Search Results (39)

Atmospheric climate science lacks the capacity to integrate thermodynamics with the gravitational potential of air in a classical quantum theory. To what extent can we identify Carnot’s ideal heat engine cycle in reversible isothermal and isentropic phases between dual temperatures partitioning heat flow with coupled work processes in the atmosphere? Using statistical action mechanics to describe Carnot’s cycle, the maximum rate of work possible can be integrated for the working gases as equal to variations in the absolute Gibbs energy, estimated as sustaining field quanta consistent with Carnot’s definition of heat as caloric. His treatise of 1824 even gave equations expressing work potential as a function of differences in temperature and the logarithm of the change in density and volume. Second, Carnot’s mechanical principle of cooling caused by gas dilation or warming by compression can be applied to tropospheric heat–work cycles in anticyclones and cyclones. Third, the virial theorem of Lagrange and Clausius based on least action predicts a more accurate temperature gradient with altitude near 6.5–6.9 °C per km, requiring that the Gibbs rotational quantum energies of gas molecules exchange reversibly with gravitational potential. This predicts a diminished role for the radiative transfer of energy from the atmosphere to the surface, in contrast to the Trenberth global radiative budget of ≈330 watts per square metre as downwelling radiation. The spectral absorptivity of greenhouse gas for surface radiation into the troposphere enables thermal recycling, sustaining air masses in Lagrangian action. This obviates the current paradigm of cooling with altitude by adiabatic expansion. The virial-action theorem must also control non-reversible heat–work Carnot cycles, with turbulent friction raising the surface temperature. Dissipative surface warming raises the surface pressure by heating, sustaining the weight of the atmosphere to varying altitudes according to latitude and seasonal angles of insolation. New predictions for experimental testing are now emerging from this virial-action hypothesis for climate, linking vortical energy potential with convective and turbulent exchanges of work and heat, proposed as the efficient cause setting the thermal temperature of surface materials. Full article

The Earth Radiation Budget (ERB), a measure of the difference between incoming solar irradiance and outgoing reflected and emitted radiant energy, is a fundamental property of Earth’s climate system. The Libera satellite mission will measure the ERB’s outgoing components to continue the long-term climate data record established by NASA’s Clouds and the Earth’s Radiant Energy System (CERES) mission. In addition to ensuring data continuity, Libera will introduce a novel split-shortwave spectral channel to quantify the partitioning of the outgoing reflected solar component into visible and near-infrared sub-components. However, converting these split-shortwave radiances into the ERB-relevant irradiances requires the development of split-shortwave Angular Distribution Models (ADMs), which demand extensive angular sampling. Here, we show how Rotating Azimuthal Plane Scan (RAPS) parameters—specifically operational cadence and azimuthal scan rate—affect the observational coverage of a defined scene and angular space. Our results show that for a fixed number of azimuthal rotations, a relatively slow azimuthal scan rate of 0.5° per second, combined with more time spent in the RAPS observational mode, provides a more comprehensive sampling of the desired scene and angular space. We also show that operating the Libera instrument in RAPS mode at a cadence between every fifth day and every other day for the first year of space-based operations will provide sufficient scene and angular sampling for the observations to achieve radiance convergence for the scenes that comprise more than half of the expected Libera observations. Obtaining radiance convergence is necessary for accurate ADMs. Full article

A critical comprehension of the impact of snow cover on urban bidirectional reflectance is pivotal for precise assessments of energy budgets, radiative forcing, and urban climate change. This study develops a numerical model that employs the Monte Carlo ray-tracing technique and a snow anisotropic reflectance model (ART) to simulate spectral albedo and bidirectional reflectance, accounting for urban structure and snow anisotropy. Validation using three flat surfaces and MODIS data (snow-free, fresh snow, and melting snow scenarios) revealed minimal errors: the maximum domain-averaged BRDF bias was 0.01% for flat surfaces, and the overall model-MODIS deviation was less than 0.05. The model’s performance confirmed its accuracy in reproducing the reflectance spectrum. A thorough investigation of key factors affecting bidirectional reflectance in snow-covered urban canyons ensued, with snow coverage found to be the dominant influence. Urban coverage, building height, and soot pollutant concentration significantly impact visible and infrared reflectance, while snow grain size has the greatest effect on shortwave infrared. The bidirectional reflectance at backward scattering angles (0.5–0.6) at 645 nm is lower than forward scattering (around 0.8) in the principal plane as snow grain size increases. These findings contribute to a deeper understanding of snow-covered urban canyons’ reflectance characteristics and facilitate the quantification of radiation interactions, cloud-snow discrimination, and satellite-based retrieval of aerosol and snow parameters. Full article

