Academic literature on the topic 'Vertical cloud overlap'

Abstract Observational studies have shown that the vertical overlap of cloudy layers separated by clear sky can exceed that of the random overlap assumption, suggesting a tendency toward minimum overlap. In addition, the rate of decorrelation of vertically continuous clouds with increasing layer separation is sensitive to the horizontal scale of the cloud scenes used. The authors give a heuristic argument that these phenomena result from data truncation, where overcast or single cloud layers are removed from the analysis. This occurs more frequently as the cloud sampling scale falls progressively below the typical cloud system scale. The postulate is supported by sampling artificial cyclic and subsequently more realistic fractal cloud scenes at various length scales. The fractal clouds indicate that the degree of minimal overlap diagnosed in previous studies for discontinuous clouds could result from sampling randomly overlapped clouds at spatial scales that are 30%–80% of the cloud system scale. Removing scenes with cloud cover exceeding 50% from the analysis reduces the impact of data truncation, with discontinuous clouds not minimally overlapped and the decorrelation of continuous clouds less sensitive to the sampling scale. Using CloudSat–CALIPSO data, a decorrelation length scale of approximately 4.0 km is found. In light of these results, the previously documented dependence of overlap decorrelation length scale on latitude is not entirely a physical phenomenon but can be reinterpreted as resulting from sampling cloud systems that increase significantly in size from the tropics to midlatitudes using a fixed sampling scale.

Abstract The assimilation of cloud- and rain-affected radiances in numerical weather prediction systems requires fast and accurate radiative transfer models. One of the largest sources of modeling errors originates from the assumptions regarding the vertical and horizontal subgrid-scale variability of model clouds and precipitation. In this work, cloud overlap assumptions are examined in the context of microwave radiative transfer and used to develop an accurate reference model. A fast cloud overlap algorithm is presented that allows for the accurate simulation of microwave radiances with a small number of radiative transfer calculations. In particular, the errors for a typical two-column approach currently used operationally are found to be relatively large for many cases of cloudy fields containing precipitation, even those with an overall cloud fraction of unity; these errors are largely eliminated by using the new approach presented here, at the cost of a slight increase in computation time. Radiative transfer cloud overlap errors are also evident in simulations when compared to actual satellite observations, in that the biases are somewhat reduced when applying a more accurate treatment of cloud overlap.

Abstract The representation of subgrid horizontal and vertical variability of clouds in radiation schemes remains a major challenge for general circulation models (GCMs) due to the lack of cloud-scale observations and incomplete physical understanding. The development of cloud-resolving models (CRMs) in the last decade provides a unique opportunity to make progress in this area of research. This paper extends the study of Wu and Moncrieff to quantify separately the impacts of cloud horizontal inhomogeneity (optical property) and vertical overlap (geometry) on the domain-averaged shortwave and longwave radiative fluxes at the top of the atmosphere and the surface, and the radiative heating profiles. The diagnostic radiation calculations using the monthlong CRM-simulated tropical cloud optical properties and cloud fraction show that both horizontal inhomogeneity and vertical overlap of clouds are equally important for obtaining accurate radiative fluxes and heating rates. This study illustrates an objective approach to use long-term CRM simulations to separate cloud overlap and inhomogeneity effects, based on which GCM representation (such as mosaic treatment) of subgrid cloud–radiation interactions can be evaluated and improved.

Abstract The observation and representation in general circulation models (GCMs) of cloud vertical overlap are the objects of active research due to their impacts on the earth’s radiative budget. Previous studies have found that vertically contiguous cloudy layers show a maximum overlap between layers up to several kilometers apart but tend toward a random overlap as separations increase. The decorrelation length scale that characterizes the progressive transition from maximum to random overlap changes from one location and season to another and thus may be influenced by large-scale vertical motion, wind shear, or convection. Observations from the U.S. Department of Energy Atmospheric Radiation Measurement program ground-based radars and lidars in midlatitude and tropical locations in combination with reanalysis meteorological fields are used to evaluate how dynamics and atmospheric state influence cloud overlap. For midlatitude winter months, strong synoptic-scale upward motion maintains conditions closer to maximum overlap at large separations. In the tropics, overlap becomes closer to maximum as convective stability decreases. In midlatitude subsidence and tropical convectively stable situations, where a smooth transition from maximum to random overlap is found on average, large wind shears sometimes favor minimum overlap. Precipitation periods are discarded from the analysis but, when included, maximum overlap occurs more often at large separations. The results suggest that a straightforward modification of the existing GCM mixed maximum–random overlap parameterization approach that accounts for environmental conditions can capture much of the important variability and is more realistic than approaches that are only based on an exponential decay transition from maximum to random overlap.

