Search Results (531)

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

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

Radar altimeters receive radio-wave reflections from nadir and determine surface parameters from the strength and shape of the return signal. Over the oceans, the strength of the return, termed sigma0 (${\sigma }^0$), is strongly related to the small-scale roughness of the ocean surface and is used to estimate near-surface wind speed. However, a number of other factors affect ${\sigma }^0$, and these need to be estimated and compensated for when developing long-term consistent ${\sigma }^0$ records spanning multiple missions. Aside from unresolved issues of absolute calibration, there are various geophysical factors (sea surface temperature, wave height and rain) that have an effect. The choice of the waveform retracking algorithm also affects the ${\sigma }^0$ values, with the four-parameter Maximum Likelihood Estimator introducing a strong dependence on waveform-derived mispointing and the use of delay-Doppler processing leading to apparent variation with spacecraft radial velocity. As all of these terms have strong geographical correlations, care is required to disentangle these various effects in order to establish a long-term consistent record. This goal will enable a better investigation of the long-term changes in wind speed at sea. 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

The impact of wind on precipitation measurements from the OTT Parsivel² optical transmission disdrometer is quantified using computational fluid dynamics simulations. The numerical velocity field around the instrument body and above the instrument sensing area (the laser beam) shows significant disturbance that depends heavily on the wind direction. By computing the trajectories of raindrops approaching the instrument, the wind-induced bias is quantified for a wide range of environmental conditions. Adjustments are derived in terms of site-independent catch ratios, which can be used to correct measurements in post-processing. The impact on two integral rainfall variables, the rainfall intensity and radar reflectivity, is calculated in terms of collection and radar retrieval efficiency assuming a sample drop size distribution. For rainfall intensity measurements, the OTT Parsivel² shows significant bias, even much higher than the wind-induced bias typical of catching-type rain gauges. Large underestimation is shown for wind parallel to the laser beam, while limited bias occurs for wind perpendicular to it. The intermediate case, with wind at 45°, presents non negligible overestimation. Proper alignment of the instrument with the laser beam perpendicular to the prevailing wind direction at the installation site and the use of windshields may significantly reduce the overall wind-induced bias. Full article

Ground-based weather radars are essential to better understand precipitation systems, to improve the Quantitative Precipitation Estimation (QPE), and to subsequently provide input to hydrological models. However, reflectivity measured by radars is typically affected by various sources of uncertainty, including attenuation and calibration errors. Due to these limitations, the two ground-based X-band weather radars of Cyprus, namely, at Rizoelia (LCA) and Nata (PFO), have not yet been employed for QPE. This study presents a dual neural network framework with the ultimate goal of converting the ground-based radar raw reflectivity to rainfall rate, using satellite and in situ observations. The two ground-based radars are aligned with GPM DPR using the volume-matching method. Preliminary results demonstrate the feasibility of converting raw ground-based radar reflectivity to rainfall estimates using neural networks trained with spaceborne and in situ observations. Full article

The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (15 January–26 February 2018) that deployed in situ probes and remote sensors to investigate low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud boundaries and classify cloud phases in single-layer, low-level marine boundary layer (MBL) clouds below 3 km using the HIAPER Cloud Radar (HCR) and in situ measurements. The cloud base and top heights derived from HCR reflectivity, Doppler velocity, and spectrum width measurements agreed well with corresponding lidar-based and in situ estimates of cloud boundaries, with mean differences below 100 m. A liquid water content–reflectivity (LWC-Z) relationship, LWC = 0.70Z^0.29, was derived to retrieve the LWC and liquid water path (LWP) from HCR profiles. The cloud phase was classified using HCR measurements, temperature, and LWP, yielding 40.6% liquid, 18.3% mixed-phase, and 5.1% ice samples, along with drizzle (29.1%), rain (3.2%), and snow (3.7%) for drizzling cloud cases. The classification algorithm demonstrates good consistency with established methods. This study provides a framework for the boundary and phase detection of MBL clouds, offering insights into SO cloud microphysics and supporting future efforts in satellite retrievals and climate model evaluation. Full article

