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In this study, the spherical particle model and ten nonspherical particle models describing the scattering properties of snow are evaluated for potential use in precipitation estimation from spaceborne dual-frequency precipitation radar. The single scattering properties of nonspherical snow particles are computed using discrete dipole approximation (DDA), while those of spherical particles are determined using Mie theory. The precipitation profiles from WRF output are then input to a forward radiative transfer model to simulate the radar reflectivity at Ka-band and Ku-band. The results are validated with Global Precipitation Mission Dual-Frequency Precipitation Radar measurements. Greater consistency between the simulated and observed reflectivity is obtained when using the sector- and dendrite-shape assumptions. For the case in this study, when using the spherical-shape assumption, radar underestimates the error of the cloud's top by about 300 m and underestimates the error of the cloud's area by about 15%. As snowflake shapes change with temperature, we use the range between −40 °C and −5 °C to define three temperature layers. The relationships between reflectivity (Z) and precipitation rate (R) are fitted separately for the three layers, resulting in  Z = 134.59 · R 1.184  (sector) and  Z = 127.35 · R 1.221  (dendrite) below −40 °C. [ABSTRACT FROM AUTHOR]

Radar echo extrapolation is a commonly used approach for convective nowcasting. The evolution of convective systems over a very short term can be foreseen according to the extrapolated reflectivity images. Recently, deep neural networks have been widely applied to radar echo extrapolation and have achieved better forecasting performance than traditional approaches. However, it is difficult for existing methods to combine predictive flexibility with the ability to capture temporal dependencies at the same time. To leverage the advantages of the previous networks while avoiding the mentioned limitations, a 3D-UNet-LSTM model, which has an extractor-forecaster architecture, is proposed in this paper. The extractor adopts 3D-UNet to extract comprehensive spatiotemporal features from the input radar images. In the forecaster, a newly designed Seq2Seq network exploits the extracted features and uses different convolutional long short-term memory (ConvLSTM) layers to iteratively generate hidden states for different future timestamps. Finally, the hidden states are transformed into predicted radar images through a convolutional layer. We conduct 0–1 h convective nowcasting experiments on the public MeteoNet dataset. Quantitative evaluations demonstrate the effectiveness of the 3D-UNet extractor, the newly designed forecaster, and their combination. In addition, case studies qualitatively demonstrate that the proposed model has a better spatiotemporal modeling ability for the complex nonlinear processes of convective echoes. [ABSTRACT FROM AUTHOR]

Recent upgrades to China's radar network now allow for polarimetric measurements of convective systems in central China, providing an effective data set with which to evaluate the microphysics schemes employed in local squall line simulations. We compared polarimetric radar variables derived by Weather Research and Forecasting (WRF) and radar forward models and the corresponding hydrometeor species with radar observations and retrievals for a severe squall line observed over central China on 16 March 2022. Two microphysics schemes were tested and were able to accurately depict the contrast between convective and stratiform regions in terms of the drop size distribution (DSD) and reproduce the classical polarimetric signatures of the observed differential reflectivity (ZDR) and specific differential phase (KDP) columns. However, for the convective region, the simulated DSDs in both schemes exhibited lower proportions of large drops and lower liquid water content; by contrast, for the stratiform region, the proportion of large drops was found to be too high in the Morrison (MORR) scheme. The underprediction of ice-phase processes in the convective region, particularly the riming processes associated with graupel and hail, was likely responsible for the bias toward large raindrops at low levels. In the stratiform region, raindrop evaporation in the WRF Double-Moment 6-Class (WDM6) scheme, which partially offsets the overestimation of ice-phase processes, produced ground DSDs that more closely matched the observational data, and did not exhibit the overly strong warm-rain collisional growth processes of MORR. [ABSTRACT FROM AUTHOR]

The Echus-Kasei region on Mars has been exposed to different episodic volcanic, fluvial, and glacial events in Amazonian time. The goal of the present work is to demonstrate the usefulness of radar instruments to find preserved late Amazonian subsurface structures that may have been encapsulated underneath recent lava flows on Mars. We have analysed 27 radar observations of the SHAllow RADar (SHARAD) instrument on board the Mars Reconnaissance Orbiter (MRO), over the region of Echus Chasma. We discovered the presence of subsurface reflectors in five consecutive SHARAD radargrams at a depth from 35 to 79 m beneath the structure of a lava fan that formed about 59 ± 4 Ma ago. Some vents are preserved above the surface of this lava flow, which stands at a height of 80 m above the surrounding surface. A few kilometres to the north, we find other subsurface reflectors at a depth of about 30 m and a long pit chain formed by the collapse of a lava tube. These kinds of subsurface late Amazonian structures are of interest for astrobiology because they date from the last period when the planet still experienced intense volcanic activity over regions that were previously extensively covered by water. [ABSTRACT FROM AUTHOR]

The differential propagation phase (ΦDP) of X-band dual-polarization weather radar (including X-band dual-polarization phased-array weather radar, X-PAR) is important for estimating precipitation and classifying hydrometeors. However, the measured differential propagation phase contains the backscatter differential phase (δ), which poses difficulties for the application of the differential propagation phase from X-band radars. This paper presents the following: (1) the simulation and characteristics analysis of the backscatter differential phase based on disdrometer DSD (raindrop size distribution) measurement data; (2) an improved method of the specific differential propagation phase (KDP) estimation based on linear programming and backscatter differential phase elimination; (3) the effect of backscatter differential phase elimination on the specific differential propagation phase estimation of X-PAR. The results show the following: (1) For X-band weather radar, the raindrop equivalent diameters D > 2 mm may cause a backscatter differential phase between 0 and 20°; in particular, the backscatter differential phase varies sharply with raindrop size between 3.2 and 4.5 mm. (2) Using linear programming or smoothing filters to process the differential propagation phase could suppress the backscatter differential phase, but it is hard to completely eliminate the effect of the backscatter differential phase. (3) Backscatter differential phase correction may improve the calculation accuracy of the specific differential propagation phase, and the optimization was verified by the improved self-consistency of polarimetric variables, correlation between specific differential propagation phase estimations from S- and X-band radar and the accuracy of quantitative precipitation estimation. The X-PAR deployed in Shenzhen showed good observation performance and the potential to be used in radar mosaics with S-band weather radar. [ABSTRACT FROM AUTHOR]

Accurate and timely precipitation forecasts can help people and organizations make informed decisions, plan for potential weather-related disruptions, and protect lives and property. Instead of using physics-based numerical forecasts, which can be computationally prohibitive, there has been a growing interest in using deep learning techniques for precipitation prediction in recent years due to the success of these approaches in various other fields. These deep learning approaches generally use historical composite reflectivity (CR) at the surface level to predict future time steps. However, other relevant factors related to the potential motion and vertical structure of the storm have not been considered. To address this issue, this research proposes a multisource ConvLSTM (MS-ConvLSTM) model to improve the accuracy of precipitation forecasting by incorporating multiple data sources into the prediction process. The model was trained on a dataset of radar echo features, which includes not only composite reflectivity (CR), but also echo top (ET), vertically integrated liquid (VIL) water, and radar-retrieved wind field data at different elevations. Experiment results showed that the proposed model outperformed traditional methods in terms of various evaluation metrics, such as mean absolute error (MAE), mean squared error (MSE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). [ABSTRACT FROM AUTHOR]

