Cloud Conditions Research Articles

Cloud droplet size distributions governed by condensation, collision–coalescence, and sedimentation. Part 1: Monte Carlo simulations and scaling relations for growth in a convection–cloud chamber

Abstract Atmospheric clouds are crucial to weather and climate, and the rate at which droplets collide and coalesce to form precipitation is one of the fundamental controlling processes. A convection-cloud chamber allows the interactions between aerosols and cloud droplets produced by condensation to be investigated within a turbulent environment. Studying the full range of microphysical conditions in atmospheric clouds is not possible, however, unless conditions for droplet growth by collision and coalescence are also achieved. In this study, we explore the conditions favorable to collision-coalescence growth in convection-cloud chambers, extending previous work on steady-state droplet size distributions due to condensation only to obtain analytic expressions for number concentration and supersaturation as a function of small-droplet injection rate, and for collision-coalescence rates. We derive several scaling laws and demonstrate consistency between these theoretical results and Monte-Carlo simulations of growth and precipitation within a convection-cloud chamber. Specifically, the coalescence rate is shown to scale as the square of the liquid water content, which in turn can be modulated in a convection-cloud chamber by varying the small-droplet injection rate as well as the applied temperature difference.

Satellite‐Based Solar Irradiance Forecasting: Replacing Cloud Motion Vectors by Deep Learning

Satellite‐based (SAT) methods are widely used to forecast surface solar irradiance up to several hours ahead. Herein, a cloud index‐based version of the Heliosat method is applied to infer irradiance from Meteosat Second Generation images. The cloud index (CI) is derived from images in the visible range and quantifies the impact of clouds on surface solar irradiance. Conventional SAT methods utilize cloud motion vectors (CMVs) from consecutive CI images to predict future cloud conditions and subsequently retrieve irradiance. In this study, HelioNet is introduced—a convolutional neural network (CNN) with UNet architecture designed to predict future CI situations from sequences of preceding CI images. Forecasts of two HelioNet configurations are benchmarked against CMV and persistence over a full year (2023), with lead times (LT) up to 4 h. HelioNet15 min recursively generates forecasts at 15 min resolution. HelioNethybrid begins with forecasts at 15 min resolution for , then uses a 45 min resolved model to forecast all remaining LT steps. HelioNet15 min achieves root mean square error (RMSE) improvements of >15% over the CMV model within the first hour on image level. HelioNethybrid shows superior performance for all LT across all metrics considered, with an average RMSE improvement of >11% on image and 8% at irradiance level.

Physics-Driven Image Dehazing from the Perspective of Unmanned Aerial Vehicles

Drone vision is widely used in change detection, disaster response, and military reconnaissance due to its wide field of view and flexibility. However, under haze and thin cloud conditions, image quality is usually degraded due to atmospheric scattering. This results in issues like color distortion, reduced contrast, and lower clarity, which negatively impact the performance of subsequent advanced visual tasks. To improve the quality of unmanned aerial vehicle (UAV) images, we propose a dehazing method based on calibration of the atmospheric scattering model. We designed two specialized neural network structures to estimate the two unknown parameters in the atmospheric scattering model: the atmospheric light intensity A and medium transmission t. However, calculation errors always occur in both processes for estimating the two unknown parameters. The error accumulation for atmospheric light and medium transmission will cause the deviation in color fidelity and brightness. Therefore, we designed an encoder-decoder structure for irradiance guidance, which not only eliminates error accumulation but also enhances the detail in the restored image, achieving higher-quality dehazing results. Quantitative and qualitative evaluations indicate that our dehazing method outperforms existing techniques, effectively eliminating haze from drone images and significantly enhancing image clarity and quality in hazy conditions. Specifically, the compared experiment on the R100 dataset demonstrates that the proposed method improved the peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) metrics by 6.9 dB and 0.08 over the second-best method, respectively. On the N100 dataset, the method improved the PSNR and SSIM metrics by 8.7 dB and 0.05 over the second-best method, respectively.

