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Aiming at the large-scale application of metasurface in the field of radar stealth, we present a hybrid resonance-based and dispersion substrate integrated checkerboard metasurface (CMS) for reducing the radar cross section (RCS) of flat metallic targets. Considering the frequency-dependent characteristics of such a dispersion material, a pair of single and dual resonant artificial magnetic conductor meta-atoms with the modified "crusades-like" cell topologies is employed to maximize the operating bandwidth; besides, a comprehensive and thorough investigation on the resonance mechanism is conducted in this paper to provide an intuitive physical insight of meta-atoms' reflection responses. By comparing the predicted results with simulations, the quasi-periodic effect is introduced to explain the frequency shift of 10 dB RCS reduction bandwidth. In the implementation procedure, a prototype of the designed RCS reducer with a total dimension of 180 × 180 mm2 is fabricated and measured, the 10 dB RCS reduction bandwidth of theoretical simulation and experimental measurement are basically consistent, and the performance improvement of 8 dB RCS reduction in the experimental results can be attributed to the dispersion effects of the dielectric substrate. With a better figure of merit, our efforts may serve as a useful exemplar for the economical CMS architecture in radar evasive applications. [ABSTRACT FROM AUTHOR]

Range-Doppler images represent one of the most informative radar data forms, providing range and frequency information. This study explores the performance of machine learning and deep learning techniques in classifying human activities behind walls using Range-Doppler images. Therefore, we input the HOG features of Range-Doppler images into various machine-learning approaches. Although the HOG feature enhances the performance of machine learning methods, we observe the superior performance of Convolutional Neural Network (CNN) architectures in a more complex scenario in which the number of target activity classes is higher. To obtain sufficient data for the CNN architecture, we combine the Uniform Linear Array (ULA) and stepped-frequency continuous-wave (SFCW) structures, enabling the acquisition of multi-channel data. The experimental results demonstrate both the improvement of the machine learning accuracy from 95.33% to 98.67% through the HOG + Range-Doppler approach and an approximately 6% enhancement in CNN performance achieved through the SFCW-ULA combination. [ABSTRACT FROM AUTHOR]

Micro‐Doppler effect is a vital feature of a target that reflects its oscillatory motions apart from bulk motion and provides an important evidence for target recognition with radars. However, establishing the micro‐Doppler database poses a great challenge, since plenty of experiments are required to get the micro‐Doppler signatures of different targets for the purpose of analyses and interpretations with radars, which are dramatically limited by high cost and time‐consuming. Aiming to overcome these limits, a low‐cost and powerful simulation platform of the micro‐Doppler effects is proposed based on time‐domain digital coding metasurface (TDCM). Owing to the outstanding capabilities of TDCM in generating and manipulating nonlinear harmonics during wave‐matter interactions, it enables to supply rich and high‐precision electromagnetic signals with multiple micro‐Doppler frequencies to describe the micro‐motions of different objects, which are especially favored for the training of artificial intelligence algorithms in automatic target recognition and benefit a host of applications like imaging and biosensing. [ABSTRACT FROM AUTHOR]

The morphology and motion of auroras have been widely studied due to their indications on magnetospheric processes. Here, we report a new kind of "auroral curls," which have wavelengths in the mesoscale (∼100 km) and propagate azimuthally. Utilizing data from the Chinese Antarctic Zhongshan Station (the all‐sky imager and the high‐frequency radar), the Active Magnetosphere and Planetary Electrodynamics Response Experiment and the Defense Meteorological Satellite Program, we analyze an event occurred on 23 April 2019. We find these curls are fine structures in the poleward boundary of multiple arcs. Corresponding field‐aligned currents manifest as a series of longitudinally arranged pairs, while ionospheric flow velocities nearby oscillate with periods in the Pc 5 band. Observational evidence suggests these curls are connected with ultra‐low frequency (ULF) waves, which opens the possibility of using auroras to globally image ULF waves. Plain Language Summary: Auroras caused by precipitation of magnetospheric particles contain information about physical processes happened in the magnetosphere. In this letter, we report a new kind of auroral dynamic forms observed in Antarctica. These structures present both spatial and temporal periodic characteristics, which have similar scales with those of magnetospheric ultra‐low frequency (ULF) waves. We propose these auroral forms are connected with ULF waves, which provides a potential method to globally image ULF waves by analyzing properties of these auroras. Key Points: Azimuthally propagating "auroral curls" with mesoscale wavelengths were observed in AntarcticaThese curls are fine structures in the poleward boundary of multiple arcs formed by longitudinal‐arranged field‐aligned current pairsIonospheric flow velocities nearby oscillate with periods in the Pc 5 band, indicating connections with ultra‐low frequency waves [ABSTRACT FROM AUTHOR]

To improve short‐term severe weather forecasts through assimilation of polarimetric radar data (PRD), the use of accurate and efficient forward operators for polarimetric radar variables is required. In this study, a new melting model is proposed to estimate the mixing ratio and number concentration of melting hydrometeor species and incorporated in a set of parameterized polarimetric radar forward operators. The new melting model depends only on the mixing ratio and number concentration of rain and ice species and is characterized by its independence from ambient temperature and its simplicity and ease of linearization. To assess the impact of this newly proposed melting model on the simulated polarimetric radar variables, a real mesoscale convective system is simulated using three double‐moment microphysics schemes. Compared with the output of the original implementation of the parameterized forward operators (PFO_Old) that rely on an "old" melting model which only estimates the mixing ratio of the melting species, the updated implementation with the new melting model (PFO_New) that estimates both the mixing ratio and number concentration of melting species eliminates the very large mass/volume‐weighted mean diameter (Dm) at the bottom of the melting layer and produces more reasonable melting layer signatures for all three double‐moment microphysics schemes that more closely match the corresponding radar observations. This suggests that the new melting model has more reasonable implicit estimates of mixing ratios and number concentrations of melting hydrometeor species than the "old" melting model. Plain Language Summary: Weather radars can provide information about observed precipitation that can be used to improve weather forecast models. Making use of this information requires linking the radar observations with the variables these models predict in an efficient and accurate way. One of the most pronounced signatures in weather radar observations occurs as precipitation melts from ice into rain. However, current operational forecast models do not explicitly predict melting ice as a separate category of precipitation. Therefore, existing methods for comparing radar observations to forecast models use an empirical melting model to estimate the mass of melting ice and assume melting ice has the same concentration as the model‐predicted dry ice. These assumptions result in a simulated radar melting layer signature that is unrealistically thick and low. In this study, a new melting model is proposed for estimating both the mass and concentration of melting ice, and its effects on simulated radar variables are evaluated for a severe thunderstorm case. For all of the microphysics schemes investigated, the newly proposed melting model results in improved simulations of the size of melting ice particles and simulated radar variables that are closer to those observed. Key Points: A new melting model is proposed and introduced into forward operators for simulation of polarimetric radar dataMore reasonable polarimetric signatures of the melting layer are simulated using different double‐moment microphysics schemesThese polarimetric forward operators have potential to be used in data assimilation for improving high‐resolution severe weather forecasts [ABSTRACT FROM AUTHOR]

With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model's predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions. [ABSTRACT FROM AUTHOR]