The ash dieback pandemic, caused by the invasive fungus Hymenoscyphus fraxineus, represents one of Europe's biggest threats to preserving natural biodiversity. To ensure the suppression of forest damage caused by fungi, timely recognition of the symptoms of ash dieback and further continuous monitoring on an adequate spatial scale are essential. Visual crown damage assessment is currently the most common method used for identifying ash dieback, but it lacks the spatial and temporal coverage required for effective disease suppression. Remote sensing technologies, with the capabilities of fast and repetitive retrieval of information over a large spatial scale, could present efficient supplementary methods for ash damage detection and disease monitoring. In this study, we provided a synthesis of the existing remote sensing methods and applications that considers ash dieback disease, and we described the lifecycle of the disease using the major symptoms that remote sensing technologies can identify. Unfortunately, although effective methods of monitoring biotic damage through remote sensing have been developed, ash dieback has only been addressed in two research studies in the United Kingdom and Germany. These studies were based on single-date hyperspectral and very-high-resolution imagery in combination with machine learning, using previously specified ground-truth information regarding crown damage status. However, no study exists using high-resolution imagery such as Sentinel-2 or radar Sentinel-1, although some preliminary project results show that these coarser sources of information could be applicable for ash dieback detection and monitoring in cases of Fraxinus angustifolia, which forms pure, more homogenous stands in Southern Europe. [ABSTRACT FROM AUTHOR]

Hebrus Valles is an outflow channel system in the plain-forming terrains of southeastern Utopia Planitia, Mars. These terrains may have formed through a combination of liquid water and volcanic processes, yet their nature, subsurface structure, and composition remain unclear. We investigate these terrains by mapping subsurface reflectors across 540 Shallow Radar (SHARAD) profiles and applying two complementary loss tangent inversion techniques. We find moderate loss tangent values across some subregions of Granicus Valles and Hyblaeus Fossae (tan δ = 0.0162 ± 0.0004 and tan δ = 0.019 ± 0.002, respectively), suggesting the presence of basaltic lava flows. We interpret non-detections in the other flows in Granicus Valles to be due to the presence of radar-lossy materials formed through aqueous processes, which supports the hypothesized occurrence of lahars in this region. A small area near Hebrus Valles exhibits subsurface reflectors with low to moderate loss tangents (tan δ = 0.010 ± 0.003), suggesting the presence of pristine lava flows or sedimentary materials capped by lava flows. We also find a widespread occurrence of very low-loss tangent materials near Hyblaeus Dorsa (tan δ = 0.0045 ± 0.0002), which may represent a lobe of the Medusae Fossae Formation or similar high-porosity materials buried underneath a lava flow. Together, these findings suggest that volcanic activity played a central role in the formation of terrains across the broader Hebrus Valles region.

The monitoring of Natura 2000 habitats (Habitat Directive 92/43/EEC) is a key activity ensuring the sufficient protection of European biodiversity. Reporting on the status of Natura 2000 habitats is required every 6 years. Although field mapping is still an indispensable source of data on the status of Natura 2000 habitats, and very good field-based data exist in some countries, keeping the field-based habitat maps up to date can be an issue. Remote sensing techniques represent an excellent alternative. Here, we present a new method for detecting habitats that were likely misclassified during the field mapping or that have changed since then. The method identifies the possible habitat mapping errors as the so-called “attribute outliers”, i.e., outlying observations in the feature space of all relevant (spectral and other) characteristics of an individual habitat patch. We used the Czech Natura 2000 Habitat Layer as field-based habitat data. To prepare the feature space of habitat characteristics, we used a fusion of Sentinel-1 and Sentinel-2 satellite data along with a Digital Elevation Model. We compared outlier ratings using the robust Mahalanobis distance and Local Outlier Factor using three different thresholds (Tukey rule, histogram-based Scott’s rule, and 95% quantiles in χ2 distribution). The Mahalanobis distance thresholded by the 95% χ2 quantile achieved the best results, and, because of its high specificity, appeared as a promising tool for identifying erroneously mapped or changed habitats. The presented method can, therefore, be used as a guide to target field updates of Natura 2000 habitat maps or for other habitat/land cover mapping activities where the detection of misclassifications or changes is needed.

A joint radar-communication (JRC) system utilizes the integrated transmit waveform and a single platform to perform radar and communication functions simultaneously. Admittedly, the multibeam waveform design approach could transmit the assigned waveforms in different beams with the aid of spatial and temporal degrees of freedom. However, a high sidelobe level (SLL) in the beampattern reduces energy efficiency and expands exposure probability. In this study, we propose a novel spatial–temporal joint waveform design method based on the beamforming algorithm to form a low SLL beampattern. Waveform synthesis constraints are considered to synthesize desired radar and communication waveforms at designated directions. Furthermore, we impose the constant modulus constraint to lessen the impact of the high peak-to-average ratio (PAPR). The optimization process of the whole model can be summarized as two stages. First, the covariance matrix is created by convex optimization with respect to the minimum SLL. Second, the integrated transmit waveform is tuned through an alternating projection algorithm. Based on the simulation findings, we demonstrate that the proposed method outperforms the traditional methods in terms of low SLL and waveform synthesis. Meanwhile, we validate the effectiveness of the proposed method using semi-physical experiment results. [ABSTRACT FROM AUTHOR]

Active Radar Calibrators (ARC), also called calibration transponders, are often used as reference targets for absolute radiometric calibration of radar systems due to their large achievable Radar Cross-Section (RCS). However, before using a transponder as a reference target, the hardware itself has to be calibrated. A novel method, called the three-transponder method, was proposed some years ago and allows for RCS calibration of digital transponders without using any RCS targets as reference. In this paper, this technique is further refined and applied to a setup utilizing only one digital transponder. The accurate measurement design is described and a novel, elaborated data processing scheme is developed to minimize remaining noise and clutter effects in the data. A comprehensive error analysis is presented in the second part of this paper. [ABSTRACT FROM AUTHOR]

Spaceborne snow water retrievals over oceans are assessed using a multiyear coincident dataset of CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR). Various factors contributing to differences in snow water retrievals between CPR and DPR are carefully considered. A set of relationships between radar reflectivity (Ze) and snow water content (SWC) at Ku- and W-bands is developed using the same microphysical assumptions. It is found that surface snow water contents from CPR are much larger than those from DPR at latitudes above 60°, while surface snow water contents from DPR slightly exceed those from CPR at latitudes below 50°. Coincident snow water content profiles between CPR and DPR are further divided into two conditions. One is that only CPR detects the falling snow. Another is that both CPR and DPR detect the falling snow. The results indicate that about 88% of all snow water content profiles are under the first condition and usually associated with light snowfall events. The remaining snow water content profiles are generally associated with moderate and heavy snowfall events. Moreover, CPR surface snow water contents are larger than DPR ones at high latitudes because most light snowfall events are misdetected by DPR due to its low sensitivity. DPR surface snow water contents exceed CPR ones at low latitudes because CPR may experience a significant reduction in backscattering efficiency of large particles and attenuation in heavy snowfall events. The low sensitivity of DPR also causes a noticeable decrease in detected snow layer depth. The results presented here can help in developing global snowfall retrieval algorithms using multi-radars. [ABSTRACT FROM AUTHOR]

