Cloud Scheme Research Articles

Secure Key Agreement and Authentication Protocol for Message Confirmation in Vehicular Cloud Computing

With the development of vehicular ad-hoc networks (VANETs) and Internet of vehicles (IoVs), a large amount of useful information is generated for vehicle drivers and traffic management systems. The amount of vehicle and traffic information is as large as the number of vehicles and it is enormous when compared to vehicle calculation and storage performance. To resolve this problem, VANET uses a combined cloud computing technology, called vehicular cloud computing (VCC), which controls vehicle-related data, and helps vehicle drivers directly or indirectly. However, VANETs remain vulnerable to attacks such as tracking, masquerade and man-in-the-middle attacks because VANETs communicate via open networks. To overcome these issues, many researchers have proposed secure authentication protocols for message confirmation with vehicular cloud computing. However, many researchers have pointed out that some proposed protocols use ideal tamper-proof devices (TPDs). They demonstrated that realistic TPDs cannot prevent adversaries attack. Limbasiya et al. presented a message confirmation scheme for vehicular cloud computing using a realistic TPD in order to prevent these problems. However, their proposed scheme still has security weaknesses over a TPD and does not guarantee mutual authentication. This paper proposes a secure key agreement and authentication protocol to address the security weaknesses inherent in the protocol of Limbasiya et al. The suggested protocol withstands malicious attacks and ensures secure mutual authentication for privacy-preserving. We prove that the proposed protocol can provide session key security using Real-Or-Random (ROR) model. We also employed Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool to show that the proposed protocol is able to defeat replay and man-in-the-middle attacks. Furthermore, we established that the proposed protocol can resist other malicious attacks by conducting the informal security analysis. We proved that our proposed protocol is lightweight and suitable for VCC environments.

A Secure and Lightweight Fine-Grained Data Sharing Scheme for Mobile Cloud Computing.

With the explosion of various mobile devices and the tremendous advancement in cloud computing technology, mobile devices have been seamlessly integrated with the premium powerful cloud computing known as an innovation paradigm named Mobile Cloud Computing (MCC) to facilitate the mobile users in storing, computing and sharing their data with others. Meanwhile, Attribute Based Encryption (ABE) has been envisioned as one of the most promising cryptographic primitives for providing secure and flexible fine-grained “one to many” access control, particularly in large scale distributed system with unknown participators. However, most existing ABE schemes are not suitable for MCC because they involve expensive pairing operations which pose a formidable challenge for resource-constrained mobile devices, thus greatly delaying the widespread popularity of MCC. To this end, in this paper, we propose a secure and lightweight fine-grained data sharing scheme (SLFG-DSS) for a mobile cloud computing scenario to outsource the majority of time-consuming operations from the resource-constrained mobile devices to the resource-rich cloud servers. Different from the current schemes, our novel scheme can enjoy the following promising merits simultaneously: (1) Supporting verifiable outsourced decryption, i.e., the mobile user can ensure the validity of the transformed ciphertext returned from the cloud server; (2) resisting decryption key exposure, i.e., our proposed scheme can outsource decryption for intensive computing tasks during the decryption phase without revealing the user’s data or decryption key; (3) achieving a CCA security level; thus, our novel scheme can be applied to the scenarios with higher security level requirement. The concrete security proof and performance analysis illustrate that our novel scheme is proven secure and suitable for the mobile cloud computing environment.

Assessment of climate models in relation to the low‐level clouds over the southern Indian Ocean

