Search Results (129)

The precise reconstruction of target scattering centers (TSCs) using sensors plays a crucial role in feature extraction and identification of non-cooperative targets. Radar sensor networks (RSNs) are well suited for this task, as they are capable of illuminating targets from multiple aspect angles and rapidly capturing reflected signals. However, the complex geometry and diverse material composition of real-world targets result in significant variations in the radar cross-section (RCS) observed at different angles. Although these RCS responses are interrelated, they exhibit considerable angular diversity. Furthermore, achieving precise spatiotemporal registration and fully coherent processing is infeasible for RSNs composed of small mobile sensor platforms, such as drone swarms. Therefore, an intelligent algorithm is required to extract and accumulate correlated and meaningful information from the target echoes received by the RSN. In this work, a novel collaborative TSC reconstruction framework for RSNs is proposed. The framework performs similarity evaluation on wide-angle high-resolution range profiles (HRRPs) to achieve adaptive angular segmentation of TSC models. It combines the expectation–maximization (EM) algorithm with an enhanced Arctic puffin optimization (EAPO) algorithm to effectively integrate echo information from the RSN in a non-coherent manner, thereby enabling accurate TSC estimation. The proposed method outperforms existing mainstream approaches in terms of spatiotemporal registration requirements, estimation accuracy, and stability. Comparative experiments on measured datasets demonstrate the robustness of the framework and its adaptability to complex target scattering characteristics, confirming its practical value. Full article

The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (15 January–26 February 2018) that deployed in situ probes and remote sensors to investigate low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud boundaries and classify cloud phases in single-layer, low-level marine boundary layer (MBL) clouds below 3 km using the HIAPER Cloud Radar (HCR) and in situ measurements. The cloud base and top heights derived from HCR reflectivity, Doppler velocity, and spectrum width measurements agreed well with corresponding lidar-based and in situ estimates of cloud boundaries, with mean differences below 100 m. A liquid water content–reflectivity (LWC-Z) relationship, LWC = 0.70Z^0.29, was derived to retrieve the LWC and liquid water path (LWP) from HCR profiles. The cloud phase was classified using HCR measurements, temperature, and LWP, yielding 40.6% liquid, 18.3% mixed-phase, and 5.1% ice samples, along with drizzle (29.1%), rain (3.2%), and snow (3.7%) for drizzling cloud cases. The classification algorithm demonstrates good consistency with established methods. This study provides a framework for the boundary and phase detection of MBL clouds, offering insights into SO cloud microphysics and supporting future efforts in satellite retrievals and climate model evaluation. Full article

Assimilation of weather radar data, as well as additional data like lightning data, in high-resolution weather forecast models is a promising method to improve short-term forecasting (nowcasting) of flash-flood events. A data assimilation system based on the Weather Research and Forecasting model is used in this study, with radar reflectivity and radial velocity data collected with X-band Doppler polarimetric radar in the area of Athens, Greece, and lightning observations obtained from a lightning detection network covering Greece. Radar data are assimilated with the four-dimensional variational method, which includes a full-hydrometeor assimilation scheme, in a nested domain of the model with a resolution of 3 km. Humidity, vertical velocity and horizontal wind divergence profiles estimated from lightning data are assimilated with a three-dimensional variation method in the parent domain of the model with a resolution of 9 km. The results from a case study are presented to show the effect of assimilating each type of data. Full article

Aerosols play an important role in global meteorology and climate, as well as in air transport and human health, but there are still many unknowns on their effects and importance, in particular for the coarser (giant and ultragiant) aerosol particles. In this study, we aim to exploit the synergy between Raman lidar and Ka-band cloud radar to enlarge the size range in which aerosols can be observed and characterized. To this end, we developed an inversion technique that retrieves the aerosol microphysical properties based on cloud radar reflectivity and linear depolarization ratio. We applied this technique to a 6-year-long dataset, which was created using a recently developed methodology for the identification of giant aerosols in cloud radar measurements, with measurements from Potenza in Italy. Similarly, using collocated and concurrent lidar profiles, a dataset of aerosol microphysical properties using a widely used inversion technique complements the radar-retrieved dataset. Hence, we demonstrate that the combined use of lidar- and radar-derived aerosol properties enables the inclusion of particles with radii up to 12 µm, which is twice the size typically observed using atmospheric lidar alone. Full article