Abstract. Using 2B-CLDCLASS-LIDAR (radar–lidar) cloud classification and 2B-FLXHR-LIDAR radiation products from CloudSat over 4 years, this study evaluates the co-occurrence frequencies of different cloud types, analyzes their along-track horizontal scales and cloud radiative effects (CREs), and utilizes the vertical distributions of cloud types to evaluate cloud-overlap assumptions. The statistical results show that high clouds, altostratus (As), altocumulus (Ac) and cumulus (Cu) tend to coexist with other cloud types. However, stratus (St) (or stratocumulus, Sc), nimbostratus (Ns) and convective clouds are much more likely to exhibit individual features than other cloud types. On average, altostratus-over-stratus/stratocumulus cloud systems have a maximum horizontal scale of 17.4 km, with a standard deviation of 23.5 km. Altocumulus-over-cumulus cloud types have a minimum scale of 2.8 km, with a standard deviation of 3.1 km. By considering the weight of each multilayered cloud type, we find that the global mean instantaneous net CREs of multilayered cloud systems during the daytime are approximately −41.3 and −50.2 W m−2, which account for 40.1 and 42.3% of the global mean total net CREs at the top of the atmosphere (TOA) and at the surface, respectively. The radiative contributions of high-over-altocumulus and high-over-stratus/stratocumulus (or cumulus) in the all multilayered cloud systems are dominant due to their frequency. Considering the overlap of cloud types, the cloud fraction based on the random overlap assumption is underestimated over vast oceans, except in the west-central Pacific Ocean warm pool. Obvious overestimations mainly occur over tropical and subtropical land masses. In view of a lower degree of overlap than that predicted by the random overlap assumption to occur over the vast ocean, particularly poleward of 40° S, the study therefore suggests that a linear combination of minimum and random overlap assumptions may further improve the predictions of actual cloud fractions for multilayered cloud types (e.g., As + St/Sc and Ac + St/Sc) over the Southern Ocean. The establishment of a statistical relationship between multilayered cloud types and the environmental conditions (e.g., atmospheric vertical motion, convective stability and wind shear) would be useful for parameterization design of cloud overlap in numerical models.

Abstract. The treatment of unresolved cloud–radiation interactions in weather and climate models has considerably improved over the recent years, compared to conventional plane-parallel radiation schemes, which previously persisted in these models for multiple decades. One such improvement is the state-of-the-art Tripleclouds radiative solver, which has one cloud-free and two cloudy regions in each vertical model layer and is thereby capable of representing cloud horizontal inhomogeneity. Inspired by the Tripleclouds concept, primarily introduced by Shonk and Hogan (2008), we incorporated a second cloudy region into the widely employed δ-Eddington two-stream method with the maximum-random overlap assumption for partial cloudiness. The inclusion of another cloudy region in the two-stream framework required an extension of vertical overlap rules. While retaining the maximum-random overlap for the entire layer cloudiness, we additionally assumed the maximum overlap of optically thicker cloudy regions in pairs of adjacent layers. This extended overlap formulation implicitly places the optically thicker region towards the interior of the cloud, which is in agreement with the core–shell model for convective clouds. The method was initially applied on a shallow cumulus cloud field, evaluated against a three-dimensional benchmark radiation computation. Different approaches were used to generate a pair of cloud condensates characterizing the two cloudy regions, testing various condensate distribution assumptions along with global cloud variability estimate. Regardless of the exact condensate setup, the radiative bias in the vast majority of Tripleclouds configurations was considerably reduced compared to the conventional plane-parallel calculation. Whereas previous studies employing the Tripleclouds concept focused on researching the top-of-the-atmosphere radiation budget, the present work applies Tripleclouds to atmospheric heating rate and net surface flux. The Tripleclouds scheme was implemented in the comprehensive libRadtran radiative transfer package and can be utilized to further address key scientific issues related to unresolved cloud–radiation interplay in coarse-resolution atmospheric models.

Abstract. The cloud overlap parameter α relates the combined cloud fraction between two altitude levels in a grid box to the cloud fraction as derived under the maximum and random overlap assumptions. In a number of published studies in this and other journals, it is found that α tends to increase with an increasing scale. In this Technical Note, we investigate this analytically by considering what happens to α when two grid boxes are merged to give a grid box with twice the area. Assuming that α depends only on scale, then between any two fixed altitudes, there will be a linear relationship between the values of α on the two scales. We illustrate this by finding the relationship when cloud cover fractions are assumed to be uniformly distributed, but with varying degrees of horizontal and vertical correlation. Based on this, we conclude that α increases with scale if its value is less than the vertical correlation coefficient in cloud fraction between the two altitude levels. This occurs when the clouds are deeper than would be expected at random (i.e. for exponentially distributed cloud depths).

