Search Results (900)

Accurate precipitation nowcasting plays an important role in disaster prevention and hydrometeorological applications, yet it remains highly challenging due to the complex spatiotemporal variability and multi-scale structural characteristics of precipitation systems. Existing deep learning methods are largely data-driven and often struggle to effectively exploit multi-source observations or learn physically meaningful representations. To address these limitations, this study proposes a Multi-Scale Frequency–Temporal Network (MS-FTNet) for precipitation nowcasting. The framework leverages Fourier transform-based frequency-domain modeling to achieve an interpretable multi-scale decomposition of precipitation dynamics. Specifically, low-frequency components capture large-scale stratiform patterns and their temporal evolution, while high-frequency components represent localized convective structures and abrupt variations. Building on this, a Global Feature Collaboration (GFC) module integrates global frequency-domain representations with multi-scale convolutional features, and an Adaptive Temporal Fusion (ATF) module enhances temporal dependency modeling. Experiments on the SEVIR dataset demonstrate that MS-FTNet consistently outperforms representative baseline models in terms of MSE, CSI, and LPIPS, particularly for heavy precipitation events and longer forecast lead times. Full article

Precipitation nowcasting is a critical part of meteorological services and applications. Recently, mainstream research has been focused on adopting deep learning-based models to generate the predictions, yet existing deep learning models face challenges with blurry predictions that fail to capture high-frequency meteorological details, difficulty modeling both local correlations and long-range spatial dependencies, and a fundamental signal–noise confusion within the diffusion process that degrades structural fidelity. In this paper, we propose FADiff, a novel frequency-aware diffusion model based on a hybrid CNN–Transformer network for radar-based precipitation nowcasting. A hybrid CNN–Transformer backbone is first designed to integrate the CNNs with the Transformers, jointly enabling the local and global feature extraction capability of the meteorological dynamics. Subsequently, a novel Frequency-Aware Module (FAM) is proposed to mitigate signal–noise confusion. By transforming features into the frequency domain via the Discrete Cosine Transform (DCT), the FAM performs content-adaptive filtering with a learnable gating mechanism, which is designed to suppress noise-dominant frequency components while benefiting high-frequency signals corresponding to real meteorological structures. Finally, these components are embedded within a latent diffusion model to form an end-to-end nowcasting framework. Extensive experiments on the CIKM and SEVIR datasets demonstrate that the proposed FADiff outperforms state-of-the-art methods across a comprehensive suite of evaluation metrics. Significantly, under high-intensity precipitation thresholds, FADiff exhibits remarkable robustness and stability, presenting its superior capability in generating meteorologically critical structures with high fidelity. Full article

The accurate and timely nowcasting of severe weather events such as short-term torrential rainfall is essential for disaster preparedness and early warning systems. Our prior studies have demonstrated a high correlation (0.92) and ~10 min time lag between in-cloud (IC) lightning and ground rainfall. In this study, based on the approach introduced by Shimizu and Uyeda, an automatic method for identifying and tracking convective storm cells, we integrate total lightning data and heavy precipitation data for further improving the prediction accuracy of torrential rainfall. High-resolution 2D weather radar composite precipitation data are collected from XRAIN, operated by MLIT, Japan, and total lightning data (TL, i.e., IC and CG) are collected from the Japanese Total Lightning Network (JTLN). The adapted algorithm is used to track lightning-frequent areas (≥5 and ≥2 pulses per 5 min) as well as heavy (≥50 mm/h) and torrential (≥80 mm/h) precipitation cells. To evaluate the predictive capability of TL, cross-correlation analyses are performed across multiple intensity thresholds and time lags. The results of correlation matrix analysis for identifying the movement of the storm and utilization towards spatiotemporal nowcasting of extreme rainfall is discussed. Full article

Against the background of intensifying global climate change, extreme precipitation events have become increasingly frequent. Improving the accuracy of short-term precipitation nowcasting is therefore essential for disaster prevention and mitigation. Traditional numerical weather prediction (NWP) approaches are constrained by computational latency and errors arising from physical parameterizations, making it difficult to satisfy real-time forecasting requirements at high spatiotemporal resolution. Using the SEVIR dataset, this study conducts a systematic comparison of two Transformer-based deep learning models—Earthformer and LLMDiff—for short-term extreme precipitation nowcasting. Model performance is evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and Success Ratio (SUCR). Results indicate that, for 0–30 min lead times, Earthformer more efficiently captures both local and long-range spatiotemporal dependencies via its Cuboid Attention mechanism and shows a slight advantage for low-intensity precipitation. As the lead time extends to 60 min, LLMDiff demonstrates stronger longer-horizon skill due to its diffusion-based probabilistic modeling and a frozen large language model (LLM) module, which enhance the representation of uncertainty and longer-term evolution of precipitation systems. However, LLMDiff tends to produce a higher false-alarm rate. Overall, Earthformer is better suited for rapid early warning of light precipitation, whereas LLMDiff is more appropriate for high-accuracy nowcasting of heavy precipitation, offering useful insights for intelligent forecasting of extreme weather. Full article

