Search Results (84)

Forecasting short-term heavy precipitation is crucial for the early warning of disasters such as flash floods, landslides, and urban flooding. However, under complex topographic conditions, traditional numerical forecasts still fall short in capturing high-resolution heavy precipitation events, and conventional radar extrapolation methods struggle to accurately characterize the nonlinear evolution of weather systems during advection, deformation, and intensity adjustment processes. To address the challenge of short-term heavy rainfall forecasting in high-altitude, complex terrain, this paper proposes Nowcast with Flow-Net (Nwf-Net), a short-term precipitation forecasting framework that integrates deep learning with multi-source meteorological data. This framework consists of a Morphological Evolution Track Module (MET) and a Rainfall Intensity Correction Module (RIC) connected in series: the former combines upper-air wind fields with traditional optical flow algorithms to jointly characterize the displacement of and morphological changes in radar echoes; the latter utilizes a deep recurrent neural network to correct the intensity of forecast results, thereby enhancing the model’s ability to characterize the evolution of strong convective echoes. Experiments in the Alpine region demonstrate that Nwf-Net achieves CSI, HSS, and F1 scores of 0.392, 0.506, and 0.546, respectively, at 32 dBz. These results outperform those of traditional numerical models and some mainstream models, indicating that Nwf-Net can accurately capture multiscale severe convective information and consistently generate precise forecasts. Full article

We propose an exploratory framework based on Topological Digital Twins (TDTs) for the monitoring and short-term forecasting of spatial dynamical systems. The approach represents the system through a reduced state built from topological descriptors obtained via persistent homology. These descriptors capture features such as connected components, cycles, and large-scale structure. The framework combines three components: an observation operator mapping spatial fields to a low-dimensional state, a reduced dynamical model evolving this state in time, and a data assimilation step aimed at improving robustness. This construction maps persistence diagrams to a finite-dimensional Euclidean space. This makes the model tractable but does not preserve the full algebraic structure of the original topological objects. We provide theoretical results supporting the stability of the representation under perturbations of the input field. The method is illustrated on a bow-echo convective system observed over Corsica on 18 August 2022, where the reduced state captures the main structural organization of the system over time. A comparison with standard nowcasting methods shows complementary behavior: pixel-based approaches provide better local accuracy, while the TDT framework better preserves the global spatial structure, as reflected by Wasserstein distances and persistence-based comparisons. Additional tests also indicate that the topological observables remain stable under small perturbations of the input field. The present study is based on a single case and should be understood as a proof of concept, rather than as a definitive validation. Future work will focus on validation on larger datasets and on the use of more advanced dynamical models. Full article

Radar echo extrapolation under severe convective conditions remains challenging because efficient prediction models still tend to suffer from strong-echo attenuation, boundary blurring, and performance degradation at longer lead times. To address these issues, this study proposes SMG-Net, a SimVP-based radar echo extrapolation model with a collaborative multistage design. The proposed framework integrates multiscale spatial enhancement, trend–disturbance differentiated temporal modeling, and gated hierarchical feature fusion to improve structural preservation and temporal stability. Experiments on a regional radar dataset show that SMG-Net achieves the lowest MSE (0.032) and the highest SSIM (0.830) among the compared models. At the 30 dBZ threshold, CSI, POD, and FAR reach 0.042, 0.045, and 0.250, respectively, indicating improved strong-echo detectability and reduced false alarms. The results further show that SMG-Net is particularly effective in preserving the morphology, boundary structure, and intensity distribution of medium- and strong-echo regions at longer lead times, while introducing only limited additional computational cost over the baseline SimVP. These findings indicate that SMG-Net improves the preservation of medium- and strong-echo structures in efficient radar echo extrapolation and has practical value for short-term precipitation nowcasting in severe convective scenarios. Full article

Accurate nowcasting of thunderstorm gales, a highly destructive form of severe convective weather, is critical for mitigating wind-related disasters and ensuring the safety of life and property. Existing deep learning approaches face challenges such as performance decay at high wind speed thresholds, limited capability in capturing extreme events, and difficulties in processing high-resolution data. To address these issues, this paper proposes a novel physics-driven multi-source fusion recurrent neural network named MSF-PhyDRNN. The model incorporates a multi-source fusion module that integrates radar composite reflectivity and surface wind field data through feature decoupling and hierarchical fusion. Additionally, we improved the recurrent unit in PhyDNet to enhance short-term wind capture and reduce redundancy, leveraging its cascaded memory and spatiotemporal propagation mechanisms. Experimental results indicate that, compared to the advanced MFWPN model, MSF-PhyDRNN achieves an average increase of 14.3% in the Critical Success Index (CSI), 27.2% in the Probability of Detection (POD), and 19.7% in the Heidke Skill Score (HSS) across the Jiangsu and South China datasets. 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

