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Abstract 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.

As an increasing number of people suffer from sleep disorders, such as insomnia or sleep apnea, sleep monitoring and management using consumer devices have gained increasing attention from research communities. As sleep quality is closely related to sleep structure based on hypnograms, the classification of sleep stages over the course of the night is important for accurate sleep monitoring. We present sleep stage classification using a smartphone equipped with ultra-wideband (UWB) radar. We focused on the development of easily accessible sleep monitoring system for the general population by placing the smartphone on a table near a bed, which is commonly used during sleep. We collected 509 nights of UWB radar and nocturnal in-laboratory polysomnography (PSG) data from various participants, including patients with apnea, using a customized Samsung Galaxy smartphone with a UWB radar chip placed on a table near the bed. A combination of 1D convolutional neural network and transformer architecture was proposed in this study, and a domain adaptation technique was applied to train the model with both large-scale respiratory signals from open database PSGs and UWB radar data to boost the performance by overcoming the lack of UWB radar data. With 5-fold validation, an epoch-by-epoch comparison between the predicted and expert-annotated four sleep stages (Wake, REM sleep, light sleep, and deep sleep) resulted in 0.76 of accuracy and 0.64 of Cohen’s kappa. This study demonstrated that sleep stages can be monitored with substantial accuracy by simply placing a smartphone on a bedtable, making it highly usable and reliable in real use cases.

In recent years, the Poisson multi-Bernoulli mixture (PMBM) filter has been established among the state-of-the art methods in target tracking. We present a method for including target-provided measurements in said filter, both when using it to track extended objects and point targets. We use messages from the Automatic identification system as an example of target-provided measurements, and radar and LiDAR as examples of exteroceptive sensors. In the point target case, we utilize several different kinematic models in parallel through the interacting multiple models framework, and compare the presented method to several common trackers and other PMBM filter configurations. The results show that our method outperforms similar methods when target-provided measurements are available. The point target variant is also shown to work in a closed-loop collision avoidance experiment in a maritime environment, demonstrating its feasibility for use in real-world applications. For the extended object tracking case, we expand upon the Gaussian process PMBM filter. The extended object method is evaluated on both simulated and experimental data, and is shown to improve the tracking performance when including target-provided measurements in comparison to when it only uses exteroceptive measurements.

For the problem of power allocation between a distributed multistatic radar network and a smart jammer, the application of non-cooperative game theory is employed to address the issue in this paper. Consequently, three scenarios of power allocation games are examined. The first two game scenarios, characterized by information asymmetry, are categorized under the Stackelberg game framework, while the final scenario, with information symmetry, is classified as a non-cooperative game. Through the power allocation analyses of the three game scenarios, it is observed that both the radar system and the jammer possess a first-mover advantage. Additionally, the existence and uniqueness of the Nash equilibrium in the games are demonstrated. Based on the best response strategies within the games, three corresponding power allocation game algorithms are proposed. Ultimately, the convergence and performance comparison of the three power allocation game algorithms are validated through simulation experiments.

This paper develops a novel stochastic geometry model to investigate design tradeoffs in a large-scale radar-aided millimeter-wave cellular network to eliminate the beam training overhead. In the proposed system, each base station comprises two sub-systems: the sensing sub-system, where radar localizes users, and the communication sub-system, where directional antennas serve the detected users. Both sub-systems operate simultaneously to eliminate the beam training overhead and reduce misalignment errors. The system is modeled under realistic fading conditions with scatterers and interferers in the environment. System parameters, such as the density of mobile users and undesired clutter, radar cross-section fluctuations, radar search duration, antenna directivity, and bandwidth are considered in the analytical model. Two scenarios are studied; the first considers antenna misalignment for the communication sub-system, where error magnitude depends on the radar sub-system accuracy. The other scenario considers perfect alignment for benchmarking. It is demonstrated that the radar search duration, which controls the radar antenna beamwidth, is a crucial parameter for optimizing the system throughput. Moreover, the beamwidth of the communication antenna exhibits a tradeoff in which a narrow beam yields higher gain and reduces interference but increases the misalignment error impact. The results demonstrate that if the beamwidths of both sub-systems are carefully chosen, the proposed system can eradicate the beam training overhead without compromising the system’s performance, in addition to significantly enhancing the average total system throughput.

In this paper, we propose an S/X-band shared-aperture array antenna with a mutual complementary design to improve the electromagnetic (EM) transparent characteristics. A unit-cell of the proposed antenna includes one dual-loop element for the S-band and $3\times 3$ dual-loop elements for the X-band. To configure the shared-aperture structure in a limited space, the S-band element is stacked on top of the X-band elements. To solve the practical engineering problems of the shared-aperture antennas, novel design techniques such as using a mutual complementary structure, a coupling compensation array, an interface layer, and an antenna modularization are employed. To verify the antenna feasibility, the fabricated unit-cell extends into a $4\times 4$ unit-cell array. The fractional bandwidth of the reflection coefficients for the proposed array are 14.7% and 15% in the S- and X-bands, respectively. In the S-band, as the steering direction of the main beam increases from 0° to 45°, the maximum gain decreases from 14.6 dBi to 11.8 dBi. In the X-band under the same conditions, the maximum gain varies from 26.6 dBi to 25.3 dBi.