Rotational excitation of methyl mercaptan (CH3SH) in collisions with molecular hydrogen

Abstract This paper presents the calculation of rate coefficients for transitions between rotational levels of the A-type and E-type levels of methyl mercaptan (CH3SH) resulting from collisions with molecular hydrogen. Radiative transfer modeling requires both radiative and collisional rates to describe the rotational populations under the usual conditions in interstellar clouds where LTE conditions do not apply. To compute the intermolecular interaction between CH3SH and H2, the explicitly correlated CCSD(T)-F12a coupled-cluster method which utilized a correlation-consistent aug-cc-pVTZ basis was employed. The computed energies were fit to a functional form suitable for use in scattering calculations. Rate coefficients were calculated over the temperature range from 5 to 100 K for transitions between the 110 lowest CH3SH rotational levels (having energies less than 107 cm−1 (ca. 150 K) within both the A-type and E-type manifolds caused by collisions with para- and ortho-H2. The rate coefficients were obtained through time-independent quantum close coupling quantum scattering calculations utilizing the calculated PES.

Quantifying the informativity of emission lines to infer physical conditions in giant molecular clouds. I. Application to model predictions

Observations of ionic, atomic, or molecular lines are performed to improve our understanding of the interstellar medium (ISM). However, the potential of a line to constrain the physical conditions of the ISM is difficult to assess quantitatively, because of the complexity of the ISM physics. The situation is even more complex when trying to assess which combinations of lines are the most useful. Therefore, observation campaigns usually try to observe as many lines as possible for as much time as possible. We have searched for a quantitative statistical criterion to evaluate the full constraining power of a (combination of) tracer(s) with respect to physical conditions. Our goal with such a criterion is twofold. First, we want to improve our understanding of the statistical relationships between ISM tracers and physical conditions. Secondly, by exploiting this criterion, we aim to propose a method that helps observers to make their observation proposals; for example, by choosing to observe the lines with the highest constraining power given limited resources and time. We propose an approach based on information theory, in particular the concepts of conditional differential entropy and mutual information. The best (combination of) tracer(s) is obtained by comparing the mutual information between a physical parameter and different sets of lines. The presented analysis is independent of the choice of the estimation algorithm ( neural network or $ minimization). We applied this method to simulations of radio molecular lines emitted by a photodissociation region similar to the Horsehead Nebula. In this simulated data, we considered the noise properties of a state-of-the-art single dish telescope such as the IRAM 30m telescope. We searched for the best lines to constrain the visual extinction or the ultraviolet illumination field . We ran this search for different gas regimes, namely translucent gas, filamentary gas, and dense cores. The most informative lines change with the physical regime ( cloud extinction). However, the determination of the optimal (combination of) line(s) to constrain a physical parameter such as the visual extinction depends not only on the radiative transfer of the lines and chemistry of the associated species, but also on the achieved mean signal-to-noise ratio. The short integration time of the CO isotopologue $J=1-0$ lines already yields much information on the total column density for a large range of ( ) space. The best set of lines to constrain the visual extinction does not necessarily combine the most informative individual lines. Precise constraints on the radiation field are more difficult to achieve with molecular lines. They require spectral lines emitted at the cloud surface ( and lines). This approach allows one to better explore the knowledge provided by ISM codes, and to guide future observation campaigns.

Super-resolution water body mapping with a feature collaborative CNN model by fusing Sentinel-1 and Sentinel-2 images

Mapping water bodies from remotely sensed imagery is crucial for understanding hydrological and biogeochemical processes. The identification of water extent is mainly dependent on optical and synthetic aperture radar (SAR) images. However, the use of remote sensing for water body mapping is often undermined by the mixed pixel dilemma inherent to traditional hard classification approaches. At the same time, the presence of clouds in optical imagery and speckle noise in SAR imagery, coupled with the difficulty in differentiating between water-like surfaces and actual water bodies, significantly compromise the accuracy of water body identification. This paper proposes a DEEP feature collaborative convolutional neural network (CNN) for Water Super-Resolution Mapping based on Optical and SAR images (DeepOSWSRM), which collaboratively leverages Sentinel-1 and Sentinel-2 imagery to address the challenges of missing data and mixed pixels. The Sentinel-1 image provides complementary water distribution information for the cloudy areas of the Sentinel-2 image, while the Sentinel-2 image enhances the perception capabilities for small water bodies in the Sentinel-1 image. Using PlanetScope imagery as the true reference data, the effectiveness of the proposed method was assessed through two experimental scenarios: one utilizing synthetic coarse-resolution imagery degraded from Sentinel-1 and Sentinel-2 data and another using actual Sentinel-1 and Sentinel-2 data, encompassing both simulated and real cloud conditions. A comparative analysis was conducted against three state-of-the-art CNN-based water mapping methods and two CNN SRM methods. The findings demonstrate that the proposed DeepOSWSRM method successfully produces accurate, fine-resolution water body maps, with its performance mainly benefiting from the fusion of SAR and optical images.

