Search Results (515)

Unmanned Aerial Vehicles (UAVs) have rapidly grown into different essential applications, including surveillance, disaster response, agriculture, and urban monitoring. However, for UAVS to steer safely and autonomously, the ability to detect obstacles and nearby aircraft remains crucial, especially under hard environmental conditions. This study comprehensively analyzes the recent landscape of obstacle and aircraft detection techniques tailored for UAVs acting in difficult scenarios such as fog, rain, smoke, low light, motion blur, and disorderly environments. It starts with a detailed discussion of key detection challenges and continues with an evaluation of different sensor types, from RGB and infrared cameras to LiDAR, radar, sonar, and event-based vision sensors. Both classical computer vision methods and deep learning-based detection techniques are examined in particular, highlighting their performance strengths and limitations under degraded sensing conditions. The paper additionally offers an overview of suitable UAV-specific datasets and the evaluation metrics generally used to evaluate detection systems. Finally, the paper examines open problems and coming research directions, emphasising the demand for lightweight, adaptive, and weather-resilient detection systems appropriate for real-time onboard processing. This study aims to guide students and engineers towards developing stronger and intelligent detection systems for next-generation UAV operations. Full article

Accurate and spatially consistent rainfall estimation is essential for hydrological modeling and flood risk mitigation, especially in mountainous tropical regions with sparse observational networks and highly heterogeneous rainfall. This study presents a comparative analysis of six radar–gauge merging methods, including three statistical approaches—Quantile Adaptive Gaussian (QAG), Empirical Quantile Mapping (EQM), and radial basis function (RBF)—and three geostatistical approaches—external drift kriging (EDK), Bayesian Kriging (BAK), and Residual Kriging (REK). The evaluation was conducted over the Huong River Basin in Central Vietnam, a region characterized by steep terrain, monsoonal climate, and frequent hydrometeorological extremes. Two observational scenarios were established: Scenario S1 utilized 13 gauges for merging and 7 for independent validation, while Scenario S2 employed all 20 stations. Hourly radar and gauge data from peak rainy months were used for the evaluation. Each method was assessed using continuous metrics (RMSE, MAE, CC, NSE, and KGE), categorical metrics (POD and CSI), and spatial consistency indicators. Results indicate that all merging methods significantly improved the accuracy of rainfall estimates compared to raw radar data. Among them, RBF consistently achieved the highest accuracy, with the lowest RMSE (1.24 mm/h), highest NSE (0.954), and strongest spatial correlation (CC = 0.978) in Scenario S2. RBF also maintained high classification skills across all rainfall categories, including very heavy rain. EDK and BAK performed better with denser gauge input but required recalibration of variogram parameters. EQM and REK yielded moderate performance and had limitations near basin boundaries where gauge coverage was sparse. The results highlight trade-offs between method complexity, spatial accuracy, and robustness. While complex methods like EDK and BAK offer detailed spatial outputs, they require more calibration. Simpler methods are easier to apply across different conditions. RBF emerged as the most practical and transferable option, offering strong generalization, minimal calibration needs, and computational efficiency. These findings provide useful guidance for integrating radar and gauge data in flood-prone, data-scarce regions. Full article

The high resolution of the Synthetic Aperture Radar (SAR) imagery, in addition to its capability to see through clouds and rain, makes it a crucial remote sensing technique. However, SAR images are very sensitive to radar parameters, the observation geometry and the scene’s characteristics. Moreover, for a complex scene of interest with targets located on a rough soil, a composite scattering between the target and the surface occurs and creates distortions on the SAR image. These characteristics can make the SAR images difficult to analyse and process. To better understand the complex EM phenomena and their signature in the SAR image, we propose a methodology to generate raw SAR signals and SAR images for scenes of interest with a target located on a rough surface. With this prospect, the entire radar acquisition chain is considered: the sensor parameters, the atmospheric attenuation, the interactions between the incident EM field and the scene, and the SAR image formation. Simulation results are presented for a rough dielectric soil and a canonical target considered as a Perfect Electric Conductor (PEC). These results highlight the importance of the composite scattering signature between the target and the soil. Its power is 21 dB higher that that of the target for the target–soil configuration considered. Finally, these simulations allow for the retrieval of characteristics present in actual SAR images and show the potential of the presented model in investigating EM phenomena and their signatures in SAR images. Full article

