Three-Dimensional Wind Field Retrieved from Dual-Doppler Radar Based on a Variational Method:Refinement of Vertical Velocity Estimates

In this paper,a scheme of dual-Doppler radar wind analysis based on a three-dimensional variational method is proposed and performed in two steps.First,the horizontal wind field is simultaneously recovered through minimizing a cost function defined as a radial observation term with the standard conjugate gradient method,avoiding a weighting parameter specification step.Compared with conventional dual-Doppler wind synthesis approaches,this variational method minimizes errors caused by interpolation from radar observation to analysis grid in the iterative solution process,which is one of the main sources of errors.Then,through the accelerated Liebmann method,the vertical velocity is further reestimated as an extra step by solving the Poisson equation with impermeable conditions imposed at the ground and near the tropopause.The Poisson equation defined by the second derivative of the vertical velocity is derived from the mass continuity equation.Compared with the method proposed by O’Brien,this method is less sensitive to the uncertainty of the boundary conditions and has better stability and reliability.Furthermore,the method proposed in this paper is applied to Doppler radar observation of a squall line process.It is shown that the retrieved vertical wind profile agrees well with the vertical profile obtained with the velocity–azimuth display(VAD)method,and the retrieved radial velocity as well as the analyzed positive and negative velocity centers and horizontal wind shear of the squall line are in accord with radar observations.There is a good correspondence between the divergence field of the derived wind field and the vertical velocity.And,the horizontal and vertical circulations within and around the squall line,as well as strong updrafts,the associated downdrafts,and associated rear inflow of the bow echo,are analyzed well.It is worth mentioning that the variational method in this paper can be applied to simultaneously synthesize the three-dimensional wind field from multiple-Doppler radar observations.

STRUCTURE OF MESO-β AND –γ-SCALE ON SOUTH CHINA HEAVY RAINFALL ON 12～13 JUNE 2005 USING DUAL-DOPPLER RADAR

The three-dimensional wind fields of the heavy rain on 12-13 June 2005 in Guangdong province are retrieved and studied with the volume scan data of the dual-Doppler radar located in the cities of Meizhou and Shantou. It is shown that the meso-β-scale and meso-γ-scale convergence lines located in the convective system at the low and middle layer play an important role in the heavy rainfall. The convergence line is the initiating and maintaining mechanism of the rain. A three dimensional kinematic structure model is also given.

Kinematic Structure of a Heavy Rain Event from Dual-Doppler Radar Observations

The detailed kinematic structure of a heavy rain event that occurred in the middle reaches of the Yangtze River was investigated using dual-Doppler radar observation. A variational analysis method was developed to obtain the three-dimensional wind fields. Before the analysis, a data preprocessing procedure was carried out, in which the temporal variation with the scanning time interval and the effect of the earth curvature on the data position were taken into account. The analysis shows that a shear line in the lower and middle levels played an important role in the rainfall event. The precipitation fell mainly on the south end of the shear line where southerly flow prevailed and convergence and updraft were obvious. With the movement and decay of the shear line, the precipitation moved and decayed correspondingly.

The Use of Dual-Doppler Radar Data in the Study of 1998 Meiyu Frontal Precipitationin Huaihe River Basin

During the Meiyu period in June and July of 1998, intensified field observations have been carried out for the project “Huaihe River Basin Energy and Water Cycle Experiment (HUBEX)”. For studying Meiyu front and its precipitation in Huaihe River basin, the present paper has performed analysis on the middle and lower level wind fields in the troposphere by using the radar data obtained from the two Doppler radars located at Fengtai district and Shouxian County. From June 29 to July 3 in 1998, the continuous heavy precipitation occurred in Huaihe River basin around Meiyu front. The precipitation process on July 2 occurred within the observation range of the two Doppler radar in Fengtai district and Shouxian County. The maximum rainfall of the Meiyu front was over 100 mm in 24 h, so it can be regarded as a typical mesoscale heavy precipitation process related to Meiyu front. Based on the wind field retrieved from the dual Doppler radar, we find that there are meso-γ scale vertical circulations in the vertical cross-section perpendicular to Meiyu front, the strong upward motion of which corresponds to the position of the heavy rainfall area. Furthermore, other results obtained by this study are identical with the results by analyzing the conventional synoptic data years ago. For example: in the vicinity of 3 km level height ahead of Meiyu front there exists a southwest low-level jet; the rainstorm caused by Meiyu front mainly occurs at the left side of the southwest low-level jet; and the Meiyu front causes the intensification of the low-level convergence in front of it. Key words Dual Doppler radar - Meiyu front - Meso—γ scale vertical circulation This research was supported by Project HUBEX (Project Number: 49794030) which is funded by the National Natural Science Foundation of China (NSFC).