Abstract. Based on four year' 2B-CLDCLASS-Lidar (Radar-Lidar) cloud classification product from CloudSat, we analyze the geographical distributions of different cloud types and their co-occurrence frequency across different seasons, moreover, utilize the vertical distributions of cloud type to further evaluate the cloud overlap assumptions. The statistical results show that more high clouds, altocumulus, stratocumulus or stratus and cumulus are identified in the Radar-Lidar cloud classification product compared to previous results from Radar-only cloud classification (2B-CLDCLASS product from CloudSat). In particularly, high clouds and cumulus cloud fractions increased by factors 2.5 and 4–7, respectively. The new results are in more reasonable agreement with other datasets (typically the International Satellite Cloud Climatology Project (ISCCP) and surface observer reports). Among the cloud types, altostratus and altocumulus are more popular over the arid/semi-arid land areas of the Northern and Southern Hemispheres, respectively. These features weren't observed by using the ISCCP D1 dataset. For co-occurrence of cloud types, high cloud, altostratus, altocumulus and cumulus are much more likely to co-exist with other cloud types. However, stratus/stratocumulus, nimbostratus and convective clouds are much more likely to exhibit individual features. After considering the co-occurrence of cloud types, the cloud fraction based on the random overlap assumption is underestimated over the vast ocean except in the west-central Pacific Ocean warm pool. Obvious overestimations are mainly occurring over land areas in the tropics and subtropics. The investigation therefore indicates that incorporate co-occurrence information of cloud types based on Radar-Lidar cloud classification into the overlap assumption schemes used in the current GCMs possible be able to provide an better predictions for vertically projected total cloud fraction.

Abstract. The radiative impacts of horizontal heterogeneity of layer cloud condensate, and vertical overlap of both condensate and cloud fraction are examined with the aid of a new radiation package operating in the GEOS-5 Atmospheric General Circulation Model. The impacts are examined in terms of diagnostic top-of-the atmosphere shortwave (SW) and longwave (LW) cloud radiative effect (CRE) calculations for a range of assumptions and overlap parameter specifications. The investigation is conducted for two distinct cloud schemes, one that comes with the standard GEOS-5 distribution, and another used experimentally for its enhanced cloud microphysical capabilities. Both schemes are coupled to a cloud generator allowing arbitrary cloud overlap specification. Results show that cloud overlap radiative impacts are significantly stronger in the operational cloud scheme where a change of cloud fraction overlap from maximum-random to generalized results in global changes of SW and LW CRE of ~4 Wm−2, and zonal changes of up to ~10 Wm−2. This is an outcome of fewer occurrences (compared to the other scheme) of large layer cloud fractions and fewer multi-layer situations where large numbers of atmospheric layers are simultaneously cloudy, both conditions that make overlap details more important. The impact of the specifics of condensate distribution overlap on CRE is much weaker. Once generalized overlap is adopted, both cloud schemes are only modestly sensitive to the exact values of the overlap parameters. When one of the CRE components is overestimated and the other underestimated, both cannot be driven simoultaneously towards observed values by adjustments to cloud condensate heterogeneity and overlap specifications alone.

Abstract Most current general circulation models (GCMs) calculate radiative fluxes through partially cloudy grid boxes by weighting clear and cloudy fluxes by the fractional area of cloud cover (Ca), but most GCM cloud schemes calculate cloud fraction as the volume of the grid box that is filled with cloud (Cυ). In this paper, 1 yr of cloud radar and lidar observations from Chilbolton in southern England, are used to examine this discrepancy. With a vertical resolution of 300 m it is found that, on average, Ca is 20% greater than Cυ, and with a vertical resolution of 1 km, Ca is greater than Cυ by a factor of 2. The difference is around a factor of 2 larger for liquid water clouds than for ice clouds, and also increases with wind shear. Using Ca rather than Cυ, calculated on an operational model grid, increases the mean total cloud cover from 53% to 63%, and so is of similar importance to the cloud overlap assumption. A simple parameterization, Ca = [1 + e(−f )(C−1υ − 1)]−1, is proposed to correct for this underestimate based on the observation that the observed relationship between the mean Ca and Cυ is symmetric about the line Ca = 1 − Cυ. The parameter f is a simple function of the horizontal (H) and vertical (V) grid-box dimensions, where for ice clouds f = 0.0880 V 0.7696 H−0.2254 and for liquid clouds f = 0.1635 V 0.6694 H−0.1882. Implementing this simple parameterization, which excludes the effect of wind shear, on an independent 6-month dataset of cloud radar and lidar observations, accounts for the mean underestimate of Ca for all horizontal and vertical resolutions considered to within 3% of the observed Ca, and reduces the rms error for each individual box from typically 100% to approximately 30%. Small biases remain for both weakly and strongly sheared cases, but this is significantly reduced by incorporating a simple shear dependence in the calculation of the parameter f, which also slightly improves the overall performance of the parameterization for all of the resolutions considered.