This article aimed to monitor vegetation using C-band radar data at a subdaily time step. To this end, radar measurements using tower-mounted antennas with a 15-min time step, along with physiology-related information (sapflow and micrometric dendrometry), were acquired quasi-continuously from March 2020 to December 2021 in an olive orchard located near Marrakech, Morocco. The article focused on temporal coherence, whose clear diurnal cycle (highest at night and lowest at the end of the afternoon) had been highlighted over tropical and boreal forests in previous studies. The results showed that coherence was highly sensitive to: wind-induced movement of scatterers, since coherence was lowest when wind speed was highest in late afternoon, and vegetation activity, especially its water dynamics, since the morning coherence drop coincided with the onset of sapflow and the daily evapotranspiration cycle, as well as the good agreement between the temporal drop rate of coherence and the daily residual variation in trunk circumference (i.e., deviation from long-term trend). Finally, coherence remained high for temporal baselines of several days, showing that sentinel-1 data (when both satellites are operational) may be well suited for such studies, especially with acquisitions made during morning passes, when wind speed is low. These results open perspectives for monitoring tree crop physiology using high-revisit-time radar observations.

Abstract 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 This paper proposes a simple non‐adaptive approach for the suppression of direct signal and clutter contributions in a passive radar system based on orthogonal frequency division multiplexing transmissions, exploiting the properties of a reciprocal filter (RF). The use of a RF allows to circumvent the limits posed by the waveform ambiguity function, producing a data‐independent time‐invariant response to a stationary point‐like target echo at the output of the range compression stage. This feature enables simple clutter cancellation strategies, based on the subtraction of delayed portions of the surveillance signal, according to the conventional moving target indication methodologies. The proposed processing scheme consists of a range compression stage based on a RF, followed by a Single Canceller. Its performance is investigated against a simulated and an experimental data set, for the case of a digital video broadcasting‐terrestial‐based passive radar scenario, and it is compared to an alternative scheme based on a conventional matched filter and adaptive cancellation techniques. The proposed approach is shown to yield a perfect cancellation of the clutter returns from a stationary scene under ideal conditions. Moreover, it proves to be robust to real‐world conditions, despite its considerably limited computational load compared to adaptive clutter cancellation techniques.

Abstract The millimetre wave radar signatures of sea lions collected from three animals in the outdoor seal pool available at the Sea Mammal Research Unit in St Andrews in the Autumn of 2021 is reported. The objective is to study the radar amplitude and Doppler signatures of the animals when their full body or part thereof is above water, which is important for the application of autonomous marine navigation. The data was collected using 24 GHz (K‐band) and 77 GHz (W‐band) Frequency Modulated Continuous Wave radars with linear polarisation. It has been demonstrated that the sea lions were very clearly detected by the radars with Signal to Noise Ratio greater than 30 dB at a range of 40 m. The calculated modal radar cross section (RCS) of the sea lions in HH polarisation at 24 and 77 GHz vary from −48 to −26 dBsm and −48 to −28 dBsm respectively, corresponding to the different body parts and the amount of exposure to the radar beam. In VV polarisation, the modal RCS value range is from −49 to −26 dBsm and −49 to −22 dBsm respectively. The corresponding maximum RCS and the Cumulative Distribution Function results are also reported.

Abstract The paper focuses on the results of experimental research on the possibility of passive space object detection using a single radio telescope from the European Low‐Frequency Array (LOFAR) network of astronomical radio telescopes. Commercial digital television (DVB‐T) transmitters were used as illuminators of opportunity in this radar system. In the conducted experiments, one LOFAR radio telescope served both as a surveillance receiver and reference receiver in a passive radar system. The greater part of the LOFAR telescope array was used to observe a space object, while a small part of the array was directed towards the illuminator of opportunity to record the reference signal. One of the most important problems to overcome with utilising the LOFAR radio telescope in such a solution was the effective suppression of the direct‐path component in a surveillance signal coming from the illuminator of opportunity, which was relatively close to the LOFAR radio telescope. The results regarding the passive detection of the International Space Station, included in the paper, confirm the possibility of observing the space object flying in Low‐Earth Orbit using the LOFAR telescope or another receiving system with a similar antenna array.

Abstract The presence and importance of small drones (unmanned aerial vehicles) are increasing. With traditional radar detection the target radar cross section and its statistical properties are needed in order to design the detector and to assess detection performance. The authors make a statistical analysis of the radar cross section of two small fixed‐wing drones using measured data from typical flights. Using data from a typical flight instead of measurements or electromagnetic calculations as a function of aspect angle is a convenient method. Work results of the authors in mean values, medians and standard deviations, in analyses of common target probability distributions and in decorrelation times for the radar cross section, are necessary for design and assessment of the detection performance. The authors suggest a new target probability distribution which agrees better with the measured data than the others tested. The authors also note significant differences between the drones.