A long-standing problem in meteor science has been the persistent presence of bias in the measured value of atmospheric scale heights obtained from radio meteor echoes. A common practice of fitting a linear function for bias correction follows the assumption that the systematic bias is devoid of seasonal asymmetry. This would be true if the mass and the velocity distribution of meteoroids remain invariant, both spatially and temporally. But so far no such convincing evidence has been published. On the contrary, fundamental arguments suggest that a universal mass function of radio meteoroids is counterintuitive. This parameter cannot remain invariant due to the intrinsic variability in the meteor response function resulting from the Earth's motion on the plane of ecliptic. In this paper, we show that an inverse relation exists between the width of meteor height distribution, expressed in unit of atmospheric scale height, and the exponent of the mass function. The overall mean of this exponent for the Sodankylä radar is |$1.91 \pm 0.02$|⁠ , modulated by a seasonal variation from the mean of the order of |$\sim \pm 0.1$|⁠. The stated inverse relation is applied to correct for the effect of mass distribution on the height distribution. Allowing for variable mass correction effectively removes the non-linear bias in the measured scale heights in the meteor ionization region. [ABSTRACT FROM AUTHOR]

Since 2021, a new surge in discrete aurora detections at Mars has been observed by the Emirates Mars Ultraviolet Spectrometer (EMUS) onboard the Emirates Mars Mission (EMM) Hope Orbiter as EMUS started to regularly obtain synoptic auroral images with a high sensitivity. Here we report on a fortuitous conjunction between EMM and Mars Express (MEX) using far ultraviolet (FUV) imaging of discrete aurora by EMM EMUS, in situ measurements of suprathermal electrons by the MEX Analyzer of Space Plasma and Energetic Atoms Electron Spectrometer (ELS), and topside radar sounding of the nightside ionosphere by the MEX Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS). In this event, EMM EMUS imaged a clear discrete aurora signature around moderately strong crustal magnetic fields on the nightside near the dusk terminator, 11 min before which MEX MARSIS measured a prominent local enhancement of the peak electron density in the nightside ionosphere and MEX ELS observed an in situ enhancement of suprathermal electrons at the corresponding location. A remarkable geographic agreement is found between the enhancements of the aurora, ionosphere, and suprathermal electrons, suggesting that the enhanced ionization and auroral emission are caused concurrently by precipitating suprathermal electrons. Subsequent images indicate that the discrete aurora slightly changed its shape in 15 min and mostly disappeared in a few hours. The MEX MARSIS measurements of the auroral ionosphere display overlapping ionospheric and surface echoes indicative of horizontal gradients of the peak electron density. Analysis of the overlapping echoes implies that the auroral ionosphere and electron precipitation could be highly structured with horizontal spatial scales on the order of several tens of km. MEX MARSIS also observed a non-auroral ionospheric enhancement with a wider spatial extent than the local auroral enhancement, suggesting alternative sources of the enhanced nightside ionosphere such as plasma transport. The comparison between the ionospheric structures measured by MEX MARSIS, suprathermal electron flux measured by MEX ELS, and discrete auroral emission imaged by EMM EMUS underscores the complexity of the auroral and non-auroral nightside ionospheres. This motivates further investigations of their sources, transport, and connections to the magnetotail dynamics of Mars. [ABSTRACT FROM AUTHOR]

The authors present the design, fabrication and testing of a W‐band monopulse antenna. This antenna consists of three main building blocks: an array with 48 × 48 slots, a tapered corporate‐feed network and a comparator. The comparator has four input ports to generate sum and difference patterns in orthogonal cardinal planes for monopulse operation. A novel octuple excitation is adopted for the corporate network to achieve a side lobe level lower than −20 dB for the sum patterns and simultaneously reduce drastically the manufacturing complexity. Diffusion bonding technology is used for fabrication. Twenty‐four etched copper sheets with a thickness of 0.2 mm are stacked to realise the prototype. The total size of the antenna is 40.76×40.76×1.51λ03 $40.76\times 40.76\times 1.51{\lambda }_{0}^{3}$, with λ0 being the wavelength at the centre frequency (94 GHz). The antenna presents an isolation better than 12 dB among the input ports in the 84–100 GHz band. The realised measured gain is 40.7 dBi at 94 GHz. The measured 3‐dB gain bandwidth is 17.4%. The difference patterns at 94.0 GHz present null depths of −24.0, −18.0, −17.5, and −17.4 dB, in the E‐, H‐, 45°‐, and 135°‐planes, respectively. [ABSTRACT FROM AUTHOR]

This paper summarizes over 60 years of radar system development at MIT Lincoln Laboratory, from early research on satellite tracking and planetary radar to the present ability to perform the centimeter-resolution imaging of resident space objects and future plans to extend this capability to geosynchronous range. [ABSTRACT FROM AUTHOR]

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-μRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%. [ABSTRACT FROM AUTHOR]

Within the context of a smart home, detecting the operating status of appliances in the environment plays a pivotal role, estimating power consumption, issuing overuse reminders, and identifying faults. The traditional contact-based approaches require equipment updates such as incorporating smart sockets or high-precision electric meters. Non-constant approaches involve the use of technologies like laser and Ultra-Wideband (UWB) radar. The former can only monitor one appliance at a time, and the latter is unable to detect appliances with extremely tiny vibrations and tends to be susceptible to interference from human activities. To address these challenges, we introduce HomeOSD, an advanced appliance status-detection system that uses mmWave radar. This innovative solution simultaneously tracks multiple appliances without human activity interference by measuring their extremely tiny vibrations. To reduce interference from other moving objects, like people, we introduce a Vibration-Intensity Metric based on periodic signal characteristics. We present the Adaptive Weighted Minimum Distance Classifier (AWMDC) to counteract appliance vibration fluctuations. Finally, we develop a system using a common mmWave radar and carry out real-world experiments to evaluate HomeOSD's performance. The detection accuracy is 95.58%, and the promising results demonstrate the feasibility and reliability of our proposed system. [ABSTRACT FROM AUTHOR]

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR's capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material's complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced. [ABSTRACT FROM AUTHOR]

Short sprints are predominantly assessed using timing gates and analyzed through parameters of the mono-exponential equation, including estimated maximal sprinting speed ( M S S ) and relative acceleration ( T A U ), derived maximum acceleration (MAC), and relative propulsive maximal power ( P M A X ), further referred to as the No Correction model. However, the frequently recommended flying start technique introduces a bias during parameter estimation. To correct this, two additional models (Estimated TC and Estimated FD) were proposed. To estimate model precision and sensitivity to detect the change, 31 basketball players executed multiple 30 m sprints. Athlete performance was simultaneously measured by a laser gun and timing gates positioned at 5, 10, 20, and 30 m. Short sprint parameters were estimated using a laser gun, representing the criterion measure, and five different timing gate models, representing the practical measures. Only the MSS parameter demonstrated a high agreement between the laser gun and timing gate models, using the percent mean absolute difference ( % M A D ) estimator ( % M A D < 10%). The MSS parameter also showed the highest sensitivity, using the minimum detectable change estimator ( % M D C 95 ), with an estimated % M D C 95 < 17%. Interestingly, sensitivity was the highest for the No Correction model ( % M D C 95 < 7%). All other parameters and models demonstrated an unsatisfying level of sensitivity. Thus, sports practitioners should be cautious when using timing gates to estimate maximum acceleration indices and changes in their respective levels. [ABSTRACT FROM AUTHOR]