A novel lidar ceilometer prototype based on divided lens optics has been designed, built, characterised, and tested. The primary applications for this manufacturable ground-based sensor are the determination of cloud base height and the measurement of vertical visibility. First, the design, which was developed in order to achieve superior performance at a low cost, is described in detail, along with the process used to develop it. The primary design considerations of optical signal to noise ratio, range-dependent overlap of the transmitter and receiver channels, and manufacturability, were balanced to develop an instrument with good signal to noise ratio, fast turn-on of overlap for detection of close range returns, and a minimised number of optical components and simplicity of assembly for cost control purposes. Second, a novel imaging method for characterisation of transmitter-receiver overlap as a function of range is described and applied to the instrument. The method is validated by an alternative experimental method and a geometric calculation that is specific to the unique geometry of the instrument. These techniques allow the calibration of close range detection sensitivity in order to acquire information prior to full overlap. Finally, signal processing methods used to automate the detection process are described. A novel two-part cloud base detection algorithm has been developed which combines extinction-derived visibility thresholds in the inverted cloud return signal with feature detection on the raw signal. In addition, standard approaches for determination of visibility based on an iterative far boundary inversion method, and calibration of attenuated backscatter profile using returns from a fully-attenuating water cloud, have been applied to the prototype. The prototype design, characterisation, and signal processing have been shown to be appropriate for implementation into a commercial instrument. The work that has been carried out provides a platform upon which a wide range of further work can be built.

Le transfert radiatif est crucial dans la modélisation de l’atmosphère, du climat, et pour la simulation du changement climatique. Les calculs de flux radiatifs au sommet de l’atmosphère et en surface permettent notamment d’estimer le bilan énergétique de la planète, grandeur dont la bonne estimation est une contrainte importante dans les simulations climatiques. De nombreux éléments interagissent avec le rayonnement dans l’atmosphère : gaz, aérosols, nuages, et différents types de surfaces (végétation, océans, neige...). Ces différents composants ne se comportent pas de la même façon avec le rayonnement solaire, dont la source est le soleil, et avec le rayonnement infrarouge, dont la source est la surface terrestre ainsi que l’atmosphère elle-même. Dans ces deux situations, les nuages, composés de gouttelettes d’eau liquide et/ou de cristaux d’eau solide, représentent une difficulté importante de modélisation. Les nuages sont des objets complexes, de part leur composition, leur géométrie, et leurs interactions multiples avec le rayonnement. L’interaction nuage-rayonnement est étudiée depuis de nombreuses années, et il a été démontré qu’elle représente un des obstacles les plus importants à l’amélioration des modèles globaux de simulation du climat. Dans cette thèse, nous nous intéressons à un des aspects clé dans la représentation de l’effet des nuages sur le rayonnement : le recouvrement vertical des nuages. Cette notion est en effet liée de manière directe à la couverture nuageuse, grandeur de premier ordre dans le calcul de l’albedo d’une scène nuageuse. Dans le cadre du recouvrement vertical des nuages, nous mettons en place un formalisme permettant d’explorer en profondeur différentes hypothèses de recouvrement des nuages, en particulier le recouvrement exponentiel-aléatoire. Nous montrons que cette hypothèse de recouvrement peut, sous certaines conditions, permettre une très bonne représentation des propriétés des nuages, à la fois géométriques et radiatives, même à partir d’un profil vertical nuageux de résolution grossière. Nous démontrons que la variabilité verticale sous-maille de la fraction nuageuse, bien que non prise en compte par les modèles atmosphériques grande échelle, peut avoir un impact significatif sur les flux solaires calculés au sommet de l’atmosphère. La prise en compte rigoureuse de la résolution verticale par le recouvrement est également un facteur important. Dans un second temps, nous incorporons ces résultats dans un code de transfert radiatif par Monte Carlo (RadForce). L’utilisation de ce nouvel algorithme, qui utilise par ailleurs une approche raie-par-raie pour les différents gaz atmosphériques, nous permet d’estimer l’altitude d’émission de chaque composant présent dans l’atmosphère. Ces nouveaux outils nous permettent d’analyser de manière nouvelle des forçages radiatifs liés aux gaz à effet de serre, ainsi que l’impact de la prise en compte du recouvrement vertical des nuages
Radiative transfer is a crucial process in atmospheric and climate modelling, as well as for climate change simulations. Computations of radiative fluxes at the top of the atmosphere and at the surface allow us to estimate the radaitive budget of the planet, which is very important to represent correctly when it comes to climate simulations. Many elements interact with the radiation in the atmosphere : gases, aerosols, clouds, and different types of surfaces (vegetation, oceans, snow...). These different components do not interact in the same way with solar radiation, that comes from the sun, and with infrared radiation, that comes from the earth’s surface and the atmosphere itself. In both situations, clouds, composed of liquid water droplets and/or solid water crystals, represent an important modeling difficulty. Clouds are complex objects, because of their composition, their geometry, and their multiple interactions with the radiation field. Cloud-radiation interaction has been studied for many years, and it has been shown that it represents one of the most important obstacles to the improvement of global climate models. In this work, we focus on one of the key aspects in the representation of the effect of clouds on radiation : vertical cloud overlap. This notion is indeed directly linked to the cloud cover, which is a quantity of first order importance in the calculation of the albedo of a cloud scene. Within the framework of the vertical cloud overlap, we develop a formalism allowing us to explore in depth various hypotheses of cloud overlap, in particular exponential-random overlap. We show that this overlap hypothesis can, under certain conditions, allow a very good representation of cloud properties, both geometric and radiative, even from a coarse resolution vertical cloud profile. We show that the vertical subgrid variability of the cloud fraction, although not taken into account by large-scale atmospheric models, can have a significant impact on the solar fluxes calculated at the top of the atmosphere. The rigorous consideration of vertical resolutions by the overlap is also an important factor. We then focus on incorporating these overlap results into a Monte Carlo radiative transfer code (RadForce). The use of this new algorithm, which also uses a line-by-line approach for the different atmospheric gases, allows us to model the emission altitudes of each atmospheric component. These new tools allow us to analyze in a new way the radiative forcings linked to greenhouse gases, as well as the impact of taking into account the vertical overlap of clouds and their vertical subgrid heterogeneity