Abstract In this article, a simple, yet effective, Bayesian scheme for tracks maintenance, promotion, and deletion in drone surveillance radar is presented. It enables the simultaneous tracking of the target body and micro‐Doppler components that originate from the motion of rotors (if any) onboard an unmanned air system. This not only delivers more accurate multi‐target tracking, but also substantially improves the radar automatic target classification capability (e.g. discriminating between drone and non‐drone targets). Challenging and diverse real staring radar datasets are used here to demonstrate the efficacy and benefits of the proposed track management approach.

Incoherent backscattering of mm-waves from natural rough surfaces is considered. A novel method is proposed to determine the statistical properties of surface scattering from range profile measurements. The method is based on modeling the road surface as a grid of uncorrelated scattering elements, described by normalized scattering matrices. Using this model, expressions are derived to estimate the mean value and covariance matrix of surface scattering from measurement data. This procedure is then applied to measurement data of four road surface types, namely: 1) dry asphalt, 2) wet asphalt, 3) asphalt covered by basalt gravel, and 4) old asphalt. Using the derived statistical normalized radar cross-section models, two novel applications are proposed. First, a procedure for synthesizing/simulating surface clutter is proposed. This procedure is subsequently used to simulate received power from surfaces comprising patches of one or multiple road surface conditions. Excellent agreement between simulation and measurement results is demonstrated. Second, a method for determining the optimal polarization of the electromagnetic sensing waves used in a single-polarized radar system is proposed. This method is based on factorizing the antenna polarization vector into two bounded parameters, allowing for numerical evaluation of the minima and maxima for targets with a specified scattering matrix. This method is further extended to work with statistical descriptions of scattering matrices by means of Monte Carlo simulations.

A promising waveform candidate for future joint sensing and communication systems is orthogonal frequency-division multiplexing (OFDM). For such systems, supporting multiple transmit antennas requires multiplexing methods for the generation of orthogonal transmit signals, where equidistant subcarrier interleaving (ESI) is the most popular multiplexing method. In this work, we analyze a multiplexing method called Doppler-division multiplexing (DDM). This method applies a phase shift from OFDM symbol to OFDM symbol to separate signals transmitted by different Tx antennas along the velocity axis of the range-Doppler map. The main focus of this work lies on the implications of DDM on the communication task. It will be shown that for DDM, the channels observed in the communication receiver are heavily time-varying, preventing any meaningful transmission of data when not taken into account. In this work, a communication system designed to combat these time-varying channels is proposed, which includes methods for data estimation, synchronization, and channel estimation. Bit error ratio (BER) simulations demonstrate the superiority of this communications system compared to ESI-based systems.

Concrete cover is the thickness of the concrete layer that protects the reinforcing steel bars (rebar) within a reinforced concrete structure. It acts as a barrier against external elements such as moisture, chemicals, and environmental factors, safeguarding the rebar from corrosion. Concrete cover measurement is performed by using various nondestructive tests such as GPS (Ground Penetrating Radar), electromagnetic test and ultrasonic. The main benefit of the concrete cover thickness measurement is to explain the causes of corrosion and identify areas that have the capability to corrode faster.

Identifying appropriate methods for any process, such as uncovering needs of juveniles in social welfare or designing interactive systems, requires intensive research and generally using a categorization system that brings methods in a systematic order. These taxonomy systems are heavily responsible for the later method usage and start the thinking process for researchers and practitioners alike in a specific direction. So making participation visible in such taxonomy systems directly affects the later method usage and makes participation more visible and easier to use. This article presents the Method Radar, a visualized categorization of methods with a focus on participation using the ladder of participation, that makes participation visible right at the beginning of any method selection. The Method Radar builds on the radar representation established in the technology sector, which allows a multi-dimensional classification. In addition, an implementation and systematic process for categorizing these methods are presented. It can be used for any form of method categorization in which participation is supposed to be thought of.

Abstract Short‐wave source localisation has an important role in many fields. A two‐stage iterative algorithm is proposed based on the ionospheric virtual height (IVH) model using the direction of arrival (DOA) and time difference of arrival (TDOA) measurements, which is divided into DOA stage and TDOA stage. The optimisation problem of the TDOA stage can be obtained by using the result of DOA stage and TDOA measurements. During the solution process, the non‐convex quadratic equation constraints are converted to linear constraints, which can lead to the algorithm converges to the global optimal solution and the optimal solution can be analytically solved. The theoretical analysis demonstrates that the localisation accuracy of both stages can reach the corresponding constrained Cramér‐Rao lower bound (CCRLB). And the accuracy of TDOA stage achieves the CCRLB of hybrid localisation. Finally, the performance of the proposed method is demonstrated by simulations.