Understanding the Impact of H2 Diffusion Energy on the Formation Efficiency of H2 on the Interstellar Dust Grain Surface

We use microscopic Monte Carlo simulation techniques to investigate the impact of H2 diffusion energy on the recombination efficiency of H2 on interstellar dust grain surfaces under diffuse and translucent cloud conditions. We constructed five models representing different possible conditions encountered by adsorbed H and H2 on interstellar dust grains. We implemented adsorption sites with multiple binding energies for surface species; the Encounter-Desorption mechanism was also included. The study focused on silicate surfaces in diffuse clouds and water ice surfaces in translucent clouds. The results show that the recombination efficiency of H2 on dust surfaces decreases as H2 diffusion energy increases. An interesting finding of this work is that considering different binding sites for H and H2 gives rise to multiple steady phases, during which the recombination efficiency remains constant with a change in H2 diffusion energy.

Formation of carbonyl sulfide (OCS) via SH radicals in interstellar CO-rich ice under dense cloud conditions

Carbonyl sulfide (OCS) is widely observed in the gas phase toward star-forming regions and was the first of the only two sulfur-bearing species to be detected in interstellar ices so far. However, the chemical network governing its formation is still not fully understood. While the sulfurization of CO and the oxidation of CS are often invoked to form OCS other mechanisms could have a significant contribution. In particular, the multistep reaction involving CO and SH is a good candidate for forming a significant portion of the OCS in dense cloud environments. We aim to constrain the viability of the $ CO SH $ route for forming solid OCS in the interstellar medium, in a similar manner as $ CO OH $ is known to produce CO2 ice. This is achieved by conducting a systematic laboratory investigation of the targeted reactions on interstellar ice analogs under dense cloud conditions. We used an ultrahigh vacuum chamber to simultaneously deposit CO H2S and atomic H at 10 K. SH radicals produced in situ via hydrogen abstraction from H2S reacted with CO to form OCS . We monitored the ice composition during deposition and subsequent warm-up by means of Fourier-transform reflection absorption infrared spectroscopy (RAIRS). Complementarily, desorbed species were recorded with a quadrupole mass spectrometer (QMS) during temperature-programmed desorption (TPD) experiments. Control experiments were performed to secure the product identification. We also explored the effect of different H2S CO mixing ratios---with decreasing H2S concentrations---on the OCS formation yield. OCS is efficiently formed through surface reactions involving CO H2S and H atoms. The suggested underlying mechanism behind OCS formation is $ CO SH HSCO $ followed by $ HSCO H OCS H2 $. The OCS yield reduces slowly, but remains significant with increasing CO H2S mixing ratios ( CO H2S = 1:1, 5:1, 10:1, and 20:1). Our experiments provide unambiguous evidence that OCS can be formed from $ CO SH $ in the presence of H atoms. This route remains efficient for large H2S dilutions ($5<!PCT!>$ with respect to CO ), suggesting that it is a viable mechanism in interstellar ices. Given that SH radicals can be created in clouds over a wide evolutionary timescale, this mechanism could make a non-negligible contribution to the formation of interstellar OCS ice.

A note of double-layer cloud detection method based on cloud base height and brightness

Double-layer cloud is a special cloud condition, and clouds in two layers have different effects on solar radiation reaching ground. Getting the shape of double-layer clouds is helpful to study the change of solar irradiance. The proposed double-layer clouds detection method is based on the cloud base height (CBH) and brightness of double-layer clouds. The CBH is calculated based on binocular parallax, which requires two images filmed synchronously by two cameras, and the brightness is offered by the brightness channel in HSV color space. The accuracy of this method is 98.5% for single-layer clouds and 89.4% for double-layer clouds. The reasons for misidentifications are (1) the brightness contrast decreases when the high-layer clouds are thick, (2) the distant low clouds have different brightness in top and bottom.