Cirrus is conventionally considered as cloud forming in the Earth's upper troposphere at temperatures somewhat below -40°C, composed of ice crystals and forming long, wispy trails. This characteristic shape, in the form of a curl of hair, results from evaporation in vertical shear of horizontal winds, and leads to its Latin name—originally proposed by Luke Howard in 1803. Here we address the nucleation, growth, and evaporation processes that influence the concentration and shape of individual particles and their role in specific atmospheric phenomena. To set the scene, figure 3.1 shows examples of such crystals collected by aircraft. In this chapter, we also address the radiation and dynamic environment in which crystals grow and subsequently evaporate. Crystal growth depends on the location of a crystal with respect to the cloud edge and the intervening cloud optical thickness; evaporation depends on larger scale processes as at fronts and cumulonimbus anvils and also at inversion interfaces where shear instability and resulting gravity waves produce significant effects over a range of scales. These effects lead to differing cloud radiative properties and ultimately control of the earth's radiation budget and overall climate (Liou 1986; Stephens et al. 1990; Liou and Takano 1994; Takano and Liou 1995; Mishchenko et al. 1996; Strauss et al. 1997; Macke et al. 1998). A growing crystal implies a supersaturated or supercooled environment with respect to the solid phase and can, in general, be considered as growth from either three-fold symmetry overlying a needle, (NASA DC-8,TOGA COARE,-48°C, deep tropical convection, 1993). The replica visually shows a uniformity of color in vertical illumination, indicating a thin crystal a few micrometers thick, uniform to ±0.05 μm. e. Replica of needles, small scalene and triangular three-fold symmetry plates, hexagonal plates, columns, and irregular crystals collected by D.L.R. Falcon in thin cirrus over the Alps, temperature -55°C, October 29,1992. (Courtesy Dr. P. Wendling.) f. Replica of crystals from the evaporating tip of a contrail formed 50 s earlier by the NASA 757 aircraft sampled from the NASA DC-8. Multiple trigonal symmetry crystals are present, with a 60° rotation (left side), along with hexagonal and scalene and triangle crystals, concentration 10/cm3. Clear sky environment over Kansas, temperature -52°C, 1840-1900Z, 4 May 1996.