The Bayan Obo Mining District, recognized as the largest rare-earth resource base worldwide, has experienced significant surface instability due to intensive mining and large-scale dumping activities. To address the challenges posed by complex geological conditions and mining-induced disturbances, this study employs dual-ascending Sentinel-1A C-band Synthetic Aperture Radar (SAR) datasets (Path 11 and Path 113) and applies the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to retrieve time-series deformation along the line-of-sight (LOS) direction for each track. Through temporal normalization and spatial matching, paired LOS observations from the two tracks were established. Based on the SAR observation geometry and under the assumption that the north–south component is negligible, a LOS projection model was constructed and a geometric decomposition was performed to derive the east–west and vertical two-dimensional deformation fields. The results indicate that the study area is generally stable, while significant subsidence occurs in the northern pit and adjacent waste-dump zones, with local maximum rates approaching 50 mm/year, predominantly controlled by the vertical component. The two-dimensional deformation analysis reveals that vertical displacement dominates surface motion, whereas east–west movement shows smaller amplitudes but clear directional concentration. In particular, the east–west slopes exhibit slightly higher velocities, suggesting a lateral adjustment tendency along this direction, likely related to the overall east–west geometric configuration of the open-pit and waste-dump areas. Time-series observations further reveal that precipitation-related surface deformation occurs with an approximate two-month delay, reflecting the hydrological–mechanical coupling processes of rainfall infiltration, pore-water pressure propagation, and dump-material consolidation. Overall, this study reveals the multi-dimensional deformation characteristics and precipitation-driven stage-wise response of the mining area, demonstrating the effectiveness of the dual-ascending SBAS-InSAR for two-dimensional deformation monitoring in highly disturbed environments, and providing a scientific basis for surface stability assessment and geohazard prevention. Full article

In this paper, the authors developed a data-driven model to classify radar measurements into precipitation (P) and non-precipitation (NP) echoes using the Random Forest machine learning algorithm. Dual-polarimetric radar variables and their local variability exhibit distinctive characteristics between P and NP echoes. The authors found that using larger search window sizes generally improves classification accuracy, though it involves a trade-off: while it helps eliminate small clusters of NP echoes, it may also suppress weak precipitation signals near storm edges. Incorporating multiscale local variability estimates computed with varying window sizes further enhances classification performance by capturing spatial-scale-dependent features characteristic of P and NP echoes. The main model uses radar variables obtained from a single scan and demonstrates consistent performance across all distances from the radar. This consistency allows reliable use of the model out to 230 km—the maximum range at which dual-polarimetric variables are used for rainfall estimation from NEXRAD radars—without significant degradation in accuracy due to range effects. Supplementing the model with independent information from GOES-16 infrared channel products further improves classification by helping to eliminate localized NP echoes remaining after the main model, particularly those caused by wind turbines that mimic precipitation in dual-polarimetric signatures. This is based on the tendency of water vapor and/or raindrops to absorb terrestrial radiation, thereby lowering brightness temperatures. A practical challenge remains near the radar, where the sampling volume is small and signal processing (e.g., sidelobe impact and ground clutter suppression) can distort radar measurements. The under-detection of precipitation in these regions is likely due to such corrupted data. This issue may be mitigated by adopting a hybrid scan strategy—such as a Constant Altitude Plan Position Indicator (CAPPI)—specifically for regions close to the radar. Full article

In July 2024, a severe rainfall event struck Sichuan Province, Southwest China, triggering deadly landslides and causing significant societal impacts. This study investigates the spatiotemporal characteristics and underlying mechanisms of the event using high-resolution surface observations, radar reflectivity, and ERA5 reanalysis data. The rainfall exhibited distinct mesoscale organization, with two primary precipitation centers identified: subregion A located within the plateau-lain transitional zone of the western Sichuan Basin, and subregion B situated over the Chengdu Plain. Synoptic-scale analysis indicated that the rainfall developed under favorable large-scale atmospheric conditions, including a mid-tropospheric trough, a pronounced low-level jet, and a well-defined Southwest Vortex (SWV), which is a dominant lower-tropospheric circulation system in this region. The evolution of rainfall was closely tied to the initiation and subsequent eastward progression of the SWV. The rainfall-producing mesoscale convective system (MCS) first formed over subregion A at approximately 2300 BST (UTC + 8) on 19 July. Vorticity budget diagnostics revealed that vertical advection and low-level convergence significantly contributed to vortex intensification during this initial phase, closely associated with the orographic lifting of low-level airflow. Convective activity in subregion B commenced roughly four hours later, coinciding with the eastward propagation of the SWV, during which horizontal vorticity advection became the primary mechanism sustaining the vortex. After 1400 BST on 20 July, the SWV weakened significantly, leading to the dissipation of the MCS and the cessation of rainfall. Full article

