Continuous Wave (CW) radars systems, especially air-coupled Ground-Penetrating Radar (GPR) or Through-Wall Imaging Radar (TWIR) systems, echo signals reflected from a stationary target with high energy, which may cause receiver saturation. Another ef...
In this study, a simplified radar echo signal model suitable for radar simulators and a Radar Cross-Section (RCS) calculation model based on the Physical Optics (PO) method was developed. A comprehensive radar target ship echo generation algorithm wa...
Precipitation nowcasting is an important tool for economic and social services, especially for forecasting severe weather. The crucial and challenging part of radar echo image prediction is the focus of radar-based precipitation nowcasting. Recently,...
Radar echo extrapolation (REE) is a crucial method for convective nowcasting, and current deep learning (DL)-based methods for REE have shown significant potential in severe weather forecasting tasks. Existing DL-based REE methods use extensive histo...
Weather radar echo is the data detected by the weather radar sensor and reflects the intensity of meteorological targets. Using the technique of radar echo extrapolation, which is the prediction of future echoes based on historical echo observations,...
The identification of some radar reflectivity signatures plays a vital role in severe thunderstorm nowcasting. A weak echo region is one of the signatures that could indicate updraft, which is a fundamental condition for hail production. However, thi...
As a spatial–temporal sequence prediction task, radar echo extrapolation aims to predict radar echoes’ future movement and intensity changes based on historical radar observations. Two urgent issues still need to be addressed in deep lear...
Radar beam blockage is an important error source that affects the quality of weather radar data. An echo-filling network (EFnet) is proposed based on a deep learning algorithm to correct the echo intensity under the occlusion area in the Nanjing S-ba...
In order to forecast some high intensity and rapidly changing phenomena, such as thunderstorms, heavy rain, and hail within 2 h, and reduce the influence brought by destructive weathers, this paper proposes a weather radar echo extrapolation method b...
Target echo cancellation is an ingenious method that protects the target of interest (TOI) from being detected by radar. Interrupted-sampling repeater jamming (ISRJ) is a novel deception jamming method for linear frequency modulation (LFM) radar coun...
In order to solve the existing problems of easy spatiotemporal information loss and low forecast accuracy in traditional radar echo nowcasting, this paper proposes an encoding-forecasting model (3DCNN-BCLSTM) combining 3DCNN and bi-directional convol...
The capability of a skywave over-the-horizon radar (SWR) to achieve the continuous observation of a wide range of ocean dynamics parameters via a single ionospheric reflection has been demonstrated by many scholars. In order to expand the method of S...
Precipitation forecasting is an immensely significant aspect of meteorological prediction. Accurate weather predictions facilitate services in sectors such as transportation, agriculture, and tourism. In recent years, deep learning-based radar echo e...
Radar echo extrapolation is a critical technique for short-term weather forecasting. Timely warnings of severe convective weather events can be provided according to the extrapolated images. However, traditional echo extrapolation methods fail to ful...
Short-term extrapolation by weather radar observations is one of the main tools for making weather forecasts. Recently, deep learning has been gradually applied to radar extrapolation techniques, achieving significant results. However, for radar echo...
This article presents an investigation into the problem of 3D radar echo extrapolation in precipitation nowcasting, using recent AI advances, together with a viewpoint from Computer Vision. While Deep Learning methods, especially convolutional recurr...
Non-precipitation echoes due to ground and sea clutter, chaff, anomalous propagation, biological targets, and interference in weather radar observations are major issues causing a decline in the accuracy of meteorological and hydrological application...
Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning...
In response to the shortcomings of current spatiotemporal prediction models, which frequently encounter difficulties in temporal feature extraction and the forecasting of medium to high echo intensity regions over extended sequences, this study prese...
Radar echo extrapolation based on real-world data is a fundamental problem in meteorological forecasting. Existing extrapolation models typically assume complete radar echo sequences, but in practice, data loss frequently occurs due to equipment fail...
Radar echo extrapolation, a critical spatiotemporal sequence forecasting task, requires precise modeling of motion trajectories and intensity evolution from sequential radar reflectivity inputs. Contemporary deep learning implementations face two ope...
Nowcasting of severe convective precipitation is of great importance in meteorological disaster prevention. Radar echo extrapolation is an effective method for short-term precipitation nowcasting. The traditional radar echo extrapolation methods lack...
In practical radar applications, the diversity of wideband transmission signals presents a significant challenge for radar target recognition systems. Traditional electromagnetic (EM) simulation methods often require the point-by-point sampling of ta...
Deep-learning-based radar echo extrapolation methods have achieved remarkable progress in the precipitation nowcasting field. However, they suffer from a common notorious problem—they tend to produce blurry predictions. Although some efforts ha...
Accurate nowcasting of severe convective storms is crucial for disaster mitigation, yet storm complexity challenges conventional deep learning models. Existing methods often use single-level radar data and lack physical constraints, limiting skill in...
