Explore the vast range of titles available.

Recent general circulation model (GCM) simulations have challenged the idea that a snowball Earth would be nearly entirely cloudless. This is important because clouds would provide a strong warming to a high-albedo snowball Earth. GCM results suggest that clouds could lower the threshold CO₂ needed to deglaciate a snowball by a factor of 10–100, enough to allow consistency with geochemical data. Here a cloud-resolving model is used to investigate cloud and convection behavior in a snowball Earth climate. The model produces convection that extends vertically to a similar temperature as modern tropical convection. This convection produces clouds that resemble stratocumulus clouds under an inversion on modern Earth, which slowly dissipate by sedimentation of cloud ice. There is enough cloud ice for the clouds to be optically thick in the longwave, and the resulting cloud radiative forcing is similar to that produced in GCMs run in snowball conditions. This result is robust to large changes in the cloud microphysics scheme because the cloud longwave forcing, which dominates the total forcing, is relatively insensitive to cloud amount and particle size. The cloud-resolving model results are therefore consistent with the idea that clouds would provide a large warming to a snowball Earth, helping to allow snowball deglaciation.

The radar observation operator for computation of polarimetric radar variables from the output of numerical cloud models is described in its most generic form. This operator is combined with the Hebrew University of Jerusalem cloud model with spectral microphysics. The model contains 7 classes of hydrometeors and each class is represented by size distribution functions in 43 size bins. The performance of the cloud model and radar observation operator has been evaluated for the case of a hailstorm in Oklahoma on 2 February 2009. It is shown that the retrieved fields of polarimetric radar variables at C and S microwave bands are generally consistent with results of observations. The relationship between microphysical and polarimetric signatures is illustrated.

Limited area cloud‐resolving model (CRM) simulations called LASAM are used to reproduce and understand negative subtropical shallow cumulus cloud feedbacks in a near‐global aquaplanet CRM (NGAqua) with 4‐K sea surface temperature (SST) warming. NGAqua spans a large tropical channel domain, with 4‐km horizontal resolution, zonally symmetric equatorially peaked SST, and no cumulus parameterization. Prior work showed that its coarsely resolved shallow cumulus increases with warming. It was suggested that with warmer SST, the moister boundary layer is destabilized by more clear‐sky radiative cooling, driving more cumulus convection. A small doubly periodic version of the same CRM is configured to analyze this low cloud increase in a simpler context. It is driven by steady thermodynamic and advective forcing profiles averaged over the driest subtropical column humidity quartile of NGAqua. Sensitivity studies separate effects of radiative cooling and free tropospheric relative humidity changes from other aspects of NGAqua's warmer climate. Enhanced clear‐sky radiative cooling explains most of the cloud increase due to SST warming, regardless of CRM model resolution and advection scheme. A boundary layer energy budget shows that the downward entrainment heat flux strengthens to balance enhanced radiative cooling, carried by a stronger updraft cloud mass flux from a larger cumulus cloud fraction. In deeper trade cumulus layers, the enhanced radiative cooling in a warming climate may be balanced by increased precipitation warming, leaving the cloud coverage area almost unchanged. With larger domain sizes, shallow cumulus self‐aggregates, especially with higher SST, marginally increasing domain‐mean cloud fraction, but this is a secondary contributor to the cloud feedback. Plain Language Summary Cumulus clouds less than 2 km deep are widespread over the subtropical oceans. Hence, even small shallow cumulus cloudiness changes affect how much the underlying oceans are warmed by sunlight and the sensitivity of climate to greenhouse gas increases. Most climate models suggest slightly decreased shallow cumulus cloudiness in a warmer climate. However, a recent study using a near‐global model with 4‐km horizontal grid spacing, much finer than conventional climate models and capable of simulating individual cumuli, found increased subtropical shallow cumulus cloudiness when the surface is uniformly warmed. We use a simpler version of the same model with a much smaller domain to represent the drier parts of the full model's subtropical oceans, reproduce its cloudiness increase, isolate the possible causes, and show the robustness of this finding. Using this simpler model, we confirm an earlier hypothesis: The cloudiness increase is mainly from more infrared cooling of the cumulus layer that holds more water vapor in a warmer climate. Shallow cumuli are largely driven by this cooling, so more infrared cooling leads to more cumuli. An important caveat is that if the cumuli were deeper so they rained more, warming could lead to more rain rather than more cloud. Key Points A 4‐K SST warming increases subtropical trade cumulus cloud cover in limited area cloud‐resolving and large eddy simulations Enhanced radiative cooling of moister boundary layer under dry free troposphere destabilizes the cloud layer and drives more shallow cumuli The response of shallow cumulus to enhanced radiative cooling is robust across all model configurations

