Multicloud Model Research Articles

A Shallow‐Deep Unified Stochastic Mass Flux Cumulus Parameterization in the Single Column Community Climate Model

AbstractCumulus parameterization (CP) in state‐of‐the‐art global climate models is based on the quasi‐equilibrium assumption (QEA), which views convection as the action of an ensemble of cumulus clouds, in a state of equilibrium with respect to a slowly varying atmospheric state. This view is not compatible with the organization and dynamical interactions across multiple scales of cloud systems in the tropics and progress in this research area was slow over decades despite the widely recognized major shortcomings. Novel ideas on how to represent key physical processes of moist convection‐large‐scale interaction to overcome the QEA have surged recently. The stochastic multicloud model (SMCM) CP in particular mimics the dynamical interactions of multiple cloud types that characterize organized tropical convection. Here, the SMCM is used to modify the Zhang‐McFarlane (ZM) CP by changing the way in which the bulk mass flux and bulk entrainment and detrainment rates are calculated. This is done by introducing a stochastic ensemble of plumes characterized by randomly varying detrainment level distributions based on the cloud area fraction of the SMCM. The SMCM is here extended to include shallow cumulus clouds resulting in a unified shallow‐deep CP. The new stochastic multicloud plume CP is validated against the control ZM scheme in the context of the single column Community Climate Model of the National Center for Atmospheric Research using data from both tropical ocean and midlatitude land convection. Some key features of the SMCM CP such as it capability to represent the tri‐modal nature of organized convection are emphasized.

Prototyping of Utilization Model for KOMPSAT-3/3A Analysis Ready Data Based on the Open Data Cube Platform in Multi-Cloud Computing Environment: A Case Study

This study introduces a multi-cloud model that combines private and public cloud services for processing and managing satellite images. The multi-cloud service is established by incorporating private clouds within organizations and integrating them with external public cloud services to utilize the data. Private clouds can maintain data security within an organization or between organizations, while public clouds offer easy processing options for general users with access accounts. The model for the private cloud service utilizes open-source OpenStack software to create virtual machines, allowing users to manage analysis ready data (ARD) of the Korea Multi-Purpose Satellite (KOMPSAT)-3/3A images simultaneously. The public cloud service through Amazon Web Services (AWS) offers four services and uses the Open Data Cube (ODC) to manage data and provide web-based time-series visualization and processing. The model utilizes OpenStack to create virtual machines, and the public cloud service through AWS offers various services using ODC to manage data. A system that handles large amounts of satellite imagery in a multi-cloud environment has benefits such as improved availability, cost savings through open-source, and enhanced scalability. We present a prototyped utilization model that can be used with the ODC user interface (UI) that applies the proposed multi-cloud model. The multi-cloud model of this study can be applied to constructing a country-scale data cube system, that deals with large-scale satellite image data. It can also be applied to systems that need to be built with data that is tailored to a specific user’s needs at any institution.

A Multicloud Model for Coastal Convection

Coastal convection is often organized into multiple mesoscale systems that propagate in either direction across the coastline (i.e., landward and oceanward). These systems interact non-trivially with synoptic and intraseasonal disturbances such as convectively coupled waves and the Madden–Julian oscillation. Despite numerous theoretical and observational efforts to understand coastal convection, global climate models still fail to represent it adequately, mainly because of limitations in spatial resolution and shortcomings in the underlying cumulus parameterization schemes. Here, we use a simplified climate model of intermediate complexity to simulate coastal convection under the influence of the diurnal cycle of solar heating. Convection is parameterized via a stochastic multicloud model (SMCM), which mimics the subgrid dynamics of organized convection due to interactions (through the environment) between the cloud types that characterize organized tropical convection. Numerical results demonstrate that the model is able to capture the key modes of coastal convection variability, such as the diurnal cycle of convection and the accompanying sea and land breeze reversals, the slowly propagating mesoscale convective systems that move from land to ocean and vice-versa, and numerous moisture-coupled gravity wave modes. The physical features of the simulated modes, such as their propagation speeds, the timing of rainfall peaks, the penetration of the sea and land breezes, and how they are affected by the latitudinal variation in the Coriolis force, are generally consistent with existing theoretical and observational studies.

