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Existing databases of RCS benchmarks lack a complex, low-observable target. This paper describes the design of such a complex and low-observable measurement model. Starting point of the design is the so-called Muldicon model, developed by the NATO/STO/AVT panel. Hot spots of the original model are identified and treated with radar-absorbing materials. Simulations on the treated model demonstrate that the model is indeed low observable. The effect of the manufacturing process of 3D-printing and separable parts is assessed experimentally on a cone-sphere; the effect is found to be negligible. These results give confidence that the model, when built, satisfies the requirements of being complex and low observable; and that artefacts of the manufacturing process will not impair its signature.

An extremely heavy rainfall event hit Guangdong province, China, from 27 August to 1 September 2018. There were two different extreme rain regions, respectively, at the Pearl River estuary and eastern Guangdong, and a record-breaking daily precipitation of 1056.7 mm was observed at Gaotan station on 30 August. This paper utilizes a suite of observations from soundings, a gauge network, disdrometers, and polarimetric radars to gain insights to the two rainfall centers. The large-scale meteorological forcing, rainfall patterns, and microphysical processes, as well as radar-based precipitation signatures are investigated. It is concluded that a west-moving monsoon depression played a critical role in sustaining the moisture supply to the two extreme rain regions, and the combined orographic enhancement further contributed to the torrential rainfall over Gaotan station. The raindrop size distributions (DSD) observed at Zhuhai and Huidong stations, as well as the observed polarimetric radar signatures indicate that the rainfall at Doumen region was characterized by larger raindrops but a lower number concentration compared with that at Gaotan region. In addition, the dual-polarization radars are used to quantify precipitation intensity during this extreme event, providing timely information for flood warning and emergency management decision-making.

Current studies rarely mention radar detection of hybrid vertical take-off and landing (VTOL) fixed-wing drones. We investigated radar signals of an industry-tier VTOL fixed-wing drone, TX25A, compared with the radar detection results of a quad-rotor drone, DJI Phantom 4. We used an X-band pulse-Doppler phased array radar to collect tracking radar data of the two drones in a coastal area near the Yellow Sea in China. The measurements indicate that TX25A had double the values of radar cross-section (RCS) and flying speed and a 2 dB larger signal-to-clutter ratio (SCR) than DJI Phantom 4. The radar signals of both drones had micro-Doppler signals or jet engine modulation (JEM) produced by the lifting rotor blades, but the Doppler modulated by the puller rotor blades of TX25A was undetectable. JEM provides radar signatures such as the rotating rate, modulated by the JEM frequency spacing interval and the number of blades for radar automatic target recognition (ATR), but also interferes with the radar tracking algorithm by suppressing the body Doppler. This work provides an a priori investigation of new VTOL fixed-wing drones and may inspire future research.

Short-range radar has become one of the latest sensor technologies for the Internet of Things (IoT), and it plays an increasingly vital role in IoT applications. As the essential task for various smart-sensing applications, radar-based human activity recognition and person identification have received more attention due to radar’s robustness to the environment and low power consumption. Activity recognition and person identification are generally treated as separate problems. However, designing different networks for these two tasks brings a high computational complexity and wastes of resources to some extent. Furthermore, there are some correlations in activity recognition and person identification tasks. In this work, we propose a multiscale residual attention network (MRA-Net) for joint activity recognition and person identification with radar micro-Doppler signatures. A fine-grained loss weight learning (FLWL) mechanism is presented for elaborating a multitask loss to optimize MRA-Net. In addition, we construct a new radar micro-Doppler dataset with dual labels of activity and identity. With the proposed model trained on this dataset, we demonstrate that our method achieves the state-of-the-art performance in both radar-based activity recognition and person identification tasks. The impact of the FLWL mechanism was further investigated, and ablation studies of the efficacy of each component in MRA-Net were also conducted.

The authors study the radar signature of a new type of wind turbine, named the Wind Lens. This design includes a flanged shroud around the turbine which concentrates the wind flow past the turbine blades and hence improves the efficiency. The design also offers improved safety and reduces acoustic noise. Furthermore, it may offer a significantly lower radar signature, which may make the design much more attractive for use in situations where conventional wind turbine designs may disturb the operation of radars. The authors present the results of an experimental trial, carried out in the UK, to measure the radar cross section (RCS) of a 5 kW Wind Lens turbine prototype and provide a reference database that can be used for comparing the Wind Lens RCS with that of conventional turbines. The authors also investigate the methods to further reduce the Wind Lens RCS and present the results of a time-varying Doppler analysis. The results show that the addition of a metallic mesh around the shroud obscures the rotating blades, and hence mitigates the RCS by 15 dB, at angles where the radar interference is highest.