Cyprus faces mounting pressure on freshwater resources from climate change, recurrent drought, and rising demand. This study evaluates the feasibility of a rain enhancement program through cloud seeding, integrating long-term rain gauge records (1991–2024), lightning climatology (2021–2025), and local X-band weather radar data (30 October 2024–4 January 2025) to quantify the frequency and characteristics of seedable clouds. Rain gauge analysis shows mean monthly rainfall exceeding 20 mm during October to April, with up to 16 rainfall events per month, indicating ample seeding opportunities. Lightning records show between 40–60 annual average thunderstorm occurrences, peaking in December (~10 days) along the Troodos Mountains in the central region and Limassol-Akrotiri in the south. Radar data analysis confirms the presence of both glaciogenic (≥25 dBZ at 5 km MSL) and hygroscopic (≥10 dBZ with ≥4 km depth) seedable cloud structures, with hotspots over the Troodos orography, southern plains, and maritime inflow zone. The combined results support the viability of an initial 7-month (October–April) cloud seeding program demonstration, integrated within a scientific framework, as a complementary and cost-effective freshwater augmentation tool for Cyprus. Full article

Advanced rider assistance systems (ARAS) play a crucial role in enhancing motorcycle safety through features such as collision avoidance, blind-spot detection, and adaptive cruise control, which rely heavily on sensors like radar, cameras, and LiDAR. However, their performance is often compromised under adverse weather conditions, leading to sensor interference, reduced visibility, and inconsistent reliability. This study evaluates the effectiveness and limitations of ARAS technologies in rain, fog, and snow, focusing on how sensor performance, algorithms, techniques, and dataset suitability influence system reliability. A thematic analysis was conducted, selecting studies focused on ARAS in adverse weather conditions based on specific selection criteria. The analysis shows that while ARAS offers substantial safety benefits, its accuracy declines in challenging environments. Existing datasets, algorithms, and techniques were reviewed to identify the most effective options for ARAS applications. However, more comprehensive weather-resilient datasets and adaptive multi-sensor fusion approaches are still needed. Advancing in these areas will be critical to improving the robustness of ARAS and ensuring safer riding experiences across diverse environmental conditions. Full article

This study investigates two extreme convective rainfall events that struck Poland in August 2024, affecting Warsaw (Okęcie) on 19 August and Zamość on 21 August. The aim is to evaluate the meteorological background, intensity, and spatial characteristics of these short-duration storms. We used high-resolution meteorological observations, radar imagery, and satellite data provided by the Institute of Meteorology and Water Management (IMGW-PIB). The storms were analyzed using temporal rainfall profiles, Chomicz α index classification, and comparison with World Meteorological Organization (WMO) thresholds for extreme precipitation. Both events exceeded national and international criteria for torrential rainfall. In Zamość, over 88.3 mm of rain fell within one hour, and 131.3 mm within three hours—ranking this episode among the most intense short-duration rainfall events in the region. Convective organization patterns, including multicellular clustering and convective training, were identified as key factors enhancing rainfall intensity. The results demonstrate the diagnostic value of combining national indices with global benchmarks in rainfall assessment. These findings support further integration of convection-permitting models and real-time nowcasting into urban hydrometeorological warning systems. Full article

Pedestrian accidents involving heavy vehicles such as trucks and buses remain a critical safety issue, primarily due to structural blind spots. While existing systems like radar-based FCW and BSD have been adopted, they are not fully optimized for pedestrian detection, particularly under adverse weather conditions. This study focused on the empirical validation of a 360-degree pedestrian collision avoidance system using multichannel ultrasonic sensors specifically designed for heavy-duty vehicles. Eight sensors were strategically positioned to ensure full spatial coverage, and scenario-based field experiments were conducted under controlled rain (50 mm/h) and fog (visibility <30 m) conditions. Pedestrian detection performance was evaluated across six distance intervals (50–300 cm) using indicators such as mean absolute error (MAE), coefficient of variation (CV), and false-negative rate (FNR). The results demonstrated that the system maintained average accuracy of 97.5% even under adverse weather. Although rain affected near-range detection (FNR up to 17.5% at 100 cm), performance remained robust at mid-to-long ranges. Fog conditions led to lower variance and fewer detection failures. These empirical findings demonstrate the system’s effectiveness and robustness in real-world conditions and emphasize the importance of evaluating both distance accuracy and detection reliability in pedestrian safety applications. Full article