An Advanced Job Scheduling Algorithmic Architecture to Reduce Energy Consumption and CO2 Emissions in Multi-Cloud

Cloud Computing is one of the emerging fields in the modern-day world. Due to the increased volume of job requests, job schedulers have received updates one at a time. The evolution of machine learning in the context of cloud schedules has had a significant impact on cost reduction in terms of energy consumption and makespan. The research article presents a two-phase process for the scheduling architecture of cloud computing where PMs are the main working unit and users are supplied to the PMs based on the work abilities of the PM in terms of resources. A minimum cost is desired in the preliminary phase of the allocation of the user to the PM. A clustered approach utilizing k-means and Q-learning was imposed to migrate the users from one PM to another PM based on Quality of Service (QoS) parameters. The proposed work has also incorporated CO2 emissions as a major evaluation parameter other than energy consumption. To support resource sharing, the deployment model is a multi-cloud model. The proposed work is evaluated against other recently proposed state of the art techniques on the basis of QoS parameters and the proposed work proved to be better in terms of efficiency at the end of the draft.

Creating a new paradigm for the local interstellar medium–A perspective

The interstellar medium close to the Sun called the local ISM (LISM) provides critical insights into physical processes and phenomena in the more distant interstellar medium in our Galaxy without the confusion of many complex structures in the sight lines to distant stars. High-resolution ultraviolet spectra are powerful diagnostic tools for understanding the LISM together with the observed properties of the outer heliosphere and hydrogen wall absorption in the astrospheres of nearby stars. For nearly 30 years the kinematic structure of the LISM has been identified from measured radial velocities of interstellar gas along many sight lines distributed across a wide range of Galactic coordinates. These data permitted the identification of three dimensional velocity vectors characterizing “clouds”. While this simple structure robustly predicts the radial velocities and associated clouds along newly observed sight lines, there are many challenges to this multi-cloud model that could lead to a revised model of the LISM. This paper reviews the assumptions and accomplishments of the multi-cloud model, and describes how the various challenges to this model could lead to a more comprehensive model with insight into physical processes and structures in the Galactic interstellar medium.

Convective momentum transport and multiscale organization in simulated shear parallel mesoscale convective systems

The way in which moist convection interacts with large scale flows is a major contemporary research issue. Organized mesoscale systems are, in particular, important for the interactions between convection and the ambient shear. Here we present numerical simulations of mesoscale systems evolving in a background shear using the Research and Weather Forecasting model. We are particularly interested in the long time integration, allowing the systems to repeatedly develop and die and effectively interact with the background shear. Starting with a typical African and equatorial jet-shear, the simulated solution goes through various phases or stages. First, a transient state, consisting of scattered squall-like systems that are aligned perpendicular to the background shear, develops and then evolves into a regime of multiscale mesoscale systems with large stratiform anvils. During the latter period the background wind changes substantially through the effect of both up scale and down scale convective momentum transport. At this stage, the systems become aligned parallel to the wind shear, with elongated stratiform anvils in which meso-beta scale convective cells evolve and propagate in the shear direction, relative to the stratiform anvils. These results are reminiscent of the development of shear parallel mesoscale convective systems observed for instance in the Eastern Pacific ITCZ and corroborate recent theoretical results obtained with a simple multi-cloud model. As such they have important implications for the parameterization of CMT in climate models.

