Radar Data Research Articles

Initiation of a Supercell by Convectively Generated Gravity Waves in the Simulated Kaiyuan Tornadic Event of 3 July 2019

Abstract An EF4-rated supercell tornado occurred on 3 July 2019 in Kaiyuan, China, causing heavy casualties. A three-level nested-grid high-resolution numerical simulation is used to investigate the initiation of the tornadic supercell. Automatic weather station (AWS) data, FY-4A visible satellite data, and Doppler radar data are used to verify the model simulation. The most important aspects of the simulated pre-supercell mesoscale convective system (MCS) and the initiation of the supercell agree with observations. Detailed investigation of the model results reveals that the initial cells form first above a convective boundary layer (CBL) on the dry side of a surface dryline. Above the CBL is a moist layer in terms of relative humidity and the layer is stable. Convectively generated gravity waves (GWs) emanating from the MCS and propagating southward along the stable layer above the CBL provide localized forcing for the actual triggering of initial cells at specific locations. The associated perturbation potential temperature and vertical velocity patterns confirm that the GWs trigger a series of cloud bands. The additional lifting by the updraft of a horizontal convective roll in the CBL underneath the GW updraft works together to promote faster growth of the initial cell that later becomes the supercell. Examination of the Scorer parameter profiles shows favorable conditions for vertical trapping of GWs along the wave guide in the stable layer, preventing the radiation of wave energy to the upper levels.

Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data

Flood mapping using Synthetic Aperture Radar (SAR) data impose limitations in fully distinguishing flood under vegetation due to false double bounce returns from inundated tree trunks along with seasonal heterogeneities devised from changing land cover settings. In addition, rapid mapping of flooded vegetation is challenging during near real time applications. In this paper a fully automatic novel supervised classification approach called polarimetric Naïve Bayes is proposed that combines polarimetric information with series of Gaussian mixture models in Naïve Bayes framework to detect various flooded vegetation classes. It also allows the user to choose class configuration and eliminates creation of Region of Interest (ROI) for supervised training. The proposed approach uses scattering information from pre monsoon PolSAR dataset in training step to create ROIs for buildings and other features. In the next step series of Gaussian Mixtures are used for density estimation for different features in Bayesian multiclass problem. The newly developed classifier applied on 2016 Assam flood event resulted in precise mapping of at least three different vegetation classes under flood such as submerged vegetation, wetlands and floating vegetation. Under optimal class configuration, the approach showed better performance compared to other supervised techniques applied on the same data set such as MLE, Mahalanobis, Minimum Euclidean distance, and SVM classifications in delineating flood, submerged vegetation, wetlands and floating vegetation with Producer’s Accuracy of 98.6%, 81.1%, 94% and 51.5% respectively and combined Overall accuracy of 95.5% for flooded vegetation class. This method also detected multiple vegetation classes with better accuracy compared to similar methods.

Estimation of surface soil moisture from Sentinel-1 synthetic aperture radar imagery using machine learning method

Surface soil moisture (SM) is a crucial variable representing the water content in soil at the topmost soil layer. Accurate data of SM are essential for various purposes, including the monitoring of drought, vegetation modeling, weather forecasting, and agriculture management. Over the past decades, microwave remote sensing has been employed to estimate SM at large scales, with the Sentinel-1 satellite mission recently offering Synthetic Aperture Radar (SAR) data and enabling to estimate SM at high spatial resolution. Machine learning (ML) techniques have further enhanced these estimations, with random forest (RF) emerging as a promising method due to its strong capabilities. However, the performance of RF in estimating SM with satellite data has not yet been thoroughly evaluated across large areas. This study presents a new RF-based model for estimating SM over Europe with SAR data from Sentinel-1, along with the climate and terrain data. The predictions from the RF model were compared against the ground measurements from the International Soil Moisture Network (ISMN), Integrated Carbon Observation System (ICOS), and official SMAP/Sentinel-1 SM products. The results demonstrated that the RF model yielded accurate predictions with a Pearson's correlation coefficient (R) of 0.847 outperforming the official SMAP/Sentinel-1 product at spatial resolutions of 1 km and 3 km, which achieved R values of 0.599 and 0.616, respectively. Additionally, the impact of vegetation cover that is represented by using multiple satellite vegetation products on the RF model was investigated. Despite a moderate correlation between backscattering coefficients and vegetation cover, no correlation was found between the RF model's errors and vegetation cover. The results suggest that the RF model and Sentinel-1 SAR data can be used to provide SM estimations at high spatial resolution, characterizing the fine-scale variations in SM, to support various applications such as precision agriculture at the small, localized scales.