AbstractAgainst the backdrop of limited fidelity of climate models in capturing low‐level cloud variability, the representation of the southern Indian Ocean (SIO) marine boundary layer (MBL) clouds and their transition from low to deep cloud have been investigated in this study. A historical run of 26 models, that featured in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and the National Centers for Environmental Prediction (NCEP)'s Climate Forecast System version 2.0 (CFSv2) models have been analysed along with different observational and reanalysis products. Observations clearly show the prominent occurrence of MBL low‐level clouds over the SIO in a 30–10°S latitudinal and 80–100°E longitudinal belt. Furthermore, there is a gradual transition from low to deep cloud towards the north of the Equator. The results reveal that the CMIP5 models were hardly able to capture the MBL low‐level clouds due to inaccurate temperature inversion and lower‐tropospheric stability (LTS) simulations. As compared to observations, the CMIP5 models show limited fidelity in capturing the transition from low to deep cloud at the north of the Equator. In this study, evaluation of one version of the CFS in which the convective parameterization is changed to the revised simplified Arakawa–Schubert (RSAS) scheme is carried out. In another CFS version (CFSR) with a more physically based cloud scheme (WRF Single‐Moment 6‐Class microphysics scheme; WSM6), comparisons are made with the default CFSv2 (CTRL) and RSAS. Only very few models, such as the MPI‐ESM‐LR, MRI, HadGEM, and NorESM models from among the 26 CMIP5 suites, and CFSCR from among the CFS suites, showed some fidelity in capturing the low‐level clouds over the SIO. This highlights that significant improvement is needed in climate models to improve the representation of the MBL over the SIO.

Developing a Cloud Scheme With Prognostic Cloud Fraction and Two Moment Microphysics for ECHAM‐HAM

AbstractWe present a new cloud scheme for the ECHAM‐HAM global climate model (GCM) that includes prognostic cloud fraction and allows for subsaturation and supersaturation with respect to ice separately in the cloud‐free and cloudy air. Stratiform clouds form by convective detrainment, turbulent vertical diffusion, and large‐scale ascent. For each process, the corresponding cloud fraction is calculated, and the individual updraft velocities are used to determine cloud droplet/ice crystal number concentrations. Further, convective condensate is always detrained as supercooled cloud droplets at mixed‐phase temperatures (between 235 and 273 K), and convectively detrained ice crystal number concentrations are calculated based on the updraft velocity. Finally, the new scheme explicitly calculates condensation/evaporation and deposition/sublimation rates for phase‐change calculations. The new cloud scheme simulates a reasonable present‐day climate, reduces the previously overestimated cirrus cloud fraction, and in general improves the simulation of ice clouds. The model simulates the observed in‐cloud supersaturation for cirrus clouds, and it allows for a better representation of the tropical to extra‐tropical ratio of the longwave cloud radiative effect. Further, the ice water path, the ice crystal number concentrations, and the supercooled liquid fractions in mixed‐phase clouds agree better with observations in the new model than in the reference model. Ice crystal formation is dominated by the liquid‐origin processes of convective detrainment and homogeneous freezing of cloud droplets. The simulated ice clouds strongly depend on model tuning choices, in particular, the enhancement of the aggregation rate of ice crystals.

Understanding Cloud Droplet Spectral Dispersion Effect Using Empirical and Semi‐Analytical Parameterizations in NCAR CAM5.3

AbstractFive parameterizations of cloud droplet spectral shape are implemented in a global climate model to investigate the dispersion effect and aerosol indirect effect (AIE). We design a series of experiments by modifying the microphysical cloud scheme of NCAR CAM5.3 (National Center for Atmospheric Research Community Atmosphere Model Version 5.3). We employ four empirical (Martin94, RLiu03, PengL03, and Liu08) and one semi‐analytical (LiuLi15) expressions for cloud droplet spectral shape parameters. Analysis focuses on the instantaneous differences in the simulated cloud microphysical properties and the comparison between model output and satellite data. The results show that RLiu03, PengL03, and LiuLi15 produce wider droplet spectrum and faster autoconversion rate, but Liu08 has a narrower droplet spectrum and slower autoconversion rate than the default parameterization (Martin94) in CAM5.3. Global dispersion effects caused by the five parameterizations modify the aerosol indirect effect by −10% (counteract) to 13% (strengthen). The simulated AIEs and dispersion effects exhibit noticeably spatial inhomogeneity. In the sensitive regions of AIE (Southeast Asia, North Pacific, and West Coast of South America), we decompose the response of shortwave cloud forcing to the change in droplet number for analysis. The varying dispersion effects can be explained by different responses of cloud properties in different spectral parameterizations.