In stratiform rainfall, the melting layer (ML) is often visible in radar observations as an enhanced reflectivity band, the so-called bright band. Despite the ongoing debate on the exact microphysical processes taking place in the ML and on how they translate into radar measurements, both model simulations and observations indicate that the radar-measured ML properties are influenced by snow microphysical processes that take place above it. There is still, however, a lack of comprehensive observations to link the two. To advance our knowledge of precipitation formation in ice clouds and provide new insights into radar signatures of snow growth processes, we have investigated this link. This study is divided into two parts. Firstly, surface-based snowfall measurements are used to develop a new method for identifying rimed and unrimed snow from X- and Ka-band Doppler radar observations. Secondly, this classification is used in combination with multifrequency and dual-polarization radar observations collected during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) experiment in 2014 to investigate the impact of precipitation intensity, aggregation, riming and dendritic growth on the ML properties. The results show that the radar-observed ML properties are highly related to the precipitation intensity. The previously reported bright band “sagging” is mainly connected to the increase in precipitation intensity. Ice particle riming plays a secondary role. In moderate to heavy rainfall, riming may cause additional bright band sagging, while in light precipitation the sagging is associated with unrimed snow. The correlation between ML properties and dual-polarization radar signatures in the snow region above appears to be arising through the connection of the radar signatures and ML properties to the precipitation intensity. In addition to advancing our knowledge of the link between ML properties and snow processes, the presented analysis demonstrates how multifrequency Doppler radar observations can be used to get a more detailed view of cloud processes and establish a link to precipitation formation.

The Precipitation Measurement Radar (PMR) onboard FengYun‐3G consists of a Ku‐/Ka‐band radar, which is characterized by similar configurations with the Dual‐frequency Precipitation Radar (DPR) carried by Global Precipitation Measurement mission Core Observatory. However, directly comparing observations from two radars is challenging due to a scarcity of their coincidences. In this study, sea surface echoes in their track intersections were employed to cross‐calibrate PMR. Then, we show that the dual‐frequency ratio (DFR) in snow stably increases with Ku‐band reflectivity in statistics, allowing for an assessment of the consistency between PMR and DPR observations. Surprisingly, our results reveal a underestimation of DFR in DPR inner swath, while observations from PMR are in good agreement with those from DPR outer swath. This study demonstrates the novel use of natural targets for spaceborne dual‐frequency radar calibration, and presents a unique view into the connection between the two spaceborne precipitation radar missions in operation. Plain Language Summary Spaceborne cloud and precipitation radars allow global characterization of hydrometeors and have been extensively used in various disciplines. Synergistic applications of radar observations from multiple satellites largely enhance the wealth of radar data offers, while the physical interpretation of radar observations relies on assured data consistency from different platforms. Similar with the Dual‐frequency Precipitation Radar (DPR) onboard Global Precipitation Measurement mission Core Observatory satellite, FengYun‐3G (FY‐3G) carries China's first spaceborne precipitation radar, a Ku‐/Ka‐band Precipitation Measurement Radar (PMR). However, it is challenging to directly compare the radar observations from the two satellites since their tracks rarely intersect simultaneously with precipitation. Here, we show how natural targets can be used for connecting observations from the two satellite missions. First, we use sea surface echoes observed by DPR in their intersections to calibrate the radar onboard FY‐3G. Then, we show that the dual‐frequency signatures of snowflakes as observed by the Ku‐/Ka‐band radar can be used to validate the radar observations from the two satellites. This is the first comparison between dual‐frequency radar observations from the two spaceborne precipitation radar missions, laying the basis for synergetic use of their observations in the future. Key Points Sea surface echoes in intersections of the tracks of FY‐3G and GPM‐CO were employed to calibrate the FY‐3G PMR in‐orbit DFR observations of snowflakes show a stable dependence on Ku‐band reflectivity, which was used to validate FY‐3G PMR calibration PMR, with added Ka‐band sensitivity, has DFR observations less affected by noise compared to DPR inner swath

