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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]

Climate change is altering the timing of seasonal events for many taxa. There is limited understanding of how northward/southward songbird migration follows or is limited by the latitudinal progression of seasonal transitions. Consistent environmental conditions that migrating birds encounter across latitudes likely represent or correlate with important resources or limiting factors for migration. We tested whether migratory passage—observed via radar—consistently tracked land surface variables and phenophases across latitudes in the US Central Flyway in both spring and fall. The daily temperatures, precipitation and vegetation greenness occurring on 10%, 50% and 90% cumulative passage dates changed substantially with latitude, indicating that most migrants experienced rapidly changing conditions as they headed north or south. Temperature did not limit the progression of migration in either season. Peak spring migration in the southern US occurred nearly 40 days after the spring green wave, the northward progression of vegetation growth, but nearly caught up to green‐up at 48° N. Spring migration phenology may have evolved to prioritize earlier arrival for breeding. Across all latitudes, peak fall migration coincided with the same land surface phenophase, an interval of 26 days prior to dormancy onset. Migrants may rely on phenological events in vegetation during fall stopovers. Considering that (a) migratory passage tracked fall land surface phenology across latitudes at a continental scale, (b) previous studies at local scales have demonstrated the importance of fruit during fall migratory stopover and (c) fruiting phenology in North America is occurring later over time while fall migration is advancing, the potential for mismatch between fall fruiting and bird migration phenology urgently needs further investigation. [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]

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]

Placing wind turbines within large migration flyways, such as the North Sea basin, can contribute to the decline of vulnerable migratory bird populations by increasing mortality through collisions. Curtailment of wind turbines limited to short periods with intense migration can minimize these negative impacts, and near‐term bird migration forecasts can inform such decisions. Although near‐term forecasts are usually created with long‐term datasets, the pace of environmental alteration due to wind energy calls for the urgent development of conservation measures that rely on existing data, even when it does not have long temporal coverage. Here, we use 5 years of tracking bird radar data collected off the western Dutch coast, weather and phenological variables to develop seasonal near‐term forecasts of low‐altitude nocturnal bird migration over the southern North Sea. Overall, the models explained 71% of the variance and correctly predicted migration intensity above or below a threshold for intense hourly migration in more than 80% of hours in both seasons. However, the percentage of correctly predicted intense migration hours (top 5% of hours with the most intense migration) was low, likely due to the short‐term dataset and their rare occurrence. We, therefore, advise careful consideration of a curtailment threshold to achieve optimal results. Synthesis and applications: Near‐term forecasts of migration fluxes evaluated against measurements can be used to define curtailment thresholds for offshore wind energy. We show that to minimize collision risk for 50% of migrants, if predicted correctly, curtailments should be applied during 18 h in spring and 26 in autumn in the focal year of model assessments, resulting in an estimated annual wind energy loss of 0.12%. Drawing from the Dutch curtailment framework, which pioneered the ‘international first’ offshore curtailment, we argue that using forecasts developed from limited temporal datasets alongside expert insight and data‐driven policies can expedite conservation efforts in a rapidly changing world. This approach is particularly valuable in light of increasing interannual variability in weather conditions. [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]

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]

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]

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]

The increasing number of space objects (SO), debris, and constellation of satellites in Low Earth Orbit poses a significant threat to the sustainability and safety of space operations, which must be carefully and efficiently addressed to avoid mutual collisions. The space situational awareness is currently addressed by an ensemble of radar and radio‐telescopes that detect and track SO. However, a large part of space debris is composed of very small and tiny metallic objects, very difficult to detect. The authors demonstrate the benefits of using deep learning (DL) architectures for small space object detection by radar observations. TIRA radio telescope has been simulated to generate range‐Doppler maps, then used as inputs for object detection exploiting You‐Only‐Look‐Once (YOLO) frameworks. The results demonstrate that the object detection by using YOLO algorithms outperform conventional target detection approaches, thus indicating the potential benefits of using DL techniques for space surveillance applications. [ABSTRACT FROM AUTHOR]

Detection of space objects is a key component of space situational awareness, which could help prevent and minimise space collisions. While there have been lots of radar systems designed to detect space objects, few of them have dealt with long baseline distributed bistatic pairs. The authors focus on the feasibility of long baseline bistatic radars, which can be extended for the multistatic case; and the performance of the multistatic system for a target at different altitudes assuming one transmitter over three different scenarios: a cluster of receivers, receivers spread throughout the world and the combination of the two previous cases. To analyse the performance the multiple‐input‐multiple‐output (MIMO) ambiguity function (AF) will be employed. The results of the MIMO AF show how the fusion of different bistatic pairs improves the detection capabilities. Moreover, when the different radar measurements are coherently summed in the MIMO AF, the uncertainty on the location of the target is reduced. [ABSTRACT FROM AUTHOR]

Equivalent‐time sampling can improve measurement or sensing systems because it enables a broader frequency band and higher delay resolution for periodic signals with lower sampling rates than a Nyquist receiver. Meanwhile, a digital down‐conversion (DDC) technique can be implemented using a straightforward radio frequency (RF) circuit. It avoids timing skew and in‐phase/quadrature gain imbalance instead of requiring a high‐speed analog‐to‐digital converter to sample an intermediate frequency (IF) signal. Therefore, when equivalent‐time sampling and DDC techniques are combined, a significant synergy can be achieved. This study provides a parameter design methodology for optimal equivalent‐time sampling using DDC. [ABSTRACT FROM AUTHOR]

Several observational precipitation products that provide high temporal (≤3 h) and spatial (≤0.25°) resolution gridded estimates are available, although no single product can be assumed worldwide to be closest to the (unknown) "reality." Here, we propose and apply a methodology to quantify the uncertainty of a set of precipitation products and to identify, at individual grid points, the products that are likely wrong (i.e., outliers). The methodology is applied over eastern North America for the 2015–2019 period for eight high‐resolution gridded precipitation products: CMORPH, ERA5, GSMaP, IMERG, MSWEP, PERSIANN, STAGE IV and TMPA. Four difference metrics are used to quantify discrepancies in different aspects of the precipitation time series, such as the total accumulation, two characteristics of the intensity‐frequency distribution, and the timing of precipitating events. Large regional and seasonal variations in the observational uncertainty are found across the ensemble. The observational uncertainty is higher in Canada than in the United States, reflecting large differences in the density of precipitation gauge measurements. In northern midlatitudes, the uncertainty is highest in winter, demonstrating the difficulties of satellite retrieval algorithms in identifying precipitation in snow‐covered areas. In southern midlatitudes, the uncertainty is highest in summer, probably due to the more discontinuous nature of precipitation. While the best product cannot be identified due to the lack of an absolute reference, our study is able to identify products that are likely wrong and that should be excluded depending on the specific application. [ABSTRACT FROM AUTHOR]