The complex power system and trading environment in China has led to higher requirements for the efficient and stable operation of the electricity market. With the continuous advancement of power system reforms, regular evaluation of the operation of the market can help us grasp its status and trends, which is of great significance for ensuring its sustainable development. In order to effectively evaluate the current operational status of the electricity market, the concept of operation health degree of power market (OHDPM) is proposed to measure whether the operation is safe, efficient, and sustainable. This paper establishes an improved model framework based on the matter-element extension theory for evaluation. In order to effectively avoid information distortion and loss in the evaluation process, this paper combines the cloud model, matter element extension theory, ideal point method (IPM), and cloud entropy optimization algorithm to deal with this problem. The matter-element extension cloud model (MEECM) can clearly represent the characteristics of the object to be evaluated. IPM is used to determine the weight of the index. For the improved matter-element extension model, the traditional rules of “3En” and “50% relevance” are taken into account, and the method of solving the entropy is optimized. Then, for the correlation degree between the object to be evaluated and the graded normal cloud, the weight vector solved by the IPM is used to weigh the cloud correlation degree, which can give a reliable evaluation result. The health evaluation index system of power market operation includes 16 sub-indicators in five categories: supply side, demand side, coordinated operation, market security, and sustainable development. In the empirical analysis, the OHDPM situation in Y Province was evaluated in May 2019. The results prove that the OHDPM level is medium, and the importance and health level of each index are given. The reliability of the power system, transaction price stability, Lerner index, residual proportion of producers, and user satisfaction have a greater impact on the health status. Finally, in order to verify the validity and stability of the model, different methods are used to evaluate the evaluation objects, and the advantages of OHDPM evaluation based on the model framework proposed in this paper are proven.

Water inrush is a serious geological hazard in underground engineering. The prediction of possibility and classification of water inrush risk has long been a global problem for the construction of deep-buried tunnels in karst areas. To solve the randomness and fuzziness in the evaluation process of water inrush risk, a novel comprehensive evaluation model was established based on the normal cloud theory. According to the systematic analysis of the influence factors of water inrush, seven factors were selected as evaluation indices, including formation lithology, unfavourable geological conditions, groundwater level, landform and physiognomy, modified strata inclination, contact zones of dissolvable and insoluble rock, and layer and interlayer fissures. Meanwhile, a hierarchy model of the influence factors was established for water inrush, and the analytic hierarchy process was adopted to determine the weighting coefficients for each evaluation index. The normal cloud theory was used to describe the cloud numerical characteristics for each evaluation index of risk classification for water inrush. Normal cloud droplets were generated to reflect the uncertain transformation between the risk levels of water inrush and the evaluation indices. Then, the synthetic degrees of certainty were calculated, and risk level of water inrush was determined. Finally, the proposed model was applied to two typical deep-buried tunnels in karst areas: Jigongling tunnel and Xiakou tunnel. The obtained results were compared with the relevant analysis results and the practical findings, and reasonable agreements were gained. The normal cloud model was found to be more accurate, feasible and effective for risk classification of water inrush prediction. It can not only meet the requirement of tunnel engineering, but also be extended to various applications.

Cirrus dominate the longwave radiative budget of the tropics. For the first time, the variability in cirrus properties and longwave cloud radiative effects (CREs) that arises from using different microphysical schemes within nudged global storm‐resolving simulations from a single model, is quantified. Nudging allows us to compute radiative biases precisely using coincident satellite measurements and to fix the large‐scale dynamics across our set of simulations to isolate the influence of microphysics. We run 5‐day simulations with four commonly‐used microphysics schemes of varying complexity (SAM1MOM, Thompson, M2005 and P3) and find that the tropical average longwave CRE varies over 20 W m−2 between schemes. P3 best reproduces observed longwave CRE. M2005 and P3 simulate cirrus with realistic frozen water path but unrealistically high ice crystal number concentrations which commonly hit limiters and lack the variability and dependence on frozen water content seen in aircraft observations. Thompson and SAM1MOM have too little cirrus. Plain Language Summary Recently, advancements in computing have made it possible for atmospheric scientists to simulate Earth's global atmosphere with higher resolution than ever before. This new generation of models, called global‐storm resolving models, have a horizontal grid spacing of just a few kilometers, which permits the formation of thunderstorms. As a result, they simulate clouds more realistically than traditionally climate and weather models and are a great tool for diagnosing cloud biases in atmospheric models. Here, we run a single global storm‐resolving model with four different representations of cloud physics called M2005, P3, SAM1MOM and Thompson. We evaluate simulated tropical cirrus, which are stratiform ice clouds at the top of the troposphere that reduce the amount of infrared radiation emitted by the Earth, with satellite and aircraft data to see which representations have the best performance. SAM1MOM and Thompson make too little cirrus causing too much infrared radiation to be emitted, M2005 makes too much cirrus, causing too little infrared radiation to be emitted, and P3 makes about the right amount. Key Points Nudged global storm‐resolving simulations are valuable for microphysics sensitivity studies Mean tropical longwave cloud radiative effect varies over 20 W m−2 depending on microphysics scheme Two‐moment schemes outperform simpler one‐moment and partial double‐moment schemes, and P3 has the smallest longwave radiative bias