Mesoscale Structure of Rainstorm Retrieved from Dual-Doppler Radar Observations Using the 4DVAR Assimilation Technique

The four-dimensional variational （4DVAR） data assimilation method was applied to dual-Doppler radar data about two Meiyu rainstorms observed during CHeRES （China Heavy Rain Experiment and Study）. The purpose of this study is to examine the performance of the 4DVAR technique in retrieving rainstorm mesoscale structure and to reveal the feature of rainstorm mesoscale structure. Results demonstrated that the 4DVAR assimilation method was able to retrieve the detailed structure of wind, thermodynamics, and microphysics fields from dual-Doppler radar observations. The retrieved wind fields agreed with the dual- Doppler synthesized winds and were accurate. The distributions of the retrieved perturbation pressure, perturbation temperature, and microphysics fields were also reasonable through the examination of their physical consistency. Both of the two heavy rainfalls were caused by merging cloud processes. The wind shear and convergence lines at middle and lower levels were their primary dynamical characteristics. The convective system was often related to low-level convergence and upper-level divergence coupled with up- drafts. During its mature stage, the convective system was characterized by low pressure at lower level and high pressure at upper level, associated with warmer at middle level and colder at lower and upper levels than the environment. However, a region of cooling and high pressure occurred in the lower and middle levels compared to warming and low pressure in the upper level during its dissipating ＇.stage. The water vapor, cloud water, and rainwater corresponded to the convergence, the updraft and the intensive reflectivity, respectively.

A Model Study of Three-Dimensional Wind Field Analysis from Dual-Doppler Radar Data

A three-dimensional wind field analysis sollware based on the Beigng-Gucheng dual-Doppler weather radar system has been built, and evaluated by using the numerical cloud model producing storm flow and hydrometeor fields. The effects of observation noise and the spatial distribution of wind field analysis error are also investigated.

ERROR COMPARISON OF WIND FIELD RETRIEVAL FROM SINGLE AND DUAL-DOPPLER RADAR OBSERVATIONS

The error distributions of the wind fields retrieved from single and dual-Doppler radar observations are given in this paper.The results indicate that the error of dual-Doppler retrieval depends on the position in the scan region of the dual-Doppler radar.The error of single-Doppler retrieval by using velocity azimuth processing(VAP)technique de- pends on the angle between the directions of wind and the radar beam.Generally,the winds retrieved from single Doppl- er radar are close to those retrieved from dual-Doppler radar.But,the error distribution of the single-Doppler retrieval is different from the dual-Doppler retrieval.We simulate the retrievals of single Doppler observation by the use of the output wind data from a 3-D numerical model of severe convection.The comparison of the simulated single-and dual-Doppler retrievals shows that the VAP may be a suitable technique for the operational analysis of mesoscale wind fields.It can also be used as a supplement to wind field retrieval in the field experiment.

Study on Retrieving Three-Dimensional Wind Using Simulated Dual-Doppler Radar Data in the Cartesian Space

This paper investigates a technique of retrieving three-dimensional windfields from the dual-Doppler weather radar radial wind which is based on the Cartesian space usingvariational method. This technology provides a simultaneous resolution of three wind components andsatisfies both the minimal dual-equation system and the continuity equation. The main advantage ofthis method is that it can remove the potential drawback of an iterative solution of Cartesiandual-Doppler analysis techniques which is a major demerit when one retrieves the vertical velocityusing mass continuity equation with iterative method. The data pre-processing technology andinterpolation are also studied. This work developed a three-dimensional Cressman weighting functionto process the interpolation. In order to test the capability and advantage of this method, onenumerical experiment based on simulating dual-Doppler radar observations is designed. Firstly, wesynthesize the dual-Doppler radar radial velocity and reflectivity from the numerical model. Then,the three-dimensional wind components are retrieved from the radial velocity and reflectivity usingthis technique. The retrieved three-dimensional wind fields are found to be quite consisted withthose previously simulated wind fields. Mean difference, root-mean-square error, and relativedeviation are defined to test the precision of the method. These statistic errors reveal theaccuracy and the advantage of this method. The numerical experiment has definitely testified thatthis technique can be used to retrieve the three-dimensional wind fields from the radial velocityand reflectivity detected by the real dual-Doppler weather radar.