We studied the characteristics of quasi-two-day wave (QTDW) using the meridional wind in the mesosphere and lower thermosphere (MLT) obtained from a meteor radar over Kototabang (KB, 0.2 ° S, 100.3 ° E) from 2003 to 2012. Atmospheric oscillations have a crucial impact on atmospheric dynamics, which contributes to more accurate space weather forecasting, thus providing a more secure space environment for human space exploration activities such as remote sensing and satellite navigation. QTDWs are typical atmospheric oscillations in the upper stratosphere, mesosphere and lower thermosphere. The occurrence time, amplitudes, periods and vertical wavelengths of QTDW events are analyzed statistically. Data obtained from the TIMED Doppler Interferometer (TIDI), which can measure wind and temperature and is onboard the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics (TIMED) satellite, are used to analyze the global distribution and spatial structure of QTDWs with different zonal wavenumbers. The characteristics of the QTDWs over KB are compared with the QTDWs at the middle latitudes using the meridional wind data from a meteor radar over Wuhan (114.4 ° E, 30.6 ° N), Beijing (116.5 ° E, 39.9 ° N) and Mohe (121.1 ° E, 50.1 ° N). The amplitudes of the QTDW and spectral analysis are calculated by the least squares fitting method. Our results demonstrate that QTDWs are present almost all year around over KB. The occurrence time, amplitudes, periods and vertical wavelengths of QTDW events with different zonal wavenumbers are determined in this study. We also find that the statistical characteristics of the QTDWs in KB are different from those at middle latitudes. The westward zonal wavenumber −4 (W4) events gradually increase with increasing latitude, whereas westward zonal wavenumbers −1, −2, and −3 (W1, W2 and W3, respectively) events all decrease with increasing latitude. [ABSTRACT FROM AUTHOR]

The Doppler shift of microwave radar sea surface echoes serves as the foundation for sea surface current field retrieval; it includes the shift caused by satellite platform motion, ocean waves, and sea surface currents. The Doppler shift caused by ocean waves is known as the wave-induced Doppler velocity (UWD), and its removal is critical for the accurate retrieval of sea surface current fields. The low-incidence Ka-band real-aperture radar rotary scan regime has the capability of directly observing wide-swath two-dimensional current fields, but as a new regime to be further explored and validated, simulation and analysis of UWD in this regime have a significant influence on the hardware design and currently observed applications of this satellite system in its conceptual stage. In this study, we simulated and investigated the impacts of radar parameters and sea-state conditions on the UWD obtained from small-incidence-angle Ka-band rotational scanning radar data and verified the simulation results with the classical analytical solution of average specular scattering point velocity. Simulation results indicate that the change in the azimuth direction of platform observation affects UWD accuracy. Accuracy is the lowest when the antenna is in a vertical side-view. The UWD increases slowly with the incidence angle. Ocean waves are insensitive to polarization in the case of small-incidence-angle specular scattering. The increase in wind speed and the development of wind waves result in a substantial increase in UWD. We classified swell by wavelength and wave height and found that UWD increases with swell size, especially the contribution of swell height to UWD, which is more significant. The contribution of the swell to UWD is smaller than that of wind waves to UWD. Furthermore, the existence of sea surface currents changes the contribution of ocean waves to UWD, and the contribution weakens with increasing wind speed and increases with wind wave development. [ABSTRACT FROM AUTHOR]

Unfrozen free and non-free water between ice crystals in flat and hummock ice in the Yellow River exists as water films with varying contents based on ice temperature. These contents can affect the radar wave velocity of the ice despite its theoretical dependence on the crystal structure and ice body components. The unfrozen water content in ice depends on the ice temperature, which is controlled by the air temperature, solar radiation, and ice thickness. Winter air temperature and radar-detected ice thickness data observed at the Shisifenzi bend in the Yellow River from 2020 to 2021 were analyzed. The unfrozen water content in the ice was the primary factor influencing the accuracy of flat ice thickness detection. The heat flux at the ice–water interface in the Yellow River was determined. The evolution of ice thickness and temperature were simulated using a one-dimensional (1D) ice thermodynamic model forced by the local weather station data (i.e., air temperature, solar radiation, wind speed, and cloud cover). On this basis, the measured ice thickness data of 13 drill holes were combined to calculate 1251 thermodynamically simulated ice thicknesses consistent with the ice thickness detection time of the radar; therefore, statistical relationships regarding the influence of air temperature and the combined action of air temperature and ice thickness on the radar wave velocity in granular and columnar ice during air temperature increases and decreases were determined. Finally, the statistical relationship between the combined influence of air temperature and ice thickness on radar wave velocity was selected as a parameterization scheme to dynamically correct the radar wave velocity of flat ice. To enhance the radar detection accuracy for flat ice thickness, the radar wave velocity of ice was parameterized as a function. Given the presence of unfrozen frazil ice and accumulated broken ice blocks in the Yellow River, radar is suggested to detect the thickness of different types of ice in future research. [ABSTRACT FROM AUTHOR]

The raindrop size distribution (DSD) is vital for applications such as quantitative precipitation estimation, understanding microphysical processes, and validation/improvement of two-moment bulk microphysical schemes. We trace the history of the DSD representation and its linkage to polarimetric radar observables from functional forms (exponential, gamma, and generalized gamma models) and its normalization (un-normalized, single/double-moment scaling normalized). The four-parameter generalized gamma model is a good candidate for the optimal representation of the DSD variability. A radar-based disdrometer was found to describe the five archetypical shapes (from Montreal, Canada) consisting of drizzle, the larger precipitation drops and the 'S'-shaped curvature that occurs frequently in between the drizzle and the larger-sized precipitation. Similar 'S'-shaped DSDs were reproduced by combining the disdrometric measurements of small-sized drops from an optical array probe and large-sized drops from 2DVD. A unified theory based on the double-moment scaling normalization is described. The theory assumes the multiple power law among moments and DSDs are scaling normalized by the two characteristic parameters which are expressed as a combination of any two moments. The normalized DSDs are remarkably stable. Thus, the mean underlying shape is fitted to the generalized gamma model from which the 'optimized' two shape parameters are obtained. The other moments of the distribution are obtained as the product of power laws of the reference moments M3 and M6 along with the two shape parameters. These reference moments can be from dual-polarimetric measurements: M6 from the attenuation-corrected reflectivity and M3 from attenuation-corrected differential reflectivity and the specific differential propagation phase. Thus, all the moments of the distribution can be calculated, and the microphysical evolution of the DSD can be inferred. This is one of the major findings of this article. [ABSTRACT FROM AUTHOR]

A dedicated signal and data processing chain is proposed for a fully polarimetric Doppler surveillance S-band radar to extract the polarimetric signatures of moving targets. To extract the target's polarimetric features, detection, clustering, and tracking steps are realized for a multi-target environment in the range-Doppler domain. A dedicated data fusion method for all four polarimetric radar channel signals is implemented to take full advantage of the additional polarimetric information and improve the detection performance. While tracking each particular target, polarization information is collected and used to describe their polarization scattering characteristics. Using the polarimetric H/A/ α decomposition technique, the polarimetric features of moving automotive targets are extracted and investigated. The developed processing chain has been applied to the signals scattered from vehicles moving in a highway. By employing both time averaging and spatial averaging of the statistical coherency matrix, the polarimetric signatures of both moving vehicles and static clutter have been presented in the two-dimensional H/ α plane. It has been found that the spatial averaging approach results in polarimetric signatures of moving vehicles that give the opportunity to directly and without consideration of the motion of the targets compare the polarization features of moving targets and static clutter. Therefore, this method can be used to improve the performance of target detection or target classification. [ABSTRACT FROM AUTHOR]

