer thickness, cloud optical thickness, cloud particle effective radius, and cloud liquid water content were all larger than those in the synoptic background of no precipitation. The spatial distribution feature manifests as gradually increased from southeast to northwest. The cloud top temperature and cloud blackbody temperature were smaller than those in the synoptic background of no precipitation. The cloud top temperature was near the cloud “seeding temperature window”, and the depth of the super-cooled layer was thick. This was in line with the “Seeder-Feeder” structure characteristic of cold stratus.

The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data

Abstract. This study proposed an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. The matching characteristics between aerosol and cloud particle size distributions and gamma distributions were studied using aircraft observation data. The feasibility of the retrieval of the particle effective radius from lidar ratios and backscatter ratios was simulated and studied. A method for inverting the effective radius and number concentration of atmospheric aerosols or small cloud droplets using the backscatter ratio was proposed, and the error sources and applicability of the algorithm were analyzed. This algorithm was suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmospheric particles and has good robustness. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 µm. For cloud droplets, it is 1.0–10 µm. An atmospheric-observation experiment was conducted using the multi-wavelength lidar developed by Xi'an University of Technology, and a thin cloud layer was captured. The microphysical parameters of aerosol and clouds during this process were retrieved. The results clearly demonstrate the growth of the effective radius and number concentration.

Studying the Aerosol Effect on Deep Convective Clouds over the Global Oceans by Applying Machine Learning Techniques on Long-Term Satellite Observation

Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models.

Evaporation and Fragmentation of the Electrified Droplets in the Polar Clouds during Spring Season as a Key Mechanism of the Ozone Depression Formation

This study focuses on the role of the charged particles in the formation of the springtime ozone depression in the polar atmosphere. Analysis of experimental data collected in the polar atmosphere indicates that the small charged particles, predominantly ion clusters, can play a key role in the ozone molecules destruction and springtime ozone depression. The formation of these particles increases strongly during the spring season in the process of evaporation and fragmentation of the cloud-charged droplets in the lower stratosphere and upper troposphere. Additionally, small charged particles can also affect the formation and accumulation of chlorine monoxide under cold conditions of the lower stratosphere. At the same time, the chlorine mechanism of ozone destruction cannot completely explain the ozone depression formation, which probably takes place not only inside the polar vortex in the lower stratosphere but outside it also, both at the altitudes of the lower stratosphere and upper troposphere. The assumption that the charged particles play an important role in the process of ozone depression formation was put forward by previous studies, but in this research work, this assumption has been additionally confirmed, which is the springtime growth of ion clusters concentration as a result of the droplet's evaporation and defragmentation in the polar atmosphere is a key mechanism of the ozone depression formation. We simulated the process of evaporation and fragmentation in the case of a 10-micron size droplet, which implies the possible catalytic cycles of ozone destruction with the ion clusters. For the first time, the role of the Earth's magnetic field and the Polar vortex wind in the unipolar charge accumulation on the cloud particles in the lower stratosphere and upper troposphere not only inside the Polar vortex but also outside of it was substantiated. This fact in our point of view can give rise to the large-scale springtime ozone depression, spreading over the midlatitudes, which size is vastly greater than it is commonly supposed.

The ion beam interceptor concept for a space station collision avoidance with space debris

Space debris poses a great threat to space stations. When a collision hazard is detected, the space station's orbit should be adjusted. An alternative approach is to apply collision avoidance measures that consist in creating an external impact on the space debris object to change its orbit and increase the miss distance slightly. Such an impact can be carried out using a ground-based or orbital laser, through the use of nano-tugs thrusters, or by placing a cloud of particles in the path of the object. This study proposes to use the plume of a thruster of an active spacecraft called an Ion Beam Interceptor. When a potential collision is detected, the interceptor undocks from the space station, approaches the space debris object and directs an ion beam, which is generated by the spacecraft's thrusters, at it. Hitting into the surface of the object, the particles of the ion beam exert a force effect on it, which leads to the required change in the orbit. After the completion of the maneuver, the interceptor returns to the space station for refueling and servicing. The objective of the study is a preliminary assessment of the feasibility and effectiveness of the proposed approach of protecting a space station from space debris impact using an active spacecraft. A mission profile of the space station protection is proposed. A series of numerical simulations are performed to determine mission parameters and fuel costs. Based on an analysis of the orbits of space objects located near the space station, the maximum angle between the orbital planes of the dangerous object and the station are determined at which it is advisable to use the Ion Beam Interceptor. The developed concept also allows to implement just-in-time collision avoidance measures for space debris objects whose orbital planes are close to the plane of the space station.