Since about 1997, the realisation that the finite Reynolds number (FRN) effect needs to be carefully taken into account when assessing the behaviour of small-scale statistics came to the fore. The FRN effect can be analysed either in the real domain or in the spectral domain via the scale-by-scale energy budget equation or the transport equation for the energy spectrum. This analysis indicates that the inertial range (IR) is established only when the Taylor microscale Reynolds number $R{e}_{\lambda }$ is infinitely large, thus raising doubts about published power-law exponents at finite values of $R{e}_{\lambda }$, for either the second-order velocity structure function $\overline{{\left(\delta u\right)}^2}$ or the energy spectrum. Here, we focus on the transport equation of $\overline{{\left(\delta u\right)}^2}$ in decaying grid turbulence, which represents a close approximation to homogeneous isotropic turbulence. The effect on the small-scales of the large-scale forcing term associated with the streamwise advection decreases as $R{e}_{\lambda }$ increases and finally disappears when $R{e}_{\lambda }$ is sufficiently large. An approach based on the dual scaling of $\overline{{\left(\delta u\right)}^2}$, i.e., a scaling based on the Kolmogorov scales (when the separation r is small) and another based on the integral scales (when r is large), yields $\overline{{\left(\delta u\right)}^2}\sim r^{2/3}$ when $R{e}_{\lambda }$ is infinitely large. This approach also yields $\overline{{\left(\delta u\right)}^n}\sim r^{n/3}$ when $R{e}_{\lambda }$ is infinitely large. These results seem to be supported by the trend, as $R{e}_{\lambda }$ increases, of available experimental data. Overall, the results for decaying grid turbulence strongly suggest that a tendency towards the predictions of K41 cannot be dismissed at least at Reynolds numbers which are currently beyond the reach of experiments and direct numerical simulations. Full article

Aerosol Optical Depth (AOD) is one of the most important optical properties of aerosols that may affect the energy budgets of our Earth–atmosphere system significantly. Currently, while regional and even global AOD knowledge has been given by various satellites or models, these products are still fraught with uncertainties. In this study, one sophisticated satellite-derived AOD product from MODIS (MODerate resolution Imaging Spectral-radiometer) and two state-of-the-art model-based AOD products from CAMS (Copernicus Atmosphere Monitoring Service) and MERRA-2 (Modern-Era Retrospective analysis for Research and Application Version 2), based on AERONET measurements from 2000–2022, analyzed the spatial distribution characteristics of global AOD. Then using the Mann-Kendall (MK) trend test, the AOD changing trends revealed by the three products were also computed and analyzed. The accuracies of these products and the reliabilities of changing trends derived are discussed and concluded finally. Our study demonstrates that MODIS products have wider applicability, matching best with AERONET globally, while CAMS and MERRA-2 products are only reliable in North America, South America, and Europe. Through comparative analysis of the AOD trends, we found that MODIS, CAMS, and MERRA-2 AOD consistently exhibited decreasing trends in eastern Asia, Europe, and eastern North America. On the other hand, different products showed increasing trends in regions like West Asia, South Asia, and South Africa, suggesting their limited reliability. The reliability assessment shows that 41.45% of the areas have consistent trends among the three products, with approximately 3.2% showing significant and consistent results. When using site trend validation, the proportions of sites with consistent trends are highest at 64.56% and 46.84% respectively. The regions with the best reliability of global trend changes are mainly distributed in North America, Europe, Australia, eastern Asia, and Central South America. This study provides new insights for validating aerosol changes using remote sensing and has the potential to enhance future monitoring and evaluation methods of aerosol products. Full article