Turbulence in clouds is known to enhance particle collision rates, as widely demonstrated for warm rain formation. A similar impact on ice growth processes is expected but a solid observational basis is missing. A statistical analysis of a 15‐month data set of cloud radar observations allows for the first time to quantify the impact of turbulence on ice aggregation and riming in Arctic low‐level mixed‐phase clouds. Increasing eddy dissipation rate (EDR), from below 10−4 to above 10−3 m2 s−3, yields larger ice aggregates, and higher particle concentration, likely caused by increasing fragmentation. In conditions more favorable to riming, higher EDR is associated with dramatically higher particle fall velocities (by up to 125%), under similar liquid water paths, indicative of markedly higher degrees of riming. Our findings thus reveal the key role of turbulence for cold precipitation formation, and highlight the need for an improved understanding of turbulence‐hydrometeor interactions in cold clouds. Plain Language Summary: Liquid and frozen precipitation mainly forms by collision and subsequent aggregation of small particles. Collisions between cloud particles, such as droplets and ice crystals, are thought to be increased by turbulence. While this effect has been intensively studied for liquid‐only clouds, the impact of turbulence on ice‐ice collisional growth (aggregation) and ice‐liquid collisional growth (riming) is expected but has so far been poorly quantified. We study the effect of turbulence on aggregation and riming based on a long‐term remote‐sensing data set of low‐level clouds containing both ice and liquid particles, recorded at the Arctic site of Ny‐Ålesund, Svalbard. Cloud radar observations are used to retrieve the dissipation rate of turbulent kinetic energy (i.e., the eddy dissipation rate; EDR), which is the relevant quantity driving increases in collision rates, and to characterize ice particle properties. We find evidence that higher EDR regimes enhance the aggregation of particles, and are associated with signatures of increased ice particle concentration, possibly caused by the production of particle fragments upon collision. In temperature regimes more favorable to riming, turbulence dramatically enhances the particles' fall velocity, denoting higher degrees of riming. Our findings thus highlight a key role of turbulence for the formation of precipitable ice. Key Points: Relation between turbulence and ice growth investigated based on long‐term remote sensing data set of Arctic low‐level mixed‐phase cloudsHigher eddy dissipation rate (EDR) correlates with larger ice aggregates, and possibly higher degrees of fragmentationHigh EDR is an essential component needed for the formation of rimed particles [ABSTRACT FROM AUTHOR]

The complexity of ice particles in the atmosphere makes it difficult to model microphysical growth processes accurately. In this study, we simulated a snowfall case over Northern China Plain using two different microphysics schemes, that is, Thompson and Morrison schemes, in the Advanced Research WRF (Weather Research and Forecasting) model. Both schemes are able to reproduce the event, albeit with a slightly weaker precipitation compared with the surface observation. However, the radar reflectivity factor in Morrison simulation is higher than the radar observation to ∼10 dBZ. Further analysis reveals that such stronger radar reflectivity in the Morrison simulation might be caused by larger collection efficiency, which would lead to more active self‐aggregation process in prediction of snow number concentration and then larger snow particle size. Sensitivity tests show that using an alternative formula of collection efficiency produces smaller radar reflectivity that is in better agreement with observations. This study highlights the accurate representation of self‐aggregation process and underscores the needs of further improvement of ice microphysics schemes for the better snowfall simulations. Plain Language Summary: Understanding how snow forms in the atmosphere is complicated. We did a case study using two different microphysics schemes in numerical weather model to simulate a snowfall event over Northern China. Both schemes did a pretty good job in simulating the snowfall, but one of them, the Morrison scheme, showed much larger radar signal compared to what was really happening. We do further analysis to find out why this was the case. It turned out that the Morrison scheme simulated the snowflakes stick together more than they actually do in nature. This made the size of snowflakes in the simulation much larger. We ran more tests by changing the way the Morrison scheme making snowflakes stick together. When we did this, the simulation results showed more accurate radar reflectivity factor. So, what did we learn? How the snowflakes stick together is a big deal. If we get it wrong, the models will give us the unreasonable estimation of radar signal. This study helps remind scientists that they need to be very careful when they use these microphysics schemes. Key Points: Radar reflectivity factor from Morrison simulation exceeds 10 dBZ compared with observations, but Thompson scheme performs betterThe overestimation of radar reflectivity factor in Morrison simulation could be attributed to larger snow particlesLarger snow particles in Morrison simulation are caused by active self‐aggregation process of snow and higher collection efficiency [ABSTRACT FROM AUTHOR]

Precipitation plays an important role in cloud and aerosol processes over the Southern Ocean (SO). The main objective of this study is to characterize SO precipitation properties associated with SO stratocumulus clouds. We use data from the Southern Ocean Clouds Radiation Aerosol Transport Experimental Study (SOCRATES), and leverage observations from airborne radar, lidar, and in situ probes. We find that for the cold‐topped clouds (cloud‐top‐temperature <0°C), the phase of precipitation with reflectivity >0 dBZ is predominantly ice, while reflectivity < −10 dBZ is predominantly liquid. Liquid‐phase precipitation properties are retrieved where radar and lidar are zenith‐pointing. Power‐law relationships between reflectivity (Z) and rain rate (R) are developed, and the derived Z–R relationships show vertical dependence and sensitivity to the presence of droplets with diameters between 10 and 40 μm. Using derived Z–R relationships, a reflectivity‐velocity (ZV) retrieval method, and a radar‐lidar retrieval method, we derive rain rate and other precipitation properties. The retrieved rain rate from all three methods shows good agreement with in‐situ aircraft estimates, with rain rates typically being quite light (<0.1 mm hr−1). We examine the vertical distribution of precipitation properties, and find that rain rate, precipitation number concentration, and precipitation liquid water all decrease as one gets closer to the surface, while precipitation size and distribution width increases. We also examine how cloud base rain rate (RCB) depends on cloud depth (H) and aerosol concentration (Na) for particles with a diameter greater than 70 nm, and find that RCB is proportional to H3.1Na−0.8 ${H}^{3.1}\,{N}_{a}^{-0.8}$. Plain Language Summary: Precipitation plays an important role over the Southern Ocean (SO), such as transferring water from the atmosphere to the ocean, and affecting clouds and aerosols (tiny airborne particles). This study aims to characterize SO precipitation properties using aircraft data that can count the number and size of cloud and precipitation droplets, as well as lidar and radar that measure light and microwaves respectively reflected by droplets. Using information from lidar, we can distinguish the precipitation phase, and we find that ice precipitation is more frequent when the amount of reflected energy measured by the radar (radar reflectivity) is larger than a certain threshold. We derived relationships between rain rate and radar reflectivity. We also find the precipitation properties inferred from radar and lidar data compare well with direct measurements from the aircraft, and the precipitation tends to be very light. We study how precipitation properties vary vertically, and find that as one gets closer to the surface, there is a decrease in precipitation droplet number and water, while there is an increase in the average size of droplets. We also find that rain rate depends on how thick the clouds are and the number of aerosols, consistent with theoretical expectations. Key Points: Liquid‐phase precipitation retrievals show good agreement with in situ observations and feature the prevalence of light rainReflectivity to rain rate relationships are developed, showing vertical dependence and sensitivity to the intermediate‐sized dropsThe below‐cloud precipitation phase with radar reflectivity >0 dBZ is mostly ice, while radar reflectivity <−10 dBZ is mostly liquid [ABSTRACT FROM AUTHOR]