In a distributed frequency-modulated continuous waveform (FMCW) radar system, the echo data collected are not continuous in the azimuth direction, so the imaging effect of the traditional range-Doppler (RD) algorithm is poor. Sparse Bayesian learning (SBL) is an optimization algorithm based on Bayesian theory that has been successfully applied to high-resolution radar imaging because of its strong robustness and high accuracy. However, SBL is highly computationally complex. Fortunately, with FMCW radar echo data, most of the time-consuming SBL operations involve a Toeplitz-block Toeplitz (TBT) matrix. In this article, based on this advantage, we propose a fast SBL algorithm that can be used to obtain high-angular-resolution images, in which the inverse of the TBT matrix can be transposed as the sum of the products of the block lower triangular Toeplitz matrix and the block circulant matrix by using a new decomposition method, and some of the matrix multiplications can be quickly computed using the fast Fourier transform (FFT), decreasing the computation time by several orders of magnitude. Finally, simulations and experiments were used to ensure the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar with polarimetric diversity is a powerful tool in remote sensing. Each pixel is described by the scattering matrix corresponding to the emission/reception polarization states (usually horizontal and vertical). The algebraic real representation, a block symmetric matrix form, is introduced to adopt a more comprehensive framework (non-restricted by reciprocity assumptions) in mapping the scattering matrix by the consimilarity equivalence relation. The proposed representation can reveal potentially new information. For example, its eigenvalue decomposition, which is itself a necessary step in obtaining the consimilarity transformation products, may be useful in characterizing the degree of reciprocity/nonreciprocity. As a consequence, it can be employed in testing the reciprocity compliance assumed with monostatic PolSAR data. Full-wave simulated polarimetric data confirm that oriented scatterers can present complex eigenvalues, even with the monostatic geometry. [ABSTRACT FROM AUTHOR]

Hail events are common in Catalonia during the warm season (May to September), but especially between June and August. These cases produce important damages to agriculture and infrastructure. The campaign of 2022 will be remembered by three different phases: the first and last phases, which were very stable and with few events, and the middle phase, which had a large number of episodes. Some of the cases had an important social impact because of the large areas affected or the economical damages. The present analysis used the vertically integrated liquid radar product for estimating the hail swaths. Hail swaths are classified according to different parameters, allowing for the characterization of the campaign and a comparison with the period of 2013–2021. The results show how the month of June had a deficit of cases with respect to the reference period (half of the cases), July presented similar values, and August had a positive anomaly, with five times more cases. In addition, the first ever case of giant hail in Catalonia occurred in August 2022, a month with more than five times the number of cases of severe and very large hail with respect to the average of the period of 2013–2021. [ABSTRACT FROM AUTHOR]

The Rover-mounted Subsurface Penetrating Radar (RoSPR) is one of the scientific payloads onboard China's first independent Mars exploration mission, Tianwen-1. The radar aims to characterize the thickness of the upper Martian soil and investigate the subsurface stratigraphy by collecting and processing the data. This article is mainly divided into two parts, the introduction of data pre-processing and analysis of pre-processed radar signals, aiming at helping scientists make more effective use of radar data. The first part describes the operating principle of the RoSPR and the procedure of radar data pre-processing at all levels. Data pre-processing is mainly designed to transfer the raw data format to a common PDS (Planetary Data System) and eliminate the influence of the instrument. In the signal analysis part, the performances of both self-check signals and echo signals of low- and high-frequency channels are analyzed, which indicate a stable radar system and are useful for background removal. Phase and time calibration is of great importance for improving data quality and making the radar data more accurate. Moreover, further processing is required to obtain clear radar images, such as filtering, background removal and gain setting. [ABSTRACT FROM AUTHOR]

X-band weather radar can provide high spatial and temporal resolution data, which is essential to precipitation observation and prediction of mesoscale and microscale weather. However, X-band weather radar is susceptible to precipitation attenuation. This paper presents an X-band attenuation correction method based on the light gradient machine (LightGBM) algorithm (the XACL method), then compares it with the ZH correction method and the ZH-KDP comprehensive correction method. The XACL method was validated using observations from two radars in July 2021, the X-band dual-polarization weather radar at the Shouxian National Climatology Observatory of China (SNCOC), and the S-band dual-polarization weather radar at Hefei. During the rainfall cases on July 2021, the results of the attenuation correction were used for precipitation estimation and verified with the rainfall data from 1204 automatic ground-based meteorological network stations in Anhui Province, China. We found that the XACL method produced a significant correction effect and reduced the anomalous correction phenomenon of the comparison methods. The results show that the average error in precipitation estimation by the XACL method was reduced by 39.88% over 1204 meteorological stations, which is better than the effect of the other two correction methods. Thus, the XACL method proved good local adaptability and provided a new X-band attenuation correction scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a method for efficient and accurate removal of grating lobes in automotive Synthetic Aperture Radar (SAR) images. Grating lobes can indeed be mistaken as real targets, inducing in this way false alarms in the target detection procedure. Grating lobes are present whenever SAR focusing is performed using data acquired on a non-continuous basis. This kind of acquisition is typical in the automotive scenario, where regulations do not allow for a continuous operation of the radar. Radar pulses are thus transmitted and received in bursts, leading to a spectrum of the signal containing gaps. We start by deriving a suitable reference frame in which SAR images are focused. It will be shown that working in this coordinate system is particularly convenient since it allows for a signal spectrum that is space-invariant and with spectral gaps described by a simple one-dimensional function. After an inter-burst calibration step, we exploit these spectral characteristics of the signal by implementing a compressive sensing algorithm aimed at removing grating lobes. The proposed approach is validated using real data acquired by an eight-channel automotive radar operating in burst mode at 77 GHz. Results demonstrate the practical possibility to process a synthetic aperture length as long as up to 2 m reaching in this way extremely fine angular resolutions. [ABSTRACT FROM AUTHOR]

Mine slope landslides seriously threaten the safety of people's lives and property in mining areas. Landslide prediction is an effective way to reduce losses due to such disasters. In recent years, micro-deformation monitoring radar has been widely used in mine slope landslide monitoring. However, traditional landslide prediction methods are not able to make full use of the diversified monitoring data from these radars. This paper proposes a landslide time prediction method based on the time series monitoring data of micro-deformation monitoring radar. Specifically, deformation displacement, coherence and deformation volume, and the parametric degree of deformation (DOD) are calculated and combined with the use of the tangent angle method. Finally, the effectiveness of the method is verified by using measured data of a landslide in a mining area. The experimental results show that our proposed method can be used to identify the characteristics of an imminent sliding slope and landslide in advance, providing monitoring personnel with more reliable landslide prediction results. [ABSTRACT FROM AUTHOR]

Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status. [ABSTRACT FROM AUTHOR]

This study investigates the sensibility of Sentinel-1 C-band backscatter to the moisture content of tree canopies over an area of about 500 km2 in north-western Portugal, with specific analysis over burnt areas. Sentinel-1 C-VV and C-VH backscatter values from 276 images acquired between January 2018 and December 2020 were assigned to five classes depending on the Drought Code (DC) scenario over several unburned and burned sites with total (>90%) forest canopy cover. Confounding variables such as tree cover and incidence angle were accounted for by masking using specific thresholds. The following results are discussed: (a) C-VV and C-VH backscatter values are inversely correlated (R2 = 0.324 to 0.438 −p < 0.001) with local incidence angle over canopies; (b) correlation is significantly stronger over very wet scenarios (DC class = 0 to 1); (c) C-VV and C-VH backscatter values can discriminate wet to dry forest environments, but they are less sensitive to the transition between dry (DC classes = 1 to 10, 10 to 100) and extremely dry environments (DC classes = 100 to 1000); (d) C-VH is more sensible than C-VV to capture burnt canopy; and (e) the C-VH polarization captures post-fire recovery after an average minimum period of 360 days after the fire event, although with less distinction for extremely wet soils. We conclude that C-band VH backscatter intensity decreases from wet to dry canopy conditions, that this behavior of the backscatter signal with respect to canopy dryness is lost after a fire event, and that after one year it is recovered. [ABSTRACT FROM AUTHOR]