Climate change is undeniably one of the most pressing and critical challenges facing humanity in the 21st century. In this context, monitoring the Earth’s Energy Imbalance (EEI) is fundamental in conjunction with greenhouse gases (GHGs) in order to comprehensively understand and address climate change. The French Uvsq-Sat NG pathfinder mission addresses this issue through the implementation of a Six-Unit CubeSat, which has dimensions of 111.3 × 36.6 × 38.8 cm in its unstowed configuration. Uvsq-Sat NG is a satellite mission spearheaded by the Laboratoire Atmosphères, Observations Spatiales (LATMOS), and supported by the International Satellite Program in Research and Education (INSPIRE). The launch of this mission is planned for 2025. One of the Uvsq-Sat NG objectives is to ensure the smooth continuity of the Earth Radiation Budget (ERB) initiated via the Uvsq-Sat and Inspire-Sat satellites. Uvsq-Sat NG seeks to achieve broadband ERB measurements using state-of-the-art yet straightforward technologies. Another goal of the Uvsq-Sat NG mission is to conduct precise and comprehensive monitoring of atmospheric gas concentrations (CO${}_2$ and CH${}_4$) on a global scale and to investigate its correlation with Earth’s Outgoing Longwave Radiation (OLR). Uvsq-Sat NG carries several payloads, including Earth Radiative Sensors (ERSs) for monitoring incoming solar radiation and outgoing terrestrial radiation. A Near-Infrared (NIR) Spectrometer is onboard to assess GHGs’ atmospheric concentrations through observations in the wavelength range of 1200 to 2000 nm. Uvsq-Sat NG also includes a high-definition camera (NanoCam) designed to capture images of the Earth in the visible range. The NanoCam will facilitate data post-processing acquired via the spectrometer by ensuring accurate geolocation of the observed scenes. It will also offer the capability of observing the Earth’s limb, thus providing the opportunity to roughly estimate the vertical temperature profile of the atmosphere. We present here the scientific objectives of the Uvsq-Sat NG mission, along with a comprehensive overview of the CubeSat platform’s concepts and payload properties as well as the mission’s current status. Furthermore, we also describe a method for the retrieval of atmospheric gas columns (CO${}_2$, CH${}_4$, O${}_2$, H${}_2$O) from the Uvsq-Sat NG NIR Spectrometer data. The retrieval is based on spectra simulated for a range of environmental conditions (surface pressure, surface reflectance, vertical temperature profile, mixing ratios of primary gases, water vapor, other trace gases, cloud and aerosol optical depth distributions) as well as spectrometer characteristics (Signal-to-Noise Ratio (SNR) and spectral resolution from 1 to 6 nm). Full article

We assess the viability of deploying commercially available multispectral and thermal imagers designed for integration on small uncrewed aerial systems (sUASs, <25 kg) on a mid-size Group-3-classification UAS (weight: 25–600 kg, maximum altitude: 5486 m MSL, maximum speed: 128 m/s) for the purpose of collecting a higher spatial resolution dataset that can be used for evaluating the surface energy budget and effects of surface heterogeneity on atmospheric processes than those datasets traditionally collected by instrumentation deployed on satellites and eddy covariance towers. A MicaSense Altum multispectral imager was deployed on two very similar mid-sized UASs operated by the Atmospheric Radiation Measurement (ARM) Aviation Facility. This paper evaluates the effects of flight on imaging systems mounted on UASs flying at higher altitudes and faster speeds for extended durations. We assess optimal calibration methods, acquisition rates, and flight plans for maximizing land surface area measurements. We developed, in-house, an automated workflow to correct the raw image frames and produce final data products, which we assess against known spectral ground targets and independent sources. We intend this manuscript to be used as a reference for collecting similar datasets in the future and for the datasets described within this manuscript to be used as launching points for future research. Full article