An improved privacy aware secure multi-cloud model with proliferate ElGamal encryption for big data storage

An improved privacy aware secure multi-cloud model with proliferate ElGamal encryption for big data storage

An improved privacy aware secure multi-cloud model with proliferate ElGamal encryption for big data storage

An improved privacy aware secure multi-cloud model with proliferate ElGamal encryption for big data storage

An archetype for mitigating the security threats in multi-cloud environment by implementing tree-based next-generation firewalls

Cloud computing on-demand dynamicity in nature of end-user that leads towards a hybrid cloud model deployment is called a multi-cloud. Multi-cloud is a multi-tenant and multi-vendor heterogeneous cloud platform in terms of services and security under a defined SLA (service level agreement). The diverse deployment of the multi-cloud model leads to rise in security risks. In this paper, we define a multi-cloud model with hybridization of vendor and security to increase the end-user experience. The proposed model has a heterogeneous cloud paradigm with a combination of firewall tracts to overcome rising security issues. The proposed work consists of three steps, firstly, all incoming traffic from the consumer end into five major groups called ambient. Secondly, design a next-generation firewall (NGFW) topology with a mixture of tree-based and demilitarized zone (DMZ) implications. Test implementation of designed topology performed by using a simple DMZ technique in case of vendor-specific model and NGFW on hybrid vendor based multi-cloud model. Furthermore, it also defines some advantages of NGFW to overcome these concerns. The proposed work is helpful for the new consumer to define their dynamic secure cloud services under a single SLA before adopting a multi-cloud platform. Finally, results are compared in terms of throughput and CPU utilization in both cases.

Evaluation of Mean State in NCEP Climate Forecast System (Version 2) Simulation Using a Stochastic Multicloud Model Calibrated With DYNAMO RADAR Data

AbstractStochastic parameterizations are continuously providing promising simulations of unresolved atmospheric processes for global climate models (GCMs). One of the stochastic multi‐cloud model (SMCM) features is to mimic the life cycle of the three most common cloud types (congestus, deep, and stratiform) in tropical convective systems. To better represent organized convection in the Climate Forecast System version 2 (CFSv2), the SMCM parameterization is adopted in CFSv2 (SMCM‐CTRL) in lieu of the pre‐existing revised simplified Arakawa–Schubert (RSAS) cumulus scheme and has shown essential improvements in different large‐scale features of tropical convection. But the sensitivity of the SMCM parameterization from the observations is yet to be ascertained. Radar data during the Dynamics of the Madden‐Julian Oscillation (DYNAMO) field campaign is used to tune the SMCM in the present manuscript. The DYNAMO radar observations have been used to calibrate the SMCM using a Bayesian inference procedure to generate key time scale parameters for the transition probabilities of the underlying Markov chains of the SMCM as implemented in CFS (hereafter SMCM‐DYNAMO). SMCM‐DYNAMO improves many aspects of the mean state climate compared to RSAS, and SMCM‐CTRL. Significant improvement is noted in the rainfall probability distribution function over the global tropics. The global distribution of different types of clouds, particularly low‐level clouds, is also improved. The convective and large‐scale rainfall simulations are investigated in detail.

Fuzzy Rule-Based Trust Management Model for the Security of Cloud Computing

In the last few years, due to the benefit of solving large-scale computational problems, researchers have been developed multicloud infrastructures. The trust-related issue in multiclouds includes more complicated content and new problems. A new trust management framework for multicloud environments is proposed in this article. The proposed framework used a combination of objective and subjective trust values to calculate the cloud service provider’s trust values. This new framework can identify and rectify fake feedbacks from other feedbacks. Another advantage of this framework is applying fuzzy rules to calculate trust values. Two main components of the proposed framework are simulated in this paper. The simulation results confirm the important role of applied components. Also, this paper proposed a framework compared with other frameworks (feedback-based model, SLA-based model, and multicloud model). Simulation results show the proposed framework increased trust values rather than other models. Also, compared with other models, our framework gives better mean trust values.

Secure and Verifiable Keyword Search in Multiple Clouds

Searchable encryption enables users to perform a keyword-based search over encrypted data. Most of the existing searchable encryption schemes are based on the single cloud server model, which has some limitations, such as the single point of failure and malicious insiders. In contrast, the multicloud server model has higher data availability and security. However, directly moving the existing keyword search schemes to the multicloud would cause larger storage and search costs, and the data security cannot be distinctly improved. In addition, different from single cloud, a challenge is how to effectively verify query integrity when some cloud servers return incorrect or incomplete results in a multicloud situation. To ensure the data security, we propose an iterative encryption method to ensure the file privacy even if multiple cloud servers collude with each other. We use Bloom filter to map keywords and construct a tree index for each cloud server to achieve secure and efficient distributed keyword search. To verify the query integrity, we propose a fast verification algorithm to embed the file content and index information into the file ID. The analysis and experiments demonstrate the security and effectiveness of our scheme.