Superparameterised cloud effects in the EMAC general circulation model (v2.50) – influences of model configuration

Abstract. A new module has been implemented in the fifth generation of the ECMWF/Hamburg (ECHAM5)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model that simulates cloud-related processes on a much smaller grid. This so-called superparameterisation acts as a replacement for the convection parameterisation and large-scale cloud scheme. The concept of embedding a cloud-resolving model (CRM) inside of each grid box of a general circulation model leads to an explicit representation of cloud dynamics. The new model component is evaluated against observations and the conventional usage of EMAC using a convection parameterisation. In particular, effects of applying different configurations of the superparameterisation are analysed in a systematical way. Consequences of changing the CRM's orientation, cell size and number of cells range from regional differences in cloud amount up to global impacts on precipitation distribution and its variability. For some edge case setups, the analysed climate state of superparameterised simulations even deteriorates from the mean observed energy budget. In the current model configuration, different climate regimes can be formed that are mainly driven by some of the parameters of the CRM. Presently, the simulated total cloud cover is at the lower edge of the CMIP5 model ensemble. However, certain “tuning” of the current model configuration could improve the slightly underestimated cloud cover, which will result in a shift of the simulated climate. The simulation results show that especially tropical precipitation is better represented with the superparameterisation in the EMAC model configuration. Furthermore, the diurnal cycle of precipitation is heavily affected by the choice of the CRM parameters. However, despite an improvement of the representation of the continental diurnal cycle in some configurations, other parameter choices result in a deterioration compared to the reference simulation using a conventional convection parameterisation. The ability of the superparameterisation to represent latent and sensible heat flux climatology is independent of the chosen CRM setup. Evaluation of in-atmosphere cloud amounts depending on the chosen CRM setup shows that cloud development can significantly be influenced on the large scale using a too-small CRM domain size. Therefore, a careful selection of the CRM setup is recommended using 32 or more CRM cells to compensate for computational expenses.

Lightweight edge‐based kNN privacy‐preserving classification scheme in cloud computing circumstance

SummaryBecause mobile terminals have limited computing and storage resources, individuals tend to outsource their data generated from mobile devices to clouds to do data operations. However, utilization of the abundant computation and storage resources of clouds may pose a threat to user's private data. In this paper, we focus on the issue of encrypted k‐nearest neighbor (kNN) classification on the cloud. In the past few years, many solutions were proposed to protect the user's privacy and data security. Unfortunately, most privacy‐preserving data mining schemes are not lightweight, which are not practical in real‐world applications. To solve this issue, we proposed a lightweight edge‐based kNN (EBkNN) classification scheme over encrypted cloud database utilizing edge computing technology. Our proposed scheme can provide several security guarantees: (i) user's data security, (ii) user's query privacy, and (iii) data access patterns. We analyzed the security of our scheme utilizing the semi‐honest security model and evaluated the performance using a synthetic dataset. The experiment results indicate that our scheme is more lightweight than the state‐of‐the‐art scheme.

A lightweight anonymous authentication scheme for secure cloud computing services

Cloud computing represents the latest technology that has revolutionized the world of business. It is a promising solution giving companies the possibility of remotely storing their data and accessing services whenever they are needed and at a lower cost. However, outsourcing IT resources also brings risks, especially for sensitive information in terms of security and privacy, since all data and resources stored in the cloud are managed and controlled by cloud service providers. On the other hand, cloud users would like cloud service providers not to know what services being accessed and how often they are using them. Therefore, designing mechanisms to protect privacy is a major challenge. One promising research area is via authentication mechanisms, which has attracted many researchers in this delicate subject. For this, several solutions have been devised and published recently to tackle this problem. Nevertheless, these solutions often suffer from different types of attacks, high computing and communication costs, and the use of complex key management schemes. To address these shortcomings, we propose an approach that ensures the optimal preservation of the privacy of cloud users to protect their personal data including identities. The suggested approach gives the cloud user the ability to access and use the services provided by cloud service providers anonymously without the providers of those services knowing their identity. We demonstrate the superiority of our proposed approach over several anonymous authentication solutions in terms of computation and communication costs.