Three ground-based radars in the Pearl River Delta successfully observed Typhoon Higos (2020), which traveled over the offshore area in the South China Sea. During the observation period, the stratiform region of the outer rainband of HIGOS became active while swirling inward, merging into an unclosed eyewall and spreading outward, but its structure was asymmetric between upwind and downwind. To understand the dynamic mechanism of the asymmetry of the stratiform region in detail, the refined wind speed distributions in the inner core of Higos was retrieved by using the radar observation data and a three-dimensional, variational, direct, data assimilation, Dual-Doppler analysis (DDA). In addition, an Observing System Simulation Experiment (OSSE) was conducted with the numerical simulations by the Weather Research and Forecasting (WRF) model and numerical emulations by Cloud Resolving Model Radar SIMulator (CR-SIM) software to validate the retrieved data. From the OSSE, the emulated retrieved data were comparable with the WRF-out data. The analysis shows that the dynamic mechanisms are different between upwind and downwind in the stratiform rainband. In the former, the inflow sinks in the middle troposphere. In addition, there is an inflow in the lower troposphere, with an outflow aloft the inflow. In the latter, however, the stratiform rainband is primarily influenced by outflow from inside the rainband and inflow from outside the monsoon-related southwesterly winds. The vertical velocity characteristics in the stratiform rainband downwind also differ from those upwind. The upwind updraft was distinct in the middle troposphere, whereas the downwind updraft was caused by the convergence of the outflow from inside the stratiform rainband and the monsoon-related southwesterly inflow in the lower troposphere. [ABSTRACT FROM AUTHOR]

To further enhance the application of dual-polarization radar in hail nowcasting, we develop an integrated convective characteristic extraction (ICCE) algorithm based on the storm cell identification and tracking (SCIT) algorithm using dual-polarization radar data and its secondary products (hydrometeor classification data and mesocyclone data). The ICCE identifies and tracks not storm cells but convective systems, and it adds other storm characteristics, such as storm microphysics (hail- and graupel-related) and storm dynamics (mesocyclone-related), to the original storm characteristics, such as storm structure (reflectivity-related) and storm tracking (motion-related). The data of four mesocyclonic hailstorms observed by the two S-band dual-polarization radars in Guangdong Province, China, are utilized, from which we draw the following conclusions: (1) ICCE excels in identifying, characterizing, matching, and tracking convective systems; and (2) the newly added storm microphysics and dynamics characteristics can more accurately quantify the relationship between mesocyclone development, hail growth, and convective system enhancement throughout the evolution of the convective system. [ABSTRACT FROM AUTHOR]

Retrieving cloud vertical structures with satellite remote-sensing measurements is highly desirable and technically challenging. In this paper, the conditional adversarial neural network (CGAN) for retrieving the equivalent cloud radar reflectivity at 94 GHz of the Cloud Profile Radar (CPR) onboard CloudSat is extended and evaluated comprehensively for different cloud types and geographical regions. The CGAN-based retrieval model was extended with additional data samples and improved with a new normalization adjustment. The model was trained with the labeled datasets of the moderate-resolution imaging spectroradiometer (MODIS) cloud top pressure, cloud water path, cloud optical thickness, and effective particle radius data, and the CloudSat/CPR reflectivity from 2010 to 2017 over the global oceans. The test dataset, containing 24,427 cloud samples, was statistically analyzed to assess the performance of the model for eight cloud types and three latitude zones with multiple verification metrics. The results show that the CGAN model possesses good reliability for retrieving clouds with reflectivity > −25 dBZ. The model performed the best for deep convective systems, followed by nimbostratus, altostratus, and cumulus, but presented a very limited ability for stratus, cirrus, and altocumulus. The model performs better in the low and middle latitudes than in the high latitudes. This work demonstrated that the CGAN model can be used to retrieve vertical structures of deep convective clouds and nimbostratus with great confidence in the mid- and lower latitude region, laying the ground for retrieving reliable 3D cloud structures of the deep convective systems including convective storms and hurricanes from MODIS cloud products and used for predicting these storms. [ABSTRACT FROM AUTHOR]

This paper describes a coherent multi-dwell processing (CMDP) method for high velocity estimation and super-resolution in search and track, while search (TWS) radar modes use an un-conventional signal processing algorithm that exploits multi-dwell transmissions. The existence of the multi-dwell waveform is necessary for visibility needs by un-folding the target's velocity and range ambiguity and is proposed to be utilized for high velocity estimation and super-resolution. In this paper, the proposed scheme is shown to result in improved velocity estimation and doppler resolution performance for un-ambiguous targets in comparison to classical radar processing. The processing concept uses the same transmitted waveform (WF) and time duration without the need to increase the time on target (TOT) through sophisticated coherent concatenation of the received dwells with velocity compensation between the dwells. The phase compensation in receive mode is implemented for each target according to its characteristics, which means that target velocities are estimated in each dwell separately. The notable result of the CMDP is the linear doppler resolution improvement obtained with the given search resources and without knowing the target characteristics in advance or the dwell delay time. Other possible benefits of this process are the ability to achieve larger detection ranges and high-angle measurement precisions in search mode due to the higher signal-to-noise ratio (SNR) of the extended dwell and the ability to track more targets due to efficient time and resource management. An outstanding opportunity to exploit the CMDP is by combining missions in phased array (PA) radars, meeting the multi-objective needs of both high spatial scan rates for illuminating the target and high doppler estimation and resolution performance. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperated range ambiguous clutter suppression method for moving target detection in the background of range-ambiguous clutter with a phased array (PA)–frequency diverse array (FDA) dual-mode radar. With the FDA mode, the range-ambiguous clutters are discriminated in the transmit spatial frequency domain, and thus the clutter covariance matrixes (CCMs) corresponding to unambiguous and ambiguous regions can be independently estimated. Therefore, the enhanced CCM can be reconstructed by using a linear combination of these distinguished CCMs from different range regions. With the PA mode, the enhanced CCM is applied, thus taking advantages of its high beampattern gain as well as alleviating the range ambiguous clutter suppression problem. Simulation results are presented to verify the effectiveness of the proposed method in range-ambiguous clutter scenarios. [ABSTRACT FROM AUTHOR]

Radar reflectivity assimilation is often used to initialize hydrometeors, to which Numerical Weather Prediction (NWP) is highly sensitive. To better initialize hydrometeors, this study further developed the background error covariance (BEC) with vertical and multivariable correlations of hydrometeor control variables (H-BEC) in the WRF three-dimensional variational data assimilation system (WRFDA-3DVar). The impacts of the H-BEC are discussed using single radar reflectivity tests and series of cycling data assimilation and forecasting experiments for five multi-type convective rainfall cases. The conclusions are summarized as follows: (1) The vertical correlations can speed up the minimization of the cost function, whereas the multivariable correlations further accelerate this minimization; (2) The vertical correlations slightly improve the precipitation forecasting and only in the first hour, while multivariate correlations lead to a larger improvement and persist into the third hour; (3) The application of H-BEC leads to a more reasonable thermodynamic and dynamical structure of the initial field, thereby improving the capability of short-term precipitation forecasting. [ABSTRACT FROM AUTHOR]