Sensitivity of ENSO Simulation to the Convection Schemes in the NESM3 Climate System Model: Atmospheric Processes

The El Niño-Southern Oscillation (ENSO) is the most prominent climate system in the tropical Pacific. However, its simulation, including the amplitude, phase locking, and asymmetry of its two phases, is not well reproduced by the current climate system models. In this study, the sensitivity of the ENSO simulation to the convection schemes is discussed using the Nanjing University of Information Science and Technology Earth System version 3.0 (NESM3) model. Three convection schemes, including the default, the default coupled with the stochastic multicloud model (SMCM), and the default used in the Coupled Model Intercomparison Project Phase 6 (CMIP6), are implemented. The model results reveal that the low-level cloud cover and surface net shortwave radiation are best represented over the tropical Pacific in the model containing the SMCM. The simulations of the ENSO behavior’s response to changes in the convection scheme are not uniform. The model results reveal that the model containing the SMCM performs best in terms of simulating the seasonal cycle of the sea surface temperature anomaly along the equatorial Pacific, the phase locking, and the power spectrum of ENSO but with a modest ENSO amplitude. Compared to the model containing the default convection scheme, the coupling of the default scheme and the SMCM provides a good simulation of the ENSO’s asymmetry, while the model containing the CMIP6 convection scheme outperforms the others in terms of the simulation of the ENSO’s amplitude. Two atmospheric feedback processes were further discussed to investigate the factors controlling the ENSO’s amplitude. The analyses revealed that the strongest positive atmospheric Bjerknes feedback and the thermodynamic damping of the surface net heat flux occurred in the model containing the CMIP6 convection scheme, suggesting that the atmospheric Bjerknes feedback may overwhelm the heat flux damping feedback when determining the ENSO’s amplitude. The results of this study demonstrate that perfectly modeling and predicting the ENSO is not simple, and it is still a large challenge and issue for the entire model community in the future.

Pulsar radio emission mechanisms: a critique

ABSTRACT We consider critically the three most widely favoured pulsar radio emission mechanisms: coherent curvature emission (CCE), beam-driven relativistic plasma emission (RPE), and anomalous Doppler emission (ADE). We assume that the pulsar plasma is 1D, streaming outwards with a bulk Lorentz factor γs ≫ 〈γ〉 − 1 ≳ 1, where 〈γ〉 is the intrinsic spread in the rest frame of the plasma. We argue that the formation of beams in a multicloud model is ineffective in the intrinsically relativistic case for plausible parameters because the overtaking takes too long. We argue that the default choice for the particle distribution in the rest frame is a Jüttner distribution and that relativistic streaming should be included by applying a Lorentz transformation to the rest-frame distribution, rather than the widely assumed relativistically streaming Gaussian distribution. We find that beam-driven wave growth is severely restricted by (a) the wave properties in pulsar plasma, (b) a separation condition between beam and background, and (c) the inhomogeneity of the plasma in the pulsar frame. The growth rate for the kinetic instability is much smaller and the bandwidth of the growing waves is much larger for a Jüttner distribution than for a relativistically streaming Gaussian distribution. No reactive instability occurs at all for a Jüttner distribution. We conclude that none of CCE, RPE, and ADE is tenable as the generic pulsar radio emission mechanism for ‘plausible’ assumptions about the pulsar plasma.