Weather radar, as a crucial component of remote sensing data, plays a vital role in convective weather forecasting through radar echo extrapolation techniques. To address the limitations of existing deep learning methods in radar echo extrapolation,...
A nonlinear grid transformation (NGT) method is proposed for weather radar convective echo extrapolation prediction. The change in continuous echo images is regarded as a nonlinear transformation process of the grid. This process can be reproduced by...
Radar echo extrapolation is a commonly used approach for convective nowcasting. The evolution of convective systems over a very short term can be foreseen according to the extrapolated reflectivity images. Recently, deep neural networks have been wid...
The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) mod...
Precipitation nowcasting is extremely important in disaster prevention and mitigation, and can improve the quality of meteorological forecasts. In recent years, deep learning-based spatiotemporal sequence prediction models have been widely used in pr...
Short-term precipitation forecasting is essential for agriculture, transportation, urban management, and tourism. The radar echo extrapolation method is widely used in precipitation forecasting. To address issues like forecast degradation, insufficie...
Precipitation nowcasting in real-time is a challenging task that demands accurate and current data from multiple sources. Despite various approaches proposed by researchers to address this challenge, models such as the interaction-based dual attentio...
Radar echo extrapolation has been widely developed in previous studies for precipitation and storm nowcasting. However, most studies have focused on two-dimensional radar images, and extrapolation of multi-altitude radar images, which can provide mor...
Radar echo extrapolation by ground-based remote sensing is essential for weather prediction and flight guiding. Existing radar echo extrapolation methods can hardly capture complex spatiotemporal features, resulting in the low accuracy of predictions...
Currently, most deep learning (DL)-based models for precipitation forecasting face two conspicuous issues: the smoothing effect in the precipitation field and the degenerate effect of forecasting precipitation intensity. Therefore, this study propose...
Synthetic Aperture Radar (SAR) target detection models are highly vulnerable to adversarial attacks, which significantly reduce detection performance and robustness. Existing adversarial SAR target detection approaches mainly focus on the image domai...
Precipitation nowcasting is critical for areas such as agriculture, water resource management, urban drainage systems, transport and disaster preparedness. In recent years, methods such as convolutional recurrent neural networks (ConvRNN) in deep lea...
Precipitation nowcasting has been gaining importance in the operational weather forecast, being essential for economic and social development. Conventional methods of precipitation nowcasting are mainly focused on the task of radar echo extrapolation...
Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is a promising scheme for high-resolution wide-swath (HRWS) imaging. After echo separation processing, a MIMO-SAR system can provide many equivalent phase centers (EPCs) in azimuth....
Recent climate change has brought extremely heavy rains and widescale flooding to many areas around the globe. However, previous flood prediction methods usually require a lot of computation to obtain the prediction results and impose a heavy burden...
The Doppler characteristics of sea surface echoes reflect the time-varying characteristics of the sea surface and can be used to retrieve ocean dynamic parameters and detect targets. On airborne, spaceborne and shipborne radar platforms, radar moves...
Currently, shore-based HF radars are widely used for coastal observations, and airborne radars are utilized for monitoring the ocean with a relatively large coverage offshore. In order to take the advantage of airborne radars, the theoretical mechani...
HF radar systems are widely and routinely used for the measurement of ocean surface currents and waves. Analysis methods presently in use are based on the assumption of infinite water depth, and may therefore be inadequate close to shore where the ra...
In several cases (e.g., thermal noise, weather echoes, …), the incoming signal to a radar receiver can be assumed to be Rayleigh distributed. When estimating the mean power from the inherently fluctuating Rayleigh signals, it is necessary to average...
The measurement error of differential reflectivity (ZDR), especially systematic ZDR bias, is a fundamental issue for the application of polarimetric radar data. Several calibration methods have been proposed and applied to correct ZDR bias. However,...
Clear-air echo studies are usually based on isotropic turbulence theory. But the theory has been considered incomplete by modern turbulence theory. The intermittence of turbulence can reveal obvious shortcomings in the existing studies of clear-air e...
Strong updrafts occur in severe thunderstorms, causing the overshooting tops, an increase in the total lightning activity, and generating a frozen drops nucleus that will produce severe weather when it collapses. The Echo Top is a measurement of the...
The SuperDARN HF radars can be used for meteor observation and inversion of mid-upper atmosphere neutral wind using observed meteor echo Doppler velocities. Aiming at the problem that the extraction of meteor echo based on echo power, Doppler velocit...
Accurate and timely precipitation forecasts can help people and organizations make informed decisions, plan for potential weather-related disruptions, and protect lives and property. Instead of using physics-based numerical forecasts, which can be co...
Globally consistent long-term radar measurements are imperative for understanding the global climatology and potential trends of convection. This study investigates the consistency of vertical profiles of reflectivity (VPR) and 20-dBZ echo-top height...
of 19