A privacy-preserving multi-keyword ranked retrieval scheme in cloud computing

ABSTRACT Big data and cloud computing could bring security problems. In order to ensure data security and user privacy, people would choose to store data in the cloud with ciphertext. How to search data efficiently and comprehensively without decryption has become the focus of this paper. In this paper, we propose an efficient privacy protection scheme. In this scheme, Elliptic Curve Cryptography (ECC) is adopted to encrypt the data. It can reduce the computing cost of encryption and decryption uploading the encrypted files and indexes to the cloud server. Then it can authorize users to generate trap door using hash conflict function, and send it to Cloud Service Provider (CSP) for searching for matched ciphertext. The CSP uses the Apriori algorithm to extend keywords and search index to match the ciphertext. In this paper, we will use the Apriori algorithm to extend the keywords’ semantics, match the index list based on these keywords, and return the requested file-set which is more consistent with the user’s search. Experiments show that compared with traditional methods, files can be encrypted, decrypted, and recovered more quickly when we use this method. It can also ensure the privacy of data and reduce the communication overhead.

Fuzzy logic and histogram of normal orientation-based 3D keypoint detection for point clouds

Point cloud processing has gained consideration for 3D object recognition and classification tasks. In this context, an important task is to detect the distinct and repeatable 3D keypoints. Many 3D keypoint detectors with low repeatability and distinctiveness have been proposed. The detection of highly repeatable and distinct keypoints is still an open problem. To address this issue, we propose a fuzzy logic and Histogram of Normal Orientation (HoNO)-based 3D keypoint detection scheme for Point Cloud (PC) data. To measure saliency, we exploit the structure of the PC and compute the eigenvalues of the covariance matrix and the HoNO to measure saliency. The histogram (HoNO) salient value is computed by the kurtosis values, which estimate the spread of the histogram. From the kurtosis and smallest eigenvalues, we compute the difference of the kurtosis values and the difference of the smallest eigenvalues of the query point against all the neighbouring points. The difference of kurtosis values and difference of smallest eigenvalues are applied to a fuzzy rule-based scheme for the keypoints detection. We compare the proposed algorithm with the state-of-the-art 3D keypoint detectors on five benchmark datasets. Experimental results demonstrate the superior performance of the proposed detector on most of the benchmark datasets both in terms of absolute and relative repeatability.

An efficient privacy-preserving data query and dissemination scheme in vehicular cloud

Vehicular Cloud (VC) improves the utilization of idle resources in vehicles by providing data services for other vehicles or entities such as data computing and storage. However, the vehicles in the VC still face privacy and security issues, especially when querying data from the road side unit. To this end, in this paper, we propose an efficient privacy-preserving data query and dissemination scheme, named EPDQD, for VC. More precisely, by using the lightweight homomorphic encryption technique and the Chinese Remainder Theorem, the road side unit can aggregate and recover multiple data query requests without knowing the linkability of the data query request and its origin vehicle. EPDQD also adopts the message authentication code and non-interactive secure session keys to achieve the authentication of data query requests and the integrity of data transmission. Detailed security analysis indicates that EPDQD can resist various security threats. In addition, the performance evaluations demonstrate that EPDQD can significantly reduce computation complexity and communication overhead.

