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In this paper, a method for the formation of the oceanic atmospheric refractive index based on Five Parameters, which uses the power information of radar sea clutter. By comparing the surface waveguides measured in some experimental sea areas in China, the feasibility of this method is verified, which is beneficial to overcome the difficulty of directly measuring the atmospheric refractive index profile under harsh conditions at sea.

A training sample selection method is proposed by fusing the generalized inner product (GIP) statistic with geographic information to construct the fused metric of the sample set. Based on this metric, more suitable training samples are selected. The experimental results demonstrate that the proposed method exhibits excellent robustness in different clutter environments, particularly in complex ground environments containing discrete moving scatterers, where its clutter suppression performance is superior to that of both GIP and knowledge‐aided space‐time adaptive processing (KA‐STAP) methods. The method expands sample‐screening dimensions through multidimensional information‐weighted fusion, enabling the selection of more suitable sample sets. Compared with the GIP and knowledge‐aided clutter suppression methods, our proposed method exhibits excellent robustness in different clutter environments and achieves improved clutter‐suppression performance in complex ground environments containing discrete moving scatterers.

This article introduces a high‐precision SAR echo simulation method aimed at providing a large‐scale SAR echo simulation algorithm that includes complex target information. This method independently simulates targets and ground clutter, calculating the shadow areas of targets and the dihedral effects between targets and the ground by constructing virtual rough surfaces and using ray tracing. Furthermore, this method can provide data sources for algorithms that improve detector performance using shadow or specular reflection characteristics. Finally, after comparing the simulation results with the publicly available MSTAR dataset, the experimental results show that this method ensures the accuracy of the results. This research provides a rich data source for the study of SAR detection and tracking algorithms and offers strong support for practical applications. This article presents a high‐precision SAR echo simulation method, aimed at providing a large‐scale SAR echo simulation algorithm that includes complex target information.This method can produce SAR video frames with extensive scene coverage, diverse target types, and continuous target states. The method takes into account the multiple scattering effects and occlusion relationships between the target and background clutter, providing reliable data sources for SAR target detection algorithms based on target shadows and target enhancement algorithms based on multiple scattering.

The objective of this research is to develop techniques for assimilating GOES-R series observations in precipitating scenes for the purpose of improving short-term convective-scale forecasts of high-impact weather hazards. Whereas one approach is radiance assimilation, the information content of GOES-R radiances from its Advanced Baseline Imager saturates in precipitating scenes, and radiance assimilation does not make use of lightning observations from the GOES Lightning Mapper. Here, a convolutional neural network (CNN) is developed to transform GOES-R radiances and lightning into synthetic radar reflectivity fields to make use of existing radar assimilation techniques. We find that the ability of CNNs to utilize spatial context is essential for this application and offers breakthrough improvement in skill compared to traditional pixel-by-pixel based approaches. To understand the improved performance, we use a novel analysis method that combines several techniques, each providing different insights into the network’s reasoning. Channel-withholding experiments and spatial information–withholding experiments are used to show that the CNN achieves skill at high reflectivity values from the information content in radiance gradients and the presence of lightning. The attribution method, layerwise relevance propagation, demonstrates that the CNN uses radiance and lightning information synergistically, where lightning helps the CNN focus on which neighboring locations are most important. Synthetic inputs are used to quantify the sensitivity to radiance gradients, showing that sharper gradients produce a stronger response in predicted reflectivity. Lightning observations are found to be uniquely valuable for their ability to pinpoint locations of strong radar echoes.

Low-frequency space-based synthetic aperture radar (SAR) is an ideal sensor for measuring forest biomass, but can suffer from ionospheric effects. The variation in total electron content (TEC), originating from ionospheric turbulence, causes the along track point spread function (PSF) to degrade in a manner which depends on ionospheric conditions. In this study, the effect of this PSF on the single point statistics (probability density function) and two point statistics (autocorrelation function (ACF)) is derived. It is shown that the K-distribution order parameter is directly proportional to the ionospheric turbulence, as quantified by CkL. The complex ACF is a measure of amplitude scintillation, and the intensity ACF is a measure of both the order parameter and the terrain correlation length. A simulation is performed which clearly shows that measuring the order parameter ratio between ionospherically disturbed and undisturbed images is a measure of CkL. This measure can be used two orders of magnitude below the point where the ionosphere causes defocusing of the SAR image. It is concluded that the usefulness of this new measure can only be verified by experimental data since the temporal stability of the underlying order parameter is unknown.