Color Measurements of Dust Clouds Transiting the White Dwarf J0328-1219

The transiting dust clouds that orbit the white dwarf (WD) J0328-1219 produce dips with nearly identical depths at g′ and R bands. This resembles a similar finding for WD 1145+017 at near-IR wavelengths, which has been interpreted as requiring an absence of small particles in the dust clouds. Whereas the lack of depth difference at optical wavelengths for J0328-1219 can be used to rule-out the presence of small particles if the dust clouds are optically thin, a constraint on the particle size distribution is not possible if the dust clouds are optically thick.

Classification of Ice Particle Shapes Using Machine Learning on Forward Light Scattering Images

Abstract Machine learning (ML) has rapidly transitioned from a niche activity to a mainstream tool for environmental research applications including atmospheric science cloud microphysics studies. Two recently developed cloud particle probes measure the light scattered in the near forward direction and save digital images of the scattering light. Scattering pattern images collected by the Particle Phase Discriminator mark 2, Karlsruhe edition (PPD-2K), and the Small Ice Detector, version 3 (SID-3) provide valuable information for particle shape and size characterization. Since different particle shapes have distinctly different light scattering characteristics, the images are ideally suited for ML. Here, results of a ML project to characterize ice particle shapes sampled by the PPD-2K in ice fog and diamond dust during a 3-yr project in Fairbanks, Alaska. About 2.15 million light-scattering pattern images were collected during 3 years of measurements with the PPD-2K. Visual Geometry Group (VGG) convolutional neural network (CNN) was trained to categorize light-scattering patterns into eight categories. Initial training images (120 each category) were selected by human visual examination of data, and the training dataset was augmented using an automated iterative method for image identification of further images which were all visually inspected by a human. Results were well correlated to similar categories identified from previously developed classification algorithms. ML identifies characteristics not included in automated analysis such as sublimation. Of the 2.15 million images analyzed, 1.3% were categorized as spherical (liquid), 43.5% were categorized as having rough surfaces, 15.3% were pristine, 16.3% were categorized as sublimating, and the remaining 23.6% did not fit into any of those categories (irregular or saturated). Significance Statement The shapes and sizes of cloud particles can be extremely important for understanding the conditions that exist in the cloud. In this study, we show that more information about cloud particle characteristics can be identified by using machine learning than by traditional means. To demonstrate this, data are analyzed from a 3-yr study of ice fog and diamond dust events in Fairbanks, Alaska. The Particle Phase Discriminator instrument collected 2.15 million light-scattering pattern images of cloud particles during ground-based measurements. Neither traditional techniques nor machine learning were able to identify all categories, but a combination of both techniques led to a more complete view of ice particle shapes.

The Influence of Space Traffic on AIM/CIPS PMC Frequencies at 80°N

AbstractWe explore the effects of lower thermospheric water vapor deposited by launch vehicle plumes on polar mesospheric cloud (PMC) frequencies at 80°N. We use July‐averaged PMC frequencies from 2007 to 2022 from the Cloud Imaging and Particle Size (CIPS) instrument on NASA's Aeronomy of Ice in the Mesosphere (AIM) satellite. Launch sites worldwide are typically located near northern mid‐latitudes. Using the orbital launch record for the same time period, we find that the number of launches correlates with PMC frequencies with a coefficient of r = 0.60, which increases to r = 0.75 when only selecting launches from 2.5 to 21.5 local time (LT), indicating a weak LT dependence on global‐scale transport to 80°N. To support our findings, we use meridional winds from the Michelson Interferometer for Global High‐resolution Imaging experiment on NASA's Ionospheric Connection Explorer satellite and winds from the Horizontal Wind Model climatology to interpret the northward motion of air parcels at 105 km. We find the launch LT window that maximizes the correlation coefficient to be consistent with the expected maximum northward motion from the diurnal variation of mid‐latitude meridional winds. Comparisons with Microwave Limb Sounder satellite observations of upper mesospheric temperature and water vapor reveal a strong dependence of cloud frequency on water vapor (r = 0.86) but not on temperature (r = −0.26), indicating that water vapor is the primary source of PMC variability for the bright PMCs at 80°N. We therefore find that launch vehicle plumes originating primarily from northern mid‐latitudes modulate PMC frequency at 80°N in July.