This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar assignment, transmit power, dwell time, and signal effective bandwidth allocation of multiple airborne radars, aiming to enhance the MTT performance under the constraints of the tolerable threshold of interference energy, platform kinematic limitations, and given illumination resource budgets. The closed-form expression for the Bayesian Cramér–Rao lower bound (BCRLB) under the consideration of spectral coexistence is calculated and adopted as the optimization criterion of the CTPRA strategy. It is shown that the formulated CTPRA problem is a mixed-integer programming, non-linear, non-convex optimization model owing to its highly coupled Boolean and continuous parameters. By incorporating semi-definite programming (SDP), particle swarm optimization (PSO), and the cyclic minimization technique, an iterative four-stage solution methodology is proposed to tackle the formulated optimization problem efficiently. The numerical results validate the effectiveness and the MTT performance improvement of the proposed CTPRA strategy in comparison with other benchmarks. Full article

Rate-splitting multiple access (RSMA) has recently received attention due to its benefits in both spectral and energy efficiencies. In this paper, we propose a hybrid RSMA-time-division multiple access (TDMA) scheme for a mobile edge computing (MEC) system, where two edge users need to offload their task data to a MEC server. In the proposed scheme, the offloading time is divided into two time phases. Specifically, we design a cognitive radio (CR)-inspired RSMA scheme, in which two users, namely the primary user and secondary user, offload their task data to the MEC server in the first time phase, while only a single user can offload task data in the second time phase. With the aim of minimizing the overall offloading delay, we formulate the offloading delay minimization problem subject to the transmit power and total energy constraints. We transform the original fractional programming non-convex problem to a convex one by using the Dinkelbach transform and propose Dinkelbach and Newton iterative algorithms to determine the optimal transmit power allocation. Specifically, we establish the optimization criteria for the three offloading schemes and derive the corresponding closed-form expressions for the optimal power allocation. Compared to the existing offloading schemes, the numerical results show that the proposed hybrid RSMA-TDMA scheme in scenarios where having a limited energy budget is superior in offloading delay compared to other offloading schemes and the sum offloading delay tends to a constant with the increase in the energy budget. Full article

Surface downward longwave radiation (SDLR) is significant with regard to surface energy budgets and climate research. The uncertainty of cloud base height (CBH) retrieval by remote sensing induces the vast majority of SDLR estimation errors under cloudy conditions; reliable CBH observation and estimation are crucial for determining the cloud radiative effect. This study presents a CBH retrieval methodology built from 10 thermal spectral data from Himawari-8 (H-8) observations, utilizing the random forest (RF) algorithm to fully account for each band’s contribution to CBH. The algorithm utilizes only infrared band data, making it possible to obtain CBH 24 h a day. Considering some factors that can significantly affect the CBH estimation, RF models are trained for different clouds using inputs from multiple H-8 channels together with geolocation information to target CBH derived from CloudSat/CALIPSO combined measurements. The validation results reveal that the new methodology performs well, with a root-mean-square error (RMSE) of only 1.17 km for all clouds. To evaluate the effect of CBH on SDLR estimation, an all-sky SDLR estimation algorithm based on previous CBH predictions is proposed. The new SDLR product not only has a resolution that is noticeably higher than that of benchmark products of the SDLR, such as the Clouds and the Earth’s Radiant Energy System (CERES) and the next-generation reanalysis (ERA5) of the European Centre for Medium-Range Weather Forecasts (ECMWF), but it also has greater accuracy, with an RMSE of 21.8 W m⁻² for hourly surface downward longwave irradiance (SDLI). Full article

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RIS) are the promising emerging technologies proposed for the 6th-Generation (6G) network to improve the capacity, reliability, and coverage of wireless communications. By integrating the UAV with RIS (RIS-UAV), the three-dimensional (3D) mobility of the UAV can be leveraged to establish strong line-of-sight links with the ground nodes, while the RIS intelligently reflect the signals toward the desired directions. However, the existing literature on RIS-UAV systems mainly assumes the use of passive elements, which suffers from the double path-loss problem. The use of active elements in RIS, which could improve the reflected link performance at the cost of increased energy consumption, has not been considered for the RIS-UAV system. Further, the energy efficiency of a RIS-UAV with active elements remains as an open direction because there is a need to investigate the feasibility of either an active or hybrid RIS-UAV implementation. This paper proposes active and hybrid RIS-UAVs and investigates the energy efficiencies of active and hybrid RIS-UAVs in comparison with existing passive RIS-UAVs and conventional UAV relays. The numerical results reveal that the proposed hybrid and active RIS-UAV relaying schemes can provide up to 14 times and 26 times improvement as compared to the passive RIS-UAV, respectively. As opposed to the active RIS-UAV that requires a larger power budget, half-duplex UAV relays that have a lower spectral efficiency, and full-duplex UAV relays that suffer from self-interference, the hybrid RIS-UAV emerges as a promising option to assist the ground communication system. Full article

