Search Results (878)

This study presents a four-year (2021–2024) radar-based analysis of warm cloud (non-glaciated) dynamics across northern Thailand, specifically characterizing their properties, kinematics, and occurrence. Utilizing high-resolution S-band dual-polarization weather radar data, a total of 20,493 warm cloud events were tracked and analyzed, with identification based on a maximum reflectivity (≥35 dBZ) and a cloud top height below the seasonal 0 °C isotherm. Occurrence exhibited a profound seasonal disparity, with the rainy season (82.68% of events) dominating due to the influence of the moist Southwest Monsoon (SWM), while the spatial distribution confirmed that convective initiation is exclusively concentrated over mountainous terrain, underscoring orographic lifting as the essential mechanical trigger. Regarding properties, while vertical development and mass are greater in the warm seasons, microphysical intensity and Duration (mean ~26 min) remain highly uniform, suggesting a constrained, efficient warm rain process. In kinematics, clouds move fastest in winter (mean WSPD ~18.38 km/h), yet pervasive directional chaos (SD > 112°) highlights the strong influence of terrain-induced local circulations. In conclusion, while topography dictates where warm clouds form, the monsoon dictates when and how robustly they develop, creating intense, short-lived events that pose significant operational constraints for localized precipitation enhancement strategies. Full article

The accurate retrieval of the raindrop size distribution (DSD) is a longstanding objective in meteorology because it underpins reliable quantitative precipitation estimation. Among remote sensors, weather radars are the primary tool for mapping DSD over wide areas, and phased-array systems in particular have demonstrated unique advantages owing to their high temporal and spatial resolution together with agile beam steering. Exploiting the underused high-resolution capability of an X-band phased-array radar, this study induced a Rainfall Regression Model (RRM). The RRM assumes a normalized gamma DSD model and retrieves its three parameters. It was then applied to a rain event influenced by the remnant circulation of Typhoon Haikui that affected Guangzhou on 8 September 2023. First, collocated disdrometer observations and T-matrix scattering simulations are used to build polynomial regressions between DSD parameters (D₀, N_w, μ) and the polarimetric variables. Validation against independent disdrometer samples yields Nash–Sutcliffe efficiencies of 0.93 for D₀ and 0.91 for log₁₀N_w. The RRM is then applied to the full volumetric radar data. Horizontal maps reveal that the surface elevation angle consistently exhibited the largest standard deviation for all three parameters. A vertical profile analysis shows that large-drop cores (D₀ > 2 mm) can reside above 2 km and that iso-value contours tilt rather than align vertically, implying an appreciable horizontal drift of raindrops within the complex remnant typhoon–monsoon wind field. By demonstrating the ability of X-band phased-array radar to resolve the three-dimensional microphysical structure of remnant typhoon precipitation, this study advances our understanding of the vertical characteristics of raindrops and provides high-resolution DSD information that can be directly ingested into severe weather monitoring and nowcasting systems. Full article

Accurate rainfall monitoring is essential for mitigating hydrometeorological disasters and understanding hydrological changes under climate change. This study presents a deep learning-based rainfall estimation framework using multispectral GEO-KOMPSAT-2A (GK2A) satellite imagery. The analysis primarily focuses on daytime observations to take advantage of visible channel information, which provides richer representations of cloud characteristics during daylight conditions. The core model, Model-HSP, is built on the Pix2PixCC architecture and trained with Hybrid Surface Precipitation (HSP) data from weather radar. To further enhance accuracy, an ensemble model (Model-ENS) integrates the outputs of Model-HSP and a radar based Model-CMX, leveraging their complementary strengths for improved generalization, robustness, and stability across rainfall regimes. Performance was evaluated over two periods—a one year period from May 2023 to April 2024 and the August 2023 monsoon season—at 2 km and 4 km spatial resolutions, using RMSE and CC as quantitative metrics. Case analyses confirmed the superior capability of Model-ENS in capturing rainfall distribution, intensity, and temporal evolution across diverse weather conditions. These findings show that deep learning greatly enhances GEO satellite rainfall estimation, enabling real-time, high-resolution monitoring even in radar sparse or limited coverage regions, and offering strong potential for global and regional hydrometeorological and climate research applications. Full article

