Search Results (508)

Accurate precipitation nowcasting is critical for many aspects of human life. A recurrent neural network (RNN) has demonstrated strong and relatively mature performance in machine learning approaches for precipitation nowcasting. However, their inherent recursive prediction structure leads to error accumulation, causing progressively blurred outputs and limiting practical applicability. To address this issue, we propose CSPP-RNN (Coupling-Similar-Precipitation-Processes RNN), a net that couples similar precipitation processes with a sequence-to-sequence RNN. For each prediction timestep, similar precipitation processes are retrieved, and their segments are then input into the encoder to obtain the corresponding hidden states. These hidden states replace the ones influenced by earlier predicted results in the recursive structure. Based on radar data from Beijing Daxing station, the comparison experiments of CSPP-RNN and ConvLSTM indicate that: (1) Over the 36–60 min lead time across the 0.1, 5.0, and 20.0 mm/h thresholds, the POD and CSI improved by 0.0334, 0.0170 on average, respectively, whereas the FAR degraded by 0.0586; (2) error accumulation was mitigated, retaining richer fine-scale structures in the predicted images; (3) the extra computational cost of coupling was controlled within an acceptable range. In conclusion, CSPP-RNN mitigates the error accumulation problem in RNN by coupling similar precipitation processes as part of the modification of the recursive prediction structure. This provides a potential new direction for optimizing the application of RNN in precipitation nowcasting. Full article

Thunderstorms are the most frequent type of severe convective weather, which pose great threats to buildings, power transmission, communication facilities, and air transportation. Their analysis and forecasting have long been challenges in meteorological operations. Currently, deep learning-based lightning forecasting has a short valid period, mostly relying on satellite imagery, radar echoes, and lightning location data, focusing on very-short-range forecasting. The longest valid period does not exceed 6 h, and the forecasting accuracy is not high. Based on the physical quantities of the ECMWF numerical prediction model and the actual observations of single-point thunderstorms, this paper constructs a single-point thunderstorm forecasting model with a long validity period (>6 h). The study integrates multi-dimensional parameters such as thermal, dynamic, water vapor, and stratification instability, introduces the second-order moist potential vorticity S as a comprehensive predictor, systematically compares the forecasting performance of eight models, such as 1D PreRNN and ConvLSTM, and verifies the actual operational capability of the model through independent cases. The results show that the 1D PreRNN model has the best overall performance in all periods, which can effectively capture the temporal evolution characteristics of meteorological physical quantities and still has stable generalization performance under unbalanced samples. The model performs well in the 1st, 2nd, and 4th periods, and especially still has significant operational reference value in the 4th period with the longest forecasting validity period; only the 3rd period is weakly affected by the small number of samples. The effect of second-order moist potential vorticity has significant time-dependent differences. Its overall improvement effect is limited in short-term forecasting, but it can provide key disturbance signals in the 4th period with the longest forecasting validity period, and the model forecasting performance drops significantly after removal. The original binary cross-entropy loss is most suitable for the unbalanced sample scenario in this study, and weighted losses are prone to overcorrection. The method in this paper can achieve stable and reliable single-point thunderstorm forecasting for more than 6 h, and can provide long-term fixed-point meteorological support for key scenarios such as aerospace and new energy stations. Full article

Predictive Maintenance (PdM) approach in Combined Sewer Systems (CSS) is gaining momentum due to advances in sensor technology, affordability and availability of data, and the rise of machine learning and data analytics. This study aims to characterize the general behavior of CSS under Dry and Wet weather conditions. To achieve this, 10 min resolution precipitation and water level data were collected from nearby SIAS stations and AMAP radar water level sensors, installed at the outlet chamber of the CSS, respectively. Precipitation data was used to segment continuous time series data into Dry Weather Flow (DWF) and Wet Weather Flow (WWF). DWF analysis exhibited unique flow patterns that strongly correlated with water consumption behaviors of households. For wet weather, a comparison was made between key rainfall parameters (depth, intensity) and peak water level data, and nonlinear relationships were observed that highlight the complex rainfall–runoff process. These findings underscore the need for separate predictive models tailored to DWF and WWF characteristics. Integrating high-resolution sensor data with machine learning models such as Long Short-Term Memory (LSTM) networks and anomaly detection, Autoencoders can enhance PdM, improving CSS management and reducing risks of blockage events and infrastructure failures. Full article