Metal–Organic Gel Leading to Customized Magnetic‑Coupling Engineering in Carbon Aerogels for Excellent Radar Stealth and Thermal Insulation Performances

Metal–organic gel(MOG)derived composites are promising multi-functional materials due to their alterable composition,identifiable chemical homogeneity,tunable shape,and porous structure.Herein,stable metal–organic hydrogels are prepared by regulating the complexation effect,solution polarity and curing speed.Meanwhile,collagen peptide is used to facilitate the fabrication of a porous aerogel with excellent physical properties as well as the homogeneous dispersion of magnetic particles during calcination.Subsequently,two kinds of heterometallic magnetic coupling systems are obtained through the application of Kirkendall effect.FeCo/nitrogen-doped carbon(NC)aerogel demonstrates an ultra-strong microwave absorption of−85 dB at an ultra-low loading of 5%.After reducing the time taken by atom shifting,a FeCo/Fe3O4/NC aerogel containing virus-shaped particles is obtained,which achieves an ultra-broad absorption of 7.44 GHz at an ultra-thin thickness of 1.59 mm due to the coupling effect offered by dual-soft-magnetic particles.Furthermore,both aerogels show excellent thermal insulation property,and their outstanding radar stealth performances in J-20 aircraft are confirmed by computer simulation technology.The formation mechanism of MOG is also discussed along with the thermal insulation and electromagnetic wave absorption mechanism of the aerogels,which will enable the development and application of novel and lightweight stealth coatings.

Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

Analysis of the joint detection capability of the SMILE satellite and EISCAT-3D radar

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.

Co-Sharing Waveform Design for Millimeter-Wave Radar Communication Systems

Millimeter-wave(mmWave)radar communication has emerged as an important technique for future wireless systems.However,the interference between the radar signal and communication data is the main issue that should be considered for the joint radar communication system.In this paper,a co-sharing waveform(CSW)is proposed to achieve communication and radar sensing simultaneously.To eliminate the co-interference between the communication and sensing signal,signal splitting and processing methods for communication data demodulation and radar signal processing are given respectively.Simulation results show that the bit error rate(BER)of CSW is close to that of the pure communication waveform.Moreover,the proposed CSW can achieve better performance than the existing waveforms in terms of range and velocity estimation.

A target parameter estimation method via atom-reconstruction in radar mainlobe jamming

Mainlobe jamming(MLJ)brings a big challenge for radar target detection,tracking,and identification.The suppression of MLJ is a hard task and an open problem in the electronic counter-counter measures(ECCM)field.Target parameters and target direction estimation is difficult in radar MLJ.A target parameter estimation method via atom-reconstruction in radar MLJ is proposed in this paper.The proposed method can suppress the MLJ and simultaneously provide high estimation accuracy of target range and angle.Precisely,the eigen-projection matrix processing(EMP)algorithm is adopted to suppress the MLJ,and the target range is estimated effectively through the beamforming and pulse compression.Then the target angle can be effectively estimated by the atom-reconstruction method.Without any prior knowledge,the MLJ can be canceled,and the angle estimation accuracy is well preserved.Furthermore,the proposed method does not have strict requirement for radar array construction,and it can be applied for linear array and planar array.Moreover,the proposed method can effectively estimate the target azimuth and elevation simultaneously when the target azimuth(or elevation)equals to the jamming azimuth(or elevation),because the MLJ is suppressed in spatial plane dimension.

Anti-swarm UAV radar system based on detection data fusion

There is a growing body of research on the swarm unmanned aerial vehicle(UAV)in recent years,which has the characteristics of small,low speed,and low height as radar target.To confront the swarm UAV,the design of anti-UAV radar system based on multiple input multiple output(MIMO)is put forward,which can elevate the performance of resolution,angle accuracy,high data rate,and tracking flexibility for swarm UAV detection.Target resolution and detection are the core problem in detecting the swarm UAV.The distinct advantage of MIMO system in angular accuracy measurement is demonstrated by comparing MIMO radar with phased array radar.Since MIMO radar has better performance in resolution,swarm UAV detection still has difficulty in target detection.This paper proposes a multi-mode data fusion algorithm based on deep neural networks to improve the detection effect.Subsequently,signal processing and data processing based on the detection fusion algorithm above are designed,forming a high resolution detection loop.Several simulations are designed to illustrate the feasibility of the designed system and the proposed algorithm.