The article presents the analysis, design, and low-cost implementation of application-specific AD converters for M-sequence-based UWB applications to minimize and integrate the whole UWB sensor system. Therefore, the main goal of this article is to integrate the AD converter's own design with the UWB analog part into the system-in-package (SiP) or directly into the system-on-a-chip (SoC), which cannot be implemented with commercial AD converters, or which would be disproportionately expensive. Based on the current and used UWB sensor system requirements, to achieve the maximum possible bandwidth in the proposed semiconductor technology, a parallel converter structure is designed and presented in this article. Moreover, 5-bit and 4-bit parallel flash AD converters were initially designed as part of the research and design of UWB M-sequence radar systems for specific applications, and are briefly introduced in this article. The requirements of the newly proposed specific UWB M-sequence systems were established based on the knowledge gained from these initial designs. After thorough testing and evaluation of the concept of the early proposed AD converters for these specific UWB M-sequence systems, the design of a new AD converter was initiated. After confirming sufficient characteristics based on the requirements of UWB M-sequence systems for specific applications, a 7-bit AD converter in low-cost 0.35 µm SiGe BiCMOS technology from AMS was designed, fabricated, and presented in this article. The proposed 7-bit AD converter achieves the following parameters: ENOB = 6.4 bits, SINAD = 38 dB, SFDR = 42 dBc, INL = ±2-bit LSB, and DNL = ±1.5 LSB. The maximum sampling rate reaches 1.4 Gs/s, the power consumption at 20 Ms/s is 1050 mW, and at 1.4 Gs/s is 1290 mW, with a power supply of −3.3 V. [ABSTRACT FROM AUTHOR]

In this paper, a 3D conformal meta-lens designed for manipulating electromagnetic beams via height-to-phase control is proposed. The structure consists of a 40 × 20 array of tunable unit cells fabricated using 3D printing, enabling full 360° phase compensation. A novel automatic synthesizing method (ASM) with an integrated optimization process based on genetic algorithm (GA) is adopted here to create the meta-lens. Simulation using CST Microwave Studio and MATLAB reveals the antenna's beam deflection capability by adjusting phase compensations for each unit cell. Various beam scanning techniques are demonstrated, including single-beam, dual-beam generation, and orbital angular momentum (OAM) beam deflection at different angles of 0°, 10°, 15°, 25°, 30°, and 45°. A 3D-printed prototype of the dual-beam feature has been fabricated and measured for validation purposes, with good agreement between both simulation and measurement results, with small discrepancies due to 3D printing's low resolution and fabrication errors. This meta-lens shows promise for low-cost, high-gain beam deflection in mm-wave wireless communication systems, especially for sensing applications, with potential for wider 2D beam scanning and independent beam deflection enhancements. [ABSTRACT FROM AUTHOR]

Transient interference often submerges the actual targets when employing over-the-horizon radar (OTHR) to detect targets. In addition, modern OTHR needs to carry out multi-target detection from sea to air, resulting in the sparse sampling of echo data. The sparse OTHR signal will raise serious grating lobes using conventional methods and thus degrade target detection performance. This article proposes a modified Alternating Direction Method of Multipliers (ADMM)-Net to reconstruct the target and clutter spectrum of sparse OTHR signals so that target detection can be performed normally. Firstly, transient interferences are identified based on the sparse basis representation and then excised. Therefore, the processed signal can be seen as a sparse OTHR signal. By solving the Doppler sparsity-constrained optimization with the trained network, the complete Doppler spectrum is reconstructed effectively for target detection. Compared with traditional sparse solution methods, the presented approach can balance the efficiency and accuracy of OTHR signal spectrum reconstruction. Both simulation and real-measured OTHR data proved the proposed approach's performance. [ABSTRACT FROM AUTHOR]

This contribution focuses on the detection of tracer particles within non-homogeneous bulk media, aiming to enhance insights into particulate systems. Polarimetric radar measurements are employed, utilizing cross-polarizing channels in order to mitigate interference from bulk media reflections. To distinguish the tracer particle in the measurements, a resonant cross-polarizing structure is constructed, facilitating the isolation of frequency signatures from the surrounding bulk clutter. In addition to characterizing the bulk and tracer components, this study provides a detailed presentation and discussion of the measurement setup, along with the employed signal processing methods. The effectiveness of the proposed methods is demonstrated through comprehensive measurements, where a tracer particle is systematically positioned at various locations. The results affirm the feasibility and efficacy of the approach, highlighting its applicability for enhanced dynamic monitoring in particulate systems within industrial processes. [ABSTRACT FROM AUTHOR]

Agriculture is considered as the important field which makes its huge contribution over the country's economic growth. The yield of food crops and the precise categorization of crops based on several characteristics are of primary importance in this agricultural industry. However, due to a lack of an effective classification method, this industry has significant issues correctly classifying the crops. In addition, classifying food crops using data mining is highly efficient as these techniques can deal with huge amounts of crop data. To this extent, this paper proposes an efficient classification model based on the cropland data extracted from the cropland images. Initially, the dataset is pre-processed based on data-mining techniques like data cleaning and data discretization. Then, the data are clustered based on their relevance using an Improved Density-based Spatial Clustering of Applications with Noise (IDBSCAN) clustering technique. Finally, classification is performed accurately using the Adaptive Capsule Transient Auto-Encoder (ACTAE). The experimental validation of a proposed approach proved its efficiency over the other existing models with an overall accuracy rate of 97% which is incomparable to the other crop classification models implemented over the cropland dataset. [ABSTRACT FROM AUTHOR]

Multisensor fusion‐based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather‐sensitive, while the frequency‐modulated continuous wave Radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method called Radar on LiDAR Map, which aims to enhance localization accuracy without relying on loop closures by mitigating the accumulated error in Radar odometry in real time. To accomplish this, we utilize LiDAR scans and ground truth paths as Teach paths and Radar scans as the trajectories to be estimated, referred to as Repeat paths. By establishing a correlation between the Radar and LiDAR scan data, we can enhance the accuracy of Radar odometry estimation. Our approach involves embedding the data from both Radar and LiDAR sensors into a density map. We calculate the spatial vector similarity with an offset to determine the corresponding place index within the candidate map and estimate the rotation and translation. To refine the alignment, we utilize the Iterative Closest Point algorithm to achieve optimal matching on the LiDAR submap. The estimated bias is subsequently incorporated into the Radar SLAM for optimizing the position map. We conducted extensive experiments on the Mulran Radar Data set, Oxford Radar RobotCar Dataset, and our data set to demonstrate the feasibility and effectiveness of our proposed approach. Our proposed scan projection descriptors achieves homogeneous and heterogeneous place recognition and works much better than existing methods. Its application to the Radar SLAM system also substantially improves the positioning accuracy. All sequences' root mean square error is 2.53 m for positioning and 1.83° for angle. [ABSTRACT FROM AUTHOR]

In this paper, a novel microwave oscillator is developed at frequencies of 5.7 and 7.5 GHz through the application of Negative Resistance and Harmonic Balance theory. The design process involves leveraging the Agilent Advance Design System (ADS) tool, ensuring exceptional electromagnetic (EM) performance. The utilization of microstrip circuit elements enhances the overall performance of the oscillator structure. Following optimization and co-simulation of nonlinear models for the compact Planar Microwave Oscillator (62 x 38 mm²), highly satisfactory results are obtained. Quantitatively, the measured output powers at 5.7 and 7.5 GHz are determined to be 9.5 dBm and 7.05 dBm, respectively. These power levels are particularly relevant for C band applications spanning 4 to 8 GHz, including areas such as satellite communication, radar, and wireless networks. [ABSTRACT FROM AUTHOR]