A deep learning approach for non-invasive Alzheimer’s monitoring using microwave radar data

Over 50 million people globally suffer from Alzheimer’s disease (AD), emphasizing the need for efficient, early diagnostic tools. Traditional methods like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans are expensive, bulky, and slow. Microwave-based techniques offer a cost-effective, non-invasive, and portable solution, diverging from conventional neuroimaging practices. This article introduces a deep learning approach for monitoring AD , using realistic numerical brain phantoms to simulate scattered signals via the CST Studio Suite. The obtained data is preprocessed using normalization, standardization, and outlier removal to ensure data integrity. Furthermore, we propose a novel data augmentation technique to enrich the dataset across various AD stages. Our deep learning approach combines Recursive Feature Elimination (RFE) with Principal Component Analysis (PCA) and Autoencoders (AE) for optimal feature selection. Convolution Neural Network (CNN) is combined with Gated Recurrent Unit (GRU), Bidirectional Long Short Term Memory (Bidirectional-LSTM), and Long Short-Term Memory (LSTM) to improve classification performance. The integration of RFE-PCA-AE significantly elevates performance, with the CNN+GRU model achieving an 87% accuracy rate, thus outperforming existing studies.

Microphysical characteristics of torrential predecessor rain events over the Yangtze River Delta Area and the related tropical cyclones

The precipitation structures and microphysical characteristics of predecessor rain events (PREs) over the Yangtze River Delta area and related tropical cyclones (TCs) from 2014 to 2019 were investigated using Dual-frequency Precipitation radar data from Global Precipitation Measurement (GPM) for drop size distributions (DSDs). Results showed that the total mean rain rate of PREs was larger than that of TCs, primarily due to higher convective and stratiform rain rates, with enhanced fractional coverage of convective rain in PREs. Examination of microphysical characteristics revealed that a greater quantity of small-sized droplets and a smaller quantity of medium- as well as large-sized droplets contributed towards PREs in comparison with TCs. The conclusion still holds when partitioning DSDs based on different precipitation rate categories. Further investigation of DSDs using gamma functions illustrated that precipitation in PREs had lower average mass-weighted diameters (Dm) and enhanced normalized number concentration (Nw) compared with TCs; partitioning precipitation into convective and stratiform components illustrated a larger Dm and lower Nw in TCs than PREs. The analysis of microphysical and thermodynamical processes using the reanalysis data indicates that relatively intense convective activity with drier conditions may be favorable to enhancing raindrop growth through collision-coalescence processes, as a result of larger Dm in TCs than PREs. The empirical relations (Z–R algorithms) applied in different rain regimes (stratiform, convective, and total PREs) revealed significant diversities, relying on weather conditions and geographical locations. Plain language summaryThe Yangtze River Delta area is an important economic belt in China. Under climate change, observed frequent occurrences of weather extremes of heavy rainfall and tropical cyclones (TCs) exert adverse effects on economic development in this region. Thus, a deep understanding of the mechanism of TCs torrential rainfall in the Yangtze River Delta area is urgently necessary. In this study, we investigated the precipitation patterns and microphysical characteristics of predecessor rain events (PREs) in the Yangtze River Delta region, and their association with TCs in the South China Sea-Western North Pacific Ocean (SCS-WNPO) area from 2014 to 2019. We found that PREs had a higher total mean precipitation rate than TCs. Further examination using gamma functions demonstrated that PREs exhibited lower average mass-weighted diameters (Dm) and higher normalized intercept parameters (Nw) than TCs. This pattern persisted when distinguishing between convective and stratiform precipitation components. We believe that our study makes a significant contribution to the literature because these results provide valuable insights into the distinct precipitation characteristics of PREs and TCs in the study region and contribute to a better understanding of tropical cyclone-related rainfall patterns, and act as a scientific basis for disaster mitigation.