This study utilizes Ka-band millimeter-wave cloud radar (MMCR), assisted by a precipitation phenomenon instrument, to conduct case studies and analyses of convective precipitation, cumulus precipitation, and stratus precipitation in the Xi’an region. Using the Doppler spectral data of the MMCR, dynamic parameters such as vertical air motion velocity (updraft and downdraft) and particle terminal fall velocity within these three types of cloud precipitation were retrieved. The results show that above the melting layer, the maximum updraft velocity in convective clouds reaches 15 m·s⁻¹, and the strong updraft drives cloud droplets to move upward at an average velocity of about 5 m·s⁻¹. The average updraft velocity in cumulus clouds is greater than that in stratus clouds, with updrafts in cumulus and stratus mainly distributed within 1.5–3 m·s⁻¹ and 1–2 m·s⁻¹, respectively. The reflectivity factor of precipitation particles (Ze) is used to correct the equivalent reflectivity factor (Ka-Ze) after attenuation correction below the MMCR melting layer. The accuracy of calculating the raindrop concentration using the Ka-Ze of MMCR was improved below the melting layer. Based on the relationship between terminal fall velocity and particle diameter and using the conversion between the MMCR power spectrum and raindrop spectrum, the concentration, fall velocity, and particle diameter of raindrops are calculated below the melting layer. The results show that the average reflectivity factor, average concentration, and average particle diameter of raindrops follow the order of convective precipitation > cumulus precipitation > stratiform precipitation. However, the average terminal fall velocity distribution of raindrop particles follows a different order: convective precipitation > stratiform precipitation > cumulus precipitation. Full article

Intense thunderstorms known as Nor’westers develop in the Eastern and North Eastern parts of India and Bangladesh before the monsoon season (March to May). The associated severe weather can cause extensive damage to property and livestock. This study uses the pre-monsoon volumetric data of S-band radar from 2013 to 2018 located in Kolkata to investigate the diurnal variation in the characteristics of the storms over Gangetic West Bengal. The cell initiation, echo top heights, maximum reflectivity, and core convective area are determined by using a flexible feature tracking algorithm (PyFLEXTRKR). The variation of the parameters in diurnal scale is examined from 211,503 individual cell tracks. The distribution of the severe weather phenomena based on radar based thresholds in spatial and temporal scale is also determined. The results show that new cell initiation peaks in the late evening and early morning, displaying bimodal variability. Most of these cells have a short lifespan of 0 to 3 h, with fewer than 5 percent of storms lasting beyond 3 h. The occurrence of hail is much greater in the afternoon due to intense surface heating than at other times. In contrast, the occurrence of lightning is higher in the late evening hours when the cell initiation reaches its peak. The convective rains are generally accompanied by lightning, exhibiting a similar diurnal temporal variability but are more widespread. The findings will assist operational weather forecasters in identifying locations that need targeted observation at certain times of the day to enhance the accuracy of severe weather nowcasting. Full article

Localizing ground-penetrating radar (LGPR) benefits from deep subsurface coupling, ensuring robustness against surface variations and adverse weather. While LGPR is widely recognized as the complement of existing vehicle localization methods, its reliance on prior maps introduces significant challenges. Channel misalignment during traversal positioning and time-dimension distortion caused by non-uniform platform motion degrade matching accuracy. Furthermore, rain and snow conditions induce subsurface water-content variations that distort ground-penetrating radar (GPR) echoes, further complicating the localization process. To address these issues, we propose a weather-resilient adaptive spatio-temporal mask alignment algorithm for LGPR. The method employs adaptive alignment and dynamic time warping (DTW) strategies to sequentially resolve channel and time-dimension misalignments in GPR sequences, followed by calibration of GPR query sequences. Moreover, a multi-level discrete wavelet transform (MDWT) module enhances low-frequency GPR features while adaptive alignment along the channel dimension refines the signals and significantly improves localization accuracy under rain or snow. Additionally, a local matching DTW algorithm is introduced to perform robust temporal image-sequence alignment. Extensive experiments were conducted on both public LGPR datasets: GROUNDED and self-collected data covering five challenging scenarios. The results demonstrate superior localization accuracy and robustness compared to existing methods. Full article