Large satellite discrepancies and model biases in representing precipitation over the Southern Ocean (SO) are related directly to the region's limited surface observations of precipitation. To help address this knowledge gap, the study investigated the precipitation characteristics and rain rate retrievals over the remote SO using ship‐borne data of the Ocean Rainfall And Ice‐phase precipitation measurement Network disdrometer (OceanRAIN) and dual‐polarimetric C‐band radar (OceanPOL) aboard the Research Vessel (RV) Investigator in the Austral warm seasons of 2016–2018. Seven distinct synoptic types over the SO were analyzed based on their radar polarimetric signatures, surface precipitation phase, and rain microphysical properties. OceanRAIN observations revealed that the SO precipitation was dominated by drizzle and light rain, with small‐sized raindrops (diameter <1 mm) constituting up to 47% of total accumulation. Precipitation occurred most frequently over the warm sector of extratropical cyclones, while concentrations of large‐sized raindrops (diameter >3 mm) were prominent over synoptic types with colder and more convectively unstable environments. OceanPOL observations complement and extend the surface precipitation properties sampled by OceanRAIN, providing unique information to help characterize the variety of potential precipitation types and associated mechanisms under different synoptic conditions. Raindrop size distributions (DSD) measured with OceanRAIN over the SO were better characterized by analytical DSD forms with two‐shape parameters than single‐shape parameters currently implemented in satellite retrieval algorithms. This study also revised a rainfall retrieval algorithm for C‐band radars to reflect the large amount of small drops and provide improved radar rainfall estimates over the SO. Plain Language Summary: Precipitation is a major component of the hydrologic cycle in high‐latitude regions including the remote Southern Ocean (SO). However, large differences continue to exist among current precipitation products in the region, owing in part to the absence of high‐quality surface observational records suitable for evaluation across a range of temporal and spatial scales. This work uses two instruments aboard the RV Investigator over the Australian sector of the SO in the Austral warm seasons of 2016–2018: the OceanRAIN disdrometer and OceanPOL radar. We found variability in radar features and surface precipitation properties among seven distinct synoptic conditions over the SO. This work also discussed two important findings related to remote sensing retrievals of SO rain. First, we demonstrated why the rainfall retrieval assumptions in satellite algorithms may need to be refined to account for the unique rainfall properties in the SO. Second, we formulated a new set of equations suitable for shipborne C‐band radars in improving rain rate estimates over the region. This work leads toward more accurate, high‐resolution estimates of precipitation over the measurement‐sparse SO to better understand a range of climatological and meteorological processes in the region. Key Points: Different synoptic types across the Southern Ocean exhibit distinctive polarimetric signatures and surface precipitation propertiesSmall raindrops of less than one millimeter contribute up to 47% of total accumulation during the Austral warm seasons over the regionA new formulation for radar rainfall estimates that reflects the large numbers of small drops over the Southern Ocean is proposed [ABSTRACT FROM AUTHOR]

LPI radar signal recognition based on convolutional neural networks usually assumes that the signal to be recognized belongs to a closed set of known signal classes. In an open electromagnetic signal environment, this type of closed‐set recognition method will experience a drastic drop in performance due to the encounter with unknown types of signals. We propose an SCNN‐SVDD model based on a combination of a lightweight convolutional neural network and a support vector data description algorithm to achieve open‐set recognition of LPI radar signals under unknown signal conditions. In this approach, Choi‐William's time‐frequency distribution is used to obtain two‐dimensional time‐frequency images of the signal to be identified, and convolutional neural networks are used to achieve high‐precision classification of known signals and extract the corresponding feature vectors. Then, the feature vectors are used as input to the SVDD algorithm and a hypersphere is constructed to detect whether the signal to be identified belongs to a known class. Experimental results show that the proposed method can detect unknown signals while maintaining high recognition accuracy for known signals. [ABSTRACT FROM AUTHOR]

With the development of electronic warfare, anti‐jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on anti‐jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti‐jamming decision‐making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti‐jamming decision‐making. This article regards the anti‐jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti‐jamming decision‐making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti‐jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles, or drones, pose a significant threat to privacy and security. To understand and assess this threat, classification between different drone models and types is required. One way in which this has been demonstrated experimentally is through this use of micro‐Doppler information from radars. Classifiers capable of exploiting differences in micro‐Doppler spectra will require large amounts of data but obtaining such data experimentally is expensive and time consuming. The authors present the methodology and results of a drone micro‐Doppler simulation framework which uses accurate 3D models of drone components to yield detailed and realistic synthetic micro‐Doppler signatures. This is followed by the description of a purpose‐built validation radar that has been developed specifically to gather high‐fidelity experimental drone micro‐Doppler data with which is used to validate the simulation. Detailed comparisons between the experimental and simulated micro‐Doppler spectra from three models of drones with differently shaped propellers are given, showing very good agreement. The aim is to introduce the simulation methodology. Validation using single propeller micro‐Doppler is provided, although the simulation can be extended to multiple propellers. The simulation framework offers the potential to generate large quantities of realistic drone micro‐Doppler signatures for training classification algorithms. [ABSTRACT FROM AUTHOR]

The Integrated Side Lobe Ratio (ISLR) problem the authors consider here consists in finding optimal sequences of phase shifts in order to minimise the mean squared cross‐correlation side lobes of a transmitted radar signal and a mismatched replica. Currently, ISLR does not seem to be easier than the general polynomial unconstrained binary problem, which is NP‐hard. In their work, the authors aim to take advantage of the scalability of quantum computing to find new optima, by solving the ISLR problem on a quantum annealer. This quantum device is designed to solve quadratic optimisation problems with binary variables (QUBO). After proposing suitable formulation for different instances of the ISLR, the authors discuss the performances and the scalability of their approach on the D‐Wave quantum computer. More broadly, their work enlightens the limits and potential of the adiabatic quantum computation for the solving of large instances of combinatorial optimisation problems. [ABSTRACT FROM AUTHOR]

For the integration detection of near space hypersonic weak targets by high pulse repetition frequency (PRF) radar, a novel method named Multi‐Hypothesis Fuzzy‐Matching Radon transform (MHFM‐RT) is proposed for the near space hypersonic target detection and tracking. For remote hypersonic target detection, to avoid range ambiguity, current radars always use a low PRF mode, which limit the number of pulse accumulations. Using the high PRF mode, the fuzzy folding will appear in the target range measurements when target trajectory crosses range fuzzy intervals. Therefore, there is a contradiction between range ambiguity and energy accumulation. The proposed method is used to match the fuzzy measurements, so as to realise the correct integration in the condition of range ambiguity. Firstly, considering the need of range ambiguity resolution, the mode of staggered PRF is used. Secondly, the first frame measurements are periodically extended for multiple‐fuzzy hypothesis. Finally, the weak target track is accumulated in MHFM‐RT domain, and the signal integration and ambiguity resolution can be realised simultaneously. The proposed method expands the Variable‐Diameter‐Arc‐Helix Radon transform (VDAH‐RT) method to fuzzy folding conditions. Compared with the existing methods, for 7‐scan measurements non‐coherent integration, the detection sensitivity of the proposed method is about 0.5–1 dB higher than that of the IMM hybrid filter algorithm, and about 1 dB higher than that of the RHT‐TBD approach, and it needs less storage space and has higher detection probability. [ABSTRACT FROM AUTHOR]

Metasurface radome is crucial to protect the radiating antenna and control electromagnetic waves flexibly. However, current design methods for metasurface radome merely focus on the single propagation direction, with limited degrees of freedom. Here, a super radome is proposed with a directional scattering response based on Janus metasurface that can realize the phase modulation of y‐polarized waves and the amplitude modulation of x‐polarized waves with a judiciously designed structure. Both numerical and experimental results illustrate two cross‐polarized transmission windows with intensity exceeding 0.9 centered at 10 and 12.2 GHz and two out‐of‐band invisibility with considerable backward radar cross‐section reduction over −10 dB within 9.3–15.4 and 7.5–23.8 GHz when the radome is illuminated by y‐ and x‐polarized waves along negative and positive z directions, respectively. Moreover, the radar cross‐section reduction is maintained for off‐normal incidence even up to 45°. This method sets up a directional degree of freedom for advanced radome design and demonstrates its enormous potential in developing next‐generation stealth antennas. [ABSTRACT FROM AUTHOR]