Rockfall events are significant natural hazards on fractured rock cliffs, often driven by environmental forcing, including thermal variations that induce stress and fatigue in rocks. This study presents the first application of Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) for high-resolution monitoring of sub-millimeter thermally induced displacements on a rock slope. An eight-day pilot experiment conducted at the La Cornalle molasse cliff (Vaud, Switzerland) revealed cyclic displacement signals with a clear 24 h periodicity, identified through Fourier and wavelet analyses, with a mean amplitude of 5 × 10⁻⁴ m. Simultaneously, infrared thermography (IRT) and a weather station recorded rock surface and air temperature variations, allowing a first estimation of the time lag between thermal forcing and mechanical response, with delays of 1–8 h relative to air temperature and 1–6 h relative to solar radiation. An analytical deformation model based on thermal diffusion predicts a daily displacement amplitude of 4.2 × 10⁻⁵ m, highlighting a significant difference with GB-InSAR observations and emphasizing the influence of structural complexity and thermo-hydro-mechanical processes in rock slopes. These results demonstrate the capability of combined high-resolution remote sensing techniques to quantify thermo-mechanical behavior in rock masses and provide a methodological framework for future investigations of rockfall-prone slopes. Full article

To address the issues of information leakage risks faced by the base station (BS) when communicating with multiple users in an integrated sensing and communication (ISAC) system, as well as the blockage of the direct link between the BS and the target to be detected, which limits sensing functionality, this paper introduces the active intelligent reflecting surface (IRS) into the ISAC system. By creating a virtual line-of-sight (LoS) path, signal blockage is effectively mitigated, while the active IRS enhances the incident signal strength and adjusts the reflection phase shifts, thereby improving the reliability and security of communication. This paper proposes a joint optimization scheme for the active IRS-assisted ISAC system, which jointly designs the BS beamforming and the IRS reflection coefficient matrix. A non-convex optimization problem is formulated with the objective of maximizing the radar output signal-to-noise ratio (SNR) subject to communication performance constraints. To solve this problem, this paper employs an iterative algorithm based on alternating optimization (AO), fractional programming (FP), and semidefinite relaxation (SDR). Simulation results demonstrate that the proposed scheme significantly outperforms the benchmark schemes without IRS assistance and with passive IRS assistance in terms of enhancing the sensing performance of the ISAC system. Full article

Tidal inundation dynamics are a principal driver of hydrological and biogeochemical processes in coastal ecosystems, controlling the exchange of carbon, nutrients, and sediments between wetlands and estuaries. In this study, we assessed the utility of L-band radar imagery in deriving tidal wetland inundated volume estimates (pixel-wise water depths), which provide a more robust characterization of wetland–estuary exchange processes than the lateral inundation state estimates. Inundation state products derived using L-band radar were combined with digital elevation models (DEMs) and synthetic tide gauging to estimate the volume of inundation. Synthetic tide gauges, models of water level produced from combined short-term field measurements and long-term monitoring stations were employed to provide calibration and validation for satellite observations for times outside of the water level sensor monitoring period (August–December 2018). Ten synthetic gauges were established across the Charles H. Wheeler Wildlife Management Area (Connecticut, USA) in a regular grid and were used to validate the radar-based inundation state and inundated volume products. To generate volumetric inundation estimates from inundation state products, we employed two bathymetric fill approaches using a DEM to constrain water surface elevations. The first approach assumed a constant water elevation fill for all inundated pixels, while the second introduced a maximum water depth constraint. While both approaches showed strong correlations with synthetic gauges, the depth constraint approach was more accurate, increasing R² from 0.87 to 0.98 and lowering RMSE from 0.79 m to 0.02 m. In this study, PALSAR-1/2 served as a proxy for the recently launched NISAR mission. Future research is planned to leverage the improved temporal sampling of the NISAR data record, combined with in-marsh water level observations (May 2025–present) and synthetic gauge estimates to improve wetland–estuary volumetric exchange characterization, which we demonstrate can be accurately estimated when paired with high-quality DEMs. Full article