A method to assess the cloud-aerosol transition zone from ceilometer measurements

Cloud and aerosol contribution to the Earth's radiative budget constitutes one of the most significant uncertainties in future climate projections. To distinguish between clouds and cloud-free air, atmospheric scientists have been using a wide range of instruments, techniques, and algorithms with various detection thresholds. However, since the change from a cloudy to a cloud-free atmosphere may be gradual and, in some cases, far from obvious, recent research is questioning where the threshold between both phases should be established. These considerations lead to contemplate a transition zone (TZ) between cloud and cloud-free conditions. In the present study, backscatter profiles obtained by a Vaisala CL31 ceilometer were processed to assess the transition zone. First, two widely used cloud detection algorithms were applied and compared: the method provided by the ceilometer manufacturer (Vaisala) and Cloudnetpy, the algorithm from ACTRIS Cloudnet, a project devoted to aerosol, clouds, and trace gases research. Second, a sensitivity analysis was applied to the backscatter and signal-to-noise ratio thresholds used for cloud detection in Cloudnetpy. This methodology has allowed us to assess the vertical distribution of clouds, aerosols, the TZ, and its frequency of occurrence. Results indicate a gradual transition in backscatter retrievals from cloud to cloud-free, where particles detected near cloud boundaries induced higher backscatter values than those found further away. Depending on the thresholds used, we observed a 9.3% (with an estimated range of uncertainty of 5.420%) variation in cloud occurrence which can be attributed to TZ conditions. Analysing the whole backscatter profile, we found as many TZ conditions as cloudy values, which emphasises the importance of studying the vertical distribution of the TZ. Moreover, the analysis of TZ occurrence in height and time revealed that such conditions concentrate below 800m during night periods, although annual height-hour distributions involve a remarkable variability among seasons. These findings highlight the importance of either including an additional phase between ‘pure clouds’ and ‘pure aerosols’ or treating them as a continuum of suspended particles in the atmosphere.

In-situ aircraft observations of aerosol and cloud microphysical characteristics of mixed-phase clouds over the North China Plain

Aerosol-cloud interactions play a vital role in climate change. This study leverages observations from the King-350 aircraft over the North China Plain on November 29, 2019, to examine aerosol and cloud microphysical characteristics of mixed-phase clouds. Through detailed vertical and spectral distributions, we investigate aerosol, cloud droplet, and ice crystal distributions in seeded clouds (SC) and natural precipitating clouds (NPC) within the same cloud system. From the vertical profile, SC and NPC have similar vertical distributions of aerosol and cloud droplets, with over 95 % of aerosols concentrated below 1600 m near the ground. Cloud droplets are more evenly distributed within the two clouds, cloud droplet number concentrations (Nc) in SC were three orders of magnitude higher than in NPC. Ice water content (IWC) and ice crystal number concentration (Ni) show distinct layer preferences—accumulating predominantly in SC's top layer and NPC's middle layer. From spectral distribution, a smaller proportion of cloud droplets (40–50 μm in diameter, the same hereafter) in SC compared to NPC. Rimed ice crystals and globular graupel (1325–1550 μm in diameter) were in SC, while plate and irregular ice crystals (300–450 μm) were in NPC with an order of magnitude higher than in SC. These microphysical differences highlight the complexity of cloud seeding efficacy, which varies based on cloud conditions and microphysical properties. In the first seeding, Ni increased by 1–2 orders of magnitude (125–300 μm) in the high Nc (Nc > 1.11 × 105 L−1) region. Seeding in low Nc (Nc < 1.11 × 105 L−1) regions was hard to be effective, especially in low Nc and low liquid water content (LWC) (LWC < 0.122 g/m3) regions. In the second seeding, ice crystals (125–250 μm) produced by the first seeding enhance the seeding efficiency. The responded regions were more sensitive to subsequent seeding, resulting in stronger reactions or longer duration.

Repeated drone photogrammetry surveys demonstrate that reconstructed canopy heights are sensitive to wind speed but relatively insensitive to illumination conditions