Vertical diffusion and cloud scheme coupling to the Charney–Phillips vertical grid in GRAPES global forecast system

AbstractSpatial aspects of the physics–dynamics coupling in the Global and Regional Assimilation and Prediction System (GRAPES) for global medium‐range numerical weather prediction (GRAPES_GFS) are studied. As a Charney–Philips (CP) grid is used for the dynamics but all physical processes are calculated on a Lorenz grid in GRAPES_GFS V2.2 and its previous versions, interpolation has to be used for potential temperature and moisture between full and half levels in the physics–dynamics coupling. Besides interpolation error, a computational mode appears in the solutions of the vertical heat diffusion equations. An idealised test using a simple one‐dimensional heat conduction equation shows definitively that inconsistency of vertical grid in the dynamics–physics coupling and an improper boundary condition can together produce the computational mode. Based on 1st‐order K‐closure, a modified parametrization scheme for implementation of the planetary boundary layer (PBL) scheme on the CP grid (PBL_CP) has been developed, together with a cloud scheme implementation on the CP grid (CLOUD_CP), such that there is no need for interpolation for both the PBL and cloud scheme coupling to the dynamics. By using the PBL_CP scheme in a case‐study, the computational mode in potential temperature and moisture predictions is shown to have been successfully eliminated and the corresponding vertical profiles appear to be reasonably smooth. Because of the improvements in potential and absolute temperature and moisture predictions, the prediction of low‐level cloud water has also been improved greatly. Meanwhile, the prediction of water vapour at high levels is more reasonable with CLOUD_CP. Consistent with these improvements in a case‐study, an overall and significant enhancement is found in 8‐day forecasts of absolute temperature, moisture, vector wind and stratocumulus with the revised model in a 4D‐Var (four‐dimensional variational) cycle experiment over a period of 3 months.Key points A revised PBL scheme called PBL_CP was developed on a Charney–Phillips grid in the GRAPES_GFS model to avoid interpolation in the coupling of vertical heat diffusion to the dynamical equations. Implementation of the PBL_CP scheme in GRAPES_GFS eliminates the computational mode in potential temperature and moisture prediction in the PBL. The revised model containing PBL_CP and cloud implementation on the CP grid has improved the GRAPES_GFS forecasts of absolute temperature, moisture, vector winds and stratocumuli significantly.

Comparison of a Spectral Microphysics and a Two-Moment Cloud Scheme: Numerical Simulations of the Cloud-Topped Marine Boundary Layer

A comparison between the spectral microphysics cloud scheme (MiStra) and the parametrized cloud scheme (PaStra) is presented. The main feature of MiStra consists of the treatment of aerosol particles, cloud droplets, and drizzle particles in a joint two-dimensional particle size distribution, whereas PaStra consists of a two-moment scheme for cloud droplets combined with a one-moment scheme for drizzle. Both cloud schemes have been implemented in a single-column model of the cloud-topped marine boundary layer. Numerical sensitivity studies are presented demonstrating that MiStra is capable of simulating in great detail the major cloud microphysical processes occurring in low-level stratiform clouds. While in MiStra no empirical parameter is available to tune the model, the empirical model parameters of PaStra have been tuned by means of the MiStra model results. By comparing the numerical results of PaStra with those of MiStra it is found that PaStra simulates the overall characteristics of the cloud-topped marine boundary layer quite well. At the same time, however, the effects of single cloud microphysical processes differ substantially from those of MiStra. Finally, it is shown that even in PaStra the inclusion of all major cloud microphysical processes is mandatory in order to obtain appropriate results.

Grover algorithm-based quantum homomorphic encryption ciphertext retrieval scheme in quantum cloud computing

Existing classical ciphertext retrieval schemes are mainly developed according to the homomorphic encryption that is a cryptographic technique based on computational complexity theory of mathematical puzzles. In quantum computing, on the one hand, most of traditional asymmetric encryption can be quickly cracked as the computational capacity of quantum computer is much higher than that of traditional digital computer. Hence, the quantum homomorphic encryption scheme is widely used in data encryption for the issue of privacy protection in quantum computing. But on the other the retrieval efficiency of homomorphic encrypted data is insufficient especially in quantum cloud computing. Therefore, this paper proposes a novel quantum homomorphic encryption ciphertext retrieval (QHECR) scheme basing on the Grover algorithm to solve the problem of homomorphic encrypted ciphertext data retrieval in quantum cloud computing. Our scheme is to improve such retrieval efficiency mentioned above where the interaction process is not required even if the T-gate exists in the circuit. In the experiment, two qubits without the T-gate are conducted on both simulations and real quantum devices by using IBM quantum information science kit (Qiskit). The results show that the proposed QHECR scheme is capable of achieving the retrieval encrypted data when the T-gate does not exist in the evaluation circuit in ciphertext environment. Moreover, a verification experiment about the T-gate key update algorithm is implemented to verify the feasibility and reliability of the proposed scheme in the Qiskit as well, which indicates that the QHECR is still available when the T-gate exists in the circuit. Since the decryption is inefficient when there are exponential T-gates in the circuit, our proposed scheme is suitable for low T-gate complexity.