The joint radar and communications (JRC) waveform often has a high range sidelobe, which will degrade the target detection performance of an automotive JRC system. To solve this problem, a joint radar and communications complementary waveform group (JRC-CWG) design method is proposed in this paper by exploiting the philosophy of the complementary sequence. In the JRC-CWG, the traditional unimodular communications waveforms, such as the binary phase shift keying (BPSK) waveform, are used to perform the communications function. The sum of the autocorrelation function (SACF) of JRC-CWG is optimized to minimize the sidelobe level. Furthermore, considering that the JRC-CWG has poor Doppler resilience, a Doppler-resilient joint radar and communications complementary waveform (DR-JRC-CWG) design method is proposed to improve the Doppler resilience. Finally, the simulation results show that the proposed JRC-CWG and DR-JRC-CWG have superior radar performances without the degradation in communications performance in terms of the bit error rate (BER). [ABSTRACT FROM AUTHOR]

In this paper, we study the multiple-input multiple-output (MIMO) radar transmit waveform design method for beampattern matching. The purpose is to design the beampattern to approximate the actual one and minimize the cross-correlation sidelobes under the constant modulus constraint (CMC). Due to the CMC, the problem is non-convex, and the existing methods solve it with relaxation, resulting in performance degradation. Different from these methods, we notice that the CMC is the product of complex circles (CC). Based on this physical characteristic, the direct beampattern matching without relaxation (DBMWR) method is proposed. To be specific, we first formulate the original problem as an unconstrained quartic problem over the CC and then solve it by the proposed method without relaxation. Simulation results show that the proposed method can achieve a balance in terms of accuracy and computation complexity compared with other methods. [ABSTRACT FROM AUTHOR]

Traditional target tracking is carried out based on the point measurements extracted from the radar resolution cells. This is not suitable for situations of low signal-to-noise ratio (SNR). In this paper, we aim to investigate the problem of the joint detection and tracking (JDT) of a target by directly using the received signals of passive multi-static radar without feeding the signals to matched filters. To this end, a novel likelihood function is proposed exploiting the statistical properties of coherent processing between the reference and surveillance signals. With such a likelihood function, the particle Bernoulli filter is employed to perform direct JDT (DJDT) of the target. A remarkable feature of the proposed method is that it is able to achieve satisfactory performance when the SNR of received signals is low. Furthermore, the proposed method cannot only achieve the existence and kinematic state of the target, but also the time-varying SNR of each receiver, which serves as an important input for sensor adjustment. The performance of the proposed method is verified via simulations. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output (MIMO) radar uses waveform diversity technology to form a virtual aperture to improve the azimuth resolution of forward-looking imaging. However, the super-resolution imaging capability of MIMO radar is limited, and the resolution can only be doubled compared with the real aperture. In the radar forward-looking image, compared with the whole imaging scene, the target only occupies a small part. This sparsity of the target distribution provides the feasibility of applying the compressed sensing (CS) method to MIMO radar to further improve the forward-looking imaging resolution. At the same time, the forward-looking imaging method for a MIMO radar based on CS has the ability to perform single snapshot imaging, which avoids the problem of a motion supplement. However, the strong noise in the radar echo poses a challenge to the imaging method based on CS. Inspired by the low-rank properties of the received radar echoes and the generated images, and considering the existing information about sparse target distribution, a forward-looking super-resolution imaging model of a MIMO radar that combines sparse and double low-rank constraints is established to overcome strong noise and achieve robust forward-looking super-resolution imaging. In order to solve the multiple optimization problem, a forward-looking image reconstruction method based on the augmented Lagrangian multiplier (ALM) is proposed within the framework of the alternating direction multiplier method (ADMM). Finally, the results of the simulation and the measurement data show that the proposed method is quite effective at improving the azimuth resolution and robustness of forward-looking radar imaging compared with other existing methods. [ABSTRACT FROM AUTHOR]

The temporal variation of received clutter and noise at a pair of oceanographic high frequency radars (HFR) operating near the geomagnetic equator in the Republic of Palau is investigated. Oceanographic HFRs process range-gated Doppler spectra from groundwave signals that are backscattered from the ocean's surface to derive maps of ocean currents. The range performance of the radars exhibited a regular diurnal signal which is determined to be a result of both ionospheric clutter and noise. The increased Clutter plus Noise Floor (C+NF) decreases the Signal to Clutter plus Noise Ratio (SCNR) which, in turn, reduces the range and quality of ocean surface current measurement. Determining the nature and origin of this degradation is critical to QA/QC of existing HFR deployments as well as performance predictions of future installations. Nighttime impacts are most severe and negatively affect ocean surface current measurements as low SCNR is found to extend across the Doppler spectra at all ranges, challenging the ability of HFR to map the ocean surface current. Daytime degradation is less severe and presents itself in a way consistent with independent observations of ionospheric clutter, specifically the diurnal temporal pattern and range where the C+NF features occur. A timeseries analysis of SCNR and C+NF is pursued to understand this relationship using received range-dependent Doppler spectra and C+NF features using image segmentation techniques. Clutter plus noise features are classified into daytime, nighttime, and no-noise feature types. The diurnal structure and variability of these features are examined, and the occurrences of each feature type are calculated. The occurrences are compared with space weather indices including a measure of geomagnetic activity, namely the EE (Equatorial Electro Jet) index (determined from magnetometers measuring the earth's magnetic field), as well as solar impacts using the F10.7 solar radio clutter index to assess the relationship of ionospheric conditions with HFR ocean surface current measurement. [ABSTRACT FROM AUTHOR]

In modern electronic warfare, the intelligence of the jammer greatly worsens the anti-jamming performance of traditional passive suppression methods. How to actively design anti-jamming strategies to deal with intelligent jammers is crucial to the radar system. In the existing research on radar anti-jamming strategies' design, the assumption of jammers is too ideal. To establish a model that is closer to real electronic warfare, this paper explores the intelligent game between a subpulse-level frequency-agile (FA) radar and a transmit/receive time-sharing jammer under jamming power dynamic allocation. Firstly, the discrete allocation model of jamming power is established, and the multiple-round sequential interaction between the radar and the jammer is described based on an extensive-form game. A detection probability calculation method based on the signal-to-interference-pulse-noise ratio (SINR) accumulation gain criterion (SAGC) is proposed to evaluate the game results. Secondly, considering that the competition between the radar and the jammer has the feature of imperfect information, we utilized neural fictitious self-play (NFSP), an end-to-end deep reinforcement learning (DRL) algorithm, to find the Nash equilibrium (NE) of the game. Finally, the simulation results showed that the game between the radar and the jammer can converge to an approximate NE under the established model. The approximate NE strategies are better than the elementary strategies from the perspective of detection probability. In addition, comparing NFSP and the deep Q-network (DQN) illustrates the effectiveness of NFSP in solving the NE of imperfect information games. [ABSTRACT FROM AUTHOR]