Abstract The formation, fall, melting, and evaporation of precipitation radically alter the dynamics of cumulus convection in several important respects. In the first place, the fall of precipitation represents an extremely important irreversible process in the atmosphere. Once precipitation reaches the surface, a net, vertically integrated quantity of heat is added to the atmosphere. In the tropics, this heating is a major term in the overall thermal budget of the atmosphere. As latent heat is converted to sensible heat in cumulus clouds, which generally cover a very small fraction of the sky (Section 6.5), much of the effect of the heating is transmitted through the atmosphere by inertia-gravity waves instead of advection. This is completely different from the advective mode of heat transfer in dry convecting layers and ex­ plains part of the very different character of moist convection. This point is explored in Sections 11.1 and 11.2.

Processing point clouds using neural network models is a relatively new approach. The first significant works in this direction date back to 2017. Point clouds are a set of vertices in threedimensional space and characterize the external surface of an object. Due to the peculiarities of such data, typical fully connected convolutional networks cannot be applied. This is due to the fact that such data should take into account not only the color component but also the entire geometric shape of the object. This paper presents a neural network model for semantic segmentation of point clouds, which are remote sensing data of the earth. The proposed model is a modification of the original DGCNN model, which is based on the graph representation of point clouds, and atrous convolution layers were used to improve the result. The training and testing of the model was carried out on the Hessigheim 3D dataset, which was obtained by scanning a suburban area. As a result of numerical experiments on model development and data set preprocessing, acceptable results were obtained for the F1 and overall accuracy metrics.

Geographic Information System (GIS) has been used to study urban form over the past decades. It is particularly useful to measure quantitative variables of urban form, such as density, clustering, proximity, accessibility, etc. Overall, GIS has been an effective tool for researchers in the field. However, GIS as its own field has continued to evolve in a rapid pace. Recent development in the area of geospatial technologies offers new possibilities with new toolsets for spatial analysis and data visualization. This paper traces recent major trends in GIS and discusses their implications to the field of urban form study. These trends include the following: 1) Increase in dimensions with 3D GIS: conventional 2D maps are being replaced by interactive 3D models generated by procedural rules stored in GIS. Along with locations and associated attributes, vertical elevation and architectural details are also represented. 2) Integration with remote sensing: remote sensing not only enables 3D visualization with imagery processing but also provides other spatial information to create meaningful analysis results. For instance, LiDAR point-cloud data allow extraction of built forms and identification of physical features and land covers. 3) Cloud-based GIS: web-based GIS services allow centralized access to location-based information. Yet through distributed mobile platforms, real-time data collection, sharing, and collaboration are done seamlessly in the cloud. 4) Integration with virtual reality: virtual reality creates immersive experiences with a perception of being physically present in a non-physical world. GIS can greatly enhance the accuracy and realism of virtual scenes with up-to-date terrain models, street networks, and 3D features. This paper identifies best practices from two recent projects in North America. It then discusses an on-going project and demonstrates the potentials of these new emerging GIS tools for study of urban form.

Abstract The main drivers contributing to the continued growth of network traffic include video, mobile broadband and machine-to-machine communication (Internet of Things, cloud computing, etc.). Two primary technologies that next-generation (5G) networks are using to increase capacity to meet these future demands are massive MIMO (Multi-Input Multi-Output) antenna arrays and new frequency spectrum. The massive MIMO antenna arrays have significant thermal challenges due to the presence of large arrays of active antenna elements coupled with a reliance on natural convection cooling using vertical plate-finned heat sinks. The geometry of vertical plate-finned heat sinks can be optimized (for example, by choosing the fin pitch and thickness that minimize the thermal resistance of the heat sink to ambient air) and enhanced (for example, by embedding heat pipes within the base to improve heat spreading) to improve convective heat transfer. However, heat transfer performance often suffers as the sensible heat rise of the air flowing through the heat sink can be significant, particularly near the top of the heat sink; this issue can be especially problematic for the relatively large or high-aspect-ratio heat sinks associated with massive MIMO arrays. In this study a vertical plate-finned natural convection heat sink was modified by partitioning the heat sink along its length into distinct sections, where each partitioned section ejects heated air and entrains cooler air. This approach increases overall heat sink effectiveness as the net sensible heat rise of the air in any partitioned section is less than that observed in the unpartitioned heat sink. Experiments were performed using a standard heat sink and equivalent heat sinks partitioned into two and three sections for the cases of ducted and un-ducted natural convection with a uniform heat load applied to the rear of the heat sink. Numerical models were developed which compare well to the experimental results and observed trends. The numerical models also provide additional insight regarding the airflow and thermal performance of the partitioned heat sinks. The combined experimental and numerical results show that for relatively tall natural convection cooled heat sinks, the partitioning approach significantly improves convective heat transfer and overall heat sink effectiveness.