High-resolution numerical weather prediction requires accurate cloud microphysical initial conditions to enhance forecasting capabilities for high-impact severe weather events such as convective storms. This study integrated Fengyun-2 (FY-2) geostationary satellite data (equivalent blackbody temperature and total cloud cover) and next-generation 3D weather radar reflectivity from the China Meteorological Administration (CMA) to construct cloud microphysical initial fields and evaluate their impact on the CMA-MESO 3 km regional model. An analysis of the catastrophic rainfall event in Henan on 20 July 2021, and a 92-day continuous experiment (May–July 2024) revealed that assimilating cloud microphysical variables significantly improved precipitation forecasting: the equitable threat scores (ETSs) for 1 h forecasts of light, moderate, and heavy rain increased from 0.083, 0.043, and 0.007 to 0.41, 0.36, and 0.217, respectively, with average hourly ETS improvements of 21–71% for 2–6 h forecasts and increases in ETSs for light, moderate, and heavy rain of 7.5%, 9.8%, and 24.9% at 7–12 h, with limited improvement beyond 12 h. Furthermore, the root mean square error (RMSE) of the 2 m temperature forecasts decreased across all 1–72 h lead times, with a 4.2% reduction during the 1–9 h period, while the geopotential height RMSE reductions reached 5.8%, 3.3%, and 2.0% at 24, 48, and 72 h, respectively. Additionally, synchronized enhancements were observed in 10 m wind prediction accuracy. These findings underscore the critical role of cloud microphysical initialization in advancing mesoscale numerical weather prediction systems. Full article

The Global Precipitation Measurement (GPM) mission’s Dual-Frequency Precipitation Radar (DPR) serves as a critical benchmark for calibrating satellite-based precipitation products, with its retrieval quality directly governing the accuracy of global precipitation estimates. While the updated version 07 (DPR-V07) algorithm introduces substantial refinements over its predecessor (DPR-V06), systematic evaluations of its operational advancements in precipitation monitoring remain limited. This study utilizes ground-based rain gauge data from Mainland China (2015–2018) to assess improvements of DPR-V07 over its predecessor’s (DPR-V06) effects. The results indicate that DPR-V07 reduces the high-altitude precipitation underestimation by 5% (vs. V06) in the west (W) and corrects the elevation-linked overestimation via an improved terrain sensitivity. The seasonal analysis demonstrates winter-specific advancements of DPR-V07, with a 3–8% reduction in the miss bias contributing to a lower total bias. However, the algorithm updates yield unintended trade-offs: the High-Sensitivity Scan (HS) mode exhibits significant detection performance degradation, particularly in east (E) and midwest (M) regions, with Critical Success Index (CSI) values decreasing by approximately 0.12 compared to DPR-V06. Furthermore, summer error components show a minimal improvement, suggesting unresolved challenges in warm-season retrieval physics. This study establishes a systematic framework for evaluating precipitation retrieval advancements, providing critical insights for future satellite algorithm development and operational applications in hydrometeorological monitoring. Full article

This study investigates the linkage between the radar reflectivity slope and raindrop size distribution (DSD) in precipitation with bright bands through coordinated C-band/Ka-band radar and disdrometer observations in southern China. Precipitation is classified into three types based on the reflectivity slope (K-value) below the freezing level, revealing distinct microphysical regimes: Type 1 (K = 0 to −0.9) shows coalescence-dominated growth; Type 2 (|K| > 0.9) shows the balance between coalescence and evaporation/size sorting; and Type 3 (K = 0.9 to 0) demonstrates evaporation/size-sorting effects. Surface DSD analysis demonstrates distinct precipitation characteristics across classification types. Type 3 has the highest frequency of occurrence. A gradual decrease in the mean rain rates is observed from Type 1 to Type 3, with Type 3 exhibiting significantly lower rainfall intensities compared to Type 1. At equivalent rainfall rates, Type 2 exhibits unique microphysical signatures with larger mass-weighted mean diameters (D_m) compared to other types. These differences are due to Type 2 maintaining a high relative humidity above the freezing level (influencing initial D_m at bottom of melting layer) but experiencing limited D_m growth due to a dry warm rain layer and downdrafts. Type 1 shows opposite characteristics—a low initial D_m from the dry upper layers but maximum growth through the moist warm rain layer and updrafts. Type 3 features intermediate humidity throughout the column with updrafts and downdrafts coexisting in the warm rain layer, producing moderate growth. Full article