Aircraft automatic detection on Station7hz interface using signal processing and artificial intelligence - ACOUDET - First method

The global project is to design a automatic noise event detection and classification system in real time using signal processing and artificial intelligence (AI). The first phase focuses on transport noise (aircrafts, trains, road vehicles, etc.). Secondly, the plan is to extend detection to construction site noise and any natural or life sources. This paper presents the first method developed to detect audible aircrafts pass-by. It focused on the development of a prototype detection algorithm, its results in situ conditions, and insights on future developments to create a robust aircraft and train noise detection system. The data collection method, aircraft audio signal particularities and a first detection method are described. The robustness of the detections is evaluated and limitation induced by real time detection are discussed. Thanks to the proprietary monitoring system Station-7hz. it has been possible to detect aircrafts in different types of sound environments in a straightforward manner, showing better results at night due to a quieter environment. Future developments based on a hybrid algorithm include AI models for audibility assessment and unwanted event filtering during aircraft pass-by. Aircraft's presence is detected via radar data and the detection of maximum acoustic levels correlated with aircraft parameters.

Marine Radar Constant False Alarm Rate Detection in Generalized Extreme Value Distribution Based on Space-Time Adaptive Filtering Clutter Statistical Analysis

The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection performance and the modeling problem of non-uniform, non-Gaussian, and non-stationary sea clutter distribution in marine radar images, in this paper, a CFAR detection method in generalized extreme value distribution modeling based on marine radar space-time filtering background clutter is proposed. Initially, three-dimensional (3D) frequency wave-number (space-time) domain adaptive filter is employed to filter the original radar image, so as to obtain uniform and stable background clutter. Subsequently, generalized extreme value (GEV) distribution is introduced to integrally model the filtered background clutter. Finally, Inclusion/Exclusion (IE) with the best performance under the GEV distribution is selected as the clutter range profile CFAR (CRP-CFAR) detector decision rule in the final detection. The proposed method is verified by utilizing real marine radar image data. The results indicate that when the Pfa is set at 0.0001, the proposed method exhibits an average improvement in PD of 2.3% compared to STAF-RCBD-CFAR, and a 6.2% improvement compared to STCS-WL-CFAR. When the Pfa is set at 0.001, the proposed method exhibits an average improvement in PD of 6.9% compared to STAF-RCBD-CFAR, and a 9.6% improvement compared to STCS-WL-CFAR.

Intelligent monitoring of marine vessel dynamics based on data mining

This study explores vessel navigation's universal characteristics and individual anomaly behavior from dispatch to destination. With the help of information technology, it introduces big data and machine learning technologies into the work of vessel traffic management and conducts statistical analysis and monitoring studies of vessel behavior from a data-driven perspective. The study was conducted by modeling vessel speed, position, course, and other navigational characteristics behavior through comprehensive statistical analysis of vessel AIS, radar, and VTS data. The study was based on the monitoring methods for anomalous behavior of vessel speed, position, and heading inconsistencies with the expected behavior identified by the model.

Strain Partitioning in the Southeastern Tibetan Plateau From Kinematic Modeling of High‐Resolution Sentinel‐1 InSAR and GNSS

AbstractFault slip rates estimated from geodetic data are being integrated into seismic hazard models. The standard approach requires modeling velocities and relative (micro‐)plate motions, which is challenging for fault‐based models. We present a new approach to directly invert strain rates to solve for slip rates and distributed strain simultaneously. We generate velocity and strain rate fields over the southeastern Tibetan Plateau, utilizing Sentinel‐1 Interferometric Synthetic Aperture Radar data spanning 2014–2023. We derive slip rates using block modeling and by inverting strain rates. Our results show a partitioning between localized strain on faults and distributed deformation. The direct inversion of strain rates matches the geodetic data best when incorporating distributed moment sources, accounting for a similar proportion to on‐fault sources. The direct strain methodology also aligns best with the independent geological slip rates, especially near fault tips. As high‐resolution strain rate fields become increasingly available, we recommend direct inversion as the preferred practice.

