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In recent years, some deep learning dehazing methods based on atmospheric scattering model mostly solve the dehazing results by using depth convolution neural networks (CNNs) to estimate the medium transmission map in the model. However, these methods usually ignored the potential correlation between the transmission map and the atmospheric light in the atmospheric scattering model, which can lead to colour distortion and incomplete dehazing in the dehazing results. To address this problem, this paper first presents a novel Haze-Veil model to increase the correlation between the model parameters by constructing an atmospheric veil term. Then, based on the proposed model, a haze-relevant end-to-end network (HRN) is designed to estimate the parameters of this model and directly output the final clear image. In addition, a cost function is designed by defining multi-object constraint cost functions to further establish the connections between the statistical attributes of the hazy image and the out of HRN. Experiments on benchmark images, which include synthesized and real images, show that HRN effectively removes haze and outperforms most of the existing and state-of-the-art dehazing methods. [ABSTRACT FROM AUTHOR]

We propose an end-to-end dehazing model based on deep learning (CNN network) and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing. Compare to the previously proposed dehazing network, the dehazing model proposed in this paper make use of the FPN network structure in the field of target detection, and uses five feature maps of different sizes to better obtain features of different proportions and different sub-regions. A large amount of experimental data proves that the dehazing model proposed in this paper is superior to previous dehazing technologies in terms of PSNR, SSIM, and subjective visual quality. In addition, it achieved a good performance in speed by using EfficientNet B0 as a feature extractor. We find that only using high-level semantic features can not effectively obtain all the information in the image. The FPN structure used in this paper can effectively integrate the high-level semantics and the low-level semantics, and can better take into account the global and local features. The five feature maps with different sizes are not simply weighted and fused. In order to keep all their information, we put them all together and get the final features through decode layers. At the same time, we have done a comparative experiment between ResNet with FPN and EfficientNet with BiFPN. It is proved that EfficientNet with BiFPN can obtain image features more efficiently. Therefore, EfficientNet with BiFPN is chosen as our network feature extraction. [ABSTRACT FROM AUTHOR]

In the face of anthropogenic climate change, forest and tree populations must adapt, migrate, or face extirpation. Assisted migration (AM) has emerged as a promising forestry strategy to mitigate the impacts of climate change, yet its role in the evolutionary and ecological processes remains unclear. Various models, including climate niche models and transfer and response functions, have been employed to guide AM efforts. Each of these models has advantages and limitations, necessitating a comprehensive understanding of their rationale and constraints for further development and improvement. In our review, we aimed to define the role of forestry AM and elucidate the scenarios in which AM becomes a critical necessity in the context of climate change. Furthermore, we provided a comprehensive overview of the rationale and development of commonly used models and functions in the realm of forestry AM. Finally, we analyzed 214 published papers related to forestry AM, examining how previous evidence either supports or opposes the implementation of AM, and the underlying reasons for the agreement or disagreement. Our findings reveal that nearly 60% of the reviewed papers express a positive stance towards AM. More than one-fifth of the papers emphasize AM's potential to enhance adaptation. Another significant aspect supporting AM is its capacity to increase production and reduce extirpation risks. However, AM can also introduce invasive risks, lead to maladaptation, and even raise moral concerns. In conclusion, we advocate for a holistic approach to AM, incorporating genetic knowledge, models accounting for biological interactions and edaphic adaptation, and the formulation of comprehensive policies. This integrated strategy will be pivotal in guiding AM practices aimed at mitigating the impacts of anthropogenic climate change. ● Explores AM's ecological and evolutionary role under climate change. ● AM enhances species adaptation and productivity. ● AM risks: invasive species and maladaptation. ● Advantages and limits of forestry AM models. ● Calls for integrated policy in AM strategy. [ABSTRACT FROM AUTHOR]

• Imaginaries influence climate knowledge and action and fix or unsettle visions of the future. • The imbrication of discourse and materiality is key to understanding the concept of imaginaries. • The medium through which imaginaries are produced and circulated matter. • Different mediums offer different affordances and constraints in shaping climate imaginaries. • Attending to the role of mediums in climate imaginaries informs environmental governance debates. Imaginaries are increasingly recognised as influential in shaping climate knowledge and action and in fixing or unsettling particular visions of the future. Whilst the imbrication of discourse and materiality is key to the distinctiveness of the concept of imaginaries, attention has tended to focus on their symbolic and discursive aspects, overlooking the role of the material dimension. In this paper we seek to bring back the material into thinking about imaginaries by critically examining the relationship between climate imaginarie s and the mediums through which they are produced and circulated. We argue that the medium is an active constituent of the productive work that imaginaries do. To construct this argument, we first distinguish the imaginary from associated debates around images and imagination, highlighting its theoretical utility in rebutting modernist dualism between the material and the ideal. Second, we propose the concept of 'medium' as a way of foregrounding the material dimensions of the imaginary without re-inscribing the binary between the virtual and the material or imputing an overly agentic or predetermined role for the latter. Deploying a co-productionist approach and foregrounding process of mediation, we signal the affordances and constraints of mediums in shaping climate imaginaries. Third, we provide a comparison between climate poetry and computerised climate scenario models to examine how each offers different potentials and limitations to the imaginaries that they co-produce, and how the medium matters to the production and circulation of climate change imaginaries. We conclude by outlining an emerging research agenda and highlighting some of the implications of attending to the role of the medium in climate change imaginaries for contemporary environmental governance debates. [ABSTRACT FROM AUTHOR]

The paper offers a new approach to modeling atmospheric turbulence consisting of turbulent cells whose size is larger than the optical beam width. Particular turbulent cells are approximated by an optical element matrix. The ray transfer matrix method is presented, through which the optical elements can be described in the matrix form. A deflection simulation was performed that indicated the behavior of the optical beam by passing through the optical element. Furthermore, the calculation of the deflection vector is described together with a cascade model of turbulent cells. The matrix calculation for the cascade of optical elements is also expressed. [ABSTRACT FROM AUTHOR]