Concerns about perceived widespread declines in insect numbers have led to recognition of a requirement for long-term monitoring of insect biodiversity. Here we examine whether an existing, radar-based, insect monitoring system developed for research on insect migration could be adapted to this role. The radar detects individual larger (greater than 10 mg) insects flying at heights of 150–2550 m and estimates their size and mass. It operates automatically and almost continuously through both day and night. Accumulation of data over a 'half-month' (approx. 15 days) averages out weather effects and broadens the source area of the wind-borne observation sample. Insect counts are scaled or interpolated to compensate for missed observations; adjustment for variation of detectability with range and insect size is also possible. Size distributions for individual days and nights exhibit distinct peaks, representing different insect types, and Simpson and Shannon–Wiener indices of biodiversity are calculated from these. Half-month count, biomass and index statistics exhibit variations associated with the annual cycle and year to year changes that can be attributed to drought and periods of high rainfall. While species-based biodiversity measures cannot be provided, the radar's capacity to estimate insect biomass over a wide area indicates utility for tracking insect population sizes. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

In the current biodiversity crisis, populations of many species have alarmingly declined, and insects are no exception to this general trend. Biodiversity monitoring has become an essential asset to detect biodiversity change but remains patchy and challenging for organisms that are small, inconspicuous or make (nocturnal) long-distance movements. Radars are powerful remote-sensing tools that can provide detailed information on intensity, timing, altitude and spatial scale of aerial movements and might therefore be particularly suited for monitoring aerial insects and their movements. Importantly, they can contribute to several essential biodiversity variables (EBVs) within a harmonized observation system. We review existing research using small-scale biological and weather surveillance radars for insect monitoring and outline how the derived measures and quantities can contribute to the EBVs 'species population', 'species traits', 'community composition' and 'ecosystem function'. Furthermore, we synthesize how ongoing and future methodological, analytical and technological advancements will greatly expand the use of radar for insect biodiversity monitoring and beyond. Owing to their long-term and regional-to-large-scale deployment, radar-based approaches can be a powerful asset in the biodiversity monitoring toolbox whose potential has yet to be fully tapped. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

Due to rapid technological innovations, the automated monitoring of insect assemblages comes within reach. However, this continuous innovation endangers the methodological continuity needed for calculating reliable biodiversity trends in the future. Maintaining methodological continuity over prolonged periods of time is not trivial, since technology improves, reference libraries grow and both the hard- and software used now may no longer be available in the future. Moreover, because data on many species are collected at the same time, there will be no simple way of calibrating the outputs of old and new devices. To ensure that reliable long-term biodiversity trends can be calculated using the collected data, I make four recommendations: (1) Construct devices to last for decades, and have a five-year overlap period when devices are replaced. (2) Construct new devices to resemble the old ones, especially when some kind of attractant (e.g. light) is used. Keep extremely detailed metadata on collection, detection and identification methods, including attractants, to enable this. (3) Store the raw data (sounds, images, DNA extracts, radar/lidar detections) for future reprocessing with updated classification systems. (4) Enable forward and backward compatibility of the processed data, for example by in-silico data 'degradation' to match the older data quality. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

This study focuses on the improvement of the tracking performance of space observation radar systems. Coherent radar systems usually provide an observation vector including four observables (range, range rate, azimuth, elevation) measured at a given epoch. Moreover, for some coherent radars, such as the TIRA system, it is possible to determine an additional parameter, the range rate, which is extracted from the collected complex amplitude. The tracking performance can be improved by increasing the observation parameter accuracy. The influence of each parameter on the overall tracking performance can be assessed by conducting a sensitivity analysis. The investigation shows that the best way to increase the accuracy of the state vector is by improving the angle estimates. The TIRA system is equipped with a tracking radar and an imaging radar. A powerful solution to improve the tracking performance is the realization of a multi-sensor data fusion between the two radars of the system. [ABSTRACT FROM AUTHOR]

Information on the spatial distribution of rainfall is required for many applications, including water and flood management. Weather radars can provide quantitative rainfall estimation over an area. Taking the example of an X-band radar installed at Mandhardev in the Western Ghats, we show that radar reflectivity data can have significant bias despite following standard calibration procedures. We report a technique to identify bias in the X-band radar reflectivity factor using collocated disdrometer observations and propose a methodology to correct it. Simultaneous data collected on 83 days during June–September of 2018 are used. Our results show that bias in the radar reflectivity factor reduced from –8.3 to –0.8 dB after correction. The estimated 83-day accumulated rainfall using uncorrected radar reflectivity data is 80% less compared to that of the disdrometer and the difference between the two reduces to 2% with correction. Bias in the estimated rainfall reduces from –30 to –0.7 mm day–1 and the RMSE reduces by 42%. There are days where daily rainfall from the two instruments agrees well with each other, while large differences exist on some days. We show that the sampling issue is one of the major sources of error, and its contribution depends on the nature/type of precipitating clouds, whereas uncertainties in the Z–R relationships account for about 15% difference. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date information on urban built-up areas is significant for managing urban growth and development. Earth Observation (EO) data are valuable sources for meeting this demand. However, the extraction of urban built-up areas from EO data is challenging due to the limitations of EO data sources. To overcome this challenge, this study follows an approach that assesses the performance of optical (Sentinel-2), radar (Sentinel-1) and fused (Sentinel-1 and Sentinel-2) data to extract urban built-up areas using machine learning algorithms including Random Forest (RF), K-Nearest Neighbors (KNN) and KDTree KNN. The results were statistically analyzed by considering the Overall Accuracy (OA) and kappa coefficient. In addition, 15 cm GSD (Ground Sample Distance) aerial photography of the study area was used to validate the results. According to the results, Sentinel-2 produced better representation and accuracy of urban built-up areas than Sentinel-1 and even the fused image. Regarding to machine learning algorithms classification performance, RF performed better in both OA and Kappa coefficient along all datasets. The research findings can have significant implications for various domains, such as urban planning, land use management and open avenues for further comparisons of different EO data sources and machine learning algorithms for built-up areas extraction. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract 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.