Research on Pedestrian and Cyclist Classification Method Based on Micro-Doppler Effect.

In the field of autonomous driving, it is important to protect vulnerable road users (VRUs) and ensure the safety of autonomous driving effectively by improving the detection accuracy of VRUs in the driver's field of vision. However, due to the strong temporal similarity between pedestrians and cyclists, the insensitivity of the traditional least squares method to their differences results in its suboptimal classification performance. In response to this issue, this paper proposes an algorithm for classifying pedestrian and cyclist targets based on the micro-Doppler effect. Firstly, distinct from conventional time-frequency fusion methods, a preprocessing module was developed to solely perform frequency-domain fitting on radar echo data of pedestrians and cyclists in forward motion, with the purpose of generating fitting coefficients for the classification task. Herein, wavelet threshold processing, short-time Fourier transform, and periodogram methods are employed to process radar echo data. Then, for the heightened sensitivity to inter-class differences, a fractional polynomial is introduced into the extraction of micro-Doppler characteristics of VRU targets to enhance extraction precision. Subsequently, the support vector machine technique is embedded for precise feature classification. Finally, subjective comparisons, objective explanations, and ablation experiments demonstrate the superior performance of our algorithm in the field of VRU target classification.

Development of a Precipitation Climate Record from Spaceborne Precipitation Radar Data. Part II: Temporal Adjustment of the Calibration Change Using the Ocean Normalized Radar Cross Section

Abstract We analyze the calibration stability of the 17-yr precipitation radar (PR) data on board the Tropical Rainfall Measuring Mission (TRMM) satellite to develop a precipitation climate record from the spaceborne precipitation radar data of the TRMM and following satellite missions. Since the PR measures the normalized radar cross section (NRCS) over the ocean surface, the temporal change in the NRCS whose variability is insensitive to the sea surface wind is regarded as the temporal change of the PR calibration. The temporal change of the PR calibration in TRMM, version 7, is found to be 0.19 dB decade−1 from 1998 to 2013. The calibration change is simply adjusted to evaluate the NRCS time series and the near-surface precipitation trend analysis within the latitudinal band between 35°S and 35°N. The NRCS time series at nadir and off-nadir are uncorrelated before the calibration adjustment, but they are correlated after the adjustment. The 0.19 dB decade−1 change of the PR calibration causes an overestimation of 0.08 mm day−1 decade−1 or 2.9% decade−1 for the linear trend of the near-surface precipitation. Even after the adjustment, agreement of the results among the satellite products depends on the analysis period. The temporal stability of the data quality is also important to evaluate the plausible trend analysis. The reprocessing of the PR data in TRMM, version 8 (or later), takes into account the temporal adjustment of the calibration change based upon the results of this study, which can provide more credible data for a long-term precipitation analysis. Significance Statement The stability of long-term data is very important for climate research so that an account of temporal calibration changes in the sensor must be made. In this study, we investigate the calibration stability of the TRMM PR data and evaluate its impact on the precipitation trend analysis. The temporal change of the PR calibration is estimated to be 0.19 dB decade−1. Compensating for this change improves the consistency of precipitation trend analysis between the PR and other precipitation datasets. The reprocessed PR data provide more probable data for long-term precipitation analysis.

Time Series Analysis of Sugarcane Plant Height and Planting Area Identification Based on DPRVI and RF Algorithm