Overview of radar detection methods for low altitude targets in marine environments

In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance of the radar detection methods under sea clutter,multipath,and combined conditions is categorized and summarized,and future research directions are outlined to enhance radar detection performance for low-altitude targets in maritime environments.

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution simultaneously.This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar.Firstly,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy.Secondly,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target tracking.These two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and resolution.Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

Probabilistic modeling of multifunction radars with autoregressive kernel mixture network

The task of modeling and analyzing intercepted multifunction radars(MFRs)pulse trains is vital for cognitive electronic reconnaissance.Existing methodologies predominantly rely on prior information or heavily constrained models,posing challenges for non-cooperative applications.This paper introduces a novel approach to model MFRs using a Bayesian network,where the conditional probability density function is approximated by an autoregressive kernel mixture network(ARKMN).Utilizing the estimated probability density function,a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains.Simulation results affirm the proposed method's efficacy in modeling MFRs,outperforming the state-of-the-art in pulse train denoising and change point detection.

Improving Satellite-Retrieved Cloud Base Height with Ground-Based Cloud Radar Measurements

Cloud base height(CBH) is a crucial parameter for cloud radiative effect estimates, climate change simulations, and aviation guidance. However, due to the limited information on cloud vertical structures included in passive satellite radiometer observations, few operational satellite CBH products are currently available. This study presents a new method for retrieving CBH from satellite radiometers. The method first uses the combined measurements of satellite radiometers and ground-based cloud radars to develop a lookup table(LUT) of effective cloud water content(ECWC), representing the vertically varying cloud water content. This LUT allows for the conversion of cloud water path to cloud geometric thickness(CGT), enabling the estimation of CBH as the difference between cloud top height and CGT. Detailed comparative analysis of CBH estimates from the state-of-the-art ECWC LUT are conducted against four ground-based millimeter-wave cloud radar(MMCR) measurements, and results show that the mean bias(correlation coefficient) is0.18±1.79 km(0.73), which is lower(higher) than 0.23±2.11 km(0.67) as derived from the combined measurements of satellite radiometers and satellite radar-lidar(i.e., Cloud Sat and CALIPSO). Furthermore, the percentages of the CBH biases within 250 m increase by 5% to 10%, which varies by location. This indicates that the CBH estimates from our algorithm are more consistent with ground-based MMCR measurements. Therefore, this algorithm shows great potential for further improvement of the CBH retrievals as ground-based MMCR are being increasingly included in global surface meteorological observing networks, and the improved CBH retrievals will contribute to better cloud radiative effect estimates.

A New Algorithm of Rain Type Classification for GPM Dual-Frequency Precipitation Radar in Summer Tibetan Plateau

In this study, a new rain type classification algorithm for the Dual-Frequency Precipitation Radar(DPR) suitable over the Tibetan Plateau(TP) was proposed by analyzing Global Precipitation Measurement(GPM) DPR Level-2 data in summer from 2014 to 2020. It was found that the DPR rain type classification algorithm(simply called DPR algorithm) has mis-identification problems in two aspects in summer TP. In the new algorithm of rain type classification in summer TP,four rain types are classified by using new thresholds, such as the maximum reflectivity factor, the difference between the maximum reflectivity factor and the background maximum reflectivity factor, and the echo top height. In the threshold of the maximum reflectivity factors, 30 d BZ and 18 d BZ are both thresholds to separate strong convective precipitation, weak convective precipitation and weak precipitation. The results illustrate obvious differences of radar reflectivity factor and vertical velocity among the three rain types in summer TP, such as the reflectivity factor of most strong convective precipitation distributes from 15 d BZ to near 35 d BZ from 4 km to 13 km, and increases almost linearly with the decrease in height. For most weak convective precipitation, the reflectivity factor distributes from 15 d BZ to 28 d BZ with the height from 4 km to 9 km. For weak precipitation, the reflectivity factor mainly distributes in range of 15–25 d BZ with height within 4–10 km. It is also shows that weak precipitation is the dominant rain type in summer TP, accounting for 40%–80%,followed by weak convective precipitation(25%–40%), and strong convective precipitation has the least proportion(less than 30%).