The Low lAtitude long Range Ionospheric raDar (LARID), which consists of two high frequency (HF) radars looking toward the east and west of Hainan Island, respectively, has been developed and installed at Dongfang (19.2°N, 108.8°E, dip lat. 13.8°N), China. This paper describes the system design of LARID and its first observational results of equatorial plasma bubble (EPB) irregularities. The antenna array of LARID is composed of a west‐looking array and an east‐looking array. Each array consists of 20 log‐periodic antennas for transmission and reception and 4 log‐periodic antennas for interferometry, and has a beam steering capability in the azimuth angles of ±24° due the boresight pointing east (or west). Observational results show that the LARID is capable of detecting backscatter echoes from EPB irregularities and the ground, with a distance of 4,000 km or more away from Hainan Island. Multiple EPB structures were continuously observed on 17 April 2023, with eastward drifts ranging between 70 and 130 m/s. Based on ray tracing simulations, the backscatter echoes of EPB irregularities were due to the 0.5‐hop and 1.5‐hop propagation modes. The distances between the successive EPB structures were estimated ranging between 500 and 900 km in longitude. The LARID observations, together with other instruments in the East and Southeast Asian sector, provided a clear picture of longitudinal variation of EPBs in 90–125°E. It is expected that the LARID will provide an important tool to study the generation and evolution of EPBs and their short‐term prediction in East and Southeast Asia. Key Points: A Low lAtitude long Range Ionospheric raDar (LARID) operating at 8–22 MHz has been developed and installed at Hainan Island, ChinaFirst long‐range observations of equatorial plasma bubble (EPB) irregularities and their zonal drifts by the LARID were presentedThe LARID is expected to play an important role in the study of EPB and its short‐term forecasting in East and Southeast Asia [ABSTRACT FROM AUTHOR]

The north and south polar layered deposits (PLD) on Mars are composed of stacks of layered ice and dust, but the SPLD is approximately twice as bright as the NPLD in 20‐MHz radar echoes. We use Shallow Radar (SHARAD) data in ∼4‐MHz bands centered on 17.5 MHz ("L") and 22.5 MHz ("H") to determine whether radar reflectivity variations are due to scattering effects related to closely spaced, near‐surface dielectric layering. We mapped the ratio of the surface echo power at the two frequencies (H/L) for both PLDs. The NPLD has large areas where H and L echo power differ, consistent with destructive interference in the H band within the uppermost ∼20 m. The SPLD is dominated by H ∼ L (unity), except for isolated regions in and near the residual CO2 cap and Australe Lingula. The H/L variations can be partly explained by near‐surface structure, where large variations in H/L match locations with numerous near‐surface reflecting interfaces, and locations where H ∼L may contain few such reflectors. There is no obvious connection between H/L and surface morphology, but the distribution of non‐unity H/L resembles the extent of a widespread, recent accumulated package (WRAP) at both poles. The spatial association between H/L and WRAP and interference indicated by H/L suggests that large regions in the NPLD—and isolated areas in the SPLD—are characterized by shallow layer(s) of consistent thickness/separation potentially deposited within the past few tens of kyr as Mars emerged from the last obliquity‐driven ice age. Plain Language Summary: The north and south poles of Mars reflect radar energy differently, where the south polar region is more reflective than the north. We used radar data from the Shallow Radar instrument to investigate how the near‐surface ice and dust layering could affect reflectivity. We found that the north polar region is more densely layered near the surface, leading to radar signal interference that changes the reflectivity. The south polar region, in contrast, has fewer near‐surface reflectors in many locations. Both poles show evidence of interference in similar locations as a widespread young deposit of ice and dust layers that formed in the past ∼300,000 years. Since our data (which describe to the top several meters of the surface) suggest interference occurs in regions similar to this young deposit, one explanation is that these regions of strong radar interference accumulated over the past ∼20,000 years. Key Points: North and south polar layered deposits have different radar surface reflectivities due in part to layering in the uppermost ∼20 mInterference effects in radar scattering occur more often in the densely layered north polar depositsDifferences in reflectivity and near‐surface structure could indicate the extent of recent ice/dust accumulation in the past ∼20 kyr [ABSTRACT FROM AUTHOR]

Rainfall frequency analysis methods are developed and implemented based on high‐resolution radar rainfall data sets, with the Baltimore metropolitan area serving as the principal study region. Analyses focus on spatial heterogeneities and time trends in sub‐daily rainfall extremes. The 22‐year radar rainfall data set for the Baltimore study region combines reflectivity‐based rainfall fields during the period from 2000 to 2011 and polarimetric rainfall fields for the period from 2012 to 2021. Rainfall frequency analyses are based on non‐stationary formulations of peaks‐over‐threshold and annual peak methods. Increasing trends in short‐duration rainfall extremes are inferred from both peaks‐over‐threshold and annual peak analyses for the period from 2000 to 2021. There are pronounced spatial gradients in short‐duration rainfall extremes over the study region, with peak values of rainfall between Baltimore City and Chesapeake Bay. Spatial gradients in 100‐year, 1 hr rainfall over 20 km length scale are comparable to time trends over 20 years. Rainfall analyses address the broad challenge of assessing changing properties of short‐duration rainfall in urban regions. Analyses of high‐resolution rainfall fields show that sub‐daily rainfall extremes are only weakly related to daily extremes, pointing to difficulties in inferring climatological properties of sub‐daily rainfall from daily rainfall analyses. Changing measurement properties are a key challenge for application of radar rainfall data sets to detection of time trends. Mean field bias correction of radar rainfall fields using rain gauge observations is an important tool for improving radar rainfall fields and provides a useful tool for addressing problems associated with changing radar measurement properties. Key Points: Rainfall frequency analysis tools based on long‐term, high‐resolution radar rainfall fields are developedSub‐daily rainfall extremes for the Baltimore study region exhibit increasing trends over 22‐year period of recordSub‐daily rainfall extremes exhibited pronounced spatial heterogeneities over the Baltimore study region [ABSTRACT FROM AUTHOR]

Meteor radar observations provide wind data ranging from 80 to 100 km altitude, while the Michaelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) onboard the Ionospheric Connection Explorer satellite offers wind data above 90 km altitude. This study aims to generate wind profiles in the mesosphere and lower thermosphere by combining the winds derived from meteor radar and MIGHTI observations over the Korean Peninsula from January 2020 to December 2021. The wind profiles derived from the two instruments are continuous at night, but they show discrepancies during the day. The atomic oxygen 557.7 nm (green line) emission intensity measured by MIGHTI peaks at approximately 100 km during the day and 94 km at night. The vertical gradient of the airglow volume emission rate is more pronounced during the day. These differences can cause day‐night differences in the MIGHTI wind retrieval accuracy, potentially leading to discrepancies during the day. Plain Language Summary: This study aims to derive vertical wind profiles in the mesosphere and lower thermosphere (MLT) by integrating wind measurements from different techniques. Neutral winds in the MLT provide a means to study the activity of various atmospheric waves originating from the lower thermosphere and their propagation to the upper thermosphere. The Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) instrument onboard the Ionospheric Connection Explorer satellite provides wind measurements above 90 km altitude. A meteor radar in Korea provides wind data in the altitude range of 80–100 km. By combining the MIGHTI and meteor radar observations, we derive extended wind profiles in the MLT. While nighttime winds driven from two different techniques show good agreement, discrepancies exist in daytime winds. Vertical gradients in airglow intensity can affect the wind retrieval from MIGHTI's airglow measurement, and this factor can be one of the sources of daytime discrepancy. Key Points: Vertical wind profiles above 80 km are derived from meteor radar and Ionospheric Connection Explorer (ICON) observations over KoreaThese observations produce continuous wind profiles at night, but discontinuity exists between two measurements during daytimeSignificant vertical variation in airglow intensity on the dayside can impact wind retrieval from ICON airglow observations [ABSTRACT FROM AUTHOR]