ABSTRACT Unoccupied aerial vehicle (UAV) based structure-from-motion (SfM) photogrammetry surveys are becoming a standard tool for ecologists to measure plant structure and biomass in non-forest ecosystems. The reproducibility of SfM survey results under different operational conditions, namely wind speed, sun elevation, and cloud condition, is poorly understood. It is also unclear to what extent commonly applied point-to-grid interpolation of derived point clouds affects inference of vegetation structure. These knowledge gaps limit the use of these methods for measuring and monitoring detection. We captured 61 UAV SfM surveys at the same study area under a range of wind/sun/cloud conditions over a 24-day period during 2021 and used generalized linear mixed effects models to test how the structural reconstructions varied with environmental conditions. Wind speed significantly influenced the canopy height reconstructions, with greater wind speeds reducing mean canopy height. Different plant species exhibited varying sensitivities to wind that are likely related to leaf attributes (size, structure, and density), growth form of the canopy, and physical properties such as limb flexibility. The movement of plants can reduce canopy height estimates derived from photogrammetric surveys, even under relatively low wind speeds. Reconstructed canopy heights were comparatively insensitive to solar elevation variations. Cloud conditions and illumination by direct sunlight had a weak, non-significant effect on reconstructed canopy height, with sunny conditions (generating shadows) resulting in a measurable but marginal reduction in mean canopy heights. When comparing canopy heights of interpolated and discontinuous derived canopy height models the results highlighted that these were insensitive to interpolation in this specific setting. We recommend measuring mean wind speeds throughout surveys where comparisons are to be made between different drone-based SfM surveys of vegetation over either time or space. Care should be taken to ensure that the effects of wind are controlled for so that inferences are valid.

Comparability of radar and optical methods in identifying surface water in a semi‐arid protected area

AbstractSurface water assumes a pivotal role in sustaining a wide range of wildlife species in semi‐arid protected areas. However, differences in surface water body typology, underlying soil type, wildlife activity, the presence of phytoplankton amongst other factors, result in high variability of surface water spectral reflectance and detection accuracy. In this study, the performance of radar and optical methods was evaluated in detecting surface water of variable spectral reflectance in Hwange National Park, Zimbabwe using Sentinel‐1 radar and Sentinel‐2 optical images for the period 2016–2023. Results demonstrated that radar methods had low surface water detection accuracy which was highly variable as shown by overall accuracy and kappa statistic measures which continuously changed over time compared with optical methods. The overall best‐performing method was the optical AWEInsh (sharpened) which showed high surface water detection accuracy and consistency (OA: 94%–100%) and (κ: 0.88–1.00) from 2016 to 2023. Therefore, optical methods present a stable and robust way for surface water monitoring in heterogeneous semi‐arid protected areas. However, radar‐based methods should be continually explored where optical‐based technologies are impeded as a result of vegetation cover and cloud conditions.

Improved Temperature‐Dependent Ice Refractive Index Compilation in the Far‐Infrared Spectrum

AbstractA new ice refractive index compilation is reported for a broad spectrum ranging from 0.0443 to 106 μm, focusing on the pronounced temperature‐dependence of ice optical properties in the far‐infrared (far‐IR) segment (15–100 μm). A sensitivity study assuming spherical particles shows that selecting ice refractive indices at 12 temperatures and 215 wavelengths in the far‐IR region gives sufficient accuracy in interpolated refractive indices for developing a new ice crystal optical property database. Furthermore, we demonstrate the differences between the bulk single‐scattering properties computed for hexagonal ice particles with this new compilation compared to a previous iteration at three far‐IR wavelengths where substantial differences are noticed between the two ice refractive index compilations. We suggest that our new ice refractive index data set will improve downstream light‐scattering applications for upcoming far‐IR satellite missions and allow robust modeling of outgoing longwave radiation under ice cloud conditions.

Formation of N–bearing complex organic molecules in molecular clouds: Ketenimine, acetonitrile, acetaldimine, and vinylamine via the UV photolysis of C2H2 ice

Context. The solid-state C2H2 chemistry in interstellar H2O-rich ice has been proposed to explain astronomically observed complex organic molecules (COMs), including ketene (CH2CO), acetaldehyde (CH3CHO), and ethanol (CH3CH2OH), toward early star-forming regions. This formation mechanism is supported by recent laboratory studies and theoretical calculations for the reactions of C2H2+OH/H. However, the analog reaction of C2H2+NH2 forming N-bearing species has been suggested to have a relatively low rate constant that is orders of magnitude lower than the value of C2H2+OH. Aims. This work extends our previous laboratory studies on O-bearing COM formation to investigate the interactions between C2H2 and NH3 ice triggered by cosmic ray-induced secondary UV photons under molecular cloud conditions. Methods. Experiments were performed in an ultra-high vacuum chamber to investigate the UV photolysis of the C2H2:NH3 ice mixture at 10 K. The ongoing chemistry was monitored in situ by Fourier-transform infrared spectroscopy as a function of photon fluence. The IR spectral identification of the newly formed N-bearing products was further secured by a quadrupole mass spectrometer during the temperature-programmed desorption experiment. Results. The studied ice chemistry of C2 H2 with NH2 radicals and H atoms resulting from the UV photodissociation of NH3 leads to the formation of several N-bearing COMs, including vinylamine (CH2CHNH2), acetaldimine (CH3CHNH), acetonitrile (CH3CN), ketenimine (CH2CNH), and tentatively ethylamine (CH3CH2NH2). The experimental results show an immediate and abundant CH2CHNH2 yield as the first-generation product, which is further converted into other chemical derivatives. The effective destruction and formation cross-section values of parent species and COMs were derived, and we discuss the chemical links among these molecules and their astronomical relevance.