Although there is a growing interest in the concept of climate justice, both in academia and society in general, there is still no consensus regarding the best models and data to represent it. Thus, the objective of this paper is to test and statistically validate the confirmatory factor model for climate justice. To do this, we applied Confirmatory Factor Analysis (CFA), which designs and tests statistically the significance of a multidimensional theoretical construct. The main results were: a) the individual freedoms derived from the capability approach and the climate actions of adaptation and mitigation were empirically confirmed as viable perspectives for assessing climate justice; b) the theoretical constructs of human development, climate actions and climate justice can be constructed based on a set of 25 indicators; and c) the three confirmatory models were successfully tested to validate a theoretical/measurement model for climate justice. We believe that the results observed will provide policymakers and national governments with a new perspective for the alignment of different types of human development programs with climate change actions, helping them on the establishment of possible links between actions taken at the national level and the definition of global goals. • Presence of synergies between the confirmatory model and the Sustainable Development Goals. • The proposition of definition of Climate Justice based on the Capability Approach. • The 25 selected indicators can guide nations in their transition to a fairer low-carbon economy. [ABSTRACT FROM AUTHOR]

The modeling of atmospheric clouds is one of the crucial elements in the natural phenomena visualization system. Over the years, a wide range of approaches has been proposed on this topic to deal with the challenging issues associated with visual realism and performance. However, the lack of recent review papers on the atmospheric cloud modeling methods available in computer graphics makes it difficult for researchers and practitioners to understand and choose the well-suited solutions for developing the atmospheric cloud visualization system. Hence, we conducted a comprehensive review to identify, analyze, classify, and summarize the existing atmospheric cloud modeling solutions. We selected 113 research studies from recognizable data sources and analyzed the research trends on this topic. We defined a taxonomy by categorizing the atmospheric cloud modeling methods based on the methods' similar characteristics and summarized each of the particular methods. Finally, we underlined several research issues and directions for potential future work. The review results provide an overview and general picture of the atmospheric cloud modeling methods that would be beneficial for researchers and practitioners. [ABSTRACT FROM AUTHOR]

This paper presents an approach to numerically simulate the inherently unsteady rime ice accretion problem on a two-dimensional airfoil and elucidate the associated variations under different icing conditions. The airflow field and the water impingement on the airfoil are obtained based on an Eulerian two-phase model. A dynamic mesh strategy is employed to unsteadily account for the changes in the ice profile and its impact on the air and droplet flow by continuously reconstructing the computational grid at each time-step through smoothing and layering mechanisms. All main icing modules including the airflow field, droplet trajectory, icing thickness profile, and mesh management are fully coupled within the same computational framework without resorting to any external tools. Classical icing theory is employed to model the rime ice roughness, and it is assumed that the ice accretes in a direction normal to the airfoil surface. The governing Reynolds-averaged Navier–Stokes (RANS) conservation equations along with the energy and continuity equations are solved to produce the velocity and temperature fields. A convective film heat transfer coefficient is computed based on the surface heat flux and a recovery temperature which takes into account the dissipative heat release in the boundary layer in the vicinity of the airfoil surface. With the implemented strategy and calculating the convective heat transfer coefficient, the water film thickness is also calculated along with the ice shape. The model is validated by comparing the local collection efficiency distribution and ice shape with experimental data, and the results show that the implemented approach provides acceptable predictions of ice accretion profiles and rates. [ABSTRACT FROM AUTHOR]

In this paper we propose a numerical model to calculate microwave absorption in the earth's atmosphere. This model combines Mont'e Carlo methods imbeded in the efficient simulation open-source toolkit Geant4, and standard physical models of the atmosphere where experimental data such as pressure, temperature and air composition have been compiled for many years. Also, from high-resolution transmission molecular absorption database, we generated spectral data at any altitude as input into our model. We were able to successfully reproduce known absorption results for a simple case previously calculated using other methods. This new approach is promising when tackling microwaves physics in more realistic atmospheric models. [ABSTRACT FROM AUTHOR]

Tree fall onto railway lines puts passengers at risk and causes large economic losses due to disruption of train services and damage to infrastructure. Railway lines in Germany are vulnerable to tree fall because of the large number of trackside trees that exist in that country with approximately 70% of all railway lines being tree-lined. In this paper we first tested whether a hybrid-mechanistic tree wind damage model, ForestGALES, could identify the sections of the railway network affected by tree fall in two federal states of Germany, Northrhine-Westphalia (NRW) and Thuringia (TH). We secondly tested whether the model, in combination with meteorological forecast models, could predict where tree fall occurred during a damaging windstorm. We used information on tree characteristics derived from LiDAR and aerial photography along the railway line network in NRW and TH to calculate the critical wind speed (CWS) at which damage is expected to happen for every individual tree as a function of its size and species, and the underlying soil. The railway network was then divided into 500 m sections and the statistics of the CWS , tree height, and species composition (broadleaf/conifer mix) within each section were calculated. Analysis of past tree fall events recorded by Deutsche Bahn AG (DB) showed that there was a significantly lower minimum CWS and significantly greater maximum tree height in sections that had recorded damage. In a second step we compared the calculated CWS values for all trees against downscaled wind speed assessments across the two federal states during Storm Friederike (named Storm David internationally) on 18 January 2018 and tested the ability of the model to discriminate sections with recorded damage during the storm. Excellent model discrimination was found with an AUC value of 0.82 and an overall model accuracy of 74.2%. The first test showed that the ForestGALES model with precise individual tree information can identify the sections of a railway network most vulnerable to tree fall. The second analysis showed, for the one storm tested, that the ForestGALES model when combined with predicted storm wind speeds can identify the most probable sections of the railway network to experience tree fall during an approaching damaging storm. Such information could be of value in firstly planning remedial work along railway lines, and secondly preparing the railway network ahead of a major storm. • Tree fall is a major hazard to the railway network in Germany. • A mechanistic model could identify sections of a railway most at risk of tree fall. • The model identified sections likely to suffer tree fall prior to a storm. • Sections with tree fall had trees with significantly greater maximum heights. • The model can be used to identify sections requiring remedial action. [ABSTRACT FROM AUTHOR]