A novel classification algorithm for precipitation types (CP) was developed to address frequent misclassification issues between shallow convection and intense stratiform precipitation using existing methods and to enhance an understanding of their physical characteristics. Based on three-dimensional radar data and temperature fields, CP integrates three approaches: Storm Labeling in Three Dimensions (SLTD), a feature parameter-based algorithm (FP), and an advanced subcategorization method. The algorithm classifies precipitation into ten types: four non-precipitating, three stratiform, and three convective categories. CP was evaluated against traditional methods (SHY and FP) through both qualitative and quantitative analyses for mid-latitude warm-season systems. The CP method demonstrated improved performance, with higher skill scores (e.g., POD: 0.567–0.571) compared to SHY (0.349–0.364) and FP (0.455–0.470). Additionally, comparative analyses of vertical mean profiles of radar reflectivity, dynamical, and microphysical variables confirmed the enhanced capability of CP in distinguishing detailed precipitation structures. Full article

Weather forecasting is essential for agriculture, yet current methods often lack the localized accuracy required to manage extreme weather events and optimize irrigation. The MAGDA Horizon Europe/EUSPA project addresses this gap by developing a modular system that integrates novel European space-based, airborne, and ground-based technologies. Unlike conventional forecasting systems, MAGDA enables precise, field-level predictions through the integration of cutting-edge technologies: Meteodrones provide vertical atmospheric profiles where traditional data are sparse; GNSS-reflectometry offers real-time soil moisture insights; and all observations feed into convection-permitting models for accurate nowcasting of extreme events. By combining satellite data, GNSS, Meteodrones, and high-resolution meteorological models, MAGDA enhances agricultural and water management with precise, tailored forecasts. Climate change is intensifying extreme weather events such as heavy rainfall, hail, and droughts, threatening both crop yields and water resources. Improving forecast reliability requires better observational data to refine initial atmospheric conditions. Recent advancements in assimilating reflectivity and in situ observations into high-resolution NWMs show promise, particularly for convective weather. Experiments using Sentinel and GNSS-derived data have further improved severe weather prediction. MAGDA employs a high-resolution cloud-resolving model and integrates GNSS, radar, weather stations, and Meteodrones to provide comprehensive atmospheric insights. These enhanced forecasts support both irrigation management and extreme weather warnings, delivered through a Farm Management System to assist farmers. As climate change increases the frequency of floods and droughts, MAGDA’s integration of high-resolution, multi-source observational technologies, including GNSS-reflectometry and drone-based atmospheric profiling, is crucial for ensuring sustainable agriculture and efficient water resource management. Full article

A novel data assimilation technique is developed to assimilate MODIS (Moderate Resolution Imaging Spectroradiometer) level two (L2) cloud products, including cloud optical thickness (COT), cloud particle effective radius (Re), cloud water path (CWP), and cloud top pressure (CTP), into the Weather Research and Forecast (WRF) model. Its impact on the analysis and forecast of Typhoon Talim in 2023 at its initial developing stage is demonstrated. First, the conditional generative adversarial networks–bidirectional ensemble binned probability fusion (CGAN-BEBPF) model ) is applied to retrieve three-dimensional (3D) CloudSat CPR (cloud profiling radar) equivalent W-band (94 Ghz) radar reflectivity factor for the typhoons Talim and Chaba using the MODIS L2 data. Next, a W-band to S-band radar reflectivity factor mapping algorithm (W2S) is developed based on the collocated measurements of the retrieved W-band radar and ground-based S-band (4 Ghz) radar data for Typhoon Chaba at its landfall time. Then, W2S is utilized to project the MODIS-retrieved 3D W-band radar reflectivity factor of Typhoon Talim to equivalent ground-based S-band reflectivity factors. Finally, data assimilation and forecast experiments are conducted by using the WRF Hydrometeor and Latent Heat Nudging (HLHN) radar data assimilation technique. Verification of the simulation results shows that assimilating the MODIS L2 cloud products dramatically improves the initialization and forecast of the cloud and precipitation fields of Typhoon Talim. In comparison to the experiment without assimilation of the MODIS data, the Threat Score (TS) for general cloud areas and major precipitation areas is increased by 0.17 (from 0.46 to 0.63) and 0.28 (from 0.14 to 0.42), respectively. The fraction skill score (FSS) for the 5 mm precipitation threshold is increased by 0.43. This study provides an unprecedented data assimilation method to initialize 3D cloud and precipitation hydrometeor fields with the MODIS imagery payloads for numerical weather prediction models. Full article