Abstract This work proposed a novel constant false alarm rate detection method based on Gaussian mixture model (GMM). The main contribution contains two aspects. First, the Gabor wavelet is used to determine the GMM component number. Second, the expression of probability of false alarm is derived based on the GMM parameter.

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]

In order to effectively improve the capability of shore-to-ship missile penetration, unmanned aerial vehicles (UAV) were used for distributed combat against enemy shipborne radar. Based on radar detection equation and interference equation, the calculation model of radar exposure area under UAV interference was established, and the configuration of UAV array was analyzed. The results show that the jamming direction, jamming power and jamming distance of UAV have important effects on the detection performance of shipborne radar. The distributed operation of UAV can effectively reduce the combat effectiveness of radar, thus providing favorable conditions for shore-to-ship missile penetration, and laying an important theoretical foundation for future research on the tactical application of UAV as a new combat style. [ABSTRACT FROM AUTHOR]

Given that the Gaussianity of observation error distribution is the fundamental principle of some data assimilation and machine learning algorithms, the error structure of radar reflectivity becomes increasingly important with the development of high resolution forecasts and nowcasts of convective systems. This study examines the error distribution of radar reflectivity and discusses what give rise to the non-Gaussian error distribution by using 6 month observations minus backgrounds (OmBs) of composites of vertical maximum reflectivity (CVMRs) in mountainous and hilly areas. By following the symmetric error model in all-sky satellite radiance assimilation, we unveil the error structure of CVMRs as a function of symmetric rain rates, which is the average of observed and simulated rain rates. Unlike satellite radiance, the error structure of CVMRs shows a sharper slope in light precipitations than moderate precipitations. Thus, a three-piecewise fitting function is more suitable for CVMRs. The probability density functions of OmBs normalized by symmetric rain rates become more Gaussian in comparison with the probability density function normalized by the whole samples. Moreover, the possibility of using third-party predictor to construct the symmetric error model are also discussed in this study. The Gaussianity of OmBs can be further improved by using a more accurate precipitation observations. According to the Jensen-Shannon divergence, a more linear predictor, the logarithmic transformation of rain rate, can provide the most Gaussian error 20 distribution in comparison with other predictors. [ABSTRACT FROM AUTHOR]

This study presents a first simultaneous observation of N 2 + Meinel (0,0) band (hereafter, N 2 + (M)) aurora by cutting-edge short-wavelength infrared imaging spectrograph (NIRAS-2) and monochromatic camera (NIRAC) installed at the Kjell Henriksen Observatory (78 ∘ N, 16 ∘ E). On January 21 2023, N 2 + (M) intensification that is associated with a band-shape aurora structure was observed by the NIRAS-2 and the NIRAC having temporal resolutions of 30 s and 20 s, respectively. In addition, the European incoherent scatter Svalbard Radar also observed electron density variations at the same time. Electron density measured at altitude range from 100 km 120 km shows similar variations as of N 2 + (M) intensity, which implies that a primary source of N 2 + (M) emissions is direct collisions of N 2 by precipitating electrons penetrating down to around 100 km altitude (up to 10 keV). However, the observation also demonstrated moderate correlations between N 2 + (M) intensity and electron density above 140 km, which implies that different N 2 + (M) generation process, N 2 charge exchange with O + , may work up to near 160 km and make a non-negligible contribution to N 2 + (M) emissions. This hypothesis would be verified with further radar observations or stereo imaging observations useful to estimate the vertical distribution of the emission layers. The N 2 + (M) is a very promising target wavelength for aurora observation because the quality of sensors is highly expected to improve further and further. Continuous observations with our new instruments will undoubtedly provide an important information of N 2 + (M) characteristics, for future missions of both balloon-borne and satellite-borne imaging. [ABSTRACT FROM AUTHOR]

The increasing amount of space debris poses a significant threat to operational satellites and space-based services. This article updates the community on the current status of the development of ATLAS, a tracking radar that is part of the EUSST network and aims to detect space objects in low Earth orbits. This article focuses on the latest activities performed: calibration of the pointing system and initial observations of space objects. The calibration procedure consisted of cross-scanning the Solar disk and yielded great results, obtaining an offset of 5.3° in azimuth and 0.10° in elevation. The first observation campaign resulted in 33 range detections of the International Space Station (ISS) with a probability of false alarm of  10 − 9 . The observations were then used to readjust the radar equation to assess the real-world performance of the system. [ABSTRACT FROM AUTHOR]