This paper concentrates on solving fuzzy dynamical differential equations (FDDEs) by use of unsupervised kernel least mean square (UKLMS). UKLMS is a nonlinear adaptive filter which works by applying kernel trick to LMS adaptive filter. UKLMS estimates multivariate function which is embedded to estimate the solution of FDDE. Adaptation mechanism of UKLMS helps for finding solution of FDDE in a recursive scenario. Without any desired response, UKLMS finds nonlinear functions. For this purpose, an approximate solution of FDDE is constructed based on adaptable parameters of UKLMS. An optimization algorithm, optimizes the values of adaptable parameters of UKLMS. The proposed algorithm is applied for solving Earth energy balance model (EBM) which is considered as a fuzzy differential equation for the first time. The method in comparison with the other existing approaches (such as numerical methods) has some advantages such as more accurate solution and also that the obtained solution has a functional form, thus the solution can be obtained at each time in training interval. Low error and applicability of developed algorithm are examined by applying it for solving several problems. After comparing the numerical results, with relative previous works, the superiority of the proposed method will be illustrated. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces the concept of renewable energy contingencies that represent long-term/extended variability of variable renewable energy (VRE) resources, namely, significant periods (e.g., days/weeks) of low wind/solar availability. These contingencies have not received much attention to date but are likely to emerge as a major issue in some countries such as India as the share of VRE increases. Using 38 years of climate model reanalysis data for wind over India, we demonstrate that low periods of wind contingency below long-term (Indian) national average of 5 m/s can extend for more than 100 days in several zones some of which are deploying large wind farms. Even in some of the best wind resource areas in India with long term average wind speed close to 7 m/s, low wind days (e.g., 5 m/s which is substantially below average) can extend up to 60 days. We propose a four-step methodology around a co-optimization based energy-ancillary services dispatch model to assess the impact of renewable contingency and implemented it for the state of Tamil Nadu, the most wind-rich state of India. We have estimated that annual renewable contingency cost impact of 5 GW additional wind in Tamil Nadu to be in the range of US$27–76 million pa. Planning analysis should embrace the concept of renewable contingency to recognize these costs and put in place necessary spinning reserve and back-up generation resources. • Introduces the concept of renewable contingency around long term variability of RE generation. • Low periods of wind contingency below long-term (Indian) national average of 5 m/s can extend for more than 100 days. • We have estimated that annual renewable contingency cost impact of 5 GW additional wind in Tamil Nadu to be in the range of US$27–76 million pa. [ABSTRACT FROM AUTHOR]

Hydrologic models require atmospheric, dynamic and static models to simulate river flow from catchments. Thus the accuracy of hydrologic modelling highly depends on the data quality. Therefore, simulation is always challenging in data-scarcity environments. In addition, physical flow measurements are infeasible in the Spatiotemporal domain, and soft computing techniques are helpful in river flow simulation in data-scarcity environments. In this research paper, an efficient and accurate Cascaded-ANFIS-based model for rainfall–runoff was proposed and evaluated using five case studies in three countries: Japan, Vietnam, and Sri Lanka. The investigation focused on predicting streamflow by the influence of past data, with each river's dataset examined to determine the best configuration of past rainfalls affecting streamflow volume. The proposed algorithm was compared against six state-of-the-art regression algorithms. The results showed that it outperformed the other algorithms in every case study except the Kalu River dataset, with zero bias calculated. The developed R-R model can be considered a generic model for streamflow prediction in data-scarcity environments, with excellent acceptability of simulated river flows against measured river flows observed across different geographic and climatic regions. [Display omitted] • Efficient Cascaded-ANFIS model for rainfall-runoff evaluated across 5 cases in 3 countries, highlighting rainfall-streamflow link in various catchments. • Six regression algorithms compared with proposed method using NSE, KGE, Bias, and RMSE. • Proposed algorithm outperformed others in all cases except Kalu River dataset. R-R model serves as generic streamflow predictor for future studies. [ABSTRACT FROM AUTHOR]

Thermal mapping has been known as a reliable technique to analyse and even predict road surface temperature in a stretch of road, rather than just a single point (e.g. road weather station location). The method itself was developed in the 1980s, and as time progressed, the technique was improved and has become more applicable. Due to other methods, such as climate modelling, becoming widely accessible and more affordable to apply, thermal mapping started being pushed out to the background as an expensive alternative. The idea for this paper arose from thermal mapping applications to Lithuanian roads that produced inconclusive results in some research areas and raised the question of whether this technique applies to flatlands as effectively as to uplands. The Czech Republic was chosen as a country with an available database and environmentally different road network. Several stretches of road thermal mapping data were analysed and compared. It was concluded, that in flat landscapes altitude has lesser predictability value for road surface temperature than in undulating uplands. In addition, thermal mapping results appear to be more inconclusive in flatlands, compared to uplands. Nevertheless, thermal mapping is a good and reliable method for determining cold spots. [ABSTRACT FROM AUTHOR]

Climate change and increasing urbanization call for the effective adaptation of cities to extreme heat. To improve the applicability of the research, sophisticated computational fluid dynamics models are being developed to capture the complexity of climate in a real urban environment, while a human-oriented paradigm is emerging concurrently. In this paper we present a synergy of these approaches by analyzing outdoor thermal exposure on five different pedestrian routes in Prague-Dejvice (Czech Republic), employing the PALM modeling system and realistic use-cases. Our simulations reveal important spatio-temporal variability in the Universal Thermal Climate Index (UTCI) in the urban neighborhood. Our findings particularly emphasize the negative effect of open spaces, such as gaps between buildings and shorter buildings, on the thermal exposure of pedestrians. These configurations allow more direct irradiation to reach ground level, while the other adverse climatic characteristics of midrise/highrise developments are largely preserved. The effect of urban greenery is quite variable during the day. Trees can reduce UTCI by up to 10 °C, but this strongly depends on the location (e.g., distance from neighboring buildings). Irrigated grass reduces UTCI by about 1.8 °C, but dried grass has little heat mitigation effect. In conclusion, our results suggest that expert-based knowledge together with sophisticated and fine-scale models can identify effective heat stress reduction measures without draconian changes to, or investments in, the urban environment. • Microscale meteorological modeling and pedestrian routes yields novel exposure information. • Fine-scale meteorological models can identify effective heat stress reduction measures. • Tree planting reductions of UTCI reach 10 K but depend strongly on local context. • Replacement of impervious surfaces with irrigated (dried) grass reduces UTCI around 1.8 K (0.1 K). [ABSTRACT FROM AUTHOR]