Ground-based ground-penetrating radar (GPR) has been applied successfully for decades in archaeological geophysics. However, there are sometimes severe problems arising in cases of rough terrain, permission to enter a site, or due to vegetation. Other issues may also make it impossible to use conventional ground-based GPR. Therefore, mounting the GPR antenna below a drone could be a potential alternative. Successful applications of drone-based GPR have already been reported, e.g., in the fields of geological mapping, glaciology, and UXO-detection. However, it is not clear whether faint archaeological remains can also be mapped using this approach. In the survey discussed below, we tested such a drone-based GPR setup at an archaeological site in Bavaria, where well-preserved Roman foundations at a shallow depth are known from previous geophysical surveys with magnetics and ground-based GPR. The aim was to evaluate the possibilities and problems arising with this new approach through a comparison with the afore-mentioned data, obtained in previous ground-based surveys of this site. The results show that under certain circumstances, the archaeological remains can be resolved while using a drone. However, the remains are much harder to detect with a lower degree of resolution and survey setup and acquisition time play a crucial role for a successful survey. Especially relevant are two factors: First, the correct choice of profile orientation, as there are strong reflections caused by near-surface features (like field boundaries) due to decoupling the antenna from the ground. Second, a very dry soil is mandatory, as otherwise too much signal is lost at the air-ground-interface. Considering these factors, drone-based GPR represents a valuable tool for modern archaeological geophysics. Full article

On 13 August 2019, a severe convective precipitation event affected the Shanghai region. At 850 hPa, a low-level shear line influenced Shanghai with surface convergence, while at 700 hPa, an inversion layer separated warm, moist lower air from colder, drier air aloft, favoring convection. Observations also revealed vertical wind shear, facilitating additional convective growth. Observations from local automatic weather stations (AWSs) and wind profiler radars (WPRs) indicate that five minutes before rainfall began, ground heat and northerly winds collided, triggering the precipitation. Both the S-band Qingpu SA radar and a novel single-polarization X-band Array weather radar system (Array Weather Radar, AWR) with three phased-array radar frontends and one radar backend captured this event. Compared with the relatively coarse spatiotemporal resolution of the Qingpu SA radar, the AWR provides high-resolution wind-field data, enabling the derivation of horizontal divergence and vertical vorticity. A detailed analysis of reflectivity, divergence, and vorticity in the AWR’s overlapping detection areas shows that, during the development and mature stages of the cell’s lifecycle, the volume of echoes with Z > 25 dBZ consistently increases, whereas echoes with Z > 45 dBZ grow in an oscillatory pattern, reaching five peaks. Moreover, at the altitudes where Z > 45 dBZ appears, regions of cyclonic vorticity emerge. Full article

This study investigates the detectability of a putative layer of regolith containing water ice in the lunar polar regions using ground penetrating radar (GPR). Numerical simulations include realistic variations in the relative permittivity of the lunar regolith, considering both density and, for the first time, the effects of temperature on permittivity profiles. We follow the case of previous theoretical studies of water migration, which suggest that water ice accumulates at depths ranging from a few centimeters to tens of centimeters, appropriate depths to explore using GPR. In particular, frequency-modulated continuous wave (FMCW) radar is well-suited for this purpose due to its high range resolution and robust signal-to-noise ratio. This study evaluates two scenarios for the presence of lunar water ice: (1) a layer of regolith containing water ice at a depth of 5 cm, with a thickness of 5 cm, and (2) a layer of regolith containing water ice at a depth of 20 cm, with a thickness of 10 cm. Our computational results show that FMCW GPR, equipped with a dynamic range of 90 dB, is capable of detecting reflections from the interfaces of these layers, even under conditions of low water ice content and using antennas with low directivity. In addition, optimized antenna offsets improve the resolution of the upper and lower interfaces, particularly when applied to the surface of ancient crater ejecta. This study highlights the critical importance of understanding subsurface density and temperature structures for the accurate detection of water-ice-bearing regolith layers. Full article

By leveraging amplitude differences between reflected and diffracted signals in Ground Penetrating Radar (GPR) data, multiple singular spectrum analysis (MSSA) is considered an attractive approach to separate diffraction, which has identified great potential in their detectability of small-scale geological structures. However, conventional MSSA encounters difficulties in pinpointing the singular value threshold that corresponds to reflection, diffraction, and noise within the singular spectrum, leading to a resolution loss of the extracted diffraction profile. To address this issue, this paper develops a new technique that incorporates multilevel wavelet transform (MWT) and MSSA to separate GPR diffraction. By first implementing the MWT on GPR data decompose, the strategy can obtain various approximate detailed coefficients of multiple transformation levels for the subsequent inverse MWT to construct the corresponding coefficient profile. The issue of coefficient profiles that depict reflections often contains residual diffractions is also addressed by performing multiple singular spectrum SVDs based on the Hankel matrix within the dominant frequency domain. Building upon this, the k-means clustering algorithm is introduced to perform MSSA for classifying singular values into k categories. The diffraction wavefield is rebuilt by combining these outcomes with the coefficient profiles that depict diffractions at various transformation levels. Numerical tests showcase that the biorthogonal wavelet basis function bior4.4 provides remarkably efficient GPR diffraction separation performance, and the number of clusters in the k-means clustering algorithm typically ranges from 9 to 15, accounting for the complexity of the wave components. Compared to plane wave deconstruction (PWD), the proposed MWT-MSSA approach reduces energy loss at the diffraction vertex, decreases residual diffraction energy within the reflection profile, and enhances computational efficiency by approximately 70–80% to facilitate the subsequent subtle imaging. Full article