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data. Full article

Intelligent reflecting surfaces (IRS) and power-domain non-orthogonal multiple access (PD-NOMA) have recently gained significant attention for enhancing the performance of next-generation wireless communications networks. More specifically, IRS can smartly reconfigure the incident signal of the source towards the destination node, extending the wireless coverage and improving the channel capacity without consuming additional energy. On the other side, PD-NOMA can enhance the number of devices in the network without using extra spectrum resources. This paper proposes a new optimization framework for IRS-enhanced NOMA communications where multiple drones transmit data to the ground Internet of Things (IoT) devices under successive interference cancellation errors. In particular, the power budget of each drone, PD-NOMA power allocation of IoT devices, and the phase shift matrix of IRS are simultaneously optimized to enhance the total spectral efficiency of the system. Given the system model and optimization setup, the formulated problem is coupled with three variables, making it very complex and non-convex. Thus, this work first transforms and decouples the problem into subproblems and then obtains the efficient solution in two steps. In the first step, the closed-form solutions for the power budget and PD-NOMA power allocation subproblem at each drone are obtained through Karush–Kuhn–Tucker (KKT) conditions. In the second step, the subproblem of efficient phase shift design for each IRS is solved using successive convex approximation and DC programming. Numerical results demonstrate the performance of the proposed optimization scheme in comparison to the benchmark schemes. Full article

Blazars are observed to emit non-thermal radiation across the entire electromagnetic spectrum from the radio to the very-high-energy $\gamma$-ray region. The broadband radiation measured from a blazar is dominated by emission from a relativistic plasma jet which is assumed to be powered by a spinning supermassive black hole situated in the central region of the host galaxy. The formation of jets, their mode of energy transport, actual power budget, and connection with the central black hole are among the most fundamental open problems in blazar research. However, the observed broadband spectral energy distribution from blazars is generally explained by a simple one-zone leptonic emission model. The model parameters place constraints on the contributions from the magnetic field, radiation field, and kinetic power of particles to the emission region in the jet. This in turn constrains the minimum power transported by the jet from the central engine. In this work, we explore the potential of machine learning frameworks including linear regression, support vector machine, adaptive boosting, bagging, gradient boosting, and random forests for the estimation of the mass of the supermassive black hole at the center of the host galaxy of blazars using the best-fit emission model parameters derived from the broadband spectral energy distribution modeling in the literature. Our study suggests that the support vector machine, adaptive boosting, bagging, and random forest algorithms can predict black hole masses with reasonably good accuracy. Full article

In recent years, scale-by-scale energy transport in wall turbulence has been intensively studied, and the complex spatial and interscale transfer of turbulent energy has been investigated. As the enhancement of heat transfer is one of the most important aspects of turbulence from an engineering perspective, it is also important to study how turbulent heat fluxes are transported in space and in scale by nonlinear multi-scale interactions in wall turbulence as well as turbulent energy. In the present study, the spectral transport budgets of turbulent heat fluxes are investigated based on direct numerical simulation data of a turbulent plane Couette flow with a passive scalar heat transfer. The transport budgets of spanwise spectra of temperature fluctuation and velocity-temperature correlations are investigated in detail in comparison to those of the corresponding Reynolds stress spectra. The similarity and difference between those scale-by-scale transports are discussed, with a particular focus on the roles of interscale transport and spatial turbulent diffusion. As a result, it is found that the spectral transport of the temperature-related statistics is quite similar to those of the Reynolds stresses, and in particular, the inverse interscale transfer is commonly observed throughout the channel in both transport of the Reynolds shear stress and wall-normal turbulent heat flux. Full article