This paper introduces a machine-learning-driven approach for automated Nocturnal Low-Level Jet (NLLJ) identification using observations of wind profiles from a Radar Wind Profiler (RWP). The work discussed here is an effort to lay the groundwork for a systematic study of the Mid-Atlantic NLLJ's formation mechanisms and their influence on nocturnal and diurnal air quality in major urban regions by establishing a general framework of NLLJ features and characteristics with an identification algorithm. Leveraging a comprehensive wind profile dataset maintained by the Maryland Department of Environment's RWP network, our methodology employs supervised machine learning techniques to isolate the features of the south-westerly NLLJ. This methodology was developed to illuminate spatiotemporal patterns and nuanced characteristics of NLLJ events, unveiling their significant role in shaping the planetary boundary layer. This paper discusses the construction of this methodology, its performance against known NLLJs in the current literature, intended usage, and a preliminary statistical analysis. First light results from this analysis have identified a total of 90 south-westerly NLLJs from May–September of 2017–2021 as captured by the RWP stationed in Beltsville, MD (39.05°, -76.87°, 135 m ASL). A composite of these 90 jets is presented to better illustrate many of the bulk parameters, such as core height, duration, and maximum wind speed, associated with the onset and decay of the Mid-Atlantic NLLJ. We hope our study equips researchers and policymakers with further means to monitor, predict, and address these nocturnal dynamics phenomena that frequently influence boundary layer composition and air quality in the U.S. Mid-Atlantic and Northeastern regions. [ABSTRACT FROM AUTHOR]

Hailstorms, while fascinating from a meteorological perspective, pose significant risks to communities, agriculture, and infrastructure. In regions such as the Central Andes of Peru, the characteristics and frequency of these extreme weather events remain largely uncharted. This study fills this gap by investigating the historical frequency and vertical structure of hailstorms in this region. We analyzed historical hailstorm records dating back to 1958 alongside 4 years of observations (2017–2021) from the Parsivel2 disdrometer and a cloud-profiling radar MIRA35c. Our findings indicate a trend of decreasing hail frequency (- 0.5 events per decade). However, the p value of 0.07 suggests the need for further investigation, particularly in relation to environmental changes and reporting methods. The results show that hailstorms predominantly occur during the austral summer months, with peak frequency in December, and are most common during the afternoon and early evening hours. The analysis of radar variables such as reflectivity, radial velocity, spectral width, and linear depolarization ratio (LDR) reveals distinct vertical profiles for hail events. Two case studies highlight the diversity in the radar measurements of hailstorms, underscoring the complexity of accurate hail detection. This study suggests the need for refining the Parsivel2 algorithm and further understanding its classification of hydrometeors. Additionally, the limitations of conventional radar variables for hail detection are discussed, recommending the use of LDR and Doppler spectrum analysis for future research. Our findings lay the groundwork for the development of more efficient hail detection algorithms and improved understanding of hailstorms in the Central Andes of Peru. [ABSTRACT FROM AUTHOR]

In this paper, we dive into the exciting world of wireless communication, focusing on how millimeter-wave technology and Multiple-Input Multiple-Output phased array antennas are shaping the future of 5G and the upcoming 6G technologies. We cover the latest advancements in millimeter-wave and beamforming technologies, emphasizing their role in enhancing network security and efficiency in automotive vehicles through dual radar communication. Our discussion spans the benefits, applications, challenges, and solutions of these technologies individually from millimeter-wave to beamforming technologies and joint radar communications, alongside a look at their theoretical and practical implementations. We emphasize the integration of beamforming technology in joint radar communications for future automotive vehicles and its impact on automotive systems, smart cities, and the Internet of Things (IoT). Looking ahead, we discuss the potential of these technologies to transform future technology landscapes while also addressing the security implications of merging communication and radar capabilities. This paper aims to provide a clear view of the advancements and prospects of millimeter-wave, beamforming, and dual radar communication technologies. [ABSTRACT FROM AUTHOR]

Aircraft icing refers to the accumulation of ice on the surface and components of an aircraft when supercooled water droplets collide with the aircraft above freezing levels (at altitudes at which the temperature is below 0 °C), which requires vigilant monitoring to avert aviation accidents attributable to icing. In response to this imperative, the Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has developed a real-time icing detection algorithm. We utilized 3D dual-polarimetric radar variables, 3D atmospheric variables, and aircraft icing data and statistically analyzed these variables within the icing areas determined by aircraft icing data from 2018–2022. An algorithm capable of detecting icing potential areas (icing potential) was formulated by applying these characteristics. Employing this detection algorithm enabled the classification of icing potential into three stages: precipitation, icing caution, and icing warning. The algorithm was validated, demonstrating a notable performance with a probability of detection value of 0.88. The algorithm was applied to three distinct icing cases under varying environmental conditions—frontal, stratiform, and cumuliform clouds—thereby offering real-time observable icing potential across the entire Korean Peninsula. [ABSTRACT FROM AUTHOR]

In the realm of short-range radar applications, the focus on detecting "low, slow, and small" (LSS) targets has escalated, marking a pivotal aspect of critical area defense. This study pioneers the use of one-dimensional convolutional neural networks (1D-CNNs) for direct slow-time dimension radar feature extraction, sidestepping the complexity tied to frequency and wavelet domain transformations. It innovatively employs a network architecture enriched with multi-frequency multi-scale deformable convolution (MFMSDC) layers for nuanced feature extraction, integrates attention modules to foster comprehensive feature connectivity, and leverages linear operations to curtail overfitting. Through comparative evaluations and ablation studies, our methodology not only simplifies the analytic process but also demonstrates superior classification capabilities. This establishes a new benchmark for efficiently classifying low-altitude entities, such as birds and unmanned aerial vehicles (UAVs), thereby enhancing the precision and operational efficiency of radar detection systems. [ABSTRACT FROM AUTHOR]

There is growing demand for the high-fidelity characterization of satellites in Geosynchronous Earth Orbit (GEO) to support Space Domain Awareness (SDA). This is particularly true for newly launched satellites, where it is necessary for satellite providers to ascertain whether components have deployed properly. Conventional wideband radar systems are capable of imaging satellites provided that (i) they have sufficient power aperture and bandwidth, and (ii) they observe enough target aspect change to generate a resolved image. While wideband radars are used routinely for characterizing satellites in Low-Earth Orbit (LEO), powerful radars with sensitivity sufficient for large GEO ranges (36,000 km or greater) are lacking. Thus, researchers often rely on more widely available high-power narrowband tracking radars for GEO characterization. In this paper, we present a novel range-Doppler-time (RDT) tensor processing technique for GEO characterization with narrowband radar. This technique encapsulates the strengths of previously proposed methods for narrowband-radar characterization at GEO, providing a generalized approach that can be applied in a variety of settings. The technique generates fully resolved 2D images of rotating GEO satellites in low-bandwidth scenarios. In cases where aspect change is limited, the technique provides detailed Doppler information for enhanced satellite status monitoring. This work presents a comprehensive quantitative analysis of the technique that considers the impact of key parameters on characterization performance. Simulated radar data, and radar data collected in a compact range on a scaled satellite model, are used to evaluate the technique. [ABSTRACT FROM AUTHOR]