Agricultural areas in semi-arid regions modify low-level atmospheric conditions through changes in heat and moisture surface fluxes and enhanced evapotranspiration. This study aims to investigate the influence of near-ground-level relative humidity (RH) on local precipitation characteristics in a relatively flat, mid-latitude, semi-arid agricultural region, divided into a rainfed and an irrigated area with high evapotranspiration contrast in summer. The region was selected for the Land Surface Interactions with the Atmosphere over the Iberian Semi-Arid Environment (LIAISE) international field campaign in 2021 and here is studied using Automatic Weather Station observations and C-band weather radar data covering six years. Summer RH records show clear contrasts between irrigated and non-irrigated areas, unlike rain gauge and radar-derived rainfall, which do not exhibit substantial differences. A closer analysis indicates that RH differences between irrigated and non-irrigated areas before rainfall tend to diminish for several hours after the rainfall onset. This suggests that the presence of rainfall is temporally more important than whether the terrain is irrigated or not. Examination of radar reflectivity (Z) profiles considered convective and non-convective cases averaged during the first 30 and 180 min from the precipitation onset. Results indicated a dependence on ground-level RH for convective cases, leading to higher Z values with higher RH, clearer for the first 30 min averaged profiles. Finally, a linear relation was found between the lowest 1 km radar Z value and collocated RH for the first 30 min period of convective precipitation, increasing Z with RH. These results point out that, despite no differences in precipitation amounts found over contiguous irrigated and non-irrigated areas, there is a local impact of low-level moisture on convective rainfall. Full article

This study presents a numerical modeling approach that utilizes millimeter-wave (mm-Wave) Frequency-Modulated Continuous-Wave (FMCW) radar to reconstruct and classify five weapon types: grenades, knives, guns, iron rods, and wrenches. A dataset of 1000 images of these weapons was collected from various online sources and subsequently used to generate 3605 samples in the MATLAB (R2022b) environment for creating reflectivity-added images. Background reflectivity was considered to range from 0 to 0.3 (with 0 being a perfect absorber), while object reflectivity was set between 0.8 and 1 (with 1 representing a perfect electric conductor). These images were employed to reconstruct high-resolution weapon profiles using a monostatic two-dimensional (2D) Synthetic Aperture Radar (SAR) imaging technique. Subsequently, the reconstructed images were classified using a Convolutional Neural Network (CNN) algorithm in a Python (3.10.14) environment. The CNN architecture consists of 10 layers, including multiple convolutional, pooling, and fully connected layers, designed to effectively extract features and perform classification. The CNN model achieved high accuracy, with precision and recall values exceeding 98% across most categories, demonstrating the robustness and reliability of the model. This approach shows considerable promise for enhancing security screening technologies across a range of applications. Full article

We present a technique for the detection of vertebrate skeletons buried at shallow depths through the use of a ground-penetrating radar (GPR). The technique is based on the acquisition of high-resolution data by medium-to-high frequency GPR antennas and the analysis of the radar profiles by a new forward modelling method that is applied on a set of representative traces. This approach allows us to obtain synthetic traces that can be used to build detailed reflectivity diagrams that plot spikes with a distinct amplitude and polarity for each reflector in the ground. The method was tested in a controlled experiment performed at the top of Cerro Los Quesos, one of the most fossiliferous localities in the Ica Desert of Peru. We acquired GPR data at the location of a partially buried fossil skeleton of a large whale and analyzed the reflections associated with the bones using the new technique, determining the possible signature of vertebrae, ribs, the cranium (including the rostrum), and mandibles. Our results show that the technique is effective in the mapping of buried structures, particularly in the detection of tiny features, even below the classical (Ricker and Rayleigh) estimates of the vertical resolution of the antenna in civil engineering and forensic applications. Full article

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies. Full article