Understanding the diurnal variation of horizontal wind in the atmospheric boundary layer is important for weather and climate research and wind energy applications. Here we analyze the hourly data from 91 wind profiler radar sites in China and observe that the power spectral density of horizontal wind in lower troposphere approximately follows the −5/3 power law in the mesoscale range over the ocean and coastal areas. However, in inland areas, the slopes of the power spectra are significantly greater than −5/3. We characterize the temporal and spatial variations of maximum wind speed and low level jets and find that the thermal wind effect may partially contribute to the high percentage of low‐level jets observed in the southeastern coast of China and Hainan Island. While the ERA5 reanalysis reproduces wind spectrum well for time scales >1 day, its spectrum diverges significantly from that of profiler data at shorter time scales. Plain Language Summary: Understanding how the horizontal wind changes throughout the day in the lower part of the atmosphere is important for studying weather and climate and using wind energy. In this study, we looked at data from 91 radar sites in China that measure wind every hour. We found that the pattern of how the wind changes follows a specific mathematical relationship, called a power law, where the wind decreases in a particular way with decreasing spatial and temporal scales. This pattern holds true mostly over the ocean and coastal areas, but in areas further inland, the wind behaves a bit differently. We also studied how the low‐level jet (LLJ), which are fast winds at low altitudes, change over time and space. We discovered that the difference in temperature across the region contributes to the occurrence of these LLJs in coastal areas in southeast China and Hainan Island. Finally, we compare our findings with the ERA5 reanalysis, which demonstrates excellent agreement in reproducing the wind spectrum for time scales greater than 1 day. However, the spectrum derived from the ERA5 reanalysis diverges significantly from the profiler data at shorter time scales. Key Points: Wind spectrum density from wind profiler radars over China shows a less negative slope over inland sites than the −5/3 power law over oceanTemporal and spatial variation of maximum wind speed in the lower troposphere and low level jets are characterizedWind spectrum from ERA5 reanalysis is realistic (deficient) for time scales >1 day (<1 day) compared with profiler data [ABSTRACT FROM AUTHOR]

Multibiomarker studies are useful to evaluate the early warning signs of environmental degradation, and their unified responses are often assessed through two common indexes, Integrated Biomarker Response (IBR) and Integrated Biological Responses version 2 (IBRv2). The R package IBRtools allows users to calculate both IBR and IBRv2 while simultaneously incorporating all the biomarkers under evaluation. The package includes functions for calculating the indexes IBR and IBRv2 and obtaining their standardized values, as well a function for radar chart creation and three example datasets. Here we describe the main algorithms involved in IBR and IBRv2 calculations, a description of the novel package and illustrate a workflow using data examples available on the package to guide the user on how to accurately acquire the values for either the IBR index or the IBRv2 index. The IBRtools package provides a user‐friendly platform for R users to obtain IBR index and IBRv2 values, making it straightforward even for large datasets. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Radar, Sonar & Navigation. It discusses the advances in AI-assisted radar sensing applications and the challenges that hinder its adoption in this field. The special issue of the journal features nine papers that address these challenges and offer innovative ideas and experimental results. The papers cover a range of topics, including health monitoring, human activity recognition, voice identification, elderly care health monitoring, track-to-track association, signal pre-processing, traffic congestion alleviation, and target recognition. The authors express their gratitude to the contributors and reviewers and believe that the research presented will inspire further exploration and innovation in this field. [Extracted from the article]

The authors present multiple machine learning‐based methods for distinguishing maritime targets from sea clutter. The main goal for this classification framework is to aid future millimetre wave radar system design for marine autonomy. Availability of empirical data at this frequency range in the literature is scarce. The classification and anomaly detection techniques reported here use experimental data collected from three different field trials from three different millimetre wave radars. Two W‐band radars operating at 77 and 94 GHz and a G‐band radar operating at 207 GHz were used for the field trial data collection. The dataset encompasses eight classes including sea clutter returns. The other targets are boat, stand up paddleboard/kayak, swimmer, buoy, pallet, stationary solid object (i.e. rock) and sea lion. The Doppler signatures of the targets have been investigated to generate feature values. Five feature values have been extracted from Doppler spectra and four feature values from Doppler spectrograms. The features were trained on a supervised learning model for classification as well as an unsupervised model for anomaly detection. The supervised learning was performed for both multi‐class and 2‐class (sea clutter and target) classification. The classification based on spectrum features provided an 84.3% and 80.1% validation and test accuracy respectively for the multi‐class classification. For the spectrogram feature‐based learning, the validation and test accuracy for multi‐class increased to 93.3% and 88.7% respectively. For the 2‐class classification, the spectrum feature‐based training accuracies are 88.1% and 86.8%, whereas with the spectrogram feature‐based model, the values are 95% and 94.1% for validation and test accuracies respectively. A one class support vector machine was also applied to an unlabelled dataset for anomaly detection training, with 10% outlier data. The cross‐validation accuracy has shown very good agreement with the expected anomaly detection rate. [ABSTRACT FROM AUTHOR]

Millimetre‐wave radar has been widely used in health monitoring and human activity recognition owing to its improved range resolution and operation in a variety of environmental conditions. With the MIMO antenna array, 4D radar is increasingly employed in autonomous driving, while its application in assisted living is recent and therefore the value added compared to the increase in signal processing and hardware requirements is still an open question. A model for 4D Time‐division multiplexing (TDM) multiple‐input‐multiple‐output (MIMO) frequency‐modulated Continuous wave radar is established using the human activities from the HDM05 motion capture dataset. The simulator produces an end‐to‐end simulation, including four human motions (jumping Jack, kick, punch, and walk), signal time of flight, noise, MIMO signal processing, and classification. Different pre‐processing and point cloud‐based methods are compared to obtain an average classification accuracy of 90% with PointNet. This study simulates a specific 4D TDM MIMO radar configuration to benchmark signal pre‐processing algorithms, which can also assist other researchers to generate range‐Doppler‐time (range‐Doppler time) point cloud data sets for human activities testing different radar configurations, array configurations, and activities saving valuable time in human resources and hardware development before prototyping to assess expected performances. [ABSTRACT FROM AUTHOR]

Radar‐based human activity recognition is considered as a competitive solution for the elderly care health monitoring problem, compared to alternative techniques such as cameras and wearable devices. However, raw radar signals are often contaminated with noise, clutter, and other artifacts that significantly impact recognition performance, which highlights the importance of prepossessing techniques that enhance radar data quality and improve classification model accuracy. In this study, two different human activity classification models incorporated with pre‐processing techniques have been proposed. The authors introduce wavelet denoising methods into a cyclostationarity‐based classification model, resulting in a substantial improvement in classification accuracy. To address the limitations of conventional pre‐processing techniques, a deep neural network model called Double Phase Cascaded Denoising and Classification Network (DPDCNet) is proposed, which performs end‐to‐end signal‐level classification and achieves state‐of‐the‐art accuracy. The proposed models significantly reduce false detections and would enable robust activity monitoring for older individuals with radar signals, thereby bringing the system closer to a practical implementation for deployment. [ABSTRACT FROM AUTHOR]

Building on previous radar‐based human activity recognition (HAR), we expand the micro‐Doppler signature to 6 domains and exploit each domain with a set of handcrafted features derived from the literature and our patents. An adaptive thresholding method to isolate the region of interest is employed, which is then applied in other domains. To reduce the computational burden and accelerate the convergence to an optimal solution for classification accuracy, a holistic approach to HAR optimisation is proposed using a surrogate model‐assisted differential evolutionary algorithm (SADEA‐I) to jointly optimise signal processing, adaptive thresholding and classification parameters for HAR. Two distinct classification models are evaluated with holistic optimisation: SADEA‐I with support vector machine classifiers (SVM) and SADEA‐I with AlexNet. They achieve an accuracy of 89.41% and 93.54%, respectively. This is an improvement of ∼11.3% for SVM and ∼2.7% for AlexNet when compared to the performance without SADEA‐I. The effectiveness of our holistic approach is validated using the University of Glasgow human radar signatures dataset. This proof of concept is significant for dimensionality reduction and computational efficiency when facing a multiplication of radar representation domains/feature spaces and transmitting/receiving channels that could be individually tuned in modern radar systems. [ABSTRACT FROM AUTHOR]