Multi-instrument synergistic observation is vital for studying cloud and precipitation physics. However, using the nearest scan time for matching inevitably introduces temporal mismatches. Here we employ three advection correction methods for temporal matching in weather radar and spaceborne radar observations: Lucas–Kanade (LK), Variational Echo Tracking (VET), and Anisotropic Diffusion (AD). These methods calculate the movement speed of the storms using optical flow methods, and then determine their positions based on the elapsed time between instruments. Next, we conducted a quantitative assessment of the performance of these three methods based on the consistency of storm morphology and rainfall rates. Our results demonstrate that all three advection correction methods effectively reduce the discrepancies in morphology and rainfall rate among multi-source data. Without correction, the Coincidence Rate (CR) and Structural Similarity (SSIM) were 30.96% and 0.689 in the US and 29.44% and 0.670 in China, respectively. In comparison, applying the LK, VET, and AD methods increased those indices to 32.94%, 32.72%, 32.85% and 0.718, 0.715, 0.716 in the US, and 31.34%, 31.17%, 31.24% and 0.696, 0.694, 0.693 in China, respectively. The rainfall rate inconsistencies were also effectively reduced after advection correction. The performances among the three methods were similar. Overall, the LK method performed slightly better than AD, followed by VET. Full article

Intercomparison studies between satellite-based and ground-based radar systems are essential for advancing radar technologies and improving precipitation retrieval algorithms. This study conducted a systematic literature review of Global Precipitation Measurement Mission (GPM) Dual-Frequency Precipitation Radar (DPR) and ground-based radar intercomparison studies using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) method, focusing on peer-reviewed literature published between 2014 and 2024. The review synthesizes current knowledge of DPR precipitation detection and estimation, including the application of DPR in ground-based radar calibration, and discussions on retrieval methods and attenuation correction algorithms. Most studies used a volume-matching method to compare observations between datasets and examine S- and C-band radars from national networks. Most analyses occurred over the Northern Hemisphere, and individual ground-based radars were more frequently compared to DPR rather than examining mosaics. Beyond summarizing existing studies, this review identifies systematic, geographic, methodological, and algorithmic gaps that constrain comprehensive validation of DPR products. Recurrent bias patterns—such as precipitation-type-dependent errors and attenuation-related uncertainties—highlight priority areas for algorithm refinement and targeted validation campaigns. By synthesizing validation strategies and recurring performance limitations, this work provides a structured reference for future intercomparison studies, supports more standardized validation practices, and informs the development of improved precipitation retrieval algorithms, ground-based radar calibration practices, and next-generation satellite radar missions. 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

This dataset compiles continuous hydraulic and water quality observations from the combined sewer overflow structure at the outlet of the Graz-West R05 catchment in Austria, covering the period from 2008 to 2011. It integrates high-resolution in-sewer measurements of flow rate, water level, flow velocity and water quality parametres (COD, TSS, temperature), complemented by laboratory analyses of discrete grab samples. Water quality parametres were monitored using an in situ UV/VIS spectrometer installed on a floating pontoon. Additional locally calibrated COD values derived from laboratory measurements are included. The in-sewer data were acquired at 1 or 3 min intervals depending on flow conditions. Flow rates, water levels and overflow discharges were monitored using radar and ultrasonic sensors. Three nearby tipping-bucket rain gauges provided time-stamped precipitation increments, enabling the detailed reconstruction of wet-weather dynamics. A hydrodynamic SWMM model of the catchment, including geospatial information and dry-weather calibration, is included to support modelling applications. This combination of long-term measurements and a calibrated hydrodynamic model supports the development, testing and validation of process-based, statistical or data-driven approaches for simulating combined sewer system behaviour and pollutant dynamics. Full article