Atmospheric refractivity governs the propagation behavior of electromagnetic waves in the lower troposphere. Accurate spatial characterization of this parameter is essential for optimizing communication, radar, and navigation systems. This study presents a geostatistical framework for generating high-resolution refractivity maps using Universal Kriging (UK) applied to meteorological observations from a dense network of automatic weather stations in the Galician region (NW Spain). The methodology explicitly models the non-stationary vertical structure of the atmosphere by decomposing the refractivity field into a deterministic altitude-dependent drift and a stochastic residual component characterized by an exponential variogram. Validation, performed using independent test stations bounding the regional vertical profile, demonstrates that the UK approach significantly outperforms Ordinary Kriging (OK). UK not only reduces mean errors and improves linear agreement, but critically minimizes systematic bias and extreme outlier occurrences ($P_{95}$). Beyond accurate spatial interpolation, the dynamically estimated vertical drift retrieves the macroscopic refractivity gradient, serving as a direct, real-time diagnostic tool to classify anomalous radio-frequency (RF) propagation regimes (e.g., super-refraction and ducting) and supporting robust decision-making in complex topographies. Full article

Based on the Advanced Geostationary Radiation Imager (AGRI) and Geostationary High-speed Imager (GHI) information in the Fengyun-4B (FY-4B) satellite, we propose a convective initiation (CI) nowcasting algorithm for Sichuan Province, China. The algorithm optimizes satellite reflectance by considering multi-channel brightness differences, visible reflectance, and cloud-top cooling by exploiting the Farneback optical flow, where the cloud is followed by false cooling due to cloud motion. Moreover, the high temporal resolution of GHI enables the detection of early cumulus cloud growth. The algorithm was developed using daytime CI events in the coverage area of Mianyang radar station from 22 July to 9 August 2023, and the remaining areas in the Chengdu scan area were used for validation. The results showed that the proposed method achieves a probability of detection (POD) of 83.1%, a false alarm ratio (FAR) of 33.0%, and a critical success index (CSI) of 58.9%. Compared with the AGRI-only method and the SATCAST algorithm, the POD increases by 5.4% and 8.4%, respectively, while the CSI improves by 1.3% and 2.3%. The average lead time reaches 34.2 min, which is 4.6 min longer than AGRI-only and 7.9 min longer than SATCAST. This suggests that AGRI and GHI data improve the spatiotemporal resolution of CI nowcasting. This approach improves the early detection of convective initiation under the climatic background of warm cloud convection in Sichuan, offering new insights for short-term warnings of regional convective weather. Full article

In this work, we propose a localization method based on an exogenous radar with multi-base station and the synchronization signal block (SSB) in 5G downlink signals. We combine physical cell identities (PCIs)-based identification with the extensive cancellation algorithm (ECA) to reconstruct and cancel the present strongest SSB signal, thereby obtaining reference signal receiving power (RSRP) values of them in descending order of strength. Then, we designed a two-stage localization method. Firstly, we determined the target’s coarse location based on the directional characteristics of different SSB beams. Subsequently, we compared the RSRP values extracted from the actually received signals against those pre-obtained when the target is at various reference points. The reference point corresponding to the closest match was selected as the estimated target position. We conducted simulations under various signal-to-noise ratio (SNR) levels, reference point densities, and signal jitter conditions. The simulation results demonstrate that the method outperforms techniques such as Fang’s method for time difference of arrival (Fang-TDOA) and observed time difference of arrival (OTDOA). Full article