The continuous release of global precipitation products offers a stable data source for flood forecasting in areas without rainfall gauges. However, due to constraints of forecast timeliness, only no/short-lag precipitation products can be utilised for flood forecasting, but these products are prone to significant errors. Therefore, the keys of flood forecasting in areas lacking rainfall gauges are selecting appropriate precipitation products, improving the accuracy of precipitation products, and reducing the errors of precipitation products by combination with hydrology models. This paper first presents the current no/short-lag precipitation products that are continuously updated online and for which the download of long series historical data is supported. Based on this, this paper reviews the utilisation methods of multi-source precipitation products for flood forecasting in areas with insufficient rainfall gauges from three perspectives: methods for precipitation product performance evaluation, multi-source precipitation fusion methods, and methods for coupling precipitation products with hydrological models. Finally, future research priorities are summarized: (i) to construct a quantitative evaluation system that can take into account both the accuracy and complementarity of precipitation products; (ii) to focus on the improvement of the areal precipitation fields interpolated by gauge-based precipitation in multi-source precipitation fusion; (iii) to couple real-time correction of flood forecasts and multi-source precipitation; and (iv) to enhance global sharing and utilization of rain gauge–radar data for improving the accuracy of satellite-based precipitation products. Full article

In this study, the relationship between radar reflectivity and rain rate (Z–R) was investigated. The analysis was conducted using data collected by the OTT Parsivel¹ disdrometer during the periods 2012–2014 and 2019–2025 in Warsaw, Poland. As a first step, the parameters a and b of the power-law Z–R relationship were estimated separately for three precipitation types: rain, sleet (rain with snow), and snow. Subsequently, observational data from all 12 months of the annual cycle were used to derive Z–R relationships for 118 individual precipitation events. To date, only a few studies of this kind have been conducted in Poland. In the analysis limited to rain events, the estimated parameters (a = 265, b = 1.48) showed relatively minor deviations from the classical Z–R function for convective rainfall, Z = 300R^1.4. However, the parameter a deviated more noticeably from the Z = 200R^1.6 relationship proposed by Marshall and Palmer, which is commonly used to convert radar reflectivity into rainfall estimates, including in the Polish POLRAD radar system. The dataset used in this study included rainfall events of varying types, both stratiform and convective, which contributed to the averaging of Z–R parameters. The values for the parameter a in the Z–R relationship estimated for the other two categories of precipitation types, sleet and snow, were significantly higher than those determined for rain events alone. The a values calculated for individual events demonstrated considerable variability, ranging from 80 to 751, while the b values presented a more predictable range, from 1.10 to 1.77. The highest parameter a values were observed during the summer months: June, July, and August. The variability in the Z–R relationship for individual events assessed in this study indicates the need for further research under diverse meteorological conditions, particularly for stratiform and convective precipitation. Full article

The relationship between radar reflectivity (Z) and rainfall intensity (R) plays a crucial role in estimating precipitation and serves as a foundation for flood risk assessment. However, empirical Z–R relationships often introduce considerable uncertainty, making the correction of rainfall estimation errors a key challenge in remote-sensing-based applications. Developing an effective approach to reduce these deviations is, therefore, essential to improve the accuracy of radar-based precipitation measurements. This study aims to develop a methodology for analyzing radar-derived precipitation using dual-polarization radar measurements, with validation based on rain gauge observations. Three well-established Z–R relationships—Marshall–Palmer, Muchnik, and Joss—were applied to radar reflectivity values measured at two heights, 1 km and 1.5 km above ground level. The Marshall–Palmer relationship applied at a height of 1.5 km yielded the smallest deviations from rain gauge measurements. Both the mean absolute error (MAE) and average precipitation difference at this height were consistent, amounting to 1.99 mm, compared to 2.32 mm at 1 km. The range of deviations in all cases was 0.54–7.64 mm at 1.5 km and 0.65–7.18 mm at 1 km. Furthermore, all tested Z–R relationships demonstrated a strong linear correlation with rain gauge data, as indicated by a Pearson correlation coefficient of 0.98. These findings enable the identification of the most accurate Z–R relationships and optimal measurement heights for radar-based precipitation estimation. These results may have important implications for operational applications and the calibration of radar precipitation products. Full article