Supercooled liquid water (SLW) in mixed-phase clouds significantly influences precipitation efficiency and aviation safety. However, a comprehensive understanding of its vertical structure has been hampered by a lack of sustained, vertically resolved observations over the North China Plain. This study presents the first systematic analysis of SLW vertical distribution and microphysics in this region, utilizing a year-long dataset (2022) from synergistic ground-based instruments in Beijing. Our retrieval approach integrates Ka-band cloud radar, microwave radiometer, ceilometer, and radiosonde data, combining fuzzy-logic phase classification with a liquid water content inversion constrained by column liquid water path. Key findings reveal a distinct bimodal seasonality: SLW primarily occurs at mid-to-upper levels (4–7.5 km) during spring and summer, driven by convective lofting, while winter SLW is confined to lower altitudes (1–2 km) under stable atmospheric conditions. The temperature-dependent occurrence probability of SLW clouds has an annual maximum at −12 °C. The diurnal variation in SLW in summer shows peaks in the afternoon and at night, corresponding to convective cloud activity. Spring, autumn, and winter do not exhibit strong diurnal variations. Retrieved microphysical properties, including liquid water content and droplet effective radius, are consistent with in situ aircraft measurements, validating our methodology. This analysis provides a critical observational benchmark and offers actionable insights for improving cloud microphysics parameterizations in models and optimizing weather modification strategies, such as seeding altitude and timing, in this water-stressed region. Full article

Quantitative precipitation estimation (QPE) from radar reflectivity is fundamental for weather nowcasting and water resource management. Conventional Z-R relationship formulas, derived from Rayleigh scattering theory, rely heavily on empirical parameter fitting, which limits the estimation accuracy and generalization across different precipitation regimes. Recent deep learning (DL)-based QPE methods can capture the complex nonlinear relationships between radar reflectivity and rainfall. However, most of them overlook fundamental physical constraints, resulting in reduced robustness and interpretability. To address these issues, this paper proposes FusionQPE, a novel Physics-Constrained DL framework that integrates an adaptive Z-R formula. Specifically, FusionQPE employs a Dense convolutional neural network (DenseNet) backbone to extract multi-scale spatial features from radar echoes, while a modified squeeze-and-excitation (SE) network adaptively learns the parameters of the Z-R relationship. The final rainfall estimate is obtained through a linear combination of outputs from both the DenseNet backbone and the adaptive Z-R branch, where the trained linear weight and Z-R parameters provide interpretable insights into the model’s physical reasoning. Moreover, a physical-based constraint derived from the Z-R branch output is incorporated into the loss function to further strengthen physical consistency. Comprehensive experiments on real radar and rain gauge observations from Guangzhou, China, demonstrate that FusionQPE consistently outperforms both traditional and state-of-the-art DL-based QPE models across multiple evaluation metrics. The ablation and interpretability analysis further confirms that the adaptive Z-R branch improves both the physical consistency and credibility of the model’s precipitation estimation. 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

With the upgrading of geostationary meteorological satellites, their capabilities in Convective Initiation (CI) identification have been enhanced. To improve the applicability of the ARGI-based CI algorithm in central and eastern China, this study uses Fengyun-4A data, integrates radar and precipitation data to construct a True_CI dataset, and defines False_CI events (satellite-identified events without radar or precipitation signals) for comparative analysis. The results show that True_CI events tend to have longer durations, larger cloud cluster areas, and lower central cloud-top brightness temperature (BT) during development. They exhibit distinct features such as reduced differences between water vapor and infrared channels, increased cloud optical thickness, and ice-phase transformation 30 min before CI occurrence—features absent in most False_CI events. Based on these comparative findings, a new satellite-based CI definition is proposed with a set of reference thresholds, which should be adjusted for different latitudes and seasons. The evaluation of the Defined_CI events (defined using the CI definition) via True_CI events indicates that the CI definition on satellite cloud imagery proposed in this study is reliable, and suggests that further research on the pre-CI environmental conditions of weak convection is needed. Supported by hyperspectral data or numerical model products, such research will help clarify which cloud clusters are prone to developing into convective weather. 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