This study proposes a high-isolation four-port wideband MIMO antenna array designed for sub-6 GHz 5G, IoT, and radar applications. The array is fabricated on a polycarbonate substrate with overall dimensions of 500 × 500 mm² (ε_r = 2.8, h = 1 mm). Four orthogonally arranged modified circular patches with triangular ground planes and optimized inter-element spacing (D₁ = 90 mm) are employed in the antenna’s design to achieve an impedance bandwidth of 0.7–7.0 GHz (Fractional Bandwidth (FBW) > 163.63%) with |S_ii| < −10 dB across all ports. The measurement results indicate that the inter-port isolation is better than 15 dB (worst-case) across the 0.7–7 GHz band, exceeding 25 dB over 63.5% of the bandwidth (with a peak of approximately 50 dB); the envelope correlation coefficient (ECC) is ultra-low (<0.008); the total active reflection coefficient (TARC) is less than −10 dB for primary multi-port excitations; the mean effective gain (MEG) is balanced (≈−3 dB); and the group delay is consistent (~0.5 ns). With a maximum realized gain of 10 dBi, the antenna exhibits omnidirectional radiation patterns, showing a significant correlation between the simulation (CST Microwave Studio) and measurement results. The proposed antenna is particularly well-suited for use in high-throughput sub-6 GHz 5G base stations and wideband wireless systems, offering superior port isolation through multi-mode resonance without the need for metamaterials and outperforming existing four-port designs. Full article

Atmospheric refraction often influences the localization accuracy of ground-based radar for detecting space targets. Traditional methods generally utilize the measured troposphere and ionosphere data from the local station for atmospheric refraction correction and thus neglect the influence of atmospheric horizontal inhomogeneity. However, in practice, a horizontally inhomogeneous ionosphere often causes considerable residual errors in the measured range and elevation angle after refraction correction, especially for targets with low elevation angles. The ionospheric electron density profile along the wave propagation path is significantly different from that in the vertical direction of the local station, which further brings about challenges in the modeling and correction of atmospheric refraction errors. To address the above challenge, the effect of a horizontally inhomogeneous ionosphere on the range and elevation angle measured by ground-based radar is analyzed, and a geographic division modeling strategy for the ionospheric electron density along the propagation path for atmospheric refraction correction is proposed in this paper. The simulation results show that the oblique electron density distribution obtained from the proposed model agrees well with the results calculated by the International Reference Ionosphere (IRI) model, and the proposed methodology effectively suppresses residual errors in radar atmospheric refraction correction in the low-elevation detection case. Full article

The Earth’s lithosphere, the rigid outermost layer of the planet, is composed of numerous tectonic plates of varying sizes that move over the underlying asthenosphere. The motion and interaction of these plates give rise to a wide range of geodynamic processes. Accurate monitoring of these processes is essential for maintaining a stable, up-to-date, and reliable terrestrial reference frame. This study investigates the horizontal and vertical motions of the Antarctic Plate resulting from its interactions with adjacent plates. Tectonic plate movements can be determined using several space-geodetic techniques, including Global Navigation Satellite Systems (GNSS), Very Long Baseline Interferometry (VLBI), Satellite Laser Ranging (SLR), and Interferometric Synthetic Aperture Radar (InSAR). Among these methods, GNSS is currently the most widely used, as plate motions can be derived from continuous observations recorded at permanent stations and processed using scientific or commercial software. Within the scope of this research, GNSS data collected between 2020 and 2023 were processed using the GAMIT/GLOBK V.10.7 software package to estimate the coordinates and velocities of stations located on the Antarctic, South American, African, and Australian Plates in the ITRF14 reference frame. Furthermore, plate-fixed solutions were generated to analyze the relative motion of the Antarctic Plate with respect to neighboring plates. The results indicate that the Antarctic Plate moves at an average velocity of approximately 4–18 mm/year in the ITRF14 frame. The plate diverges from both the African and Australian Plates and exhibits predominantly strike-slip motion relative to the South American Plate. A comparison with existing global plate motion models demonstrates that the obtained velocities are consistent within 0–5 mm/year. Full article