China's southeast coastal areas frequently suffer from storm surge due to the attack of tropical cyclones (TCs) every year. Hazards induced by TCs are complex, such as strong wind, huge waves, storm surge, heavy rain, floods, and so on. The atmospheric and oceanic hazards cause serious disasters and substantial economic losses. This paper, from the perspective of hazard group, sets up a multi-factor evaluation method for the risk assessment of TC hazards using historical extreme data of concerned atmospheric and oceanic elements. Based on the natural hazard dynamic process, the multi-factor indicator system is composed of nine natural hazard factors representing intensity and frequency, respectively. Contributing to the indicator system, in order of importance, are maximum wind speed by TCs, attack frequency of TCs, maximum surge height, maximum wave height, frequency of gusts ≥ Scale 8, rainstorm intensity, maximum tidal range, rainstorm frequency, then sea-level rising rate. The first four factors are the most important, whose weights exceed 10% in the indicator system. With normalization processing, all the single-hazard factors are superposed by multiplying their weights to generate a superposed TC hazard. The multi-factor evaluation indicator method was applied to the risk assessment of typhoon-induced atmospheric and oceanic hazard group in typhoon-prone southeast coastal cities of China. [ABSTRACT FROM AUTHOR]

The paper presents results of measurements of selected thermal-physiological properties of military clothing fabrics in simulation of climatic conditions in selected areas of the world. The aim of the research was to test and compare old and new sandwich structures of military clothing and provide description of their basic characteristics relating to selected utility properties such as heat resistance R, thermal conductivity A and vapour resistance Ret. The result of the research is delivery of suitable sandwich structures of clothing according to real climatic conditions, under which they are used. The results of experimental measurement allow to verify functionality and suitability of military clothing used in various areas of the world by objective measurement in simulation of real climatic conditions under European standard EN 60721-2-1:2014 that sets the types of climate characterized by the values of air temperature and humidity. [ABSTRACT FROM AUTHOR]

Port based transportations are key elements governing the operation of global maritime logistics. In China, the southeast coastline suffers from typhoons frequently every year and causes many disruptions to port operations and thus economic losses. Quantitative evaluation of coastal ports' economic losses induced by typhoons is quite demanding and can serve as guidance for port operation planning and disaster prevention. In this paper, a framework based on a hybrid wind numerical model for assessing the direct and indirect economic losses of coastline caused by typhoon-induced extreme wind is proposed. The estimation of port disruptions and their durations is realized based on the typhoon hybrid wind numerical model. For the direct economic losses, this model considers losses to the port based on the loading/unloading operations of containers. The disruption days and economic losses of 31 ports along China's coastline are selected. These include ports from the Bohai Rim Port Cluster, Yangtze River Delta Port Cluster, Southeastern Coastal Port Cluster, Pearl River Delta Port Cluster, and Southwestern Coastal Port Cluster. The analysis takes the record of typhoons from 2006 to 2020 for this investigation. The influence of time-varying variables on economic losses, such as economic growth rate, has been considered. For the indirect economic losses, the input-output models of 11 different provinces (or municipalities) are employed to analyze each sector. Features of adopting a climate model in the economic evaluations are discussed. [ABSTRACT FROM AUTHOR]

Mathematical morphing of historical weather data to match the projected climate change scenario is a commonly used method to generate future weather files for building energy simulation. It is known for preserving the local weather characteristics, which is particularly important when designing or analyzing high-performance buildings. Researchers may use one of the available morphing tools. Unfortunately, current tools use future climate data from outdated climate models with lesser accuracy, coarser spatial resolution, and fewer climate scenarios than recent models. In addition, these tools are closed, preventing others from adding corrections, updating, and developing them further. This paper presents an open-source, cross-platform, and state-of-the-art morphing tool that generates future hourly weather data for the whole building performance simulation. The novel tool is applied to a real-case office building in Coimbra, Portugal. In the SSP5-8.5 scenario, simulations show thermal energy needs and electricity use will reduce by 60% in 2050 and 77% in 2080 for heating and will increase by 67% in 2050 and 121% in 2080 for cooling. As a result, the building's global electricity consumption will increase by 24% in 2050 and by 53% in 2080. The study is extended to other locations in Europe, concluding that the total HVAC energy needs will remain constant or even decrease in heating-dominated climates, mainly due to the significant drop in the heating demand in the future. • A new future weather morphing research tool is presented. • It uses the latest data from the CMIP6 experiments (EC-Earth3). • The open-source code promotes research transparency and scientific discovery. • The tool overcomes issues and implementation closedness from other tools. • An application to a real-case office building shows the impact of climate change. [ABSTRACT FROM AUTHOR]

A case study in Thessaloniki provided microclimate data at street canyons with varying orientation and aspect ratio. Seventeen canyons characterized by diverse geometries including four different canyon axis orientations and a range of aspect ratios from 0.6 to 3.3 as well as variations of vegetation and pavement material, were monitored during summer and winter. Measurements are used to calibrate microclimate simulation models, examine results accuracy and produce further data for larger areas or different climatic periods. This paper presents the simulation studies run with ENVI-met software for models of the study area at different scale used to evaluate results accuracy depending on model resolution, influence of the surrounding built environment and of material and geometric details. The large area - low resolution models take into account the urban environment surrounding the study areas which influences thermal and airflow conditions and provide urban microclimate maps. The small area - high resolution models take into account fine geometric elements, individual building heights, tree species, canopies, and pavement materials and provide detailed microclimate data and pedestrian comfort indices which can inform urban space design. [ABSTRACT FROM AUTHOR]

Numerous studies of hygrothermal transfers through walls have highlighted the impacts of this phenomenon on energy consumption and indoor conditions. However, urban scale models that aim to simulate weather conditions and urban heat island effects within cities neglect moisture transfer through walls. The objective of this paper is to propose a method for solving this phenomenon that could be integrated into an urban climate model. This kind of integration requires the numerical schemes to be adapted as well as the spatiotemporal scales. The proposed method is based on an Implicit/Explicit discretisation scheme and a decoupled numerical approach for solving. Numerical stability is ensured by reducing the time step in critical moments, which are detected by five tests. This method is validated by comparison with a reference model (Delphin ®), on several study cases using different wall compositions and climates. A mesh sensitivity analysis is performed and shows that low permeability walls require a finer mesh than walls made of highly hygroscopic or capillary active materials. Furthermore, the more layers there are in the wall, the finer the mesh size needs to be. This numerical method allowed a balance to be found between computational cost and accuracy level in accordance with the expectations for an urban climate model. The future integration of this method into an urban climate model will make it possible to carry out energy/climate simulations, including the hygrothermal behaviour of walls, and thus to study its impact at urban scale. • The numerical scheme is suitable for solving moisture transfer at the urban scale. • The resolution method is based on a decoupled numerical approach. • The fixed timestep is reduced at critical moments which are detected by five tests. • Validation is performed by intermodel comparison for several walls and climates. • The mesh sensitivity analysis is carried out for fifteen walls. [ABSTRACT FROM AUTHOR]