Wildfires are among the biggest sources of aerosol emissions, in particular black and organic carbon. These aerosols modify meteorological processes through direct and indirect aerosol effects. Despite our knowledge of the main aerosol-meteorology interactions, their role in the atmosphere often remains uncertain depending on weather conditions. This study presents the analysis of the main aerosol effects observed during the severe wildfire event that occurred in the Chornobyl Exclusion Zone (CEZ) in the north of Ukraine in April 2020. The study is based on seamless modeling using the Environment-High Resolution Limited Area Model (Enviro-HIRLAM). Simulations were performed at 15 km horizontal resolution with downscaling to 5 and 2 km resolution. The model covered 40 vertical levels with a 3-hour data output. Based on the emissions derived from the IS4FIRES and IASA ECLIPSE, it was simulated the three-dimential distribution, transportation, and deposition of black carbon (BC) and organic carbon (OC), including other main aerosol types (dust, sea salt, and sulfates) for considering real aerosol content. Aerosol components were divided into groups by their size (Aitken, accumulation, and coarse modes) and solubility (soluble or insoluble). Enviro-HIRLAM simulations included runs with aerosol effects (direct (DAE), indirect (IDAE), and both (DAE+IDAE) aerosol effects included) and a reference (REF) run without aerosol effects. Elevated BC and OC content significantly modified meteorological conditions, mainly because of DAE. During the period of clear-sky conditions, aerosols caused local 2-m air temperature decrease up to -3°C. At the same time, cloudy conditions caused the opposite effect, and the 2-m air temperature increased up to 5°C, especially on the edge of the stationary front, which was observed on April 14, 2020. Emitted aerosols affected the moisture regime and resulted in drier conditions with a lower amount of precipitation. These effects were prevailing despite elevated BC and OC content, which influenced cloud formation in different ways depending on weather conditions. The changes were also observed for wind. However, the suggestion is that the observed wind changes because of DAE and IDAE might happen due to the spatial shifts in wind patterns as a result of the overall impact, not because of a direct influence on air pressure at the local scale. The presented results were obtained within individual grant INFRAIA-2016-1-730897 “High Performance Computing Europa-3 (HPC-Europa3) Transnational Access programme” while conducting the project “Integrated modeling for assessment of potential pollution regional atmospheric transport as result of accidental wildfires” (2020–2022).

Abstract Sucker-rod pumps (SRP) are the most common form of artificial lift in depleted oil wells. Given vast datasets collected from years of operation, many operators are enacting digital technology to generate automated artificial lift systems. However, their online monitoring and optimization come with many challenges. Therefore, the individually engineered artificial lift is an imminent solution for long-term production, while maintaining cost efficiency. The key to make sucker-rod pumps operate effectively lies in downhole condition diagnostics. The emerging big-data analytics have provided relatively precise downhole condition forecasting based on available data, enabling better decision making. This study focuses on developing a testing digital SRP application, while leveraging analytical approaches to diagnose its operational anomalies. This study presents an experimental and analytical workflow to monitor sucker-rod pumps and perform diagnostics using a designed Interactive Digital Sucker-Rod Pumping Unit (IDSRP). This unit consists of a vertical 50-ft long facility with a downhole rod pump at the bottom and proper instrumentation, capable of simulating a rod-pumped wellbore's operation. A linear actuator is used to provide the rod string's reciprocating movement and simulate different surface units and operating scenarios. The system utilizes the application of Pulse-width Modulation (PWM) technique and data-acquisition system (DAQ) to obtain analog results through signals detected by sensor. The surface dynamometer cards, and time-driven pressure and rate data are collected to train a cloud-based analytics software platform. The wave equation of Gibbs is used to draw the downhole pump card from the surface card. Some of the tested scenarios are normal pump operation with varying rates and varying levels of pumping off at the pump inlet. The applied online prototype is designed to provide a step towards digitized automation systems. The setup is used to generate datasets for the rod-pump's operation at varying pump speeds, stroke lengths, and rod movement profiles. The collected data include the flowrate, bottomhole and surface pressures, and the dyno cards. The digitized transformation algorithms develop these physics-based inputs to generate predictive models, thus classifying operational conditions or failures of the pump. The model dynamically categorizes the pumps into key states of ideal condition and over-pumping with a regression fit accuracy of higher than 0.7 and overall classification accuracy of 92%. The novelty of this setup consists not only of its mechatronic design but also allows thorough performance monitoring of the pump, thus easily validating models. The results have the potential of becoming a tool to optimize and shorten the downtime for repairing pump failures.