With the modernisation of agriculture, the time series analysis of sugarcane growth and the identification of planting areas play an important role in the investigation and detection of agriculture. The study conducted a time series analysis of sugarcane plant height and identification of planting areas based on dual polarisation radar vegetation index and random forest algorithm. Firstly, dual-polarisation radar vegetation index data of sugarcane were obtained using Sentienl-1A radar remote sensing data for time-series analysis of sugarcane plant height. Then the random forest model was applied to the regression and classification of Sentinel-2A remote sensing images for sugarcane plantation area identification. According to the findings, sugarcane field 4 had an R2 value of 0.835 in the quadratic regression model inversion, and the average absolute difference between the model’s predicted plant height and the actual value was only 7.3%. It shows that the dual-polarization radar vegetation index has great reliability and is capable of accurately predicting sugarcane plant height during the pre-growth stage. The user and producer accuracies are as high as 0.911 and 0.989, respectively, and the overall accuracies and Kappa coefficients are as high as 0.976 and 0.932, respectively, indicating that the Random Forest with multi-temporal phase and multi-feature is able to effectively identify sugarcane planting areas. The accuracy under multi-temporal phase and multi-feature is better than the single-temporal phase. It offers a trustworthy way for developing contemporary agricultural policy and development planning.

Unwrap Intractable C‐Band Coseismic Interferograms: An Improved SNAPHU Method With Range Offset Gradients as Prior Information

AbstractC‐band Interferometric Synthetic Aperture Radar (InSAR) data are widely used to map coseismic deformation. However, phase unwrapping errors are commonly distributed near faults owing to decorrelation and steep phase gradients from short radar wavelengths. Here, we propose an improved SNAPHU phase‐unwrapping algorithm that considers the prior information of the range offset gradients (P‐SNAPHU) to overcome the constraints imposed by the phase continuity assumption. P‐SNAPHU exploits median filtering of homogeneous pixels for initial denoising of range offset and then refines range offset gradients divisionally through saliency extraction. The derived gradients are used to estimate the expected values of the cost functions for the low‐coherence fringes. The synthetic experiments show significant improvements in the phase‐unwrapping accuracy of the P‐SNAPHU method compared with classical unwrapping methods. We apply the P‐SNAPHU method to unwrap Sentinel‐1 coseismic interferograms of three large (Mw > 6.5) strike‐slip earthquake events that the existing classical methods could not successfully unwrap. In the comparison of the unwrapped interferograms with the external global navigation satellite system (GNSS) displacements and those from the classical methods, we find that P‐SNAPHU significantly reduces the phase unwrapping errors with the mean absolute error of 7.2, 2.3, and 1.8 cm for the 2023 Kahramanmaraş earthquake doublet, the 2016 Kumamoto earthquake, and the 2019 Ridgecrest earthquakes, respectively. Based on the unwrapped results derived from P‐SNAPHU, an estimate is made regarding the shallow slip deficit of the 2023 Kahramanmaraş earthquake doublet, which is approximately 7%. Therefore, P‐SNAPHU is useful for developing and applying C‐band InSAR data for large earthquakes and volcanic activity.

Observation of sporadic [formula omitted] layer altitude partially modulated by the Traveling Ionospheric Disturbances at high latitudes over Zhongshan station

At Zhongshan (69° S, 76° E) Antarctica we investigate the sporadic E (Es)-layer virtual height modulation, observed by an ionosonde, during the passage of the Medium-Scale Traveling Ionospheric Disturbances (MSTIDs), observed by a SuperDARN radar. Two events were identified, on 04 October 2011 at 07:00 - 12:00 UT and 29 February 2012 at 00:00 - 04:00 UT with periods of ∼15.0 and ∼12.0 min, respectively. The magnitude of average height modulation of the Es-layer was ∼3.7 to ∼17.1 and ∼0.5 to ∼7.3 km, respectively, with the same periods as the MSTIDs. Ray tracing during the events shows that the likely MSTID propagation was up to ∼300 km in the ionospheric F-region. The computed ion vertical drift velocity using SuperDARN radar and magnetometer data, and Es-layer altitude modulation observed by the ionosonde have moderate to strong positive correlation of 0.71 ± 0.22 and 0.51 ± 0.16, respectively. We show that the MSTIDs polarization electric field, which is mapped down from the F-region along the near-vertical magnetic field, moderately contributes to the modulation of the Es layer altitude via the E×B drift mechanism.