• A brief historical overview of micro and urban climate CFD modelling is provided. • Three major limitations and challenges of the current CFD models is discussed. • Mesh generation should be optimized in respect to many aspects of urban climate. • Connections between cities' bigdata and CFD model inputs is a challenging topic. • Oversimplification can results in a significant departure from the reality. Despite the popularity of the micro/urban climate CFD modeling as a powerful approach to simulate convective exchanges in urban areas, yet its application faces three profound limitations, including (1) computational barriers, (2) data acquisition, and (3) over-simplifications of underlying physics. Computational resources are not qualitatively studied to be allocated to their best of performance in urban climate models. Moreover, bigdata of city components and inhabitants are sometimes inaccessible or difficult to be effectively interpreted to be fed into CFD models. Furthermore, commonly adopted oversimplifications, and misinterpretation of underlying physics of urban climate can substantially render falsified results, no matter if they look otherwise followed by extravagant visual reports. This paper, hence, aims to explore the capabilities and limitations of urban climate CFD modeling. It further scrutinizes the common oversimplifications in the modeling techniques, potentially resulting in CFD capacities to be lost in the translation. The paper describes the extend to which CFD tools can be the favourable options and otherwise, while it underpins the areas in which further research is needed to conform urban climate CFD models as practical design and decision-making tools. It also offers a brief overview in the recent advancements in response to the mentioned challenges. [ABSTRACT FROM AUTHOR]

Under changing climate and burgeoning food production demands, climate-smart agriculture (CSA) practices are the need of the hour. Physically-based crop models have been designed, which require large sets of input variables, besides being timeworn, tedious, and not user-friendly in the current situation. There is a rapid advancement in the modeling approaches in agricultural practices. But, still, there is a shortage of comprehensive information related to the recent technological advancement in the modeling applications toward CSA and its prospects. The paper reviews, critically assess, and discusses the present state-of-art modeling technologies related to the CSA. Special emphasis is placed on highlighting the current research trends in the different crop simulation models and their CSA applications. The assessment and analysis of the combined implications of crop simulation and hydrological models form another vital area of our review. Subsequently, the suitability and future scope of AI in crop simulation models is thoroughly assessed. Finally, the ways to address future challenges for CSA through the models and AI-based approaches are evaluated. We have selected and critically reviewed the literature to deliberate on the various aspects of modeling and their impact on CSA. Overall, we attempted to distinctly bring out the different latest and advanced technologies and their potential roles in modeling exercises towards CSA. The current review also collates and presents new and innovative modeling approaches toward CSA with techno-feasible solutions. The review involves the simulation-optimization framework for coupling hydrological and climate models with crop models that presently have the utmost importance for the CSA. Concurrently it also highlights the overarching significance of different models and suggests refined solutions that enable better crop and environment estimation, field management, and decision-making. [Display omitted] • Reviewed existing and emerging technologies for strengthening agro-based modeling. • Chronological trends in the development of agroecological models are examined. • Review offers an ideal comparative analysis of modeling approaches. • Depicts the application of hydrological models with the crop models in agriculture. • Applications and drawbacks of different modeling approaches are delineated. [ABSTRACT FROM AUTHOR]

As climate change adaptation is increasingly discussed and becoming a mainstream concept, different types of users are asking themselves if and when they should develop an adaptation strategy, often not knowing where to begin. Climate experts, on the other hand, have access to an enormous amount of data that could be useful to users but often do not know how to translate it into something practical. Both users and experts can be linked through two timescales, the system lifespan and climate vulnerability. While the system lifespan relies on the user's estimation of his planning timeframe, the climate vulnerability is estimated from climate model projections and observations. We propose a simple tool to relate user and climate expert knowledge by combining the two timescales. To be reliable, the interconnection implies a dialogue to first identify what sensitive climate variable will impact the system and subsequently the extent of the impact. Climate data can then be used to identify, with the use of a simple graph, how sensitive a system is likely to be and help users position themselves about the urgency of adaptation. The concept has been successfully presented and applied to the tourism industry, notably the ski industry, which is showcased in this paper. [ABSTRACT FROM AUTHOR]

Copyright of Atmosphere -- Ocean (Taylor & Francis Ltd) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The urban and built-up class is the most complex land category interacting with the atmosphere. In assessment of urban heat island intensity (UHII), urban canopy models (UCM) are coupled to mesoscale models to account for the momentum, heat, and turbulence kinetic energy of buildings. Since considering the actual structure of an urban area is not feasible, ideal urban geometries are proposed. Moreover, realistic estimation of mean height of buildings, deviation of building heights, frontal area density, and plan area density of a city is required for urban climate modeling. In this paper, we have selected four neighborhoods in Montreal (Canada) to compare UHII using a typical urban structure that has been proposed by earlier studies with UHII using the urban morphology of Montreal in current research. Simulations with Weather Research and Forecasting model coupled to a multi-layer urban canopy model and building energy model suggest that the UHII of Montreal can be underestimated by up to 1.2 °C (at 9 am) in July 14th, 2005, when urban morphology is not accurate. The energy consumption of buildings is also underestimated by a factor of three. The UCM has a detailed procedure that considers the multi-reflection between urban surfaces to estimate the energy budget of the canopy. Major portion of the absorbed energy in the canopy is from the solar radiation on urban surfaces, which is highly dependent on their sky view factors. We propose a new parameterization for sky view factor of walls and compare it with other parameterizations and approximated field data. Implementing the new parameterization decreases the predicted UHII and the energy consumption by 0.4 °C and 1% in the simulation period, respectively. [ABSTRACT FROM AUTHOR]