This paper presents a comprehensive real-time forecasting and mapping cycle of a regional flood event, encompassing quantitative precipitation forecasting, runoff production and routing, and inundation mapping. The objective of this study is to highlight the significant uncertainties inherent in each step of the fully automated cycle, despite the utilization of state-of-the-art models and remote sensing technologies. The case study focuses on a significant flood event that occurred in the Turkey River and Upper Iowa River, in rural Iowa, United States, resulting in localized damage and disruption to several small communities. The novelty of this study is that it demonstrates the limited utility of satellite-based remote sensing in the absence of other forecasting and mapping system elements, emphasizing the need for the timely integration of information from diverse sources to accurately forecast and map floods. To achieve this, we assembled and analyzed precipitation data from weather radars, streamflow estimates derived from river stages and rating curves, and cross-sectional data from river channels to characterize the movement of the flood wave. These data were integrated into hydrologic and hydraulic models to generate flood inundation estimates for the more severely affected areas. Remote sensing imagery was obtained and used as reference to assess the accuracy of the modeled inundated areas. Our findings illustrate that, despite the increasing availability of satellite data sources, there are still significant limitations to tracking inundation using satellite remote sensing, particularly for medium-sized basins. Flood modeling processes are not merely complementary to satellite-based flood estimation, but essential for comprehensive flood risk assessment. Full article

The Southwest China vortex (SWV) is a high-impact mesoscale cyclonic vortex that typically originates over Sichuan Province, China, and frequently produces hazardous rainfall. Yet systematic knowledge of the structural and microphysical properties of SWV precipitation remains insufficiently quantified. Using Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM/DPR) observations from 2014 to 2022, this study investigates the vertical structure and macro- and microphysical characteristics of SWV precipitation, and quantifies their differences across life-cycle stages and precipitation types. The mature stage is characterized by higher echo tops, stronger radar reflectivity, higher strong-echo altitudes, and larger near-surface rainfall, together with a clearer melting-layer bright band and a stronger post-melting shift toward larger drops and lower number concentrations. The developing stage is weakest and shows the largest fraction of coalescence–breakup balance signatures, whereas the dissipating stage features enhanced evaporation- and breakup-related signals. Among precipitation types, deep strong convection exhibits the greatest vertical extent with enhanced ice/mixed-phase growth; stratiform precipitation produces stronger radar echoes and higher rainfall rates than deep weak convection despite similar echo-top heights; and shallow precipitation is characterized by smaller drops, higher concentrations, and active warm-rain spectral evolution. These findings provide satellite-based constraints for microphysics parameterization evaluation and improved numerical prediction of SWV-related rainfall over complex terrain. Full article

Acoustic precipitation enhancement (APE) is an emerging non-chemical weather-modification technique, yet quantitative three-dimensional evidence of its impact on rainy clouds remains scarce. This study investigates a stratiform precipitation event over the Bayinbuluke Basin in the central Tianshan Mountains of northwestern China, 29–30 August 2024, using an X-band phased-array weather radar (X-PAR) coordinated with an upward-directed acoustic source. Rapid volumetric scans and sector-aligned range-height indicators were combined to reconstruct the three-dimensional cloud structure before, during, and after acoustic operation. During acoustic operation, the results were stronger and more persistent than during the non-operation period, with localized values exceeding 40 dBZ. Within the 3 km influence zone, low-level reflectivity increased across all azimuthal sectors with clear directional dependence. Dual-ratio analysis showed statistically significant enhancement in the windward sector (247°, $DR$ = 1.91, $p$ = 0.0004) and the leeward sector (137°, $DR$ = 1.51, $p$ = 0.008), indicating that acoustic-induced responses extended beyond the primary radiation sector and propagated downstream with cloud advection. These results, based on a single stratiform precipitation case, demonstrate that volumetric X-PAR observations can detect localized cloud-structure responses during acoustic operation. Full article

This study, conducted in the framework of the LIAISE field campaign in NE Spain (May–September 2021), investigates how near-surface relative humidity influences early-stage rainfall characteristics when precipitation is most affected by temperature and relative humidity before rainfall onset. Two instrumented sites were examined, using disdrometers, Micro Rain Radar (MRR), C-band weather radar data, and automatic weather stations. Rainfall events were first classified as stratiform or convective using weather radar data based on a texture analysis of the reflectivity field. Then, only stratiform events were selected and further classified into dry and moist categories according to the upper and lower terciles of near-surface (2 m) relative humidity at the rainfall onset (dry < 54%; moist > 72%). Results show that during dry events, the time delay between the detection of precipitation at ~750 m above ground level (AGL) (by MRR or C-band radar) and its arrival at the surface (measured by the disdrometer) is consistently longer than during moist events, indicating possible evaporation of raindrops during their descent. Surface drop size distributions also differ: dry cases have generally fewer small drops (with diameters < 0.8 mm) but relatively more large drops, leading to higher radar reflectivity values despite similar surface rainfall amounts. However, reflectivity observed aloft by C-band radar and MRR does not present the dependence on relative humidity found at ground level. Findings reported here increase our understanding of the impact of low-level conditions on precipitation characteristics and microphysical associated processes and may contribute to improve correction schemes in operational weather radar quantitative precipitation estimates. Full article