Operation and maintenance (O&M) events resulting from environmental factors (e.g., precipitation, temperature, seasonality, and unexpected weather conditions) are among the primary sources of operating costs and downtime in run-of-river small hydropower plants (SHPs). This paper presents a data-driven methodology for predicting such long events using machine learning models trained on historical power production, weather radar, and forecast data. Case studies on two Slovenian SHPs with different structural designs and levels of automation demonstrate how environmental features—such as day of year, rain duration, cumulative amount of rain, and rolling precipitation sums—can be used to forecast long events or shutdowns. The proposed approach integrates probabilistic classification outputs with threshold-consistency smoothing to reduce noise and stabilize predictions. Several algorithms were tested—including Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting, and k-Nearest Neighbors (k-NN)—across varying feature combinations for O&M model development, with cross-validation ensuring robust evaluation. The models achieved an F1-score of up to 0.58 in SHP1 (k-NN), showing strong seasonality dependence, and up to 0.68 in SHP2 (Gradient Boosting). For SHP1, the best model (k-NN) correctly detected 36 long events, while 15 were misclassified as no events and 38 false alarms were produced. For SHP2, the best model (Gradient Boosting) correctly detected 69 long events, misclassified 23 as no events, and produced 42 false alarms. The findings highlight that probabilistic machine learning-based forecasting can effectively support predictive O&M planning, particularly for manually operated or service-operated SHPs. Full article

In this study, a machine learning-based quality control algorithm for heavy rainfall was developed by integrating automatic weather station observations with remote sensing data, minute-level data, and metadata. Based on heavy rainfall samples from 1 June 2022 to 31 December 2024, the performances of four gradient boosting models—eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and Gradient Boosted Regression Trees (GBRT)—significantly outperformed precipitation-threshold-based conventional methods, including regional extreme value checks, temporal consistency checks, and others. Specifically, the XGBoost in particular achieves an increase in precision by 0.110 and recall by 0.162. This translates to a substantial reduction in both false alarms (higher precision) and missed detections (higher recall) of anomalous heavy rainfall events, thereby significantly enhancing the reliability of the quality-controlled data. The radar composite reflectivity, satellite cloud-top temperature, and minute-level precipitation were identified as dominant contributors to model predictions. The integration of multi-sensor observations effectively addressed limitations inherent in conventional threshold-based approaches. Through SHapley Additive exPlanations (SHAP)-based interpretability analysis, the model’s decision logic was shown to align with meteorological physical principles. Characteristic patterns such as combinations of low radar reflectivity and elevated cloud-top temperatures were flagged as anomalous rainfall events, typically corresponding to manual operational errors. Moreover, the model identified anomalous minute-level precipitation extremes to be critical signals for detecting instrument malfunctions, data encoding and transmission errors. The physical consistency of the model’s reasoning enhances its trustworthiness and supports its potential for operational implementation in heavy rainfall quality control. Full article

Every year, thunderstorms initiating in the eastern Pyrenees cause a wide range of adverse phenomena, not only in the mountainous areas but also in the surrounding regions. Events such as heavy rainfall leading to flash floods, large or giant hail, and strong winds are common in this area. These phenomena cause significant damage and have major impacts on the population. We used remote sensing data, specifically weather radar, to identify areas that are more prone to convection initiation. This initial analysis covers the period from 2022 to 2024 and is intended to serve as the foundation for a more extensive study. The aim of this study is to characterize the diurnal convection cycle over the Pyrenees. Additionally, we plan to develop a technique that can be applied to other mountainous regions where similar data are available. The steps are as follows: (1) identifying events with precipitation over the area; (2) selecting cases associated with diurnal convection; (3) applying algorithms to determine the tracks of convective cells; and finally, (4) selecting the initial points of these trajectories. The result is a map highlighting these “hotspot” areas, which will allow us to incorporate other variables in the future, both meteorological and non-meteorological, to identify the main factors influencing the characteristics of each event. Full article

A major scientific challenge is understanding how precipitation systems will change under global warming. In particular, extreme precipitation events associated with hail and graupel are of significant concern. In this study, we evaluated the performance of the flagGraupelHail product from the Dual-Frequency Precipitation Radar (DPR) aboard the GPM Core Observatory using high-resolution dual-polarization observations from Multi-Parameter Phased Array Weather Radar (MP-PAWR). The analysis focused on a convective system that developed in a humid environment over the Tokyo region of Japan, providing a valuable assessment within a climatic regime that has been underrepresented in previous studies. A bias correction for MP-PAWR reflectivity, derived from XRAIN network comparisons, yielded good agreement with KuPR observations from the DPR. A new grid-matching method, suitable for comparing vertically varying hydrometeor particle types and available only for MP-PAWR, was also introduced. The comparison revealed that DPR flagGraupelHail detections generally corresponded to regions of graupel occurrence identified by the MP-PAWR GHratio, defined as the number of graupel/hail grids within a DPR observation volume, although DPR tended to detect fewer events. To improve detection performance, we introduced a new indicator, STH35-FH—the height difference between the 35 dBZ echo top and the 0 °C level—as a complementary parameter to the PTI value used to determine flagGraupelHail. Incorporating STH35-FH improved the consistency between DPR and MP-PAWR detections, reducing false positives and enhancing overall detection accuracy. These results demonstrate the value of combining ground-based and spaceborne radar observations to improve global precipitation retrievals, particularly in humid environments. This approach will contribute to more accurate global graupel/hail estimation by spaceborne precipitation radar and a better understanding of how global warming affects precipitation systems. Full article