The millimeter‐wave radar sensor is widely used for urban traffic surveillance because of its weather resistance and high detection accuracy. Methods such as fuzzy theory, pattern recognition, and artificial neural networks have been integrated into the research of traffic state discrimination. However, research on systematically describing the fusion of sensors and traffic state discrimination algorithms to alleviate urban road congestion is still lacking, especially based on millimeter‐wave radar. Thus, the authors propose an urban traffic congestion alleviation system framework. First, the design and deployment of the millimeter‐wave radar system, including waveforms, signal processing flow, and target tracking, are demonstrated to achieve vehicle information acquisition and output. Then, the appropriate traffic parameters are obtained by analysing traffic state influencing factors and the radar data characteristics. Finally, a traffic conditions identification algorithm combining spectral clustering and neural network algorithm is presented to realise road congestion level classification. The system is applied to real urban intersections rather than simulation or approximate real simulation. According to the current road congestion level, regulate the traffic light state to achieve road vehicle driving command. Experiments show that the proposed system can effectively reduce road congestion by 20% compared to the current fixed traffic light system. [ABSTRACT FROM AUTHOR]

Summary: Given the significant impact of sleep on overall health, radar technology offers a promising, non‐invasive, and cost‐effective avenue for the early detection of sleep disorders, even prior to relying on polysomnography (PSG)‐based classification. In this study, we employed an attention‐based bidirectional long short‐term memory (Attention Bi‐LSTM) model to accurately predict sleep stages using 60 GHz frequency‐modulated continuous‐wave (FMCW) radar. Our dataset comprised 78 participants from an ongoing obstructive sleep apnea (OSA) cohort, recruited between July 2021 and November 2022, who underwent overnight polysomnography alongside radar sensor monitoring. The dataset encompasses comprehensive polysomnography recordings, spanning both sleep and wakefulness states. The predictions achieved a Cohen's kappa coefficient of 0.746 and an overall accuracy of 85.2% in classifying wakefulness, rapid‐eye‐movement (REM) sleep, and non‐REM (NREM) sleep (N1 + N2 + N3). The results demonstrated that the models incorporating both Radar 1 and Radar 2 data consistently outperformed those using only Radar 1 data, indicating the potential benefits of utilising multiple radars for sleep stage classification. Although the performance of the models tended to decline with increasing OSA severity, the addition of Radar 2 data notably improved the classification accuracy. These findings demonstrate the potential of radar technology as a valuable screening tool for sleep stage classification. [ABSTRACT FROM AUTHOR]

We show that Traveling Ionospheric Disturbances (TIDs) may affect the Farley Buneman Instability (FBI) and Gradient Drift Instability (GDI) echoes referred to as the Near Range Echoes (NREs) in the SuperDARN radar backscatter from the lower part of the E‐region. TIDs and NREs are observed concomitantly by the Zhongshan SuperDARN radar (69.38°S, 76.38°E) in the far and near ranges, respectively. At the moment, there is no study about the effects of TIDs on the NREs caused by the FBI using the SuperDARN radars. The GDI are more likely to occur at a lower altitude while FBI occurs at a slightly higher altitude in the lower part of the ionospheric E‐region. We use the Spearman Correlation Coefficient (SCC) to show that a part of the NREs backscatter power could be statistically explained by the MSTIDs backscatter power received by the same radar. We also investigate the simultaneous occurrence rate of the NREs and MSTIDs during the 24th solar cycle. Seasonal variability shows that MSTIDs‐NREs events over Zhongshan mostly occur in summer and equinoxes during local night and morning. The majority of these events lasted between ∼4 and ∼8 hr. Most events disappeared early in the morning. Statistics of the Spearman correlation coefficient values show that ∼9% of NRE amplitude modulation could be due to the MSTIDs. There are almost equal numbers of negative and positive Spearman correlation coefficient values. The relative velocity between the E‐region NREs and the F‐region MSTIDs switching the electric field polarities between the crests and troughs could be the cause of those equal number of the Spearman correlation coefficient values. The orientation of the ionospheric current relative to the MSTID polarization electric field may also play a significant role in the reported Spearman correlation coefficient values. We argue that in some cases, the TIDs might have been close enough to the NREs altitude to modulate them directly by transporting the plasma up and down through shear or compression. Plain Language Summary: SuperDARN radar Near Range Echoes (NREs) were observed at an altitude range of 95–125 km and are caused by the Gradient Drift Instability (GDI) and Farley Buneman Instability (FBI). Medium Scale Traveling Ionospheric Disturbances (MSTIDs) are wave‐like perturbations of plasma density that propagate in the ionosphere. They are caused by any major ionospheric energy input such as the Atmospheric Gravity Waves (AGWs), Joule heating, Perkins instability, and so on. It was argued that the MSTIDs partially modulate the NREs backscatter power associated with the GDI through the polarization electric field. We use the Zhongshan SuperDARN radar to show that apart from GDI, FBI related echoes are also partially modulated by the MSTIDs. We also record the number of MSTID‐NRE events during 2010–2019 and found that the majority of them occurs in summer and equinoxes during local night and morning while the minority occurred in the winter of the southern hemisphere. We also perform a statistical study of the Spearman correlation coefficient values of all events recorded for this study. Key Points: First demonstration of TIDs partially modulating Farley Buneman (FB) and Gradient Drift (GD) wavesFarley Buneman and Gradient Drift Instabilities generate NREsSpearman rank correlation analysis shows that statistically ∼9% of NRE amplitude modulation could be due to the MSTIDs [ABSTRACT FROM AUTHOR]

Summary: In order to ensure that the electromagnetic compatibility and mutual interference of multiple radar network systems meet the requirements, a 55‐W gallium nitride (GaN) high harmonics suppression (HSs) high power amplifier (HPA) microwave monolithic integrated circuit (MMIC) with an operating frequency of 5.1–6.3 GHz is designed for radar network system. An HSs method using self‐resonant capacitor integrated in the output matching network of HPA is proposed to improve the HSs characteristics. The measured results show that under the condition of 100‐μs pulse width and 10% duty cycle, the HSs are greater than 48 dBc while the output power is greater than 55 W, the power gain is 21 dB, and the power additional efficiency (PAE) is 54%, which is suitable for use in radar network system. The size of the HPA is 3.55 × 4.05 mm2. [ABSTRACT FROM AUTHOR]