Polarimetric radars provide variables like the specific differential phase (KDP) to detect fingerprints of dendritic growth in the dendritic growth layer (DGL) and secondary ice production, both critical for precipitation formation. A key challenge in interpreting radar observations is the lack of in situ validation of particle properties within the radar measurement volume. While high KDP in snow is usually associated with high particle number concentrations, only few studies attributed KDP to certain hydrometeor types and sizes. To address this, we combined surface in situ observations from the Video In Situ Snowfall Sensor (VISSS) with remote sensing data from a polarimetric W-band radar and an X-band radar, along with modeling approaches. Data were collected during the CORSIPP project, part of the ARM SAIL campaign (winter 2022/2023, Colorado Rocky Mountains). We found that at W-band, KDP > 2 ° km−1 can result from a broad range of particle number concentrations, between 1 and 100 L−1. Blowing snow and increased ice collisional fragmentation in a turbulent layer enhanced observed KDP values. T-matrix simulations indicated that high KDP values were primarily produced by particles smaller than 0.8 mm in the DGL and 1.5 mm near the surface. Discrete dipole approximation simulations based on VISSS data suggested that dendritic aggregates larger than 2.5 mm contributed 10 %–20 % to the measured W-band KDP near the surface. These findings highlight the complexity of interpreting W-band KDP in snowfall and emphasize the need for combined in situ observations and radar forward simulations to better understand snowfall microphysical processes.

In this study, we present radar polarimetric characterizations of the storm producing 201.9 mm hourly rainfall on 20 July 2021 in Zhengzhou, China. We employed the separation signatures of enhanced polarimetric observations to investigate hydrometeor size sorting processes, and developed an algorithm to quantify the size sorting directions. Analysis of coupled polarimetric observations unraveled multi‐directional size sorting (MSS) occurred as a low‐level differential reflectivity ZDR backwards arc signature encompassing the rainfall center during the most intensive rainfall period. The rainfall intensification is in step with the increase of size sorting directions. Model simulations with two‐moment microphysics scheme suggest that the presence of arc‐shaped updrafts is conducive to MSS and increased rain rates around the rainfall center. This work sheds novel insights into the kinematics‐driven microphysics in extreme rainfall storms, warranting the potential of using coupled polarimetric signatures for warning catastrophic extreme rainfall events. Plain Language Summary Raindrops of varying sizes have different fall speeds. In presence of storm‐relative winds, the differential sedimentation between large and small raindrops leads to different sorting distances, redistributing the regional characteristic sizes of raindrops. In radar polarimetric measurements, differential reflectivity ZDR is representative of the characteristic size of raindrops and specific differential phase KDP is proportional to the rain water content. The coupled low‐level polarimetric separation signatures in rainfall storms are indicative of the raindrop size sorting as driven by the storm kinematics. In this study, we identified unusual polarimetric separation signatures characterized by an unprecedented ZDR backwards arc in the storm producing 201.9 mm hourly rainfall accumulation. Different from previously observed ZDR arc in a single inflow, ZDR backwards arc is suggestive of multi‐directional size sorting as driven by arc‐shaped updrafts. We found that the intensification of rain rates is associated with increased size sorting directions. Detection of this novel radar polarimetric signature in operational services is recommended for warning such extreme rainfall events in the future. Key Points Radar polarimetric separation signatures were used to characterize size sorting in the extreme rainfall storm MSS as driven by arc‐shaped updrafts in the storm is evidenced by an unprecedented ZDR backwards arc Increase of size sorting directions is correlated to rainfall intensification

While prior research has shown that characteristics of the supercell environment can indicate the likelihood of tornadogenesis, it is common for tornadic and nontornadic supercells to coexist in seemingly similar environments. Thus, some small-scale factors must support tornadogenesis in some supercells and not in others. In this study we examined polarimetric radar signatures of proximate pretornadic and nontornadic supercells in seemingly similar environments to determine if these radar signatures can indicate which proximate supercells are pretornadic and which are nontornadic. We gathered a collection of proximity supercell groups and developed a method to quantify environmental similarity between storms. Using this method, we selected pretornadic–nontornadic supercell pairs in close proximity in space and time having the most similar environments. These pairs were run through an automated tracking algorithm that quantifies polarimetric signatures in each supercell. Supercells with larger differential reflectivity ( Z DR ) column areas were more likely to become tornadic within the next 30 min compared to neighboring supercells with smaller Z DR column areas. In about two-thirds of pairs, the pretornadic supercell had a larger Z DR column area than the nontornadic supercell prior to its maximum low-level rotation, which is consistent with much prior work. The Z DR arcs could not discriminate between pretornadic and nontornadic supercells, and hailfall area was larger in pretornadic supercells. The separation distance between the specific differential phase ( K DP ) foot and the Z DR arc was larger in pretornadic supercells, yet was a limited result due to the small sample size used for comparison.