Joint transceiver beamforming is a fundamental and crucial research task in the field of signal processing. Despite extensive efforts made in recent years, the joint transceiver beamforming of frequency diverse array (FDA)-based multiple-input and multiple-output (MIMO) radar has received relatively less attention and is confronted with some tricky challenges, such as range–angle decoupling and the interaction between multiple performance metrics. In this paper, we initially derive the generalized ambiguity function of the FDA-MIMO radar to explore the intrinsic correlation between its waveform design and resolution. Following that, the joint beamforming optimization is formulated as a nonconvex bivariate quadratic programming problem (NBQP) with the aim of maximizing the Signal-to-Interference-Noise Ratio (SINR) of the FDA-MIMO radar system. Building upon this, we introduce an innovative alternating manifold optimization with nested iteration (AMO-NI) algorithm to address the NBQP. By incorporating manifold optimization into iterative updates of transmit waveform and receiving filter, the AMO-NI algorithm considers the interdependencies among the optimization variables. The algorithm efficiently and expeditiously finds global optimum solutions within a finite number of iterations. Compared with other methods, our approach yields a superior beampattern and higher SINR. [ABSTRACT FROM AUTHOR]

The proposed "Guanlan" ocean science satellite, led by China's Laoshan Laboratory, includes an interferometric radar altimeter (IRA) as a key payload. As an integral part of its development, an airborne IRA experiment was conducted on 6 November 2021, with a flight path of approximately 90 km in the South China Sea. This study investigates the IRA's ability to observe ocean sea surface height (SSH) across scales ranging from meters to mesoscale. The sea surface height anomaly (SSHA) of the IRA is aligned with the SSHA of the AVISO at scales greater than 30 km, but also demonstrates the ability to capture small-scale SSHA changes in two dimensions. We analyzed wavenumber spectra of SSHA obtained from the airborne IRA, ICESat-2, and SARAL/AltiKa satellite for this region. The results show a good agreement in power spectral density (PSD) levels between ICESat-2, SARAL/AltiKa and IRA at scales larger than 30 km. Within the submesoscale range of 1–10 km, the IRA SSHA spectrum exhibits a distinctly negative slope and the lowest energy level. The minimum PSD level of the IRA fell in the range of 10−4–10−3 m2/cycle/km, at scales around 1 km, which is more than an order of magnitude lower than that of ICESat-2, forming a spectral gap that is in agreement with the theoretical expectation. Furthermore, IRA-derived wave direction and significant wave height matched well with the MFWAM wave data. The results of this study underscore the considerable potential of airborne IRA in capturing SSHA across a range of scales, from oceanic waves to submesoscale. [ABSTRACT FROM AUTHOR]

The large quantity of resident space objects orbiting Earth poses a threat to safety and efficient operations in space. Radar sensors are well suited to detecting objects in space including decommissioned satellites and debris, whereas the more commonly used optical sensors are limited by daylight and weather conditions. Observations of three non-operational satellites using a VHF radar system are presented in this paper in the form of micro Doppler signatures associated with rotational motion. Micro Doppler signatures are particularly useful for characterising resident space objects at VHF given the limited bandwidth resulting in poor range resolution. Electromagnetic simulations of the micro Doppler signatures of the defunct satellites are also presented using simple computer-aided design (CAD) models to assist with interpretation of the radar observations. The simulated micro Doppler results are verified using the VHF radar data and provide insight into the attitude and spin axis of the three resident space objects. As future work, this approach will be extended to a larger number of resident space objects which requires a automated processing. [ABSTRACT FROM AUTHOR]

The Yulong porphyry copper belt in eastern Tibet is located in the middle of Tethys–Himalayan metallogenic mega-province, which is one of the three major porphyry copper metallogenic mega-provinces. The Yulong copper belt belongs to the super porphyry copper belt and represents one of the most important copper mineralization prospecting areas in China. A significant quantity of research data shows that this study area belongs to the environment of intracontinental collision and compression, with a complex geological structure, magmatic rock development and excellent metallogenic geological background. However, because this area is located in an alpine and high-altitude area, it is difficult to carry out any traditional field geological surveys, and the existing studies of both prospecting and prediction are relatively weak. This study focused on information extraction for alteration minerals in the Yulong metallogenic belt and its surroundings based on multispectral data and hyperspectral data, establishing a spectral library of alteration minerals in this area. Based on Sentinel-1A radar data and Landsat-8 OLI color synthesis data, the linear structure of the study area was interpreted. On this basis, the information extraction results relating to alteration minerals obtained from multi-source remote sensing data, linear structure interpretation results and the geochemical exploration data of the study area were superimposed to comprehensively analyze the metallogenic geological conditions and mineralization characteristics in the area, establish remote sensing prospecting indicators there and optimize the potential areas for prospecting, providing technical support for the next step of prospecting and exploration in the area. [ABSTRACT FROM AUTHOR]

This paper details the design and implementation of a harmonic frequency-modulated continuous-wave (FMCW) radar system, specialized in detecting harmonic tags and achieving precise range estimation. Operating within the 2.4–2.5 GHz frequency range for the forward channel and 4.8–5.0 GHz for the backward channel, this study delves into the various challenges faced during the system's realization. These challenges include selecting appropriate components, calibrating the system, processing signals, and integrating the system components. In addition, we introduce a single-layer passive harmonic tag, developed specifically for assessing the system, and provide an in-depth theoretical analysis and simulation results. Notably, the system is characterized by its low power consumption, making it particularly suitable for short-range applications. The system's efficacy is further validated through experimental evaluations in a real-world indoor environment across multiple tag positions. Our measurements underscore the system's robust ranging accuracy and its ability to mitigate self-interference, showcasing its significant potential for applications in harmonic tag detection and ranging. [ABSTRACT FROM AUTHOR]

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for wide angle monitoring of vital signs in smart home applications. The person is tracked using an indoor radar. Upon detecting the person to be static, the radar automatically focuses its beam on that location, and subsequently breathing and heart rates are extracted from the reflected signals using continuous wavelet transform ( C W T ) analysis. In this way, leveraging the radar's on-chip processor enables real-time monitoring of vital signs across varying angles. In our experiment, we employ a commercial multi-input multi-output (MIMO) millimeter-wave FMCW radar to monitor vital signs within a range of 1.15 to 2.3 m and an angular span of − 44.8 to + 44.8 deg. In the Bland–Altman plot, the measured results indicate the average difference of − 1.5 and 0.06 beats per minute (BPM) relative to the reference for heart rate and breathing rate, respectively. [ABSTRACT FROM AUTHOR]

With global warming and intensified human activities, extreme convective precipitation has become one of the most frequent natural disasters. An accurate and reliable assessment of severe convective precipitation events can support social stability and economic development. In order to investigate the accuracy enhancement methods and data fusion strategies for the assessment of severe convective precipitation events, this study is driven by the horizontal reflectance factor (ZH) and differential reflectance (ZDR) of the dual-polarization radar. This research work utilizes microphysical information of convective storms provided by radar variables to construct the precipitation event assessment model. Considering the problems of high dimensionality of variable data and low computational efficiency, this study proposes a dual-polarization radar echo-data-layering strategy. Combined with the results of mutual information (MI), this study constructs Bayes–Kalman filter (KF) models (RF, SVR, GRU, LSTM) for the assessment of severe convective precipitation events. Finally, this study comparatively analyzes the evaluation effectiveness and computational efficiency of different models. The results show that the data-layering strategy is able to reduce the data dimensions of 256 × 256 × 34,978 to 5 × 2213, which greatly improves the computational efficiency. In addition, the correlation coefficient of interval III–V calibration period is increased to 0.9, and the overall assessment accuracy of the model is good. Among them, the Bayes–KF-LSTM model has the best assessment effect, and the Bayes–KF-RF has the highest computational efficiency. Further, five typical precipitation events are selected for validation in this study. The stratified precipitation dataset agrees well with the near-surface precipitation, and the model's assessment values are close to the observed values. This study completely utilizes the microphysical information offered by dual-polarized radar ZH and ZDR in precipitation event assessment, which provides a wide range of application possibilities for the assessment of severe convective precipitation events. [ABSTRACT FROM AUTHOR]