This paper presents a method for predicting atmospheric duct conditions from a clutter power spectrum using deep learning. To accurately predict the duct conditions, deep learning with a binary classification is applied to the proposed refractivity from the clutter (RFC) method. The input data set is the artificial clutter data that are generated via the Advanced Refractive Prediction System (AREPS) simulation software Ver. 3.6 in conjunction with random atmospheric refractive indices. The output of the RFC method is then predicted via binary classification, indicating whether the atmospheric conditions are duct or non-duct. For the cross-validation, the clutter power spectrum data are generated based on real atmospheric refractivity data. The results show that the DNN trained with 5600 pieces of data (validation accuracy of 95.99%) exhibits a binary classification accuracy of 98.36%. The deep neural network (DNN) trained with 28,000 pieces of data (validation accuracy of 98.20%) achieves a binary classification accuracy of 99.06% with an F1-score of 0.9921. [ABSTRACT FROM AUTHOR]

The processes of ocean dynamics are complex near the Pearl River Estuary and are not clear due to a lack of abundant observations. The spatial characteristics of the spring sea surface currents in the adjacent waters of the Pearl River Estuary were analyzed using the current data observed by a three-station high-frequency surface wave radar system (HFSWRS). Compared with the two-station HFSWRS, the deviation of current velocity and direction observed by the three-station HFSWRS from the underway measurements decreased by 42.86% and 38.30%, respectively. The analyzed results show that the M2 tidal current is the dominant current among all the tidal constituents, followed by K1, with angles of inclination ranging from 130° to 150°. The tidal flow is dominated by northwest–southeast back-and-forth flow. In the southern part of the observed area, which is far from the coastline, the tidal current ellipses exhibit a circular pattern. The prevalent tidal current type in this region is irregularly semi-diurnal, and the shallow water constituents also have a significant effect. The tidal energy in the adjacent waters of the Pearl River Estuary is affected by potential energy flux and kinetic energy flux. As the water depth and currents velocity increase in the southeast direction, the tidal energy flux increases. In the nearshore zone, the direction of tidal energy flux varies along the coastline. The changes in the residual current within the observed area are correlated with the sea surface wind field. Based on the high-precision sea surface current observed by the three-station HFSWRS, the characteristics of the ocean dynamic processes near the Pearl River Estuary are analyzed comprehensively, which provides important reference and confidence for the application of the developing new radar observing network with about 10 radar stations near the Pearl River Estuary. [ABSTRACT FROM AUTHOR]

Optimizing jamming strategies is crucial for enhancing the performance of cognitive jamming systems in dynamic electromagnetic environments. The emergence of frequency-agile radars, capable of changing the carrier frequency within or between pulses, poses significant challenges for the jammer to make intelligent decisions and adapt to the dynamic environment. This paper focuses on researching intelligent jamming decision-making algorithms for Intra-Pulse Frequency Agile Radar using deep reinforcement learning. Intra-Pulse Frequency Agile Radar achieves frequency agility at the sub-pulse level, creating a significant frequency agility space. This presents challenges for traditional jamming decision-making methods to rapidly learn its changing patterns through interactions. By employing Gated Recurrent Units (GRU) to capture long-term dependencies in sequence data, together with the attention mechanism, this paper proposes a GA-Dueling DQN (GRU-Attention-based Dueling Deep Q Network) method for jamming frequency selection. Simulation results indicate that the proposed method outperforms traditional Q-learning, DQN, and Dueling DQN methods in terms of jamming effectiveness. It exhibits the fastest convergence speed and reduced reliance on prior knowledge, highlighting its significant advantages in jamming the subpulse-level frequency-agile radar. [ABSTRACT FROM AUTHOR]

RADARs and cameras have been present in automotives since the advent of ADAS, as they possess complementary strengths and weaknesses but have been underlooked in the context of learning-based methods. In this work, we propose a method to perform object detection in autonomous driving based on a geometrical and sequential sensor fusion of 3+1D RADAR and semantics extracted from camera data through point cloud painting from the perspective view. To achieve this objective, we adapt PointPainting from the LiDAR and camera domains to the sensors mentioned above. We first apply YOLOv8-seg to obtain instance segmentation masks and project their results to the point cloud. As a refinement stage, we design a set of heuristic rules to minimize the propagation of errors from the segmentation to the detection stage. Our pipeline concludes by applying PointPillars as an object detection network to the painted RADAR point cloud. We validate our approach in the novel View of Delft dataset, which includes 3+1D RADAR data sequences in urban environments. Experimental results show that this fusion is also suitable for RADAR and cameras as we obtain a significant improvement over the RADAR-only baseline, increasing mAP from 41.18 to 52.67 (+27.9%). [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]