Accurate, real-time estimation of rainfall from Doppler radars remains a challenging problem, particularly over complex terrain where vertical beam sampling, atmospheric effects, and radar quality limitations introduce significant biases. In this work, we leverage citizen science rain gauge observations to develop a deep learning framework that corrects biases in radar-derived surface precipitation rates at high temporal resolution. A key step in our approach is the construction of piecewise-linear rainfall accumulation functions, which align gauge measurements with radar estimates and allow for the generation of high-quality instantaneous rain rate labels from rain gauge observations. After validating gauges through a two-stage temporal and spatial consistency filter, we train an adapted ResNet-101 model to classify rainfall intensity from sequences of surface precipitation rate estimates. Our model substantially improves precipitation classification accuracy relative to NOAA’s operational radar products within observed spatial regions, achieving large gains in precision, recall, and F1 score. While generalization to completely unseen regions remains more challenging, particularly for higher-intensity rainfall, modest improvements over baseline radar estimates are still observed in low-intensity rainfall. These results highlight how combining citizen science data with physically informed accumulation fitting and deep learning can meaningfully improve real-time radar-based rainfall estimation and support operational forecasting in complex environments. Full article

Remote sensing precipitation products are essential for the systematic analysis of precipitation characteristics and changes. This study conducts a comparative evaluation of precipitation products from rain gauge stations, Climate Prediction Center morphing technique (CMORPH), Tropical Rainfall Measuring Mission precipitation radar (TRMM PR) version 7 and Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar Ku band (DPR KuPR) version 6 orbital observations during the common observational period (April–September 2014) across South China. The spatial patterns and probability density function of rain rates from four precipitation products show similar features. However, average rain rates from CMORPH (0.2–2.6 mm/h) tend to be smaller than those from rain gauge (0.1–4.4 mm/h) in temporal and spatial distribution. Conversely, average rain rates from TRMM PR and GPM KuPR (0.4–10.0 mm/h) are generally larger and exhibit more pronounced monthly changes. Despite notable differences in the number of detection samples, TRMM and GPM exhibit comparable spatiotemporal distributions and vertical structures, including rain-rate profiles, storm top heights and liquid (ice) water path. This confirms the consistency of space-borne precipitation radars and provides a foundation for analyzing long-term precipitation trends. Further analysis reveals that light rain rates from CMORPH have relatively small deviations, while rain rates generally tend to underestimate the rain rate compared to rain gauge. In contrast, TRMM PR and GPM KuPR tend to generally overestimate rain rates. Meanwhile, CMORPH (1.5–6.0 mm/h) shows larger deviations from rain gauge than TRMM and GPM, and the bias progressively increases as rain rates rise, as indicated by root mean square error results. Several statistical metrics suggest that although the missing detection rates of TRMM and GPM are higher than those of CMORPH (probability of detection 10–60%), their false detection rates are spatially lower (false alert ratio 10–30%) in Middle-East China. This study aims to provide valuable insights for enhancing precipitation retrieval algorithms and improving the applicability of remote sensing precipitation products. Full article

The evaluation of raindrop size distribution (DSD) is a crucial subject in radar meteorology, as it determines the relationship between radar reflectivity (Z) and rainfall rate (R). The coefficients (a and b) of the Z-R relationship vary significantly due to several factors (e.g., climate and rainfall intensity), rendering the characterization of local DSD essential for improving radar quantitative precipitation estimation. This study used a unique network of 21 disdrometers with high spatio-temporal resolution in Mexico City to investigate changes in the local drop size distribution (DSD) resulting from seasonal fluctuations, rain rates, and topographical regions (flat urban and mountainous). The results indicate that the DSD modeling utilizing the normalized gamma distribution provides an adequate fit in Mexico City, regardless of geographical location and season. Regional variation in DSD’s slope, shape, and parameters was detected in flat urban and mountainous areas, indicating that distinct precipitation mechanisms govern rainfall in each season. Severe rain intensities (R > 20 mm/h) exhibited a more uniform and flatter DSD shape, accompanied by increased dispersion of DSD parameter values among disdrometer locations, particularly for intensities exceeding R > 60 mm/h. The coefficients a and b of the Z-R relationship exhibit significant geographic variability, dependent on the city’s topographic gradient, underscoring the necessity for regionalization of both coefficients within the metropolis. Full article

This study presents an objective scheme to construct hurricane bogus vortices based on satellite microwave scatterometer observations of sea surface wind vectors. When specifying a bogus vortex using Fujita’s formula, the required parameters include the center position and the radius of the maximum gradient of sea level pressure (R₀). We first propose determining the tropical cyclone (TC) center position as the cyclonic circulation center obtained from sea surface wind observations and then establishing a regression model between R₀ and the radius of 34-kt sea surface wind of scatterometer observations. The radius of 34-kt sea surface wind (R₃₄) is commonly used as a measure of TC size. The center positions determined from HaiYang-2B/2C/2D Scatterometers, MetOp-B/C Advanced Scatterometers, and FengYun-3E Wind Radar compared favorably with the axisymmetric centers of hurricane rain/cloud bands revealed by Advanced Himawari Imager observations of brightness temperature for the western Pacific landfalling typhoons Doksuri, Khanun, and Haikui in 2023. Furthermore, regression equations between R₀ and the scatterometer-determined radius of 34-kt wind are developed for tropical storms and category-1, -2, -3, and higher hurricanes over the Northwest Pacific (2022–2023). The bogus vortices thus constructed are more realistic than those built without satellite sea surface wind observations. Full article