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

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

Hailstorms frequently develop in Yun-Gui Plateau, Western China, which bring about significant economic damage. Due to the high terrain, these storms are typically shallow, rapidly evolving, and challenging to forecast. An X-band phased-array radar (XPAR) network is set up at Weining in Yun-Gui Plateau to study these storms. To explore these XPAR data for numerical prediction of hailstorms in this region, we implement the Weather Research and Forecast (WRF) model and Hydrometeor and Latent Heat Nudging (HLHN) method to assimilate the data and conduct prediction experiments. The XPAR data was evaluated along with the operational Severe Weather Automatic Nowcast (SWAN) system radar mosaic data. Furthermore, a humidity adjustment scheme is used to overcome inconsistency of the humidity field and related prediction errors. The model results show that in comparison to the SWAN data, assimilating XPAR data in 1-min intervals significantly reduces the model error, and improves the representation of rapid hail cloud evolution. Additionally, adjusting the model humidity based on vertically integrated liquid (VIL) derived from the radar data can effectively correct model analyses of humidity and temperatures, suppressing spurious convection, thus improving the hailstorm forecast. Overall, we recommend joint assimilation of the high spatiotemporal resolution XPAR data along with SWAN radar data with the improved WRF-HLHN for hailstorm prediction over the study region, and the algorithm can be promptly adapted to forecasting hailstorms in other regions. Full article

Radar echo extrapolation is a core technique for 0–2 h nowcasting, yet existing deep learning models often struggle with non-linear atmospheric motion and intensity attenuation due to insufficient feature decoupling. To address these limitations, this paper proposes AFTA-Net, a novel encoder–decoder architecture. The model introduces an Axial Fusion Block (AFB) that employs a parallel decomposition strategy to explicitly separate temporal evolution from spatial morphology, preserving structural integrity while capturing motion trends. Furthermore, a Tri-Axis Factorized Attention (TAFA) mechanism is designed to sequentially recalibrate feature representations across Time, Channel, and Spatial dimensions, thereby enhancing sensitivity to high-frequency convective signals and suppressing background noise. Extensive experiments on the Jiangsu radar dataset demonstrate that AFTA-Net significantly outperforms representative baselines. Notably, at the critical 30 dBZ threshold for severe weather, the model achieves a CSI of 0.2506 and an HSS of 0.3430. Full article

To advance marine thunderstorm forecasting and enhance the operational utility of lightning data, this study developed a novel very low-frequency (VLF) lightning data assimilation scheme for the South China Sea region. The three-dimensional graupel mixing ratio field was successfully inverted from VLF lightning detection data through the application of an empirical formula linking lightning frequency to graupel mass, a database of graupel mixing ratio profiles, and a distance-weighted diffusion scheme. This reconstructed field was then subjected to horizontal diffusion and assimilated into the Weather Research and Forecasting (WRF) model using the Grid Nudging module within the WRF–Four-Dimensional Data Assimilation (WRF-FDDA) system. A quantitative evaluation of 37 nocturnal marine convective cases was conducted using Fengyun-4A(FY-4A) satellite observations. The results demonstrate that the proposed assimilation method significantly enhances short-term (0–6 h) forecast performance. Specifically, the Fractions Skill Score (FSS) derived from the Advanced Geosynchronous Radiation Imager (AGRI) data increased rapidly during the early forecast stage, exceeding a value of 0.9. Meanwhile, the Lightning Mapping Imager Event (LMIE) product evaluation showed a high probability of detection (POD) of 85% for lightning forecasts, with a false alarm ratio (FAR) of only 9%. These findings indicate that the assimilation approach improves the accuracy of capturing the spatial structure and evolution of convective systems. Although the degree of improvement diminished with longer lead times, the results confirm the value of VLF lightning data in initializing convective-scale processes and underscore its practical value in marine nowcasting applications. Full article

Geostationary atmospheric motion vectors (e.g., FY4A AMVs) are routine mid-upper atmospheric observations used in numerical weather prediction (NWP) models, yet their complex spatiotemporal errors and assimilation limitations, i.e., high-temporal/coarse-spatial data and large-scale-adjustment/direct-assimilation scheme, leave unclear impacts of AMVs assimilation on nowcasting forecasts. To this end, a Nudging-Forced–3DVar scheme (NFV) is designed within a multi-scale (i.e., 12, 4, and 1 km) regional NWP framework to exploit AMVs characteristics; ablation experiments for the Zhengzhou “7.20” rainstorm isolate Nudging and 3DVar impacts on assimilation and nowcasting. Results show the following: (1) large-scale Nudging and high-resolution 3DVar both improve mid-upper analyses, with the former ingesting more observations; (2) Nudging retains large-scale background updates but yields significant misses, whereas 3DVar intensifies rainfall extremes yet blurs fine structures; (3) NFV merges its strengths, modulating deep convection through upper-level systems and markedly improving rainfall spatiotemporal patterns. Therefore, NFV is recommended for the FY4A AMVs’ future numerical nowcasting, which provides useful guidance for the regional application of geostationary 3D winds. Full article