Featured Application: Object detection method based on three-dimensional GPR data of railroad tracks using AI, trained solely on simulation data. Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations. [ABSTRACT FROM AUTHOR]

An exceptionally strong westward propagating quasi‐6‐day wave (Q6DW) with zonal wavenumber 1 in connection with the rare 2019 Southern Hemispheric Sudden Stratospheric Warming (SSW) is observed by two meteor radars at 30°S and is found to modulate and interact with the diurnal tide and gravity waves (GWs). The diurnal tide is amplified every 6 days and a prominent 21 hr child wave attributed to Q6DW‐diurnal tide nonlinear interaction occurs. Q6DW modulation on GWs is confirmed as the 4–5 day periodicity in GW variances. Simultaneously, the Q6DW appears to shift its period toward the periodicity of the modulated GW variances. Enhancement is also observed in the first results of meteor radar observed Q6DW Eliassen‐Palm flux, which may facilitate the global perturbation and persistence of this Q6DW. We conclude that the observed SSW triggered Q6DW‐tide and Q6DW‐GW interactions play an important role in coupling the lower atmospheric forcings to ionospheric variabilities. Plain Language Summary: Our work provides observational evidence for the 6‐day planetary wave‐tide and 6‐day planetary wave‐gravity wave interactions at the Earth's mesosphere and lower thermosphere. The results strongly support the theory that wave‐wave interactions are the primary mechanism coupling planetary waves to ionospheric variability and provide an additional mechanism as the 6‐day wave modulation on the gravity waves. We utilize measurements from two meteor radars to diagnose planetary wave characteristics and identify wave‐wave interactions, and compute the first‐time meteor radar observed Eliassen‐Palm flux. Enhancement is observed in the Eliassen‐Palm flux of 6‐day wave following the SSW maximum phase, which demonstrates that energy of the 6‐day wave is enhanced and therefore, facilitates the global perturbation and persistence of the 6‐day wave for an extended time period. While meteor radar observations are widely used to investigate planetary waves and tides, high meteor detection rate is required for further studying temperature perturbations and small scale waves (e.g., gravity waves). Thus, this work also highlights the capability of a modern multi‐static meteor radar system, Chilean Observation Network De meteOr Radars, in resolving oscillations of small spatial scales over a broad range of periods, and for calculating Eliassen‐Palm flux of planetary waves. Key Points: A dominating W1 Q6DW is observed at 30°S and its Eliassen‐Palm flux is enhanced during the 2019 SH SSWQ6DW amplifies the diurnal tide every 6 days and a strong 21 hr child wave is observedQ6DW modulates the gravity wave variances and its frequency appears to shift accordingly [ABSTRACT FROM AUTHOR]

The planetary boundary layer (PBL) over the Tibetan Plateau (TP) exerts a significant influence on regional and global climate, while its vertical structures of turbulence and evolution features remain poorly understood, largely due to the scarcity of observation. This study examines the vertical profile and daytime variation of turbulence dissipation rate (ε) in the PBL over the TP using the high-resolution (6 min and 120 m) measurements from the radar wind profiler (RWP) network, combined with the hourly data from the ERA5 reanalysis. Observational analyses show that the magnitude of ε below 3 km under all-sky conditions exhibits large spatial discrepancy over the six RWP sites over the TP. Particularly, the values of ε at Minfeng and Jiuquan over the northern TP and Dingri over the southern TP are roughly an order of magnitude greater than those at Lijiang, Ganzi and Hongyuan over the eastern TP. This could be partially attributed to the difference of land cover across the six RWP sites. In terms of the diurnal variation, ε rapidly intensifies from 0900 local standard time (LST) to 1400 LST, and then gradually levels off in the late afternoon. Under clear-sky conditions, both ε and planetary boundary layer height (zi) are greater, compared with cloudy-sky conditions. This reveals that clouds would suppress the turbulence development and deduce zi. In the lower PBL (0.2< z / zi <0.5, where z is the height above ground level), the dominant influential factor for the development of turbulence is the surface-air temperature difference (Ts – Ta). By comparison, in the upper PBL (0.6< z / zi <1.0), both the and vertical wind shear (VWS) affect the development of turbulence. Above the PBL (1.0< z / zi <2.0), the shear production resulting from VWS dominates the variation of turbulence. Under cloudy-sky conditions, clouds are found to decrease the surface total solar radiation, thereby reducing Ts – Ta and surface sensible heat flux. This weakened sensible heat flux tends to inhibit the turbulent motion within PBL especially in the lower PBL and decrease the growth rate of zi. On the other hand, the strong VWS induced by clouds enhances the turbulence above the PBL. The findings obtained here underscore the importance of RWP network in revealing the fine-scale structures of the PBL over the TP and gaining new insight into the PBL evolution. [ABSTRACT FROM AUTHOR]

Objective: Unusual clinical course. Background: Endometriosis is a common cause of chronic pelvic pain among women globally. Pharmacological therapy for endometriosis includes non-steroidal anti-inflammatory drugs (NSAIDs) and hormonal contraceptives, while surgical therapy often involves either laparoscopic excision and ablation of endometriosis implants or open surgery. Surgical therapy is one of the mainstays of treatment especially for extrapelvic endometriomas. However, little guidance exists for the treatment of non-palpable or intermittently palpable lesions of this nature. Case Report: A 33-year-old woman with a previous cesarean section presented with complaints of intermittent discomfort in the area between her umbilicus and the surgical incision, for the previous 7 years, that worsened during her menstrual cycle. A 3×3-cm area of fullness was only intermittently palpable during various clinic visits, but was visualizable on computed tomography and magnetic resonance imaging. Given the lesion's varying palpability, a Savi Scout radar localization device was placed into the lesion pre-operatively to aid with surgical resection. The mass was excised, pathologic examination revealed endometrial tissue, and the patient had an uncomplicated postoperative course with resolution of her symptoms. Conclusions: Surgical removal of extrapelvic endometrioma lesions can be made difficult by varying levels of palpability or localizability due to a patient's menstrual cycle. The Savi Scout, most commonly used in breast mass localization, is a useful tool in guiding surgical excision of non-palpable or intermittently palpable extrapelvic endometrioma lesions. [ABSTRACT FROM AUTHOR]