To achieve effective wireless transmission margin and larger bandwidth at lower cost, hydrometeor models roles are of primary importance. The almost perpetual existence of clouds in tropical climates makes cloud models all the more fundamental. Details of four years station spectrum analyzer data, five years climatological data and fifty – eight years radiosonde data used in this research were earlier published. The radiosonde data was used to obtain existing primary cloud models’ predicted cloud attenuation cumulative distributions for the station and it was also used to deduce the new algorithm’s parameters for the station. The every minute measured and logged station cloud attenuation data using spectrum analyzer was used to deduce the station cloud attenuation cumulative distribution for comparison with that of other existing cloud models. The simulation program was run to generate the new cloud attenuation algorithm’s parameters, which defines the cloud attenuation model for the station. Thus the new model only fundamentally requires station radiosonde data. The cloud cover data and all others are needed only for graphical comparisons and corroboration. Thus the new tropical cloud attenuation algorithm can be used to develop the cloud attenuation model for any station climatic zone by using the methodology earlier published. Collected spectrum analyzer data, climatological data and acquired radiosonde data were used to compute projected attenuation values for each cloud attenuation model at propagation signal frequencies between 12 GHz to 50 GHz. The predicted values were extracted and analysed statistically. With respect to frequency, the new cloud attenuation model’s cumulative distribution proportionally averaged the characteristics of the cumulative distributions deduced from the station radiosonde data and that of the spectrum analyzer data as shown by the graphical figures. The results show that convergence of the range of predicted attenuation values by each of the cloud models increases directly with frequency.

Scientific surrounding rock classification (SRC) of underground water-sealed oil reservoir caverns is greatly significant for reducing risks and costs during construction. As to underground water-sealed caverns, not only the stability and deformation of the surrounding rock, but also the requirement of the water-sealed function must be considered. At present, there is no special standard for the SRC of underground water-sealed caverns. Based on the cloud theory and principal component analysis (PCA) method, a novel analytical model was proposed for SRC of underground water-sealed caverns. Five indexes: rock strength, rock integrity, discontinuity surface characteristics, groundwater, and the angle between the horizontal plane and the main discontinuity were selected to establish a multi-index evaluating system. With enough measured samples, the weights of the five indexes were determined based on the PCA method. According to cloud theory, five indexes affecting the SRC were investigated to establish five corresponding single-index normal cloud models. Subsequently, the single-index certainty degrees to every grade of surrounding rock were calculated. Finally, combined with the weights, a multi-index certainty degree was calculated as guidance to classify the surrounding rock. The proposed novel model was applied to a case study, i.e., the first underground water-sealed oil reservoir in China. The evaluating results were in broad agreement with the excavation results. The present findings imply that the proposed novel model is an effective method for the SRC in underground water-sealed caverns and can provide some useful references to similar projects.

Utilizing cloud models to analyze the uncertainty and consequences of dam failure factors. Enhanced Analytic Hierarchy Process (AHP) with a scale criterion based on index scale and an expert score constraint mechanism. Focus on the downstream area of the Zipingpu Dam for assessing the uncertainty of dam comprehensive evaluation factors. The final outcome is represented by three numerical eigenvalues of the cloud model, determining the weights of each factor in the evaluation index system. This approach offers a novel method for the comprehensive evaluation of dam failure consequences.

The multi-dimensional cloud model is proposed as the expansion of the one-dimensional cloud model. The features of ambiguity and stochasticity in complex information situations are considered; thus, this optimized model can be utilized upon multiple value classifications and ordering via which the objects' attributes of physical and social can be reflected. Therefore, this promoted model is wildly used. This paper provides a knowledge graph by reviewing the theoretical research of the multi-dimensional cloud model and its related bibliographies, and Cite Space is applied here to give a visualization conclusion. In recent years, a multitude of theories and methods have emerged to address the challenges posed by fuzzy and stochastic uncertainty in various domains, such as image segmentation, data mining, prediction techniques, and comprehensive evaluation of multiple metrics and dimensions using uncertain linguistic variables.