Impacts of the stochastic multicloud parameterization on the simulation of Western North Pacific summer rainfall

This study investigated the sensitivity of the western North Pacific (WNP) summer precipitation to the convection schemes and discussed the associated dynamical processes. Two convection schemes were compared: one is the default mass-flux convection scheme used in the state-of-the-art ECHAM6.3 atmosphere model and the other incorporates the Stochastic Multicloud Model (SMCM) into ECHAM6.3. Incorporation of the SMCM reduces the bias of cloud cover and shortwave and longwave radiation by regulating the shortwave and longwave cloud radiative forcing over the WNP. Compared to the default model, the modified model with the SMCM alleviates the dry bias in the WNP, which is associated with enhanced ascending motion. The moist static energy balance revealed that improved simulation of precipitation in the modified model is contributed by enhanced horizontal advection of moist enthalpy and increased net energy in the atmosphere, which is attributed to increased total cloud cover, over the WNP. Additionally, intensified latent energy advection over the WNP dominates enhanced horizontal advection of moist enthalpy in the modified model. On the other hand, the moisture budget analysis of the WNP demonstrated that strengthened convergence of moisture flux in the modified model plays the most influential role in reducing precipitation bias. Further analysis unraveled that enhanced zonal-mean moisture transported by the stationary eddy zonal flow convergence in the WNP dominates intensified zonal moisture convergence, thus increased horizontal convergence of moist flux in the modified model.

Effects of a stochastic multicloud parameterization on the simulated Asian‐Australian monsoon rainfall in an AGCM

AbstractThe Asian‐Australian monsoon (AAM) plays a vital role in modulating the global and local climates. Current numerical models, including climate system models (CSMs) and atmospheric general circulation models (AGCMs), have difficulties in capturing the fundamental and intrinsic features of the AAM. By applying the stochastic multicloud model (SMCM) to the state‐of‐the‐art ECHAM6.3 atm model, we investigated the impacts of SMCM on the simulation of AAM precipitation. Though the modified model with the SMCM improves simulations of the AAM precipitation in June–July–August–September season, the solstice mode, the AAM domain, and the AAM precipitation intensity, deficiencies also exist in other aspects, that is, simulations of rainfall in December–January–February–March and April–May seasons, and the equinox mode. Analyses also unravelled the modified model improves in simulating the spatial patterns of seasonal variation of precipitation in the Indian monsoon region, western North Pacific monsoon region, and Australian monsoon region in terms of pattern correlation coefficient and normalized root‐mean square error scores. The enhanced simulation of solar radiation in the modified model favours improving the performance of the simulated solstice mode. In addition, moisture budget analysis was applied to investigate physical processes that modulate the variations and distributions of AAM precipitation. Analyses revealed that variations and distributions of the vertical integral of moisture flux convergence is closely associated with changes in precipitation, while the stratification of vertical moist transport is associated with changes in precipitation over land in the AAM region. In addition, the modified model influences the AAM precipitation mainly through the variations of moisture flux convergence. The present work potentially provides a method to improve the simulated capability of AGCMs.

Improved leading modes of interannual variability of the Asian-Australian monsoon in an AGCM via incorporating a stochastic multicloud model

In the present study, we investigated improvements in simulating the two major modes of the Asian-Australian monsoon (AAM) interannual variability by incorporating the stochastic multicloud model (SMCM) into the state-of-the-art ECHAM6.3 atmospheric model. Model results showed that the modified ECHAM6.3, i.e., with the SMCM, improves the simulation of seasonal evolution of anomalous rainfall and low-level circulation. Analysis revealed that the improvement in the simulation of precipitation anomalies of the first mode is associated with the improvement in both enhanced and suppressed convection. In addition, the enhanced easterly vertical shear over the Maritime Continent in the modified ECHAM6.3 contributes to the improvement in an equatorial asymmetry of precipitation anomalies of the first mode. Moreover, the moisture budget analysis demonstrated that the modified ECHAM6.3 improves the seasonal anomalous rainfall of the first mode by ameliorating the vertical mass integral of the moist flux divergence. In addition, the second mode of the AAM interannual variability produced by the modified ECHAM6.3 potentially exerts stronger influence on the ENSO variability compared to the default ECHAM6.3.