Exploring the integration of InSAR data into climate‐driven creep models to assess slow‐moving landslide dynamics

AbstractThe deformation behavior of slow‐moving large landslides is often governed by rainfall characteristics. Based on observational data such as precipitation, deformation measurement, and pore water pressure measurements in the slip zone, in many cases a strong correlation between strong rainfall events, a time‐delayed increase of pore water pressures in the slip zone, and, simultaneously to this, an increase of the deformation rate of the landslide can be found. Based on such detailed data, calculation models, which couples the relation between rainfall characteristics and the development of pore water pressures in the slip zone on one hand and the deformation behavior of the slope on the other, can be developed and be used for a better understanding and a prediction of deformation behavior of such slow‐moving landslides. Climate change issues will lead to a change in rainfall frequency and magnitude and annual temperature distribution characteristics in several regions worldwide, which will also lead to changes in the deformation behavior of such large landslides. In this contribution, satellite‐based interferometric synthetic aperture radar (InSAR) data are discussed to be used as source for deformation measurements as bases for prediction models describing the rainfall‐triggered deformation behavior of slow‐moving landslides.

Different Origin Field‐Aligned Currents and Their Relationship With Auroral Intensity

AbstractThe ionospheric field‐aligned current (FAC) consists of three components related to ionospheric vortices and gradients of Pedersen and Hall conductances, with the first term being of magnetospheric origin and the latter two being of ionospheric origin (Eq. 1). We calculate each of them between 2010 and 2016 using line‐of‐sight velocity data from the Super Dual Auroral Radar Network radar and auroral electron precipitation data from the Defense Meteorological Satellite Program. Then, we investigate the contribution of each FAC component, as well as the relationship between each FAC component and auroral activity. Our result shows that ionospheric‐origin FACs contribute up to 54% (dawn) and 42% (dusk) within the auroral oval. Both magnetospheric‐origin FACs and ionospheric‐origin FACs initially increase and then stabilize as the SuperMAG electrojet (SME) index increases. After stabilization, these two currents have almost equally important roles, with an average contribution rate of 56% and 44%, respectively. Additionally, the ionospheric‐origin FACs are found to have a higher dependency on auroral intensity. We also find that magnetospheric‐origin FACs exhibit distribution patterns similar to the R1/R2 FAC systems because their directions are determined by vorticities. When the SME index exceeds 200 nT, the contribution from the Pedersen conductance gradient consistently exhibits downward currents near 70° magnetic latitude in the dusk and predawn sectors due to the combined effects of velocities and Pedersen conductance gradients. However, the distribution pattern from the Hall conductance gradient is not so clear due to the uncertainty in the angle between velocity and Hall conductance gradient.

Preliminary Observations of the 5 April 2024 Mw 4.8 New Jersey Earthquake

Abstract On 5 April 2024, 10:23 a.m. local time, a moment magnitude 4.8 earthquake struck Tewksbury Township, New Jersey, about 65 km west of New York City. Millions of people from Virginia to Maine and beyond felt the ground shaking, resulting in the largest number (>180,000) of U.S. Geological Survey (USGS) “Did You Feel It?” reports of any earthquake. A team deployed by the Geotechnical Extreme Events Reconnaissance Association and the National Institute of Standards and Technology documented structural and nonstructural damage, including substantial damage to a historic masonry building in Lebanon, New Jersey. The USGS National Earthquake Information Center reported a focal depth of about 5 km, consistent with a lack of signal in Interferometric Synthetic Aperture Radar data. The focal mechanism solution is strike slip with a substantial thrust component. Neither mechanism’s nodal plane is parallel to the primary northeast trend of geologic discontinuities and mapped faults in the region, including the Ramapo fault. However, many of the relocated aftershocks, for which locations were augmented by temporary seismic deployments, form a cluster that parallels the general northeast trend of the faults. The aftershocks lie near the Tewksbury fault, north of the Ramapo fault.