The availability of reliable and spatially distributed rainfall data is a key element flood and landslide risk assessment, both for forecasting and post-event analysis. In this context, this study evaluates the contribution of radar-based rainfall estimates to enhancing the spatial accuracy of precipitation fields with respect to those derived from rain gauge networks alone. The analysis was conducted over a ~100 km² area in the Liguria Region, north-western Italy, characterized by a dense rain gauge network, with an average density of one gauge per 10 km², and covers seven years of hourly rainfall observations. Radar-derived rainfall fields, available at a 1 × 1 km² spatial resolution, were locally corrected across the study area by interpolating gauge-based local correction factors through an Inverse Distance Weighting (IDW) scheme. The corrected radar fields were then assessed through Leave-P-Out Cross-Validation and rainfall-intensity-based classification, also simulating scenarios with progressively reduced gauge density. The results demonstrate that radar-corrected estimates systematically provide a more accurate spatial representation of rainfall, especially for high-intensity events and in capturing the actual magnitude of local rainfall peaks, even in areas covered by a dense rain gauge network. Regarding the implications for rainfall-induced landslide hazard assessment, the analysis of 56 landslides from the ITALICA (Italian Rainfall-Induced Landslides Catalogue) database showed that including radar information can lead to significant differences in the estimation of rainfall thresholds for landslide initiation compared with gauge-only data. Full article

Quantitative precipitation estimation (QPE) is one of the primary applications of weather radar. Over the last several decades, dual-polarization radars have significantly improved QPE accuracy by providing additional observational variables that offer more microphysical information about precipitation particles. In this work, we review QPE methods for dual-polarization radars and summarize their advantages and disadvantages from both theoretical and practical perspectives. The development paths and current status of operational QPE systems in the United States, China, and France are examined. We demonstrate how dual-polarization radars have improved QPE accuracy in these systems not only directly through the application of polarimetric QPE methods, but also indirectly through the more accurate identification of non-meteorological echoes, the mitigation of the partial blockage effect, and the detection of melting layers. The challenges are discussed for dual-polarization radar QPE, including the quality of polarimetric variables, QPE quality in complex terrain, estimation of surface precipitation with observations within or above the melting layer, and polarimetric QPE methods for snow. Full article

Weather radar reflectivity plays a critical role in precipitation estimation and convective storm identification. However, due to terrain limitations and the uneven spatial distribution of radar stations, oceanic regions have long suffered from a lack of radar observations, resulting in extensive monitoring gaps. Geostationary meteorological satellites have wide-area coverage and near-real-time observation capability, offering a viable solution for synthesizing radar reflectivity in these regions. Most previous synthesis studies have adopted fixed time-window data partitioning, which introduces significant noise into visible-light observations under large-scale, low-illumination conditions, thereby degrading synthesis quality. To address this issue, we propose an integrated deep-learning method that combines illumination-based classification and reflectivity synthesis to enhance the accuracy of radar reflectivity synthesis from geostationary meteorological satellites. This approach integrates a classification network with a synthesis network. First, visible-light observations from the Himawari-8 satellite are classified based on illumination conditions to separate valid signals from noise; then, noise-free infrared observations and multimodal fused data are fed into dedicated synthesis networks to generate composite reflectivity products. In experiments, the proposed method outperformed the baseline approach in regions with strong convection (≥35 dBZ), with a 9.5% improvement in the critical success index, a 7.5% increase in the probability of detection, and a 6.1% reduction in the false alarm rate. Additional experiments confirmed the applicability and robustness of the method across various complex scenarios. Full article