A Secure Authentication and Key Agreement Scheme for IoT-Based Cloud Computing Environment

The integration of Internet of things (IoT) and cloud computing technology has made our life more convenient in recent years. Cooperating with cloud computing, Internet of things can provide more efficient and practical services. People can accept IoT services via cloud servers anytime and anywhere in the IoT-based cloud computing environment. However, plenty of possible network attacks threaten the security of users and cloud servers. To implement effective access control and secure communication in the IoT-based cloud computing environment, identity authentication is essential. In 2016, He et al. put forward an anonymous authentication scheme, which is based on asymmetric cryptography. It is claimed that their scheme is capable of withstanding all kinds of known attacks and has good performance. However, their scheme has serious security weaknesses according to our cryptanalysis. The scheme is vulnerable to insider attack and DoS attack. For overcoming these weaknesses, we present an improved authentication and key agreement scheme for IoT-based cloud computing environment. The automated security verification (ProVerif), BAN-logic verification, and informal security analysis were performed. The results show that our proposed scheme is secure and can effectively resist all kinds of known attacks. Furthermore, compared with the original scheme in terms of security features and performance, our proposed scheme is feasible.

Log as a Secure Service Scheme (LASS) for Cloud

Log as a Secure Service Scheme (LASS) for Cloud

Performance of the NCMRWF convection‐permitting model during contrasting monsoon phases of the 2016 INCOMPASS field campaign

AbstractThis study uses INCOMPASS aircraft, radiosonde and satellite observations for verifying hydrometeors and associated state variables predicted by the regional model of the NCMRWF (NCUM‐R) for contrasting phases of the 2016 monsoon. INCOMPASS flights B957 and B975 took place between Lucknow in northern India and Bhubaneswar near the east coast, and represent a contrast between dry pre‐monsoon and active monsoon conditions, respectively. A moist profile above 4 km in Bhubaneswar measured on B957 showed a dry‐air intrusion being eroded by mid‐level clouds, whereas the Lucknow profile showed a drier, pre‐monsoon profile. Aerosol extinction coefficient and cloud‐top height measured using lidar showed an influx of continental aerosol, and intermittent multiple clouds below the aircraft in the mid‐troposphere and boundary layer. Measurements from B975 match well with cyclonic wind patterns estimated from satellite observations and the convective tendency represented in radiosonde profiles. Extensive clouds were detected below 5 km during the active monsoon. Two model formulations for cloud representation (prognostic cloud and prognostic condensate, PC2, and diagnostic schemes, Diag) are compared with observations during the campaign. Vertical structures of state variables from both schemes are generally in agreement along the flight tracks. Surface energy budget and cloud diagnoses indicate higher cloud cover in Diag consistent with lower surface temperatures through reduced surface downwelling short‐wave flux than in PC2, while the latent‐heat flux is found to be insensitive to cloud scheme chosen. In‐cloud water content is larger in PC2 for lower cloud fraction, and the autoconversion process is faster with respect to Diag. Higher total condensed‐water content in the model with respect to aircraft measurements and an enhanced light precipitation bias with respect to satellite data is common to both cloud schemes. Further work to improve the representation of clouds and precipitation for the tropical implementation of the model is clearly warranted.