Abstract This paper proposes an integrated approach to model High Permeability Streaks (HPS) using the case study of heterogeneous carbonate Reservoir B, utilizing static and dynamic data. Modelling the HPS is critical as they play an important role in fluid dynamics within the reservoir. The impact is observed from 60 years of development, where flood front movement is captured by rich density of Pulsed Neutron and recently drilled open hole logs. Injection water is overriding from tighter lower subzones (injected zones) to permeable upper subzones of the reservoir, thereby leaving the tighter lower subzones unswept. Gas cusping down to the oil zone occurs through the HPS resulting in non-uniform gas cap expansion, which leads to early gas breakthrough in producers near the gas cap. The problem with characterizing HPS is associated with their thickness- in Reservoir B it ranges from 0.5 to 2.5ft and occur in multiple subzones in the upper part of the reservoir. The standard triple combo suite of logs does not have the resolution to detect these thin HPS. In addition, the cored interval of the HPS is mainly disintegrated which is attributed majorly to well sorted grain-supported lithofacies. Therefore, sampling for porosity & permeability via Routine Core Analysis (RCA) and Capillary pressure as well as pore throat distribution using Mercury Injection Capillary Pressure (MICP) method is extremely difficult. This results in a gap in the input dataset for the static models, where the higher permeability samples are not captured in logs or cores and are therefore under-represented. Current approach to unify this gap is to use permeability multipliers, which does not honor geological trends. The HPS in Reservoir B has added complexities when compared to other regional HPS. Not only are they multiple and distributed across subzones, there is also preferential movement of water through the HPS within the same area. Of the 3 upper subzones that have HPS, in some areas, water injected in lower subzone will override the HPS in the middle and move right to the HPS in the top subzone, thereby ignoring the hierarchical flood front movement from bottom to the top. A robust workflow was developed in order to address and resolve the above mentioned uncertainties related to High Permeability Streaks. The proposed integrated workflow consisted of five stages: Developing a robust geological conceptual model Mapping spatial distribution & continuity Capturing the vertical presence in cored & uncored wells (depth & thickness) Permeability Quantification of HPS using Well Test Measurements Modelling High Permeability Streaks The paper highlights the utilization of a range of static (core, Routine Core Analysis (RCA), image logs, OH logs) and dynamic data (Pulse Neutron Logs (PNL's), later drilled Open Hole Logs, Production Logging Tools (PLTs) and well test data). Quantitative (HPS depth indicated by water saturation profile indicated by waterflood movement) and Qualitative (Flooding observed but HPS depth is uncertain) depth indicators/flags were generated from the data set and became the foundation of the modelling the HPS. The first step in the workflow is to establish a robust geological conceptual model. For Reservoir B, certain facies contribute to HPS, which are mainly leached Rudist Rudstones and Coated grain Algal Floatstones as well as well sorted Skeletal Grainstones. Based on core observations, they have confirmed vertical stratigraphic presence in each subzone (top, mid, base) which is attributed to storm events. These were consequently mapped using average thickness from core descriptions and revised using contributing facies trend maps and qualitative dynamic observations. These maps served as basis for probability trend distribution for static rock type models. The vertical presence of HPS was increased from 10% to 30% by re-introducing them in the missing core intervals using quantitative dynamic flags and thickness from isochores. Consequently, permeability were assigned in the missing section using the proposed permeability enhancement technique that honors the verified well test measurements. Based on the above improvements, the HPS intervals were mapped to the static rock type with best reservoir quality (SRT 1), which is also linked to certain geological attributes (i.e. lithofacies, diagenetic overprint & depositional environment). The enhanced permeability in the identified HPS intervals is also reflected as upgraded SRT (from lower SRT 2 to best SRT 1). The overall impact is observed by improvement of poro-perm cloud, with added control points for HPS SRT (1), which is vital for permeability modelling. The updated permeability model, captures high perm streaks in terms of vertical presence and magnitude. By introducing higher permeability in the upper subzones of the reservoir, the water overriding/gas cusping phenomena could then be mimicked in the dynamic model. The proposed methodology is an integrated workflow that maximizes the input from each disciplines (G&G, Petrophysics and Reservoir Engineering) to create a robust static model through incorporation of high permeability streaks. The use static and dynamic data, has helped to establish HPS existence/preference, which then could be used to upgrade the permeability/SRT. This will in turn lead to a better static model and a better history match in the dynamic model. It will also led to better remaining in place prediction and enable accurate prediction for future field development, especially where EOR is involved.