As an important remote sensing technology, millimeter-wave radar is used for environmental sensing in many fields due to its advantages of all-day, all-weather operation. With the increasing use of radars, inter-radar interference becomes increasingly critical. Severe mutual interference degrades radar signal quality and affects the performance of post-processing, e.g., synthetic aperture radar (SAR) imaging and target tracking. Aiming to deal with mutual interference, we propose an interference mitigation method based on variational mode decomposition (VMD). With the characteristics that the target is a single-frequency sine wave and the interference is a broadband signal, VMD is used for decomposing the radar received signal and separating the target from the interference. Interference mitigation is then implemented in each decomposed mode, and an interference-free signal is obtained through the reconstruction process. Simulation results of multi-target scenarios demonstrate that the proposed method outperforms existing decomposition-based methods. This conclusion is also confirmed by the experimental results on real data. [ABSTRACT FROM AUTHOR]

A uniformity index for the axis ratios (Uar) derived from dual polarization weather radar data is proposed for raindrop area identification and analysis. The derivation of this new parameter is based on radar scattering simulations and assumptions. Uar is between 0 and 1 and can be calculated from the differential reflectivity (ZDR) and the copolar correlation coefficient (ρhv), which reflects the uniformity of the axis ratio (r) of the particle group. For raindrops, Uar is close to 1 under ideal conditions, but is clearly different from that of ice particles whose value is close to 0. Studies conducted during two convective weather events observed by X-band and S-band radar are presented to show the Uar features. In convective areas, high Uar presents a U-shaped vertical structure. One branch corresponds to the ZDR column, while the other branch is located at the rear of the convective cloud zone and is lower in altitude, representing the process of ice particles melting into raindrops and then being transported upward by a strong updraft. In stratiform cloud areas, a more than 95% overall identification ratio is obtained when the threshold of Uar is set to 0.2~0.3 for discriminating rain layers. [ABSTRACT FROM AUTHOR]

Sea ice thickness is one of the essential characteristics of sea ice. Sea ice lead detection is the key to sea ice thickness estimation from radar altimetry data. This research studies ten different surface type classification methods, including supervised learning, unsupervised learning, and threshold methods, being applied to the HY-2B radar altimeter data collected in October 2019 in the Arctic Ocean. The Sentinel-1 Synthetic Aperture Radar (SAR) images were used for training and validation of the classifiers. Compared with other classifiers, the supervised Bagging ensemble learning classifier showed excellent and robust performance with overall accuracy up to 95.69%. In order to assess the performance of the Bagging classifier in practical applications, lead fractions from January 2019 to March 2021 based on the HY-2B radar altimeter data were mapped using the trained Bagging classifier and compared to the CryoSat-2 L2I data product. The results of the lead fraction showed the monthly variability of ice lead, and the ice lead had a reasonable spatial distribution and was consistent with CryoSat-2 L2I data products. According to these results, the Bagging classifier can provide an essential reference for future studies of Arctic sea ice thickness and sea level estimation from HY-2B radar altimeter data. [ABSTRACT FROM AUTHOR]

Radar automatic target recognition based on high-resolution range profile (HRRP) has become a research hotspot in recent years. The current works mainly focus on closed set recognition, where all the test samples are assigned to the training classes. However, radar may capture many unknown targets in practical applications, and most current methods are incapable of identifying the unknown targets as the 'unknown'. Therefore, open set recognition is proposed to solve this kind of recognition task. This paper analyzes the basic classification principle of both recognitions and makes sure that determining the closed classification boundary is the key to addressing open set recognition. To achieve this goal, this paper proposes a novel boundary detection algorithm based on the distribution balance property of k-nearest neighbor objects, which can be used to realize the identification of the known and unknown targets simultaneously by detecting the boundary of the known classes. Finally, extensive experiments based on measured HRRP data have demonstrated that the proposed algorithm is indeed helpful to greatly improve the open set performance by determining the closed classification boundary of the known classes. [ABSTRACT FROM AUTHOR]

In recent years, sparse direction-of-arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar has attracted extensive attention and been extensively studied, especially the method based on the classic least absolute shrinkage and selection operator (LASSO) estimator. The alternating-direction method of multipliers (ADMM) is an effective method for solving this problem at the cost of introducing an additional user parameter. To avoid introducing an additional user parameter, this paper adopts an equivalent transformation in the form of the generalized SParse Iterative Covariance-based Estimation (qSPICE) cost function to obtain a mean squared minimized form of the cost function. Then, the problem is transformed into a sparse optimization problem in the form of a weighted LASSO. Next, this unconstrained optimization problem is decomposed into three subproblems, which are solved separately to reduce the dimension of each problem and thus reduce the overall computational complexity based on ADMM. Simulation results and measured data indicate that the proposed method significantly outperforms the traditional super-resolution DOA estimation method and ADMM-LASSO method and slightly outperforms qSPICE in terms of resolution and sidelobe suppression capability. In addition, the proposed method has a much lower computational complexity and substantially fewer iterations than qSPICE. [ABSTRACT FROM AUTHOR]

In this study, the quantitative precipitation estimation (QPE) capability of three X-band dual-polarization phased array radars (PAR) in Guangzhou, South China, was demonstrated, with an S-band operational dual-polarization radar as the benchmark. Rainfall rate (R) estimators based on the specific differential phase (KDP) for summer precipitation for both X-band and S-band radars were derived from the raindrop size distributions (DSDs) observed by a 2-dimensional video disdrometer (2DVD) in South China. Rainfall estimates from the radars were evaluated with gauge observations in three events, including pre-summer rainfall, typhoon precipitation, and local severe convective precipitation. Observational results showed that radar echoes from the X-band PARs suffered much more severely from attenuation than those from the S-band radar. Compared to S-band observations, the X-band echoes can disappear when the signal-to-noise ratio drops to a certain level due to severe attenuation, resulting in different estimated rainfall areas for X- and S-band radars. The attenuation corrected by KDP had good consistency with S-band observations, but the accuracy of attenuation correction was affected by DSD uncertainty and may vary in different types of precipitation. The QPE results demonstrated that the R(KDP) estimator produced better rainfall accumulations from the X-band PAR observations compared to the S-band observations. For both the X-band and S-band radars, the estimates of hourly accumulated rainfall became more accurate in heavier rainfall, due to the decreases of both the DSD uncertainty and the impact of measurement errors. In the heavy precipitation area, the estimation accuracy of the X-band radar was high, and the overestimation of the S-band radar was obvious. Through the analysis of the ZH-ZDR distribution in the three weather events, it was found that the X-band PAR with the capability of high spatiotemporal observations can capture minute-level changes in the microphysical characteristics, which help improve the estimation accuracy of ground rainfall. [ABSTRACT FROM AUTHOR]

