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This work addresses the problem of semantic scene understanding under fog. Although marked progress has been made in semantic scene understanding, it is mainly concentrated on clear-weather scenes. Extending semantic segmentation methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both labeled synthetic foggy data and unlabeled real foggy data. The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog). CMAda is extensible to other adverse conditions and provides a new paradigm for learning with synthetic data and unlabeled real data. In addition, we present four other main stand-alone contributions: (1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; (2) a new fog density estimator; (3) a novel fog densification method for real foggy scenes without known depth; and (4) the Foggy Zurich dataset comprising 3808 real foggy images, with pixel-level semantic annotations for 40 images with dense fog. Our experiments show that (1) our fog simulation and fog density estimator outperform their state-of-the-art counterparts with respect to the task of semantic foggy scene understanding (SFSU); (2) CMAda improves the performance of state-of-the-art models for SFSU significantly, benefiting both from our synthetic and real foggy data. The foggy datasets and code are publicly available.

This study addresses the ongoing challenge for learning-based methods to achieve accurate object detection in foggy conditions. In response to the scarcity of foggy traffic image datasets, we propose a foggy weather simulation algorithm based on monocular depth estimation. The algorithm involves a multi-step process: a self-supervised monocular depth estimation network generates a relative depth map and then applies dense geometric constraints for scale recovery to derive an absolute depth map. Subsequently, the visibility of the simulated image is defined to generate a transmittance map. The dark channel map is then used to distinguish sky regions and estimate atmospheric light values. Finally, the atmospheric scattering model is used to generate fog simulation images under specified visibility conditions. Experimental results show that more than 90% of fog images have AuthESI values of less than 2, which indicates that their non-structural similarity (NSS) characteristics are very close to those of natural fog. The proposed fog simulation method is able to convert clear images in natural environments, providing a solution to the problem of lack of foggy image datasets and incomplete visibility data.

Intermittent turbulence in general refers to a brief turbulent burst, which is the main mechanism of scalar diffusion in the stable boundary layer (SBL). The impacts of intermittent turbulence on a radiation fog were investigated based on the measurements at a 255-m meteorological tower and the Weather Research and Forecasting model. Observational results showed that intermittent turbulence inhibited fog formation. As intermittent turbulence weakened, radiation fog formed in the SBL. During the fog development and maturity stage, intermittent turbulence at the high levels promoted the vertical development of fog. However, the downward propagation of intermittent turbulence did not reach the surface. Low intermittent strength of turbulence and weak turbulent mixing at 40 m indicated that there was a barrier layer hindering the transmission up and down. The barrier effect led to explosively reinforced fog at the surface. Intermittent turbulence is not considered in the original Yonsei University (YSU) scheme, leading to the underestimation of the simulated turbulent diffusion coefficient ( k m ). The average ratio of observed k m to simulated k m was 4.30 during the fog episode. Thus, three sensitivity experiments – a double k m , a quadruple k m from the original YSU scheme, and an updated YSU scheme – were designed to study the contributions of the increase in k m to fog evolution. The results showed that the increase in k m can improve the simulation of fog-top height and correct the onset timing of fog. Thus, an improvement in the original YSU scheme is necessary for a reasonable description of intermittent turbulence.

A common approach used for multi-source observation data blending is the fusion method. This study assesses the applicability of the first-generation fusion sea surface temperature (SST) product of the China Meteorological Administration (CMA) in the Yellow–Bohai Sea region for numerical weather predictions. First, daily and 6 h fusion SST measurements are compared with data derived from 21 buoy sites for 2019 to 2020. The error analysis results show that the root-mean-square error (RMSE) of the daily SST ranges from 0.64 to 1.36 °C (overall RMSE of 0.996 °C). The RMSE of the 6 h SST varies from 0.64 to 1.73 °C (overall RMSE of 1.06 °C). According to the simulation result, the SST difference could affect the value and location distribution of liquid water content in the fog area. A lower SST is favorable for increasing the liquid water content, which fits the mechanisms of advection fog formation by warm air flowing over colder water.