Compact multi-channel airborne radar stations increasingly rely on an LED-based visible light communication (VLC) service link under radio-frequency spectrum restrictions and strict end-to-end delay constraints. Despite the directional nature of optical links, the VLC channel remains vulnerable to active optical interference and signal injection; furthermore, when an AI-enabled integrity monitor is embedded into the receiver, the AI decision layer becomes a direct target of evasion and online poisoning. This paper proposes a lightweight, interpretable AI-based attack detection architecture in which a Poisson photon-counting observation model is used to form physically consistent features over the preamble and control-sequence interval, while the final decision is produced by an AI ensemble combining a monotonic logistic detector and a one-class detector. The considered threat profile includes sustained illumination and synchronized flashes (jamming/blinding), spoofing via false preambles, replay of recorded fragments, and online data poisoning during self-calibration. The adequacy of solutions is assessed using the detection probability P_D (ensemble: P_D ≥ 0.90 for DC-jamming mean-count increment Δλ_DC ≈ 7.56, pulsed-interference mean-count increment Δλ_pulse ≈ 12.89, and spoofing signal-scaling factor α ≈ 1.02), the false-alarm probability P_FA = 0.045, and the per-packet end-to-end latency (bounded by the observation-window duration LΔT = 20 μs, where window length L = 20 and interval duration ΔT = 1 μs), which confirms real-time CPU operation without GPU acceleration. Full article

Numerical weather prediction (NWP) models are essential for precipitation forecasting but are constrained by coarse spatial resolutions (10–50 km), which fail to capture fine-scale variations required for regional disaster prevention, particularly in complex terrain. While statistical and machine learning downscaling methods have been developed to bridge this resolution gap, they predominantly operate as “black boxes” without explicit physical guidance, leading to predictions that violate meteorological principles and systematic underestimation of extreme precipitation events. To address these limitations, this study aims to develop a Physics-Informed Machine Learning framework that explicitly integrates multi-scale topographic modulation and physical consistency constraints into precipitation downscaling. Specifically, a Random Forest model enhanced with Multi-Scale Structural Similarity (MS-SSIM) loss and Physical Constraint Enhancement (MSSSIM-PCE-RF) was constructed. The model introduces elevation gradient weights at low-resolution layers and micro-topographic parameters (slope, surface roughness) at high-resolution layers, while enforcing physical consistency between precipitation intensity, radar reflectivity, and ground observations via the Z-R relationship. Based on hourly data from 2252 meteorological stations in Jiangxi Province (2021–2022), coupled with topographic factors (DEM, slope, aspect) and Normalized Difference Vegetation Index (NDVI), a technical framework of “data fusion–feature synergy–machine learning–spatial reconstruction” was established. Results demonstrate that the MSSSIM-PCE-RF model achieves a validation R² of 0.9465 and RMSE of 0.1865 mm, significantly outperforming the conventional RF model (R² = 0.9272). Notably, errors in high-altitude, steep-slope, and high-vegetation areas are reduced by 45.3%, 42.0%, and 43.1%, respectively, with peak precipitation period errors decreasing by 37.2%. Multi-scale topographic analysis reveals significant orographic lifting effects at 250–1000 m elevations, peak precipitation at 12–15° slopes, and abundant precipitation on south/southeast aspects. By explicitly embedding topographic modulation and physical consistency constraints, the model effectively alleviates systematic underestimation of extreme precipitation in complex terrain, providing high-resolution data support for transmission line disaster prevention and micro-meteorological risk assessment. Full article

Time synchronization and positioning of bistatic radar transceivers is required to coordinate and meaningfully merge the measurements made between them. It simultaneously allows the radar transceivers to change their position throughout time. Despite their acknowledged vulnerabilities, Global Navigation Satellite Systems (GNSSs) are the preferred source for Positioning, Navigation and Timing (PNT) services. Because of these vulnerabilities however, research on possible signal sources to obtain alternative positioning, navigation and timing (A-PNT) is of interest. This present work proposes the use of a narrow-band transponder installed on a geostationary (GEO) satellite to be used as one anchor for a future time transfer. A channel calibration is made between the transceiver station and the chosen satellite. Diverse models are used to estimate the channel effects throughout the signal propagation path, estimate the time delay, and correct the measurements, accordingly. The available channel bandwidth on the proposed satellite is 2.7 kHz, limiting the accuracy of the time measurements. After integration of multiple pulses, a time accuracy of approximately 1 μs is obtained. The range measurements are compared against satellite positions propagated from publicly available two-line element sets (TLEs). The obtained results suggest that, after calibration, the expected accuracy and a good repeatability is obtained. Thus, making the QO-100 satellite a suitable anchor for the proposed technique. Full article