Lightning activity in India an important cause of increase in fatalities

With Recent increase in deaths due to lightning activity in India we are posed with a question - are these fatalities related to increase in the lightning activity owing to climate change? The IPCC report (2013) projects global warming of 1-5 °C by the end of 21st Century. The global warming is closely related to increased concentration of greenhouse gases. Previous studies have shown that on different temporal and spatial scales small increases in surface temperature leads to increase in thunderstorm and lightning activity. The lightning induced deaths are on the rise especially in the tropical south Asia and Africa but IPCC report does not explicitly deals with lightning activity and its future projection. Studies on this aspect become all the more important as the subgrid scale phenomena such as convective clouds and hence lightning are poorly resolved and taken in to account by climate models. In order to address this issue we have analyzed trends of lightning activity, surface temperature, upper tropospheric water vapour, cloud ice, Convective Available Potential Energy (CAPE) and aerosols. We also present correlation of these parameters with lightning activity using lightning flash rate data of Lightning Imaging Sensor aboard TRMM, TRMM Level -2 Precipitation Radar data, gridded temperature data of IMD, aerosol data acquired by MODIS. The result indicates that upper tropospheric temperature rise is more than surface temperature rise. This imply stable atmosphere with fewer thunderstorms. The increased convection transports additional water vapour into upper troposphere. The water vapour acts as green house has by absorbing infrared radiation emitted by the surface of the Earth. This results in more warming in the upper troposphere than at the surface and stabilizing of the atmosphere. However, results also show that within the thunderstorm the instability measured by CAPE is positively correlated with lightning activity. This paradox of stabilization of global mean atmosphere with increase in lighting activity leads us to conclude that tough thunderstorm activity has subdued but those develop are much more explosive producing more lightning activity and perhaps lead to more fatalities.

EKF-based parameter estimation method for radar maneuvering target with unknown time information

Moving target detection (MTD) is a research hotspot in radar signal processing. Generally, the time information of non-cooperative moving targets entering and leaving a radar coverage area is unknown, which would lead to severe performance loss for target parameter estimation, detection, and imaging. Unlike our previous research work, this paper addresses the motion parameters estimation and refocusing problem for a radar maneuvering target with unknown entry and departure time. A computationally efficient method that utilizes extended Kalman filtering (EKF) for phase tracking is proposed to estimate the entry and departure times. The proposed method first performs range cell migration correction (RCMC) on the pulse compression echo signal. Then, the maneuvering target signal is modeled as a polynomial phase signal (PPS) and utilizes the EKF to construct a binary state-space equation for polynomial phase tracking. Finally, by comparing the phase tracking results of the noise cell and the signal cell, one can derive estimates for the entry/departure time and motion parameters. Compared with existing methods, the proposed method avoids multi-dimension searching on the parameter space, so it has a prominent advantage in computational complexity. Moreover, the core of the proposed method lies in tracking the polynomial phase, which is not constrained by the order of target motion, and has wider applicability in practice. Both simulated and public radar data are used to validate the effectiveness of the proposed method.

Remote Gait Analysis Using Ultra-Wideband Radar Technology Based on Joint Range-Doppler-Time Representation.

In recent years, radar technology has been extensively utilized in contactless human behavior monitoring systems. The unique capabilities of ultra-wideband (UWB) radars compared to conventional radar technologies, due to time-of-flight measurements, present new untapped opportunities for in-depth monitoring of human movement during overground locomotion. This study aims to investigate the deployability of UWB radars in accurately capturing the gait patterns of healthy individuals with no known walking impairments. A novel algorithm was developed that can extract ten clinical spatiotemporal gait features using the Doppler information captured from three monostatic UWB radar sensors during a 6-meter walking task. Key gait events are detected from lower-extremity movements based on the joint range-Doppler-time representation of recorded radar data. The estimated gait parameters were validated against a gold-standard optical motion tracking system using 12 healthy volunteers. On average, nine gait parameters can be consistently estimated with 90-98% accuracy, while capturing 94.5% of participants' gait variability and 90.8% of inter-limb symmetry. Correlation and Bland-Altman analysis revealed a strong correlation between radar-based parameters and the ground-truth values, with average discrepancies consistently close to 0. Results prove that radar sensing can provide accurate biomarkers to supplement clinical human gait analysis, with quality similar to gold standard assessment. Radars can potentially allow a transition from expensive and cumbersome lab-based gait analysis tools toward a completely unobtrusive and affordable solution for in-home deployment, enabling continuous long-term monitoring of individuals for research and healthcare applications.