An efficient public key secure scheme for cloud and IoT security

According to the National Institute of Standard and Technology (NIST), the security level of RSA is safe when it is N-bit modulus ≥2048 bits. Because of this, the processing time to generate asymmetric keys also increases. Taking this into account, an efficient and non-shareable Public Key Exponent Secure Scheme (ENPKESS) is proposed by using a non-linear Diophantine equation to have high security against side-channel attacks like timing attacks. This scheme has three-stage of encryption and two-stage of decryption, whereas other schemes like ESR and RSA has one level in encryption and decryption. Due to this, extraction of the secret key is extremely hard to determine from our public exponents α,R,N. Our methodology is well suited for secure cloud computing environments used in the Internet of Things (IoT). Here we have also applied the Knapsack method to encrypt our ENPKESS keys to enrich high security in cloud systems. We show a strong performance evaluation on standard RSA, Enhanced and Secured RSA Key Generation Scheme (ESRKGS), and ENPKESS on its key generation, encryption and decryption by varying the N-bit moduli size up to 10K bits. From the overall result, ENPKESS consumes 89% of standard RSA and 27% of ESRKGS.

Sensitivity of climate models in relation to the “pool of inhibited cloudiness” over South of the Bay of Bengal

AbstractRealistic simulation of cloud variability and rainfall by the coupled models still remains a challenge particularly over the Asian Summer Monsoon (ASM). The simulation of the “pool of inhibited cloudiness” (hereafter referred to as PIC) and associated cloud variability have been analysed in the historical run of 26 models which participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and it is shown that the current state of the art general circulation models (GCMs) still have difficulties in properly simulating the PIC. The pool covers an area greater than 1 million km2 between 3°–13°N and 77°–90°E over the southwest Bay of Bengal (BoB); persisting throughout the ASM and interestingly it is surrounded by the deep convective region. The majority of the models could not simulate cloud variability over the ASM. The performance of National Centre for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) version 2.0, which is being used for operational monsoon prediction over the Indian region, is also tested to evaluate its fidelity in capturing PIC. The moist convective process in the default CFSv2 is found to be one of the major sources of uncertainty in its monsoon prediction. In this study, evaluation of the default CFSv2 (CTRL) and suites of modified CFSv2 have been carried out. The default version of CFSv2 has the simplified Arakawa Schubert (SAS) as convection scheme and Zhao and Carr (ZC) as microphysics. In another modification, the SAS is changed to revised simplified Arakawa Schubert (RSAS) keeping the microphysics unchanged. Further, a more physically based cloud scheme (WRF Single Moment 6‐class microphysics—WSM6) is used with SAS and RSAS for comparison of simulation of PIC. Among the CMIP5 models, ACESS‐1‐0, GFDL‐CM3, HadGEM2‐CC, HadGEM2‐ES are able to represent the PIC reasonably well. CFSCR has shown an improved fidelity in comparison to the CTRL, CTRL‐WSM and RSAS and other CMIP5 models. The impact of cloud microphysics in CTRL‐WSM and CFSCR appears to play an important role in the simulation of the PIC.

Algorithmic differentiation for cloud schemes (IFS Cy43r3) using CoDiPack (v1.8.1)

Abstract. Numerical models in atmospheric sciences not only need to approximate the flow equations on a suitable computational grid, they also need to include subgrid effects of many non-resolved physical processes. Among others, the formation and evolution of cloud particles is an example of such subgrid processes. Moreover, to date there is no universal mathematical description of a cloud, hence many cloud schemes have been proposed and these schemes typically contain several uncertain parameters. In this study, we propose the use of algorithmic differentiation (AD) as a method to identify parameters within the cloud scheme, to which the output of the cloud scheme is most sensitive. We illustrate the methodology by analyzing a scheme for liquid clouds, incorporated into a parcel model framework. Since the occurrence of uncertain parameters is not limited to cloud schemes, the AD methodology may help to identify the most sensitive uncertain parameters in any subgrid scheme and therefore help limiting the application of uncertainty quantification to the most crucial parameters.