This review article provides an overview of various aspects of lunar exploration, including missions to the Moon, collection and analysis of lunar sample data in laboratories, and the processing and analysis of remote sensing data, particularly using radar techniques. Both remote sensing and in-situ methods are critical for advancing our understanding of the lunar surface and its properties. This review article focuses on the identification and quantification of water–ice deposits located in areas such as Permanently shadowed areas (PSRs) and the lunar poles (section " Lunar Poles and PSRs: A Special Environment "). These volatile resources have the potential to serve as valuable sources of fuel for future missions, making it crucial for the lunar community to determine their abundance and distribution. After thoroughly examining lunar samples using high-precision laboratory techniques, many preconceptions were dispelled which is highlighted in the section " Laboratory Investigation of Lunar samples ". But as in-situ observations are difficult to acquire, especially terrestrial bodies samples, remote sensing techniques allow the global understanding of the surface. The article specifically highlights the importance of understanding the electrical characteristics of the lunar surface and how radar inversion can provide valuable information in this regard. The " Conclusion " section of this review article serves as a key takeaway for readers, underscoring the critical role that both in-situ and remote sensing techniques play in advancing our understanding of the Moon. [ABSTRACT FROM AUTHOR]

Rain cells are the most common units in the natural precipitation system. Enhancing the understanding of these rain cell characteristics can significantly improve the cognition of the precipitation system. Previous studies have mostly analyzed rain cells from a single radar data. In this study, we merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) with the muti-channel cloud-top radiance measured by the visible and infrared scanner (VIRS) and the muti-channel brightness temperature measured by the TRMM microwave imager (TMI). The rain cells were identified within the PR orbit, and the swath truncation effect was eliminated. We used two methods for rain cell identification: the minimum bounding rectangle (MBR) method and the best fit ellipse (BFE) method, and compared the differences between these two methods in describing the rain cell characteristics. The results indicate that both methods can better reflect the geometric characteristics of rain cells. Compared with the MBR method, the BFE method can obtain a smaller rain cell area, and the filling ratio is better. However, the MBR method can simplify the data storage volume. Consequently, we employed the MBR method to analyze the precipitation structure of two typical rain cell precipitation cases. The results show that the new rain cell dataset can be used for the analysis of rain cell precipitation parameters and visible/infrared and microwave signals, which provides valuable data for comprehensive studies on the rain cell structural characteristics and furthers the understanding of precipitation mechanisms. The data which were used in this paper are freely available at https://doi.org/10.5281/zenodo.8352587 (Wu et al., 2023). [ABSTRACT FROM AUTHOR]

Objective: To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology. Methods: This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury's imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value. Results: RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI's lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring. Conclusion: The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology. Critical relevance statement: The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation. Keypoints: • Radiology artificial intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap. [ABSTRACT FROM AUTHOR]

X-band radar observations with a transmitting frequency of 9.4 GHz were conducted for over 10 h at the mouth of Tenryu River, Japan, to estimate surface velocity distributions during floods in 2010 and 2020 and analyse changes in the flow field caused by dredging in 2014. X-band radar is an imaging radar that can collect backscatter of floating objects flowing downstream with the flood flow every 2 s. The observation area extends approximately 1000 m × 1000 m. Particle image velocimetry is applied to the image sequences to guess the surface flow pattern. Flow direction in the main river channel changed smoothly, while surface flow speeds ranged from 1 to 3 m s–1 depending on the discharge. Water levels were computed using the estimated surface flow velocities, river cross-section from survey data, and observed discharge for indirect validation: it agreed within an error of 0.3 m with the observed water level. This paper highlights radar's potential for estimating surface velocity distribution during floods and harsh weather conditions. [ABSTRACT FROM AUTHOR]

Monitoring of lake ice is important to maintain transportation routes, but in recent decades the number of in situ observations have declined. Remote sensing has worked to fill this gap in observations, with active microwave sensors, particularly synthetic-aperture radar (SAR), being a crucial technology. However, the impact of wet conditions on radar and how interactions change under these conditions have been largely ignored. It is important to understand these interactions as warming conditions are likely to lead to an increase in the occurrence of slush layers. This study works to address this gap using the Snow Microwave Radiative Transfer (SMRT) model to conduct forward-modelling experiments of backscatter for Lake Oulujärvi in Finland. Experiments were conducted under dry conditions, under moderate wet conditions, and under saturated conditions. These experiments reflected field observations during the 2020–2021 ice season. Results of the dry-snow experiments support the dominance of surface scattering from the ice–water interface. However, conditions where layers of wet snow are introduced show that the primary scattering interface changes depending on the location of the wet layer. The addition of a saturated layer at the ice surface results in the highest backscatter values due to the larger dielectric contrast created between the overlying dry snow and the slush layer. Improving the representation of these conditions in SMRT can also aid in more accurate retrievals of lake ice properties such as roughness, which is key for inversion modelling of other properties such as ice thickness. [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]