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 predicting small-scale convective systems. To address this, we propose DIFF-3DRformer, a novel deep learning framework for 3D radar echo extrapolation. This model unifies a mesoscale evolution network, embedded with 3D advection equation neural operators and a 3D continuity equation-informed loss function, and a convective-scale denoising generative network based on a diffusion model, within an end-to-end architecture optimized for prediction accuracy. Evaluated on severe storm events over Jiangsu, China, DIFF-3DRformer demonstrates robust predictive skill across various convective scales. It outperforms NowcastNet, improving the comprehensive score by 44.8% for reflectivity thresholds ≥35 dBZ. Utilizing 19 vertical levels of radar data as input significantly enhances the morphology and intensity prediction of convective echoes, boosting performance by 4.63% compared to using only composite reflectivity. Furthermore, the incorporation of physical constraints refines the forecasted echo structure and spatial placement, yielding additional improvements. DIFF-3DRformer provides accurate short-term evolution forecasts of convective systems, offering a promising solution for developing nowcasting methods that directly characterize the 3D structure of convective storms. Full article

Urban coastal areas along the Mediterranean are exposed to short-duration convective rainfall, producing infrastructure disruptions and flood-related impacts. This study analyzes 45 rainfall episodes in the Metropolitan Area of Barcelona between 2014 and 2022, combining radar products, rain gauge observations, and urban-scale impact datasets. Storm radar tracking enabled the identification of key spatiotemporal features and assessment of short-term forecasting performance. Convective cells were typically short-lived, lasting less than 30 min in most cases. The main goal of the research has been the comparison between VIL density (DVIL) radar field and short-duration rainfall intensity provided by rain gauges. This is the first study comparing both data types, being a pioneer in this field. We have found a linear relationship between both data types, with weaker values for larger values. More persistent cells had higher DVIL values, observing a difference in behavior with a break point at 2 g/m³. The tracking and nowcasting system were evaluated based on its ability to anticipate convective precipitation. It achieved good scores values (POD of 0.73 and FAR of 0.33), considering the difficulties of tracking this type of convective system. Finally, false alarms associated with elevated DVIL values suggested the difficulty of capturing storm severity by surface-based precipitation measurements. Full article

This study presents a practical early-warning approach for heavy rainfall detection using the temporal dynamics of satellite-derived Cloud-Top Temperature (CTT). A rapid rise followed by a sharp fall in CTT is identified as a precursor signal of convective intensification. By quantifying the $rise-peak-fall-trough$ pattern and the $peak$-to-$trough$ amplitude ($swing$), a $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ window—representing a potential heavy-rainfall candidate period—is defined. The observed lead time between the onset of CTT decline and the subsequent radar-observed rainfall surge is calculated, while an estimated lead time is inferred from the steepness of CTT fall in the absence of a surge. Application to eight heavy rainfall events in Korea (July 2025) yielded a probability of detection (POD) of 87.5%, indicating that potential heavy rainfall could be detected approximately 1.3–8.6 h in advance. Compared with radar-based nowcasting, the CTT $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method retained predictive skill up to 3 h before numerical model guidance became effective, suggesting that satellite-based signals can bridge the forecast gap in short-term prediction. This work demonstrates a clear methodological novelty by introducing a physical interpretable, pattern-based metric. Quantitatively, the $\mathrm{W}\mathrm{A}\mathrm{T}\mathrm{C}\mathrm{H}$ method improves early-warning capability by providing 1–3 h of additional lead time relative to radar nowcasting in rapidly evolving convective environments. Overall, this framework provides an interpretable, low-cost module suitable for operational early-warning systems and flood preparedness applications. Full article

In numerical weather prediction (NWP) models, radar data are commonly utilized to retrieve relative humidity fields, thereby mitigating forecast errors arising from uncertainties in the initial moisture field. This study addresses the degradation in convective structure retrieval capability caused by temporal biases in NWP when using spatial neighborhood sampling methods with radar data for relative humidity field retrieval. We developed a time-extended Bayesian retrieval method and constructed a dual-polarization radar data assimilation framework compatible with the China Meteorological Administration Mesoscale Model (CMA-MESO). The core of this approach lies in expanding the Bayesian retrieval sample library by integrating forecast fields from two hours before and after the assimilation time, thereby improving the inadequate performance of traditional spatial sampling under model temporal bias. When applied to a heavy rainfall event in North China in July 2023, this method effectively reduced retrieval errors. The subsequent assimilation of retrieved humidity fields enhanced the Threat Scores for 0–6 h precipitation forecasts and demonstrated improvement in overprediction bias. This confirms that the time-extended strategy can effectively enhance moisture field analysis and nowcasting accuracy by overcoming the inherent limitations of spatial-only sampling. Full article