Enhancing signal-to-noise ratio in active laser imaging under cloud and fog conditions through combined matched filtering and neural network

Active laser imaging utilizes time-of-flight and echo intensity measurements to generate distance and intensity images of targets. However, scattering caused by cloud and fog particles, leads to imaging quality deterioration. In this study, we introduce a novel approach for improving imaging clarity in these environments. We employed a matched filtering method that leverages the distinction between signal and noise in the time domain to preliminarily extract the signal from one-dimensional photon-counting echo data. We further denoised the data by utilizing the Long Short-Term Memory (LSTM) neural network in extracting features from extended time-series data. The proposed method displayed notable improvement in the signal-to-noise ratio (SNR), from 7.227 dB to 31.35 dB, following an analysis of experimental data collected under cloud and fog conditions. Furthermore, processing positively affected the quality of the distance image with an increase in the structural similarity (SSIM) index from 0.7883 to 0.9070. Additionally, the point-cloud images were successfully restored. These findings suggest that the integration of matched filtering and the LSTM algorithm effectively enhances beam imaging quality in the presence of cloud and fog scattering. This method has potential application in various fields, including navigation, remote sensing, and other areas susceptible to complex environmental conditions.

On the retrieval of cloud optical thickness from spectral radiances - A sensitivity study with high albedo surfaces

Measurements of spectral zenith radiance in the 320–950 nm wavelength range have been carried out since 2021 at the Thule High Arctic Atmospheric Observatory (THAAO, https://www.thuleatmos-it.it/, 76.5° N, 68.8° W, 225 m a.s.l) located in Pituffik, northern Greenland. This study evaluates whether such observations could provide, in principle, scientifically meaningful cloud optical depth (τ) estimates, what would be the limitations, which are the necessary auxiliary measurements, and which radiance wavelengths would be better suited for the goal. Although clouds play a critical role in the Arctic, a climatology of τ in high albedo conditions is particularly difficult to obtain. THAAO might have the instrument capabilities to provide such a long-term dataset. We use a radiative transfer package to simulate visible spectra with different cloud and surface conditions, assuming homogeneous overcast sky and liquid water clouds of fixed geometrical thickness. Simulations are run to reproduce typical conditions encountered at THAAO when measurements are carried out. We find that the assumption of a broadband albedo instead of a spectrally-resolved one is the source of the largest uncertainties. Tests on size and phase of cloud particles showed that a 50% uncertainty in reff leads to a ∼10% error in τ, and that a 10% contamination of ice crystals in a low-level liquid water cloud leads to an error in τ estimates of less than 5%. All the tests showed that the most critical τ range is between the thin and thick cloud regimes (τ ∼ 7–15), where the retrievals can be less reliable. Otherwise, tests suggest that in the environmental conditions that characterize late spring and summer at THAAO, and given the observatory measurements capabilities, estimates of τ for low-level clouds could be accurately retrieved both in high and low surface albedo conditions by means of zenith radiance measurements in the UV-Vis-NIR range.

Laboratory simulations of ice growth in space: An expected nonuniform ice mantle composition

Context. In dense, cold molecular regions, gas-phase chemical species freeze out onto grain surfaces. These icy condensates become an important reservoir of volatile elements and feedstock for molecular diversity. Aims. While there is a fairly general agreement on the chemical composition of icy mantles, there are differences in how the various molecular components are perceived to be present. Should the materials composing the ice be mixed or are they segregated into distinct chemical zones? Methods. To answer such a question, we performed a few exploratory experiments that allowed the adsorbing surface (mimic dust grains) to slowly relax to very low temperatures while gas-phase mixtures of H2O, NH3, and CO embed onto it. Results. We find that mantles are far from being uniform, and they could evolve into completely mixed ices only if the ambient temperature undergoes a catastrophic collapse. Conclusions. Under the typical conditions of an interstellar dense cloud, ices present a high degree of molecular segregation, with possible consequences on the ice chemistry and the desorption mechanisms.