The goal of this study is to analyze the impacts of urban heat-island control, via increased surface albedo, on regional and urban meteorology, emissions, and ozone air quality over a range of summer conditions in California. The ozone air-quality impacts of heat-island control are then converted into precursor-emission equivalents. Linked atmospheric models used in this effort show that significant cooling of the urban canopy and boundary layers can be achieved, particularly during the daytime, but that warming can also occur. The air-quality improvements are significant where surface albedo is increased. Downwind of modified areas, the air-quality impacts can be positive or negative depending on meteorological conditions affecting the formation, transport, removal, and mixing of ozone and its precursors. Overall, and accounting for both positive and negative impacts of increased urban albedo, the models show beneficial net effects for California. The results do not account for additional benefits such as reduced cooling energy use and associated reductions in emissions from point sources, or the potential negative impacts on heating energy use in winter. These have been addressed elsewhere. This paper is a summary of results from a study and detailed report (Taha, 2013a) that can be accessed at http://www.energy.ca.gov/2013publications/CEC-500-2013-061/CEC-500-2013-061.pdf . [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Oceanography is the property of Instituto Oceanografico da Universidade de Sao Paulo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Offshore support vessels (OSVs) have important economic and environmental impacts on coastal communities in the Gulf Coast region and these impacts depend on the quantity and spatial distribution of vessel activity, which in turn depends on the type and geographic distribution of exploration and development. In this paper, port activity is quantified according to offshore activity expected to arise from current and new lease sales categorized according to water depth, planning area, and vessel class. Between 53,000 and 119,000 trips per year are expected to be required to support exploration, development and production in the Gulf of Mexico from 2012 to 2017, and approximately half of all trips are expected to emanate from Port Fourchon, Louisiana, with the next largest port, Cameron, Louisiana, contributing an additional 14% of activity. During the 2012–2017 period, the majority of trips are expected to support shallow water production operations. [ABSTRACT FROM AUTHOR]

Copyright of Atmosphere -- Ocean (Taylor & Francis Ltd) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Adverse weather such as fog will reduce drivers' visual recognition ability, and important visual information in the driving will be weakened and even lost, which brings risks to driving safety. This paper sets out to explore causes for the decline in driving visual ability in foggy sections at night based on drivers' visual recognition demand mechanism. The purpose is to study the influence of light source characteristics on visual distance under open traffic conditions. Through an outdoor visual recognition test, data were collected on the visual recognition distances of 12 car drivers to standard gray small targets, under different light source characteristics and meteorological visibility conditions. Results revealed that the latter conditions, along with illuminance and correlated color temperature affected drivers' visual recognition ability in low meteorological visibility sections at night. At the same illuminance and meteorological visibility, a light source with high correlated color temperature could improve visual distance. The multivariate power function model of meteorological visibility, illuminance at the target and driver's visual distance in foggy areas were obtained by fitting. The research methods and conclusions could provide technical reference for improving traffic visual distance at night, and thus help lay a foundation for road construction and traffic control technology in foggy areas. • Visibility, illumination and CCT will all affect drivers' visual distance. • Meteorological visibility is different from traffic safety visual distance. • Visual distance increases with the increase of visibility, illumination and CCT. • Road lighting with high CCT can improve visual distance in foggy sections at night. • Established a model between traffic visual distance, visibility and illumination. [ABSTRACT FROM AUTHOR]

Gaining insight into the response of surface ozone (O 3) formation to its precursors plays an important role in the policy-making of O 3 pollution control. However, the real atmosphere is an open and dissipative system, and its complexity poses a great challenge to the study of nonlinear relations between O 3 and its precursors. At present, model-based methods based on reductionism try to restore the real atmospheric photochemical system, by coupling meteorological model and chemical transport model in temporal and spatial resolution completely. Nevertheless, large inconsistencies between predictions and true values still exist, due to the great uncertainty originated from emission inventory, photochemical reaction mechanism and meteorological factors. Recently, based on field observations, some nonlinear methods have successfully revealed the complex emergent properties (long-term persistence, multi-fractal, etc) in coupling correlation between O 3 and its precursors at different time scales. The emergent properties are closely associated with the intrinsic dynamics of atmospheric photochemical system. Taking them into account when building O 3 prediction model, is helpful to reduce the uncertainty in the results. Nonlinear methods (fractal, chaos, etc) based on holism can give new insights into the nonlinear relations between O 3 and its precursors. Changes of thinking models in methodology are expected to improve the precision of forecasting O 3 concentration. This paper has reviewed the advances of different methods for studying the sensitivity of O 3 formation to its precursors during the past few decades. This review highlights that it is necessary to incorporate the emergent properties obtained by nonlinear methods into the modern models, for assessing O 3 formation under combined air pollution environment more accurately. Moreover, the scaling property of coupling correlation detected in the real observations of O 3 and its precursors could be used to test and improve the simulation performance of modern models. [Display omitted] • The model-based methods for evaluating O 3 -NOx-VOC sensitivity are reviewed. • The progress of nonlinear methods in O 3 photochemical system study are reviewed. • Holism can reveal the macro-response of O 3 to its precursors at different timescales. • The use of reductionism in complex atmospheric system will produce uncertainty. • The combination of holism and reductionism in O 3 sensitivity study is expected. [ABSTRACT FROM AUTHOR]

There is currently a significant gap in knowledge about the emission and deposition of mercury (Hg) from coal-fired power plants in Australia. To fill this gap, we propose a novel method that combines several sources of information (stratigraphic data, hydrodynamic modelling and atmospheric modelling), to identify the sources and fates of Hg emitted from coal-fired power plants. The stratigraphic record from Lake Macquarie (Australia) shows that mercury deposition increased up to 7-times since the 1950s, which is when coal-fired power plants were commissioned in the catchment. The stratigraphy also shows a decrease in Hg deposition with power plant retrofits. Using results from multiple models (statistical modelling, hydrodynamic modelling, particle density modelling and atmospheric emissions modelling), we found that ash dams contribute little Hg to Lake Macquarie. Instead, most of the Hg contamination in the lake is a result of atmospheric emissions from the power plants, and these power plants are also depositing Hg in the urban areas to the west of the lake. Our results demonstrate that the multi-proxy approach demonstrated in the paper can be used to provide clues as to the source of Hg, so that appropriate mitigation strategies and regulatory frameworks can be implemented. [Display omitted] • Multiple lines of evidence used to identify source and fate of Hg. • Hg in aquatic sediments at case study site comes from atmospheric emissions, not ash dams. • Hg mitigation strategies at coal-fired power plants should reduce Hg in atmospheric emissions. [ABSTRACT FROM AUTHOR]