Surface air pressure is one of the most important parameters used in Numerical Weather Prediction (NWP) models. Although it has been measured using weather stations on the ground for many decades, the numbers of measurements are sparse and concentrated on land. Few measurements from buoys and ships are available over ocean. Global measurements can only be achieved by using remote sensing from Space, which is challenging; however, a novel design using Differential Absorption Radar (DAR) can provide a potential solution. The technique relies on two facts: first the electromagnetic fields are absorbed mainly by oxygen and water vapor, and second that oxygen is well mixed. In this work we discuss a space‐borne concept, which aims at providing, over the ocean, consistent, and regular observations for determining surface air pressure from space by a design of a multi‐tone radar operating on the upper wing of the O2 absorption band with tones from 64 to 70 GHz. Simulations of radar vertical profiles based on the output of a state of‐the‐art microphysical retrievals applied to the A‐Train suite of sensors are exploited to establish the performance of such a system for surface pressure determination. In particular the identification and quantification of errors introduced by the presence of water vapor, cloud liquid water and rain water and the potential of a correction via the three‐tone method is discussed. Errors introduced by surface measurement noise and temperature profile uncertainties are discussed as well. Results show that accuracy between 2 and 5 hPa is at reach. Plain Language Summary: Pressure is an important atmospheric variable. A radar‐based remote sensing techinque to measure surface pressure is proposed. The methodology is based on the darkening of the surface return when moving closer to the center of the 60 GHz oxygen absorption line. Results demonstrate that measurements of surface pressure with errors of few hPa are feasible. Key Points: Differential absorption radars with multiple tones within the oxygen band to enable surface pressure measurements with few hPa accuracyCloud, rain and water vapor produce biases in the surface pressure that must be correctedErrors can be minimized by adopting tunable frequencies and frequency diversity [ABSTRACT FROM AUTHOR]

Fast settling phase locked loops (PLLs) play a pivotal role in many applications requiring rapid attainment of a stable frequency and phase. In modern communication standards, these PLLs are extensively utilized to guarantee precise compliance with dynamic resource allocation requirements. In processors, these PLLs manage dynamic voltage frequency scaling. Moreover, the fast-settling PLLs expedite the scanning of frequency spectra in sophisticated electronic radar set-ups, proving particularly advantageous for imaging and scanning radar applications. The rapid response exhibited by these PLLs is also harnessed in quantum technologies, catering to the urgent need for precise frequency adjustments to manipulate qubit states effectively. The strategies employed to attain fast-settling PLLs are primarily classified into five broad techniques in this article: enhanced phase frequency detection, hybrid multiple subsystems, VCO start-up, gear shift, and look-up table or finite state machine. This article explores the fundamental operational principles encompassing these techniques and presents optimal settling times for each method reported in the literature. Finally, the architectures utilizing these techniques will be evaluated based on their figure of merit (FoM), settling time, and tuning range. [ABSTRACT FROM AUTHOR]

Convection‐generated gravity waves (CGWs) transport momentum and energy, and this momentum is a dominant driver of global features of Earth's atmosphere's general circulation (e.g., the quasi‐biennial oscillation, the pole‐to‐pole mesospheric circulation). As CGWs are not generally resolved by global weather and climate models, their effects on the circulation need to be parameterized. However, quality observations of GWs are spatiotemporally sparse, limiting understanding and preventing constraints on parameterizations. Convection‐permitting or ‐resolving simulations do generate CGWs, but validation is not possible as these simulations cannot reproduce the CGW‐forcing convection at correct times, locations, and intensities. Here, realistic convective diabatic heating, learned from full‐physics convection‐permitting Weather Research and Forecasting simulations, is predicted from weather radar observations using neural networks and a previously developed look‐up table. These heating rates are then used to force an idealized GW‐resolving dynamical model. Simulated CGWs forced in this way closely resembled those observed by the Atmospheric InfraRed Sounder in the upper stratosphere. CGW drag in these validated simulations extends 100s of kilometers away from the convective sources, highlighting errors in current gravity wave drag parameterizations due to the use of the ubiquitous single‐column approximation. Such validatable simulations have significant potential to be used to further basic understanding of CGWs, improve their parameterizations physically, and provide more restrictive constraints on tuning with confidence. Plain Language Summary: Thunderstorms generate waves in the atmosphere that can generate turbulence at commercial aircraft cruising altitudes and further aloft. At these higher altitudes, they eventually break, not only generating turbulence, but also exerting forces that affect the large‐scale flows in the middle atmosphere. While these waves have been known to be important since at least the 1980s, they are difficult to observe. They can be simulated, but weather models do not simulate thunderstorms in the correct locations at the right times, meaning the simulated waves cannot be directly compared against observations. Here, weather radar observations are used as input to a look‐up table and a neural network to force realistic thunderstorm motions and waves within a simplified weather model. This method was able to reproduce a satellite‐observed case with notable skill. In one of the first simulations of thunderstorm‐generated waves comparable to satellite observations, these waves travel 100s of kilometers away from the thunderstorms, conflicting with assumptions made in weather and climate models. Key Points: If realistic convective diabatic heating is supplied at the correct places and times, GW‐resolving models can reasonably reproduce Convection‐generated gravity waves (CGWs)While location and type of convective storm do influence latent heating, these difference are of second order importance for CGW forcingDrag due to convection‐generated GWs from a compact source region is spread over O(1,000) km due to lateral propagation [ABSTRACT FROM AUTHOR]

The multifunctional metasurface offers a high degree of flexibility in manipulating electromagnetic waves. However, the majority of its functions are limited to the reflection or transmission space in a single band, restricting the utilization of electromagnetic information. This paper proposes a three-channel multifunctional frequency multiplexing coding metasurface based on the Fabry-Perot cavity principle. It consists of two layers of orthogonal metal gratings and a cross-shaped, oblique open loop structure in the intermediate layer. Simulation results reveal that at an incidence of 22 GHz, the polarization conversion and focusing functions of the transmitted wave are accomplished. Similarly, at an incidence of 31 GHz, the beam deflection function of the reflected wave is observed. Furthermore, at an incidence of 32 GHz, the radar scattering cross-section reduction function of the reflected wave is achieved. In addition to achieving high efficiency, miniaturization, and compactness, the proposed metasurface effectively enhances the spatial utilization of electromagnetic information. As a result, potential applications in multifunctional integrated systems, including wireless communication, sensing technologies, and radar systems, are vast. [ABSTRACT FROM AUTHOR]

The object of this study is the process of detecting stealth aerial vehicles by a network of two small-sized radars with decentralized signal processing. The main hypothesis of the study assumed that combining two small-sized radars into a network could improve the quality of detection of stealth aerial vehicles with decentralized signal processing. The improved method for detecting a stealth aerial vehicle by a network of two small-sized radars with decentralized processing, unlike the known ones, provides for the following: – each radar emits its own probing signal; – each radar receives only its own signal; – coordinated filtering in the reception system of each radar of its signal; – quadratic detection of its signal in each radar; – finding the sum of detected signals in each radar at the output of its matched filter; – preliminary detection of the signal is carried out by each radar separately; – in each range element, the signal is compared with the threshold level; – when the threshold level in the range element is exceeded, such range element is assigned a value of one, otherwise – zero; – the sequence of zeros and ones obtained in this way in each radar of the network is transmitted to the central processing point; – at the central processing point, a decision is made about the presence or absence of a stealth aerial vehicle in the range element. Such a decision is made based on the results of the combined processing of binary sequences coming from the radars according to the "k out of m" criterion. It was established that when detecting a stealth aerial vehicle by a network of two small-sized radars, decentralized information processing provides a higher value of the conditional probability of correct detection, by (19–26) % on average [ABSTRACT FROM AUTHOR]