Lightning is associated with severe thunderstorm events and causes hundreds of deaths annually in Brazil. Additionally, it is responsible for losses amounting to millions in Brazil’s electricity and telecommunication sectors. Between November 2011 and March 2012, the CHUVA-Vale do Paraíba (CHUVA-Vale) campaign was conducted in the Vale do Paraíba region and the Metropolitan Area of São Paulo (MASP), located in southeastern São Paulo state, Brazil, to enhance the understanding of cloud processes, including lightning. During the campaign, several instruments were available: a meteorological radar, lightning location systems, rain gauges, a vertical-pointing radar, a surface tower, and others. In this context, the main goal of this study was to evaluate the temporal evolution of lightning properties, such as frequency, type (cloud-to-ground (CG) and intracloud (IC) lightning), peak current, length, and duration, in the MASP between November 2011 and March 2012. To achieve this objective, lightning data from the Brazilian Lightning Detection Network (BrasilDAT) and the São Paulo Lightning Mapping Array (SPLMA) were utilized. The maximum amount of lightning for the BrasilDAT (322,598 events/month) occurred in January, while for the SPLMA (150,566 events/month), it occurred in February, suggesting that thunderstorms displayed typical summer behavior in the studied region. Most of lightning registered by the BrasilDAT were concentrated between 2:00 and 5:00 pm local time, with a maximum of 5.0 × 10⁴, 6.2 × 10³, and 95 events/month.hour for IC, −CG, and +CG lightning, respectively. These results are associated with the favorable conditions of diurnal atmospheric instability caused by surface heating. Regarding the lightning properties from the SPLMA, longer-duration lightning (up to 0.4 s) and larger spatial extension (up to 14 km) occurred during the nighttime period (0–6:00 am local time), while the highest lightning frequency (up to 9 × 10⁴ events month⁻¹ h⁻¹) was observed in the afternoon (3–4:00 pm local time). Full article

Quantitative precipitation estimation using polarimetric radar in attenuation-prone frequency (X-band) in tropical regions characterized by convective rain systems with high intensities is a major challenge due to strong attenuations that can lead to total signal extinction over short distances. However, some authors have addressed this issue in Benin since 2006 in the framework of the African Monsoon Multidisciplinary Analysis program. Thus, with an experimental setup consisting of an X-band polarimetric weather radar (Xport) and a network of rain gauges, investigations have started on the subject with the aim of improving rainfall estimates. Based on simulated polarimetric variables and using a Multilayer Perceptron artificial neural network, several bi-variable and tri-variable algorithms were assessed in this study. The data used in this study are of two categories: (i) simulated polarimetric variables (Rayleigh reflectivity $Z$, horizontal attenuation $A_h$, horizontal reflectivity $Z_h$, differential reflectivity $Z_{dr}$, and specific differential phase $K_{dp}$) and rainfall intensity (R) obtained from Rain Drop Size Distribution (DSD) measurements used for algorithm evaluation (training and testing); (ii) polarimetric variables measured by the Xport radar and rainfall intensity measured by rain gauges used for algorithm validation. The simulations are performed using the T-matrix code, which leverages the scattering properties of spheroidal particles. The DSD measurements taken in northwest Benin were used as input for this code. For each spectrum, the T-matrix code simulates multiple variables. The simulated data (first category) were divided into two parts: one for training and one for testing. Subsequently, the best algorithms were validated with the second category of data. The performance of the algorithms during training, testing, and validation was evaluated using metrics. The best selected algorithms are $A1:R(Z,K_{dp})$ and $A12:R(Z_{dr},K_{dp})$ (among the bi-variable); $B2:R(Z_h,Z_{dr},K_{dp})$ and $B3:R(A_h,Z_{dr},K_{dp})$ (among the tri-variable). Tri-variable algorithms outperform bi-variable algorithms. Validation with observation data (Xport measurements and rain gauge network) showed that the algorithm $B3:R(A_h,Z_{dr},K_{dp})$ performs better than $B2:R(Z_h,Z_{dr},K_{dp})$. Full article