1- Spatial framework for equitable transportation electrification in Oakland, California is presented. 2- Bus routes and census tracts with highest environmental burden are identified. 3- Solar rooftop PV in blocks to power electric buses is explored and quantified. 4- Public and private transportation incentives are discussed in a local context for equitable urban mobility. This paper implements a framework that catalogs and maps opportunities at a city level to support the electrification of the transportation system in an equitable way. Leveraging on the use of hyper-local air quality data, paired with sociodemographic indicators, we evaluate the framework in the City of Oakland, CA under a context of public and private transport areas to prioritize interventions and incentives, while reducing environmental hazard exposure under an equity focus. Following the recognition of racial and socio-economic disparities, the subsequent steps of implementing operational strategies are to identify specific bus routes in the city in which decarbonization would render the highest impact with respect to the distribution of environmental burden, and to identify regions in which private vehicle interventions would render the highest impact with respect to racial and social equity and mobility. [ABSTRACT FROM AUTHOR]

[Display omitted] • Climate services coproduction should result in transdisciplinary knowledge production. • Our coproduction framework goes from awareness raising to co-development. • Participatory processes gradually involve more specialised stake(&knowledge)holders. • Scientists become co-learners and empower users to contribute to shared knowledge. • Successful coproduction integrates both the process and the end product or service. While knowledge coproduction between climate scientists and climate information users has become a common theme in the climate services discourse, the interface between climate service providers and users is an aspect of climate services projects that still calls for more attention. This is due in part to the dominance of the physical sciences in these projects, as well as the prevalence of an instrumental and narrow interpretation of coproduction. Following up on the World Meteorological Organisation's Guidance on Good Practices for Climate Services User Engagement, and incorporating insights from the social and human sciences, we develop a coproduction framework for climate services to help establish a smooth and effective interface between scientists and stakeholders. This framework is intended for research and innovation projects developing climate knowledge and services. The coproduction framework comprises three realms: (i) engagement using various communication channels; (ii) involvement through interviews, workshops and webinars; and (iii) empowerment of stakeholders and scientists through focused relationships. This incremental participatory process involves stakeholders in increasingly profound ways: from a broad stakeholder group identified through awareness-raising campaigns, on to potential users with whom we exchange knowledge, and then to a set of "champion users" who co-develop the service and pioneer its use in decision-making processes. This paper illustrates the application of the coproduction framework in PRIMAVERA, an EU H2020-funded project for designing, running and testing new high-resolution global climate models and evaluating their outputs. While PRIMAVERA provided ground breaking scientific findings that could potentially benefit various stakeholders and support climate risk assessment activities, these results are highly specialised and their added value has yet to be assessed. Accordingly, the user engagement component of the project faced the challenging task of both motivating stakeholders' participation in the project and motivating future users of potential services based on PRIMAVERA data. The trial of the framework in PRIMAVERA provided key lessons for enhancing coproduction in research and innovation projects. We demonstrate how the role of scientists gradually shifted in this coproduction cycle from masters of knowledge(Roux et al., 2017) to co-learners, and how the involvement of the project's interdisciplinary team and their interaction with stakeholders served to move the project towards transdisciplinary knowledge production. [ABSTRACT FROM AUTHOR]

This paper used a simulation-based method to predict the life cycle energy performance of residential buildings in different climate zones of China. After comparison, GISS-E2-R was selected as the general climate model (GCM) for generating future weather data, and 15 locations in different climate zones of China were involved. To predict the life cycle building performance, we created a Python procedure to generate future weather data files for every year from 2020 to 2099 for each location and performed simulations on a parametric simulation tool. According to the simulation results, heating energy is expected to decrease, and cooling energy is expected to increase in the future under the influence of climate change. The more climate change there is, the greater the heating energy and cooling energy of the building change. There are three trends in residential building energy demand. For the locations where heating energy dominates, there will be a significant decrease in total energy demand. For the locations in central China, the increase in cooling energy will offset part of the decrease in heating energy; therefore, there will be a relatively small drop in the total energy demand. For the locations where cooling energy dominates, the total energy demand is expected to rise. Finally, a simple sensitivity analysis was conducted to explore the impact of the passive design of the window-to-wall ratio (WWR), solar heat gain coefficient (SHGC), wall construction and roof construction on the life cycle energy demand under representative concentration pathway (RCP) 8.5 and 4.5. • The applicability of multiple general climate models in China was evaluated. • Weather files from 2020 to 2099 were generated in batch for building simulation. • There are three trends in energy demand of residential building in China. • The life cycle energy demand in varying climate and locations was obtained. • The impact of passive design on life cycle energy demand was studied. [ABSTRACT FROM AUTHOR]

Daytime atmospheric oxidation chemistry is conventionally considered to be driven primarily by the OH radical, formed via photolytic sources. In this paper we examine how, during winter when photolytic processes are slow, chlorine chemistry can have a significant impact on oxidative processes in the urban boundary layer. Photolysis of nitryl chloride (ClNO 2) provides a significant source of chlorine atoms, which enhances the oxidation of volatile organic compounds (VOCs) and the production of atmospheric pollutants. We present a set of observations of ClNO 2 and HONO made at urban locations in central England in December 2014 and February 2016. While direct emissions and in-situ chemical formation of HONO continue throughout the day, ClNO 2 is only formed at night and is usually completely photolyzed by midday. Our data show that, during winter, ClNO 2 often persists through the daylight hours at mixing ratios above 10–20 ppt (on average). In addition, relatively high mixing ratios of daytime HONO (> 65 ppt) provide a strong source of OH radicals throughout the day. The combined effects of ClNO 2 and HONO result in sustained sources of Cl and OH radicals from sunrise to sunset, which form additional ozone, PAN, oxygenated VOCs, and secondary organic aerosol. We show that radical sources such as ClNO 2 and HONO can lead to a surprisingly photoactive urban atmosphere during winter and should therefore be included in atmospheric chemical models. Image 1 • High ClNO 2 concentrations at sunrise cause it to persist until sunset above 10–20 ppt. • Daytime HONO accounts for over 90% of OH formation in the winter urban atmosphere. • Sustained sources of Cl and OH throughout the day enhance the oxidation of VOCs. • Production of O 3 and secondary pollutants increases and continues into the afternoon. [ABSTRACT FROM AUTHOR]