Understanding drop size distribution (DSD) variability has important implications for remote sensing and numerical modeling applications. Twelve disdrometer datasets across three latitude bands are analyzed in this study, spanning a broad range of precipitation regimes: light rain, orographic, deep convective, organized midlatitude, and tropical oceanic. Principal component analysis (PCA) is used to reveal comprehensive modes of global DSD spatial and temporal variability. Although the locations contain different distributions of individual DSD parameters, all locations are found to have the same modes of variability. Based on PCA, six groups of points with unique DSD characteristics emerge. The physical processes that underpin these groups are revealed through supporting radar observations. Group 1 (group 2) is characterized by high (low) liquid water content (LWC), broad (narrow) distribution widths, and large (small) median drop diameters D 0 . Radar analysis identifies group 1 (group 2) as convective (stratiform) rainfall. Group 3 is characterized by weak, shallow radar echoes and large concentrations of small drops, indicative of warm rain showers. Group 4 identifies heavy stratiform precipitation. The low latitudes exhibit distinct bimodal distributions of the normalized intercept parameter N w , LWC, and D 0 and are found to have a clustering of points (group 5) with high rain rates, large N w , and moderate D 0 , a signature of robust warm rain processes. A distinct group associated with ice-based convection (group 6) emerges in the midlatitudes. Although all locations exhibit the same covariance of parameters associated with these groups, it is likely that the physical processes responsible for shaping the DSDs vary as a function of location.

Range sidelobe modulation (RSM) limits the performance of pulse‐agile radars. This letter proposes an expectation–maximisation (EM)‐based range–Doppler (RD) estimator that learns the clutter and target distributions and adjusts a range–Doppler prior map. By optimising the shrinkage strength via prior weighting, the proposed method improves RSM suppression in the region of interest. Simulation results validate its effectiveness.

Feature extraction from the normalized transformation domain is a key technique in target detection. Traditional normalization approaches assume that matrix elements follow a normal distribution, but any deviations from this assumption can lead to significant systematic errors. This article presents a novel method that modifies the normalization process in the fractional Fourier transform (FRFT) domain by incorporating a threshold mechanism to counteract the effects of non‐normal distributions. Three modified FRFT features are then extracted from this modified FRFT domain. Furthermore, a target detection method that utilizes these three adjusted features is proposed. Experimental results based on measured data indicate that the modified FRFT features exhibit superior classification capabilities for sea clutter and targets compared to the original ones. Additionally, the experiments also demonstrate that under the same conditions, the proposed detection method outperforms traditional FRFT feature detector and the tri‐feature based detector. Our manuscript introduces a novel method that modifies the normalization process in the fractional Fourier transform (FRFT) domain by incorporating a threshold mechanism to counteract the effects of non‐normal distributions. Additionally, we propose a target detection method that utilizes three new features extracted from this modified FRFT domain.

Due to the advantages of wide coverage, continuous remote monitoring, and high measurement accuracy, the spaceborne multi‐channel surveillance radar has been widely used for the air moving target detection and tracking. In this paper, the minimum detection velocity (MDV) definition for the aerial target detection performance evaluation in a spaceborne multi‐channel radar system is analysed, where three kinds of MDV definitions based on empirical formula, output signal‐to‐clutter‐plus‐noise ratio (SCNR) loss criterion, and output SCNR criterion are discussed in detail based on theoretical analysis and simulation verification. The analysis results will provide valuable reference for the practical spaceborne radar system designment with the air target detection mode.

The Earth's surface radar reflection is one of the most important signals received by spaceborne radar systems. It is used in several scientific applications, including geolocation, terrain classification, and path-integrated attenuation estimation. A simulator based on the ray-tracing approach has been developed to reproduce the clutter reflectivity and the Doppler velocity signal for a conically scanning spaceborne Doppler radar system. The simulator exploits topographic information through a raster digital elevation model, land types from a regional classification database, and a normalized radar surface cross-section look-up table. The simulator is applied to the WInd VElocity Radar Nephoscop (WIVERN) mission, which proposes a conically scanning W-band Doppler radar to study in-cloud winds. Using an orbital model, detailed simulations for conical scans over the Piedmont region of Italy, which offers a variety of landscape conditions, are presented. The results highlight the strong departure of the reflectivity and Doppler velocity profiles in the presence of marked orography and the significant gradient in the surface radar backscattering properties. The simulations demonstrate the limitations and advantages of using the surface Doppler velocity over land as an antenna-pointing characterization technique. They represent the full strength range of the surface radar clutter over land surfaces for the WIVERN radar. The surface clutter tool applies to other spaceborne radar missions, such as the nadir-pointing EarthCARE and CloudSat Cloud Profiling Radar (CPR), or the cross-track scanning Global Precipitation Measurement (GPM) precipitation radars.