Toward a Stochastic Relaxation for the Quasi‐Equilibrium Theory of Cumulus Parameterization: Multicloud Instability, Multiple Equilibria, and Chaotic Dynamics

AbstractThe representation of clouds and organized tropical convection remains one of the biggest sources of uncertainties in climate and long‐term weather prediction models. Some of the most common cumulus parameterization schemes, namely, mass‐flux schemes, rely on the quasi‐equilibrium (QE) closure, which assumes that convection consumes the large‐scale instability and restores large‐scale equilibrium instantaneously. However, the QE hypothesis has been challenged both conceptually and in practice. Subsequently, the QE assumption was relaxed, and instead, prognostic equations for the cloud work function (CWF) and the cumulus kinetic energy (CKE) were derived and used. It was shown that even if the CWF kernel serves to damp the CWF, the prognostic system exhibits damped oscillations on a timescale of a few hours, giving parameterized‐cumulus‐clouds enough memory to interact with each other, with the environment, and with stratiform anvils in particular. Herein, we show that when cloud‐cloud interactions are reintroduced into the CWF‐CKE equations, the coupled system becomes unstable. Moreover, we couple the CWF‐CKE prognostic equations to dynamical equations for the cloud area fractions, based on the mean field limit of a stochastic multicloud model. Qualitative analysis and numerical simulations show that the CKE‐CWF‐cloud area fraction equations exhibit interesting dynamics including multiple equilibria, limit cycles, and chaotic behavior both when the large‐scale forcing is held fixed and when it oscillates with various frequencies. This is representative of cumulus convection variability, and its capability to transition between various regimes of organization at multiple scales and regimes of scattered convection, in an intermittent and chaotic fashion.

Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection

AbstractStochastic parameterizations are increasingly becoming skillful in representing unresolved atmospheric processes for global climate models. The stochastic multicloud model, used to simulate the life cycle of the three most common cloud types (cumulus congestus, deep convective, and stratiform) in tropical convective systems, is one example. In this model, these clouds interact with each other and with their environment according to intuitive‐probabilistic rules determined by a set of predictors, depending on the large‐scale atmospheric state and a set of transition time scale parameters. Here we use a Bayesian statistical method to infer these parameters from radar data. The Bayesian approach is applied to precipitation data collected by the Shared Mobile Atmospheric Research and Teaching Radar truck‐mounted C‐band radar located in the Maldives archipelago, while the corresponding large‐scale predictors were derived from meteorological soundings taken during the Dynamics of the Madden‐Julian Oscillation field campaign. The transition time scales were inferred from three different phases of the Madden‐Julian Oscillation (suppressed, initiation, and active) and compared with previous studies. The performance of the stochastic multicloud model is also assessed, in a stand‐alone mode, where the cloud model is forced directly by the observed predictors without feedback into the environmental variables. The results showed a wide spread in the inferred parameter values due in part to the lack of the desired sensitivity of the model to the predictors and the shortness of the training periods that did not include both active and suppressed convection phases simultaneously. Nonetheless, the resemblance of the stand‐alone simulated cloud fraction time series to the radar data is encouraging.

Improving the simulation of the climatology of the East Asian summer monsoon by coupling the Stochastic Multicloud Model to the ECHAM6.3 atmosphere model

The East Asian summer monsoon (EASM) has significant impacts on local and global hydrological and climatic systems. However, most current atmospheric and coupled models have difficulty in capturing the climatological mean state of the EASM. The present study investigated the possible improvement of EASM simulation via coupling the Stochastic Multicloud Model (SMCM) to the state-of-the-art ECHAM6.3 atmosphere model. Evaluated by the pattern correlation coefficient and root-mean-square error, the modified ECHAM6.3, i.e., with SMCM coupling, showed better performance in simulating the EASM in terms of the precipitation pattern and intensity, the pattern of the western North Pacific subtropical high, the monsoon onset, and its seasonal evolution. Analyses also revealed that the modified ECHAM6.3 outperformed the default ECHAM6.3 in terms of large-scale circulation and vertically integrated moisture transport. Finally, it was that the intensified land–sea thermal contrast in both the meridional and zonal directions, as well as the stronger meridional temperature gradient in the upper troposphere and enhanced diabatic heating over the Tibetan Plateau, which are associated with the stochastic cloud, were the main reasons for the better performance of the modified ECHAM6.3 in simulating the EASM.