The objective of this paper was to incorporate source-meteorological interaction information from two commonly employed atmospheric dispersion models into the land use regression technique for predicting ambient nitrogen dioxide (NO 2), sulphur dioxide (SO 2), and particulate matter (PM 10). The study was undertaken across two regions in Durban, South Africa, one with a high industrial profile and a nearby harbour, and the other with a primarily commercial and residential profile. Multiple hybrid models were developed by integrating air pollution dispersion modelling predictions for source specific NO 2 , SO 2 , and PM 10 concentrations into LUR models following the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology to characterise exposure, in Durban. Industrial point sources, ship emissions, domestic fuel burning, and vehicle emissions were key emission sources. Standard linear regression was used to develop annual, summer and winter hybrid models to predict air pollutant concentrations. Higher levels of NO 2 and SO 2 were predicted in south Durban as compared to north Durban as these are industrial related pollutants. Slightly higher levels of PM 10 were predicted in north Durban as compared to south Durban and can be attributed to either traffic, bush burning or domestic fuel burning. The hybrid NO 2 models for annual, summer and winter explained 60%, 58% and 63%, respectively, of the variance with traffic, population and harbour being identified as important predictors. The SO 2 models were less robust with lower R2 annual (44%), summer (53%) and winter (46%), in which industrial and traffic variables emerged as important predictors. The R2 for PM 10 models ranged from 80% to 85% with population and urban land use type emerging as predictor variables. Image 1 • Results demonstrated seasonal and spatial variation of air pollution concentrations in Durban. • The incorporation of dispersion model estimates served to further explain air pollution differences that occur over Durban. • The presence of the harbor variable across all NO 2 hybrid models specifies that ship emissions are significant locally. While these results demonstrate the influence of land use variables on air pollution concentrations, our study highlighted the importance of incorporating emission source information in order to capture local variations due to emission patterns. [ABSTRACT FROM AUTHOR]

Ammonia (NH 3) emitted into the atmosphere from agricultural sources may affect nearby sensitive ecosystems due to high dry deposition fluxes on vegetation and soil surfaces, contributing to critical load exceedances. Ammonia fluxes near sources are simulated by either short-range atmospheric models or regional models using large grid cell sizes. However, studies are missing on the comparison of the results simulated by these two types of models. This paper presents the effect of model formalism, input factors, especially grid cell size and wind speed and the choice of deposition threshold on the spatial patterns of NH 3 dry deposition fluxes and deposition threshold exceedances. We used the Eulerian chemistry-transport model CHIMERE and the Gaussian plume model OPS-ST on two study domains characterised by contrasting land use. We showed that the average annual NH 3 dry deposition fluxes over each whole domain are similar for both models. By contrast, NH 3 dry deposition fluxes near sources are higher when simulated with OPS-ST that provides analytical solutions that can be sampled with small grid cell sizes (i.e. , from 25 to 1600 m in this study), than with CHIMERE, which uses large grid cell sizes (i.e. , 800 and 1600 m). As a result, the spatial patterns of deposition threshold exceedance were very different between both models. These patterns depend mainly on grid cell size, the input factors and the choice of the deposition threshold value. We show that the model formalism has a relatively small effect on the results and that the differences result mainly from the spatial resolutions to which they can be applied. Simulation results must therefore be interpreted carefully, taking into account the simulation conditions. Image 1 • Patterns of NH 3 dry deposition fluxes and exceedances depend on model formalism. • Grid cell size affects spatial patterns of dry deposition fluxes and exceedances. • Patterns of NH 3 emissions affect patterns of dry deposition fluxes and exceedances. • Models using large grid cell size miss exceedances in heterogeneous landscapes. • Spatial patterns of exceedances change according to the chosen threshold. [ABSTRACT FROM AUTHOR]

• Global Forecast System (GFS) estimations outperform data from nearest weather station. • Kriging of GFS results performance is second only to kriging of weather stations. • The nature of the GFS grid sets an upper limit to nearest neighbour errors. • GFS is a viable weather data source for thermal heating building simulation. Thermal simulations are a commonly used tool for energy efficiency analysis of buildings. Regional meteorological station networks are a prime source of weather data inputs, required for building thermal simulations. However, local measurements from weather stations are not always available, and when they are, accessing these data can be expensive. This paper analyses a novel use of a numerical weather prediction mesoscale model, the Global Forecast System (GFS) sflux model, as a source of input data for transient thermal simulations. Two interpolation techniques (nearest neighbour and universal kriging) were used to generate local weather datasets from GFS outputs at 27 locations spread over an area of 29,574 km2 in Galicia (northwest Spain). The performance of the GFS estimations was tested against weather measurements obtained from a governmental weather agency. A reference building with the most common features was selected for running thermal simulations in the TRNSYS environment, focusing on heating demand, with estimated weather data as input. The results highlight that GFS-interpolated datasets consistently performs better than using measured data from the nearest weather station. This clearly supports the use of GFS as an appropriate weather source for building simulations, providing good-quality, free and global-scale local weather inputs. [ABSTRACT FROM AUTHOR]

In this paper, a novel architecture in mixed Radio-Frequency (RF)/Free-Space-Optical (FSO) communication systems is presented. RF and FSO links based on Amplify-and-Forward (AF) relay with variable gain, are serially connected. In the proposed method, RF-FSO or FSO-RF paths are used for data transmission. The fading of RF and FSO systems are modeled by Rayleigh and Gamma–Gamma distributions, respectively. It is assumed that the Intensity Modulation with Direct Detection (IM/DD) and the heterodyne detection techniques are used in FSO links. New closed-form expressions for Outage Probability (OP) and Bit Error Rate (BER) are presented in this article. The performance of the proposed system is compared with the single RF-FSO system in different atmospheric disturbance conditions. Numerical results show that the new RF-FSO/FSO-RF system has a significant improvement over the single RF-FSO system. For S N R = 40 dB, the outage probability for our proposed system and RF-FSO are obtained, 2. 3 × 1 0 − 6 , and 3 × 1 0 − 3 , respectively. [ABSTRACT FROM AUTHOR]