The sensitivity of fog dissipation to the environmental changes in radiation, liquid-water lapse rate, free tropospheric temperature and relative humidity was studied through numerical experiments designed based on the 2007-Paris Fog observations. In particular, we examine how much of the stratocumulus-thinning mechanism can be extended to the near-surface clouds or fog. When the free troposphere is warmed relative to the reference case, fog-top descends and become denser. Reducing the longwave radiative cooling via a more emissive free troposphere favors thickening the physical depth of fog, unlike cloud-thinning in a stratocumulus cloud. Drying the free troposphere allows fog thinning and promotes fog dissipation while sustaining the entrainment rate. The numerical simulation results suggest that the contribution of entrainment drying is more effective than the contribution of entrainment warming yielding the reduction in liquid water path tendency and promoting the onset of fog depletion relative to the reference case studied here. These sensitivity experiments indicate that the fog lifting mechanism can enhance the effect of the inward mixing at the fog top. However, to promote fog dissipation, an inward mixing mechanism only cannot facilitate removing humidity in the fog layer unless a sufficient entrainment rate is simultaneously sustained.

Current IoT systems are structured around Edge, Fog, and Cloud layers to manage data and resource constraints more effectively. Although several studies have examined IoT simulators from a functional angle, few have combined technical comparisons with experimental validation under realistic conditions. This lack of integration limits the practical value of prior results and complicates tool selection for distributed architectures. This work introduces a selection and evaluation methodology for simulators that explicitly represent the Edge–Fog–Cloud continuum. Thirteen open-source tools are analyzed based on functional, technical, and operational features. Among them, iFogSim2 and FogNetSim++ are selected for a detailed experimental comparison on their support of mobility, resource allocation, and energy modeling across all layers. A shared hybrid IoT scenario is simulated using eight key metrics: execution time, application loop delay, CPU processing time per tuple, energy consumption, cloud execution cost, network usage, scalability, and robustness. The analysis reveals distinct modeling strategies: FogNetSim++ reduces loop latency by 48% and maintains stable performance at scale but shows high data loss under overload. In contrast, iFogSim2 consumes up to 80% less energy and preserves message continuity in stressful conditions, albeit with longer execution times. These outcomes reflect the trade-offs between modeling granularity, performance stability, and system resilience.

In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error derror of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain.

This review presents some of the latest achievements in sea fog research, including fog climatology, fog structure in the marine atmospheric boundary layer, and numerical simulations and forecasting of fog. With the development of atmospheric obser- vational techniques and equipments, new facts about sea fog are revealed. The mechanisms involved in the formation, development and dissipation of sea fog are further explored with the help of advanced atmospheric models.

Processing data at the cloud, generated from millions of IoT devices is not beneficial in terms of energy and communication cost. Many researchers have proposed several techniques to utilize cloud resources efficiently; however, the processing of massive data at the cloud not only consumes network resources such as network bandwidth but also affect other user services. To support delay‐sensitive applications, the concept of fog computing is introduced. Fog computing extends the computing, storage, and network services closer to the source. The existing fog simulators are mostly focused on sensors and broker nodes. However, the network, context‐aware, and mobility features are properly explored in the simulators.In this chapter, we provide a step‐by‐step guide to configure and explore the FogNetSim++ environment. The FogNetSim++ covers the network aspects such as delay, packet error rate, transmission range, handover, scheduling, and heterogeneous mobile devices. The chapters are organized as follows. The concept of fog computing is discussed in Section 11.1. Section 11.3 covers the concept of Modeling and Simulation. The FogNetSim++ architecture is covered in Section 11.4. Section 11.5 covers the Installation guide and sample test simulation. Finally, the conclusion is presented at the end.

This chapter presents a scalable software‐defined orchestration architecture to intelligently compose and orchestrate thousands of heterogeneous Fog appliances (devices, servers). Specifically, it provides a resource filtering‐based resource assignment mechanism to optimize the resource utilization and fair resource sharing among multitenant Internet of things (IoT) applications. The chapter also presents a component selection and placement mechanism for containerized IoT microservices to minimize the latency by harnessing the network uncertainty and security while considering different applications’ requirement and capabilities. It describes a fog simulation scheme to simulate the aforementioned procedure by modeling the entities, their attributes, and actions. The chapter also provides the results of practical experiences on the orchestration and simulation. It outlines numerous difficulties and challenges to develop an orchestration framework across all layers within the Fog resource stack and describes a prototype orchestration system that makes use of some of the most promising mechanisms to tackle these challenges.