Millimeter wave (MMW) radar simultaneous localization and mapping (SLAM) technology is an emerging technology in a tunnel vehicle accident rescue scene. It is a powerful tool for statistic-trapped vehicle detection with limited vision caused by darkness, heat, and smoke. A variety of SLAM frameworks have been proven to be able to obtain 3-degree-of-freedom (3-dof) pose estimation results using 2-dimention (2D) MMW radar in open space. In the application of millimeter wave radar for pose estimation and mapping in a closed environment, closed space structures and artificial targets together constitute high-intensity multi-path scattering measurement data, leading to radar false detections. Radar false detections caused by multi-path scattering are generally considered to be detrimental to radar applications, such as multi-target tracking. However, few studies analyze the mechanism of multi-path effects on radar SLAM, especially in closed spaces. In order to address the problem, this paper first presents a radar multi-path scattering theory to study the generation mechanism difference of false and radar true detection and their influences on radar SLAM 2D pose estimation and mapping in tunnel. According to the scattering mechanism differences on SLAM, a radar azimuth scattering angle signature is proposed, which allows distinguishing radar false detections from real ones. This is useful in avoiding using unreliable radar false detections to solve a radar SLAM problem. In addition, two different radar false detection revising methods combined with the CSM (correlative scan matching) algorithm are proposed in this paper. The HTMR-CSM (hard-threshold-multi-path-revised correlative scan matching) algorithm only depends on a hard threshold of radar azimuth scattering angle signature to eliminate all radar false detections as much as possible before CSM. Another idea is the STMR-CSM (soft-threshold-multi-path-revised correlative scan matching) algorithm. All the radar false detections are classified according to the distribution model of radar azimuth accuracy, and part of more reliable radar false detections are retained to estimate a more accurate pose. All the ideas in this paper are validated by using an MMW 2D radar mounted on a rail-guided robot in a tunnel. Two cars on fire were set as the targets. The experimental results show that the STMR-CSM algorithm that keeps the reliable radar false detections improves the positioning accuracy by 20% compared with CSM. [ABSTRACT FROM AUTHOR]

E-region field-aligned irregularities (FAIs) are a hot topic in space research, since electromagnetic signal propagation through ionospheric irregularities can undergo sporadic enhancements and fading known as ionospheric scintillation, which could severely affect communication, navigation, and radar systems. However, the range resolution of very-high-frequency (VHF) radars, which is widely used to observe E-region FAIs, is limited due to its bandwidth. As a technology that is widely used in atmosphere radars to improve the range resolution of pulsed radars by transmitting multiple frequencies, this paper employed the multifrequency radar imaging (RIM) technique in a Wuhan VHF radar. The results showed that the range resolution of E-region FAIs greatly improved when compared with the results in traditional single-frequency mode, and that finer structures of E-region FAIs can be obtained. Specifically, the imaging results in multifrequency mode show that E-region FAIs demonstrate an overall descending trend at night, and it could be related to the tides or gravity waves due to their downward phase velocities or even driven by downwind shear. In addition, typical quasi-periodic (QP) echoes with a time period of around 10 min could be clearly seen using the RIM technique, and the features of the echoes suggest that they could be modulated by gravity waves. Furthermore, the RIM technique can be used to obtain the fine structure of irregularities within a short time period, and the hierarchical structure of E-region FAIs can be easily found. Therefore, the multifrequency imaging RIM technique is suitable for observing E-region FAIs and their evolution, as well as for identifying the different layers of E-region FAIs. Combined with the RIM technique, a VHF radar provides an effective and promising way to observe the structure of E-region FAIs in more detail to study the physical mechanism behind the formation and evolution of ionospheric E-region irregularities. [ABSTRACT FROM AUTHOR]

The growing number of civil applications in which Unmanned Aerial Vehicles (UAVs) are involved can create many concerns for airspace security and surveillance. Gathering as much information as possible about a drone can be crucial to apply proper countermeasures if a potentially dangerous situation is detected. Of course, the presence of a UAV can be detected by radar, but it is possible to extend the system capabilities to obtain additional information. For example, in the case in which the UAV is equipped with propellers, the radar-measured rotational speed could be important information to classify the type of UAV or to reveal if it is carrying some possibly harmful payload. In addition, the rotational speed measured through radar could be used for different purposes, such as to detect a drone manumission, to estimate its maximum payload, or for predictive maintenance of the drone. Measuring the propellers' rotational speed with radar systems is a critical task, as the Doppler generated by the rotation can be very high, and it is very difficult to find commercial radar systems in the market able to handle such a high Doppler. Another problem is caused by the typically very small Radar Cross-Section (RCS) of the propellers, which makes their detection even more difficult. In the literature, common detection techniques are based on the measurement of the Doppler effect produced by the propellers to derive their rotational speed, but due to the very limited capabilities of commercial sensors, this approach can be applied only at very low values of the rotational speed. In this work, a different approach based on a Frequency-Modulated Continuous Wave (FMCW) radar is proposed, which exploits the vibration of the UAV generated by the rotation of the propellers. The phenomenon and how the sensor can detect it will be presented, which is joined with a performance analysis comparing different estimation techniques for the indirect measurement of the propellers' speed to evaluate the potential benefits of the proposed approach. [ABSTRACT FROM AUTHOR]

Currently, most deep learning (DL)-based models for precipitation forecasting face two conspicuous issues: the smoothing effect in the precipitation field and the degenerate effect of forecasting precipitation intensity. Therefore, this study proposes "time series residual convolution (TSRC)", a DL-based convolutional neural network for precipitation nowcasting over China with a lead time of 3 h. The core idea of TSRC is it compensates the current local cues with previous local cues during convolution processes, so more contextual information and less uncertain features would remain in deep networks. We use four years' radar echo reflectivity data from 2017 to 2020 for model training and one year's data from 2021 for model testing and compare it with two commonly used nowcasting models: optical flow model (OF) and UNet. Results show that TSRC obtains better forecasting performances than OF and UNet with a relatively high probability of detection (POD), low false alarm rate (FAR), small mean absolute error (MAE) and high structural similarity index (SSIM), especially at longer lead times. Meanwhile, the results of two case studies suggest that TSRC still introduces smoothing effects and slightly outperforms UNet at longer lead times. The most considerable result is that our model can forecast high-intensity radar echoes even for typhoon rainfall systems, suggesting that the degenerate effect of forecasting precipitation intensity can be improved by our model. Future works will focus on the combination of multi-source data and the design of the model's architecture to gain further improvements in precipitation short-term forecasting. [ABSTRACT FROM AUTHOR]

Non-imaging radar can achieve the detection and localization of moving targets, but it faces difficulties in the classification of targets. Aiming for the application of moving target classification by low/medium resolution non-imaging radar, this paper proposes a novel feature extraction method, based on the statistical characteristics of echo amplitude with the L-band fully polarimetric radar. A feature plane, composed of the 3rd-order central moment (skewness) and 4th-order central moment (kurtosis) as the statistical characteristics of the echo envelope, is established. In addition, two types of moving targets, pedestrians and non-motorized vehicles, are able to be effectively distinguished according to whether the echo signals of different polarizations have overlapping areas in the feature plane. The L-band fully polarimetric radar has been developed and field experiments have been conducted. The experimental results verify that the kurtosis and skewness of the fully polarimetric echo amplitude of the pedestrians are very close, and there is an overlapping area in the feature plane, while the statistical characteristics of the fully polarimetric echo signal of non-motor vehicles are different, and there is no overlapping area in the feature plane. This proposed feature extraction method has the advantage of being simple and robust, compared to the traditional imaging approach. [ABSTRACT FROM AUTHOR]

Frequency modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radars are widely applied in target localization. However, during the process, the estimation accuracy decreases sharply without considerable signal-to-noise ratio (SNR) and sufficient snapshot number. It is therefore necessary to consider estimation schemes that are valid under low signal-to-noise ratio (SNR) and snapshot. In this paper, a fast target localization framework based on multiple deep neural networks named Multi-DeepNet is proposed. In the scheme, multiple interoperating deep networks are employed to achieve accurate target localization in harsh environments. Firstly, we designed a coarse estimate using deep learning to determine the interval where the angle is located. Then, multiple neural networks are designed to realize accurate estimation. After that, the range estimation is determined. Finally, angles and ranges are matched by comparing the Frobenius norm. Simulations and experiments are conducted to verify the efficiency and accuracy of the proposed framework. [ABSTRACT FROM AUTHOR]