Thunderstorms can generate intense electrical activity, hail, and result in substantial economic and human losses. The development of very short-term forecasting tools (nowcasting) is essential to provide information to alert systems in order to mobilize most efficiently the population. However, the development of nowcasting tools depends on a better understanding of the physics and microphysics of clouds and lightning formation and evolution. In this context, the objectives of this study are: (a) to describe the environmental conditions that led to a genesis of a thunderstorm that produce hail on 7 January 2012, in the Metropolitan Area of São Paulo (MASP) during the CHUVA-Vale campaign, and (b) to evaluate the thunderstorm microphysical properties and vertical structure of electrical charge. Data from different sources were used: field campaign data, such as S-band radar, and 2- and 3-dimensional lightning networks, satellite data from the Geostationary Operational Environmental Satellite-13 (GOES-13), the Meteosat Second Generation (MSG), and reanalysis of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The thunderstorm developed in a region of low-pressure due to the presence of a near-surface inverted trough and moisture convergence, which favored convection. Convective Available Potential Energy (CAPE) of 1053.6 J kg−1 at the start of the thunderstorm indicated that strong convective energy was present. Microphysical variables such as Vertically Integrated Liquid water content (VIL) and Vertically Integrated Ice (VII) showed peaks of 140 and 130 kg m−2, respectively, before the hail reached the surface, followed by a decrease, indicating content removal from within the clouds to the ground surface. The thunderstorm charge structure evolved from a dipolar structure (with a negative center between 4 and 6 km and a positive center between 8 and 10 km) to a tripolar structure (negative center between 6 and 7.5 km) in the most intense phase. The first lightning peak (100 flashes in 5 min−1) before the hail showed that there had been a lightning jump. The maximum lightning occurred around 18:17 UTC, with approximately 350 flashes 5 min−1 with values higher than 4000 sources 500 m−1 in 5 min−1. Likewise, the vertical cross-sections indicated that the lightning occurred ahead of the thunderstorm's displacement (maximum reflectivity), which could be useful in predicting these events. [ABSTRACT FROM AUTHOR]

In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall that occurred in the region using the radar rain rate and the SWD. Subsequently the nowcasts were evaluated using both variables. Two nowcast models were employed from pySTEPS: Extrapolation and S-PROG. The results indicate that average SWD is below 2.5 K, 90 min before the onset of rainfall events, and, on average, the SWD is 2 K during rainfall events. The results from both nowcast models were accurate and produced similar results. The nowcasts performed better when SWD data were used as input, having an average Probability of Detection (PoD) above 70% and a False Alarm Rate (FAR) reaching 40% for the 15-min prediction. The nowcasts were less accurate using the radar rain rate as input for the 15-min forecast, where the PoD was maximum 70% and FAR reaching 40%. However, these nowcasts were more reliable during well-organized precipitation events. In this work, it was determined that the nowcast models provided by pySTEPS can provide valuable rain forecasts using GOES-16 satellite and radar data for the central Mexico region. [ABSTRACT FROM AUTHOR]

The success of the National Severe Storms Laboratory's (NSSL) experimental Warn-on-Forecast System (WoFS) to provide useful probabilistic guidance of severe and hazardous weather is mostly due to the frequent assimilation of observations, especially radar observations. Phased-array radar (PAR) technology, which is a potential candidate to replace the current U.S. operational radar network, would allow for even more rapid assimilation of radar observations by providing full-volumetric scans of the atmosphere every ∼1 min. Based on previous studies, more frequent PAR data assimilation can lead to improved forecasts, but it can also lead to ensemble underdispersion and suboptimal observation assimilation. The use of stochastic and perturbed parameter methods to increase ensemble spread is a potential solution to this problem. In this study, four stochastic and perturbed parameter methods are assessed using a 1-km-scale version of the WoFS and include the stochastic kinetic energy backscatter (SKEB) scheme, the physically based stochastic perturbation (PSP) scheme, a fixed perturbed parameters (FPP) method, and a novel surface-model scheme blending (SMSB) method. Using NSSL PAR observations from the 9 May 2016 tornado outbreak, experiments are conducted to assess the impact of the methods individually, in different combinations, and with different cycling intervals. The results from these experiments reveal the potential benefits of stochastic and perturbed parameter methods for future versions of the WoFS. Stochastic and perturbed parameter methods can lead to more skillful forecasts during periods of storm development. Moreover, a combination of multiple methods can result in more skillful forecasts than using a single method. Significance Statement: Phased-array radar technology allows for more frequent assimilation of radar observations into ensemble forecast systems like the experimental Warn-on-Forecast System. However, more frequent radar data assimilation can eventually cause issues for prediction systems due to the lack of ensemble spread. Thus, the purpose of this study is to explore the use of four stochastic and perturbed parameter methods in a next-generation Warn-on-Forecast System to generate ensemble spread and help prevent the issues from frequent radar data assimilation. Results from this study indicate the stochastic and perturbed parameter methods can improve forecasts of storms, especially during storm development. [ABSTRACT FROM AUTHOR]