The structure and distribution of sub-glacial water directly influences Antarctic ice mass loss by reducing or enhancing basal shear stress and accelerating grounding line retreat. A common technique for detecting sub-glacial water involves analyzing the spatial variation in reflectivity from an airborne radar echo sounding (RES) survey. Basic RES analysis exploits the high dielectric contrast between water and most other substrate materials, where a reflectivity increase ≥ 15 dB is frequently correlated with the presence of sub-glacial water. There are surprisingly few additional tools to further characterize the size, shape, or extent of hydrological systems beneath large ice masses. We adapted an existing radar backscattering simulator to model RES reflections from sub-glacial water structures using the University of Texas Institute for Geophysics (UTIG) Multifrequency Airborne Radar Sounder with Full-phase Assessment (MARFA) instrument. Our series of hypothetical simulation cases modeled water structures from 5 to 50 m wide, surrounded by bed materials of varying roughness. We compared the relative reflectivity from rounded Röthlisberger channels and specular flat canals, showing both types of channels exhibit a positive correlation between size and reflectivity. Large (> 20 m), flat canals can increase reflectivity by more than 20 dB , while equivalent Röthlisberger channels show only modest reflectivity gains of 8–13 dB. Changes in substrate roughness may also alter observed reflectivity by 3–6 dB. All of these results indicate that a sophisticated approach to RES interpretation can be useful in constraining the size and shape of sub-glacial water features. However, a highly nuanced treatment of the geometric context is necessary. Finally, we compared simulated outputs to actual reflectivity from a single RES flight line collected over Thwaites Glacier in 2022. The flight line crosses a previously proposed Röthlisberger channel route, with an obvious bright bed reflection in the radargram. Through multiple simulations comparing various water system geometries, such as canals and sub-glacial lakes, we demonstrated the important role that topography and water geometry can play in observed RES reflectivity. From the scenarios that we tested, we concluded the bright reflector from our RES flight line cannot be a Röthlisberger channel but could be consistent with a series of flat canals or a sub-glacial lake. However, we note our simulations were not exhaustive of all possible sub-glacial water configurations. The approach outlined here has broad applicability for studying the basal environment of large glaciers. We expect to apply this technique when constraining the geometry and extent of many sub-glacial hydrologic structures in the future. Further research may also include comprehensive investigations of the impact of sub-glacial roughness, substrate heterogeneity, and computational efficiencies enabling more complex and complete simulations. [ABSTRACT FROM AUTHOR]

The article focuses on nowcasting systems in European National Meteorological Services (NMS), defining nowcasting and its unique characteristics, including methods, and applications. Topics include the EUMETNET Nowcasting program's phases, various types of nowcasting systems (object-oriented, field-oriented radar and satellite, NWP models), and seamless systems (deterministic and probabilistic), highlighting their development, methodologies, and applications across European NMSs.

This two‐part study presents a comprehensive analysis of (1) the vortex structure, (2) the inner core planetary boundary layer (PBL) wind profile, and (3) the overland surface winds of Typhoon Faxai(2019) during landfall observationally (Part I) and in high‐resolution Weather Research and Forecasting Model (WRF) simulations (Part II). Part II presents a framework for evaluating the wind field in the WRF simulations of Typhoon Faxai (2019) by comparing them with the observations presented in Part I. First, WRF simulations with two different surface roughness z0$$ {z}_0 $$ tables are presented. The simulation with the default z0$$ {z}_0 $$ table in WRF is shown to largely overestimate the surface wind speed. The overestimation is largely mitigated by using larger z0$$ {z}_0 $$ based on the climatology for urban and forest areas in Japan. Next, using the modified z0$$ {z}_0 $$ table, WRF simulations with three different PBL parametrizations, namely, Mellor–Yamada–Janjić, Yonsei University, and Mellor–Yamada–Nakanishi–Niino schemes are presented, and their impacts on the simulated vortex structure, inner core PBL wind profile, and overland surface winds are evaluated. In particular, the simulated surface winds are in good agreement with the observations outside the inner core of Typhoon Faxai. However, during the passage of the eyewall, the maximum surface winds are underestimated in the simulations. We show that the underestimation of the inertial stability, associated with the excessively large inner core of Typhoon Faxai in the simulations, led to the overestimation of the eyewall PBL jet height. This, in turn, caused the underestimation of the peak surface winds, as based on the empirical model of the wind reduction factor F2 km$$ {F}_{2\;\mathrm{km}} $$ proposed in Part I. [ABSTRACT FROM AUTHOR]

In this study, we investigate the seasonal and diurnal variations in cloud occurrence frequency, as well as cloud vertical structure (CVS) characteristics under different seasons and precipitation intensities over the Guangzhou region in South China, based on the analysis of millimeter-wave cloud radar (MMCR) and ground automatic weather station rainfall observations from May 2019 to August 2021. The results showed that the occurrence frequency of clouds exhibits a bimodal distribution throughout the year, with peaks in March to June and October, reaching its highest occurrence in May at approximately 80% and its lowest from December to February at around 40%. Additionally, there are distinct diurnal variations in occurrence frequency, with the lowest rates occurring around 0005 LST, rapidly increasing after 0006 LST, and peaking during the afternoon to evening hours. Cloud top height (CTH) shows bimodal distributions during the pre-flood and post-flood seasons. The most frequently occurring range of CTH during the pre-flood season is below 3 km, accounting for approximately 43%, while during the post-flood season, it ranges from 11 to 14 km, constituting about 37%. For precipitation clouds, CTH can extend beyond 12 km, with the radar reflectivity decreasing gradually with increasing height. The highest frequencies of radar echoes are observed below 2 km and between 4 and 7 km, exhibiting clear diurnal variations, with echoes mainly below 2 km and between 4 to 6 km during the early morning, intensifying and shifting to higher altitudes during the day and reaching their maximum below 4 km during the afternoon to nighttime hours, while both the frequency and intensity increase in the height range of 4 to 12 km. Vertical profiles of radar reflectivity and cloud ice/liquid water content (IWC/LWC) exhibit similar trends under different precipitation intensities. The main differences are observed below 4 km, where both radar reflectivity and IWC/LWC generally increase with increasing precipitation intensity. These findings contribute to a better understanding of cloud characteristics in the South China region, enhance the accuracy of model simulations, and provide a scientific basis for accurate forecasting and warning of meteorological disasters. [ABSTRACT FROM AUTHOR]

Long-term observations of deep convective cloud (DCC) vertical velocity and mass flux were collected during the Observations and Modelling of the Green Ocean Amazon (GoAmazon2014/5) experiment. Precipitation echoes from a surveillance weather radar near Manaus, Brazil, are tracked to identify and evaluate the isolated DCC lifecycle evolution during the dry and wet seasons. A radar wind profiler (RWP) provides precipitation and air motion profiles to estimate the vertical velocity, mass flux, and mass transport rates within overpassing DCC cores as a function of the tracked cell lifecycle stage. The average radar reflectivity factor (Z), DCC area (A), and surface rainfall rate (R) increased with DCC lifetime as convective cells were developing, reached a peak as the cells matured, and decreased thereafter as cells dissipated. As the convective cells mature, cumulative DCC properties exhibit stronger updraft behaviors with higher upward mass flux and transport rates above the melting layer (compared with initial and later lifecycle stages). In comparison, developing DCCs have the lowest Z associated with weak updrafts, as well as negative mass flux and transport rates above the melting layer. Over the DCC lifetime, the height of the maximum downward mass flux decreased, whereas the height of the maximum net mass flux increased. During the dry season, the tracked DCCs had higher Z , propagation speed, and DCC area, and were more isolated spatially compared with the wet season. Dry season DCCs exhibit higher Z , mass flux, and mass transport rate while developing, whereas wet season DCCs exhibit higher Z , mass flux, and mass transport rates at later stages. [ABSTRACT FROM AUTHOR]