Both sets of Martian polar layered deposits (PLDs) display semi‐rhythmic reflector stratigraphy in radargrams. The southern PLDs (SPLD), among other peculiarities, also contain widespread, distinctive deposits with few to no reflections that commonly occur at or near the top of the stratigraphic column. Here, we study the low reflectivity zones (LRZs) to determine whether they exhibit radar properties indicative of non‐water ice. We find that the previously reported CO2‐ice LRZ exhibits a distinct radar signature compared to all other LRZs. We also found behavior that suggests that the other LRZs may contribute to increased radar signal attenuation, which does not support a CO2 ice composition. We observe many instances of LRZs comprising the entire SPLD column and find that the radar attenuation varies across different LRZs, suggesting unique properties within each of these deposits. Variable radar attenuation of LRZs may be connected to differences in bulk compositions, radar‐sensitive roughness characteristics, and/or distributions of stratigraphic materials. Plain Language Summary: From orbital data, we know that both of the Martian polar caps contain widespread layers of dusty water ice. However, radar data, in particular, reveal that the southern cap also contains large sections of layer‐free material. Previous studies have established that one of these occurrences of layer‐free material is composed of carbon dioxide ice. We looked at the remaining occurrences of layer‐free zones in the southern cap and found that they do not show the same radar signatures as those used to deduce the composition of the carbon dioxide unit. These other layer‐free units are often associated with decreased signal strength compared to regions where layers are present. We also see variations in the signal strength across these layer‐free units, suggesting subtle differences in their composition, material distribution, or surface roughness of the polar cap. Key Points: The large carbon‐dioxide ice deposits of Australe Mensa exhibit radar characteristics distinct from other low reflectivity zonesThe outlying low reflectivity zones show evidence of increased radar attenuation relative to layered regionsVariation in attenuation characteristics across low reflectivity zones suggests unique properties across units [ABSTRACT FROM AUTHOR]

Radar data can be of significant utility in investigating characteristics of rainfall events that cannot be studied with rain gauges alone. The recent establishment of a long‐term, quality‐controlled data set covering most of the radars on the Australian continent enables a deeper characterization of rainfall, including heavy rainfall events. This study develops a methodology to identify and characterize rainfall events from radar data and tests its utility by applying it to the regions surrounding the major cities of Brisbane, Sydney, and Melbourne. The event characteristics studied include rainfall accumulation and intensity, event duration and spatial extent, and the contribution convective areas make to the overall event rainfall. Rainfall events in Brisbane and Sydney are found to be more intense, more convective, and smaller in extent whilst producing larger rainfall accumulations than Melbourne rainfall events. Rainfall event duration and total accumulated rainfall are strongly positively correlated, as are the overall event intensity and the intensity of convective rainfall. The events that produce the largest rainfall accumulations exhibit significant differences from the events that produce the highest rainfall intensities. Overall, the study demonstrates that long‐term radar data sets in Australia provide an invaluable and rich source to study rainfall characteristics in a variety of regions at a high spatial and temporal resolution. Plain Language Summary: Weather radars produce a map of rainfall across a large area. Radars can observe characteristics of rain systems that are difficult to capture with rain gauge networks. A data set containing observations from weather radars in Australia can be used to study the characteristics of rain events. Such characteristics include how long an event lasts, how intense the rain is, how much rain falls overall, how large the event is, and how much of the rain comes from strong ascent (convection). This study develops a method of using radar to find rain events and assess their characteristics, and tests the method using Brisbane, Sydney, and Melbourne radar data. Rain events in Brisbane and Sydney have heavier rain and more rain coming from convective areas, and cover smaller areas, with more rain falling than in events in Melbourne. The longer a rain event is, the more rain is produced. An event having high rain intensity also means it produces intense rain from convective areas. Rain events that produce large rainfall amounts are distinct from those which have very intense rain. This study demonstrates that radar data sets in Australia are very useful for studying rain events. Key Points: Brisbane and Sydney rainfall events are more intense and convective than Melbourne rainfall events, which cover larger areasRainfall events with the highest accumulations and rainfall events with the highest intensities show significant differences in characterLong‐term radar data sets are useful for in‐depth analysis of rainfall and heavy rainfall event characteristics in Australia [ABSTRACT FROM AUTHOR]

This two‐part study presents a detailed analysis of (a) the vortex structure, (b) the inner core planetary boundary layer (PBL) wind profile, and (c) the overland surface winds of Typhoon Faxai (2019) during landfall observationally (Part I) and in high‐resolution Weather Research and Forecasting model simulations (Part II). Part I presents a comprehensive observational analysis of (a), (b), and (c) of Typhoon Faxai (2019) based on radar dual‐Doppler analysis and spatially dense surface wind observations over the Kanto region of Japan. An empirical model for the wind reduction factor F2 km$$ {F}_{2\;\mathrm{km}} $$ is proposed and validated with surface observations and radar velocity–azimuth display analyses at Narita and Haneda airports, both of which were affected by the passage of Faxai's inner core. Two distinct PBL jets associated with the inner rain band and the eyewall were observed over Narita Airport, which was located to the right of Faxai's track. Based on the empirical model of F2 km$$ {F}_{2\;\mathrm{km}} $$, it is found that the maximum surface wind speed observed at Narita Airport was caused by the peak of F2 km$$ {F}_{2\;\mathrm{km}} $$ due to a strong PBL jet associated with the eyewall located as close as ∼$$ \sim $$0.3 km from the ground. In addition, the wind swath analysis shows that a large area far inland on the right side of Faxai's track experienced strong surface 10‐min winds due to the combination of the passage of the eyewall PBL jet and the small surface roughness length around agricultural land such as paddy fields and farm fields. [ABSTRACT FROM AUTHOR]

The accurate prediction of high‐impact weather systems using cloud‐resolving models is still a challenge among researchers. This study evaluates the consistency of the combination of the three‐dimensional variational technique within the Gridpoint Statistical Interpolation assimilation system (GSI‐3DVAR) and nudging in the same modelling system on short‐range forecasts of three heavy rainfall events from the southwest monsoon season of 2021 over the Indian subcontinent. Three experiments have been conducted (i) control (CNTL): assimilated conventional and satellite observations; (ii) radar and lightning data assimilation (RLDA): assimilated radar reflectivity and lightning proxy reflectivity data along with all observations used in CNTL; and (iii) lightning data assimilation (LDA): same as RLDA but without the assimilation of radar data; particularly done to test the impact of assimilation of only lightning data. The model‐simulated rainfall is evaluated by using the Integrated Multi‐Satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM IMERG) rainfall data. The intercomparison of LDA and RLDA for event 1 highlighted that both represent the convective regions reasonably better than CNTL, but RLDA outperforms LDA and thus further assimilation experiments are done with RLDA. RLDA provided reasonably accurate forecasts compared to CNTL, which is evident in the spatial distribution of rainfall and area‐averaged three‐hourly accumulated rainfall. Verification metrics for the three selected heavy rainfall events reveal that an optimal forecast performance (especially in the first six hours of free forecast) is obtained by the simulation with assimilation of radar and lightning data during the pre‐forecast period, through correcting the position and timing of convective centres. The probability of detection (POD) values are higher for light rainfall categories than for the heavy rain categories. POD values were higher in RLDA than CNTL throughout simulation for all three events. For all these three selected events, fractions skill scores (FSS) of RLDA are always better than CNTL with different neighbourhood sizes for different threshold values throughout the forecast period. [ABSTRACT FROM AUTHOR]