A Research on Hybrid Cloud Computing

Dual distributed computing has arisen as a significant answer for present day ventures trying to use the benefits of both public and confidential cloud foundations. This theoretical digs into the pith of crossover distributed computing, inspecting its design, advantages, challenges, and arising patterns molding its reception. Cross breed cloud engineering consistently incorporates public and confidential cloud conditions, permitting associations to improve responsibility position in view of elements like security, consistence, execution, and cost-proficiency. By mixing the versatility and adaptability of public mists with the control and customization of private mists, dual models engage endeavors to draftsman modern IT environments custom-made to their extraordinary necessities. The advantages of mixed distributed computing are complex. Associations can powerfully scale assets, moving responsibilities between conditions to successfully oblige vacillations sought after. Furthermore, mixture mists work with information sway, empowering endeavors to hold delicate information on-premises while utilizing the dexterity of public mists for less basic responsibilities. This flexibility encourages development and speeds up computerized change drives, enabling organizations to remain deft and serious in a quickly developing scene. In any case, hybrid cloud reception likewise presents difficulties. Overseeing different cloud conditions presents intricacy, requiring powerful administration structures, robotization devices, and gifted faculty to guarantee consistent organization and security across the mixture framework. Interoperability issues, information coordination concerns, and potential seller secure in additional highlight the significance of fastidious preparation and vital dynamic in crossover cloud arrangements. Looking forward, a few patterns are reshaping the hybrid cloud scene. Edge registering is acquiring conspicuousness, driving the decentralization of information handling and stockpiling to the organization edge, subsequently enlarging mixed models with edge capacities. Besides, progressions in containerization advances, like Kubernetes, are working with compactness and responsibility portability across mixed conditions, upgrading spryness and asset use. All in all, dual distributed computing addresses a change in outlook in IT framework the executives, offering a strong mix of adaptability, versatility, and control. While reception involves difficulties, the essential reconciliation of public and confidential mists can open unmatched open doors for advancement and effectiveness in the computerized time. Embracing mixed cloud structures enables undertakings to explore intricacy, mitigate risk, and benefit from arising advancements to drive feasible development and upper hand.

Astrochemical effect of the fundamental grain surface processes

Context. Thermal diffusion is one of the basic processes for the mobility and formation of species on cosmic dust grains. The rate of thermal diffusion is determined by the grain surface temperature, a pre-exponential factor, and an activation energy barrier for diffusion. Due to the lack of laboratory measurements on diffusion, prior astrochemical models usually assume that the diffusion pre-exponential factor is the same as that for desorption. This oversimplification may lead to an uncertainty in the model predictions. Recent laboratory measurements have found that the diffusion pre-exponential factor can differ from that for desorption by several orders of magnitude. However, the newly determined pre-exponential factor has not been tested in astrochemical models so far. Aims. We aim to evaluate the effect of the newly experimentally measured diffusion pre-exponential factor on the chemistry under cold molecular cloud conditions. Methods. We ran a set of parameters with different grain temperatures and diffusion barrier energies using the NAUTILUS astro-chemical code and compared the molecular abundance between the models with the abundance obtained using the experimentally determined pre-exponential factor for diffusion and with the abundance obtained using the values commonly adopted in prior models. Results. We found that statistically, more than half of the total gas-phase and grain surface species are not affected by the new pre-exponential factor after a chemical evolution of 105 yr. The most abundant gas-phase CO and grain surface water ice are not affected by the new pre-exponential factor. For the grain surface species that are affected, compared to the commonly adopted value of the pre-exponential factor for diffusion used in the chemical models, they could be either overproduced or underproduced with the lower diffusion pre-factor used in this work. The former case applies to radicals and the species that serve as reactants, while the latter case applies to complex organic molecules (COMs) on the grain and the species that rarely react with other species. Gas-phase species could also be affected due to the desorption of the grain surface species. The abundance of some gas-phase COMs could be varied by over one order of magnitude depending on the adopted grain surface temperature and/or the ratio of diffusion to desorption energy in the model. Key species whose diffusion pre-exponential factor significantly affects the model predictions were also evaluated, and these species include CH3OH, H2CO, and NO. Conclusions. The results presented in this study show that the pre-exponential factor is one of the basic and important parameters in astrochemical models. It strongly affects the chemistry and should be determined carefully. More experiments to determine the diffusion of grain surface species are helpful for constraining their properties.