Precipitation microphysics in tropical cyclones (TCs) are often poorly represented in numerical simulations, which ultimately affects TC structure, evolution, and prediction. This provides a large incentive to better observe and understand the underlying microphysical processes in TCs in order to improve precipitation forecasts and warning operations. 112 TCs from 2014 to 2020 were matched up with overpasses from the NASA Global Precipitation Measurement (GPM) mission Dual‐Frequency Precipitation Radar (DPR) on a global scale to identify cloud properties and associated precipitation processes by quantifying vertical slopes of reflectivity in the liquid and ice phase. Further, vertical profiles of reflectivity were partitioned into different 850–200 hPa shear‐relative quadrants of each storm, different annuli from the storm center, and 5 TC ocean basins globally. Preliminary results showed the highest echo top heights occurred in the Northwest Pacific and Indian Ocean basins, with all basins and quadrants exhibiting median negative slopes of KuPR in the liquid phase. Additionally, all shear‐relative quadrants revealed negative slopes of reflectivity in the ice phase implying ice hydrometeor growth. These findings can potentially be used to improve the representation of cloud properties and the accuracy of the DPR particle size distribution algorithm in TCs. Plain Language Summary: Tropical cyclones play a key role in the Earth's water cycle by transferring heat and moisture from the tropics to the mid‐latitudes, which makes it important to better understand precipitation processes in these systems. As tropical cyclones spend a large portion of their life over the open ocean, the use of ground radar to monitor precipitation is rarely an option, which makes space‐borne radar a useful tool for quantifying precipitation. This study investigates precipitation processes in tropical cyclones on a global scale using space‐borne radar. The results suggest that precipitation mechanisms acting to increase or reduce the size of precipitation particles varied by ocean basin and in different areas relative to the storm center. These differences were determined based off the vertical profiles of radar reflectivity indicating a change in precipitation size and concentration with height. Key Points: A 7‐year global analysis of precipitation microphysics in tropical cyclones using space‐borne radarThere exists differences in microphysical processes in tropical cyclones by distance from the storm center, ocean basin, and shear‐relative quadrantAll ocean basins exhibit negative median slopes of reflectivity in both the liquid and ice phases, suggesting primarily hydrometeor growth [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are crucial in modifying the water cycle and frequently induce high‐impact weather events over eastern China. Radar and Climate Prediction Center (CPC)‐4 km satellite‐derived infrared cloud top temperature (Tb) data were used to thoroughly analyze the long‐term climatology of MCSs over eastern China, particularly in the Yangtze–Huaihe River Basin (YHR) in the warm season from 2013 to 2018. For the first time, we contrasted the effects of data set selection and threshold setting on research outcomes. The large‐scale environments of MCSs initiation were also investigated using the latest global reanalysis data ERA5. It is found that striction of thresholds, including duration, reflectivity/Tb, area, and linearity, would lead to a greater proportion of early‐morning MCSs. Satellite‐identified MCSs differed from radar‐derived ones, exhibiting afternoon diurnal peaks, faster movement speeds, longer travel distances, and expansive impact areas. The center of MCS and related precipitation shifted northward from Pre‐Meiyu to Post‐Meiyu seasons, contributing to up to 20% of total rainfall, with most MCSs moving along eastward trajectories. MCSs typically had the most substantial impact in the Meiyu season because of the most prolonged duration, largest convective core area, and strongest precipitation intensity. Warm‐season MCSs initiated ahead of midlevel troughs and were related to strong anomalous low‐level convergence and midlevel upward. The circulation anomalies were the strongest in the Pre‐Meiyu season among the three subseasons, with most moisture sourced from the southwest. Plain Language Summary: This work used radar and satellite data to analyze the climate characteristics of a kind of large thunderstorms known as mesoscale convective systems (MCSs) in the Yangtze–Huaihe River Basin (YHR) in eastern China. These systems cover large areas, lasting from a few hours to a few days. The analysis revealed that stricter tracking thresholds would lead to more early‐morning MCSs. Satellite‐identified MCSs differed from those identified by radar. They tended to have more thunderstorms in the afternoon, move faster, travel longer distances, and cover larger areas. MCSs also contributed to up to 20% of the total warm‐season rainfall in the YHR. MCSs typically had the most substantial impact in the rainy season of eastern China from mid‐June to mid‐July. Warm‐season MCSs initiated ahead of low‐pressure systems and were related to strong anomalous low‐level convergence and midlevel upward motion. During the time before the rainy season, the large‐scale forcing needed to be anomaly enough to trigger MCS formation. Most of the moisture that caused MCSs came from southwest direction instead of stemming locally during this period. Key Points: Six‐year climatology of warm‐season mesoscale convective systems (MCSs) in Yangtze–Huaihe River Basin is investigated using satellite, radar data, and ERA5Criteria setting for tracking MCSs noticeably influences the derived featuresMeiyu season MCSs featured longest duration, largest areas, and heaviest precipitation, while the background circulation anomalies are the weakest [ABSTRACT FROM AUTHOR]

An analysis of the parameter estimation uncertainty for the target location and velocity achievable using a single‐transmitter‐multiple‐receiver multistatic radar system is presented. A framework for establishing measures of multistatic radar parameter uncertainties by expansion of the bistatic radar parameter uncertainty measures is presented for systems containing omnidirectionally radiating nodes. The methodology uses analytical methods based on the Cramér–Rao Lower Bounds applied to scenarios in a two‐dimensional physical space with a single target exhibiting Doppler characteristics and a bistatic angle dependent radar cross‐section. A set of geometric descriptors is proposed to characterise the system, and parameter uncertainty results are reported as a function of these descriptors. The results indicate that angular separation between the transmitter and the centre of the receiver distribution is of greater importance than the quantity of receivers within the system when low uncertainty estimation capabilities are desired, though a minimum of two receivers must be available. The proportion of receivers within the system which contributed information crucial to obtaining the minimum estimation uncertainty is reported for systems containing different quantities of receivers. It was observed that, as the total number of receivers available increased, the proportion of receivers required to achieve the minimal uncertainty level reduces significantly. [ABSTRACT FROM AUTHOR]

With the advent of constellations of SAR satellites, and the possibility of swarms of SAR UAV's, there is increased interest in multistatic SAR image formation. This may provide advantages including allowing three‐dimensional image formation free of clutter overlay; the coherent combination of bistatic SAR geometries for improved image resolution; and the collection of additional scattering information, including polarimetric. The polarimetric collection may provide useful target information, such as its orientation, polarisability, or number of interactions with the radar signal; distributed receivers would be more likely to capture any bright specular responses from targets in the scene, making target outlines distinct. Highlight results from multistatic polarimetric SAR experiments at the Cranfield University GBSAR laboratory are presented, illustrating the utility of the approach for fully sampled 3D SAR image formation, and for sparse aperture SAR 3D point‐cloud generation with a newly developed volumetric multistatic interferometry algorithm. [ABSTRACT FROM AUTHOR]

This article is a guest editorial for the IET Radar, Sonar & Navigation journal, focusing on selected papers from the RADAR 2022 conference held in Edinburgh, UK. The conference provided an opportunity for radar specialists from 22 countries to explore the latest developments in radar systems. Key topics discussed at the conference included new radar trends, target detection (with a focus on drones), low-frequency radar, and cognitive radar. The special issue contains 17 papers based on extended work presented at the conference, covering topics such as multistatic radar, passive radar, target signatures, and advanced radar processing techniques. The authors hope that this special issue will serve as a valuable resource for further research in the field. [Extracted from the article]

Urban surveillance of slow‐moving small targets such as drones and birds in low to medium airspace using radar presents significant challenges. Detecting, locating and identifying such low observable targets in strong clutter requires both innovation in radar hardware design and optimisation of processing algorithms. To this end, the University of Birmingham (UoB) has set‐up a testbed of two L‐band staring radars to support performance benchmarking using datasets of target and clutter from realistic urban environment. This testbed is also providing the vehicle to understand how novel radar architectures can enhance radar capabilities. Some of the challenges in installing the radar at the UoB campus are highligted. Detailed benchmarking results are provided from urban monostatic and bistatic field trials that form the basis for performance comparison against future hardware modification. The solution to the challenge of interfacing the radar to the external oscillators is described and stand‐alone bench tests with the candidate oscillators are reported. The testbed provides a valuable capability to undertake detailed analysis of performance of Quantum photonic‐enabled radar and allows for its comparison with conventional oscillator technology for surveillance of low observable targets in the presence of urban clutter. [ABSTRACT FROM AUTHOR]