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This paper describes the first assessment of ISRO's indigenously developed S-band Polarimetric Doppler Weather Radar (DWR) at Sriharikota High Altitude Range (SHAR), Sriharikota (13.66°N, 80.23°E). The assessment is done during the passage of a tropical cyclone 'Phethai' and four different cases of Mesoscale convective systems. A comparison of reflectivity measured from SHAR-DWR with India Meteorological Department (IMD) DWR located at Chennai (13.07°N, 80.28°E) and Ku-band space-borne radar of the Global Precipitation Mission (GPM) is done using the common volume matching method. A good comparison is observed between SHAR-DWR with IMD-DWR and GPM-Ku radar in both space and height. The vertical structure of the cyclone is provided using Contour Frequency by Altitude Diagrams (CFAD) which show the vertical extent up to an altitude of 16 km with a reflectivity between 25 and 30 dBZ. Maximum occurrence of 30 dBZ occurs below 6 km. Bright band signatures show ~ 25 dBZ between 4 and 5 km depicting the stratiform precipitation of the cyclone. Further rain rate is estimated using both reflectivity and the polarimetric product which compared well with rain estimated from IMD-DWR and rainfall observed from rain gauges. Rain rate is observed from 5 to 35 mm hr−1. Hydrometer classification during the passage of tropical cyclone 'Phethai' shows the presence of rain below 4 km and graupels, ice crystals, and large droplets above. [ABSTRACT FROM AUTHOR]

The three-body scatter spike (TBSS), an echo artifact in radar imagery, manifests as a weak, linear echo extending radially from a core of high reflectivity. Adequately, though not indispensably, indicating the presence of large hail in convective storms, the automatic identification of the TBSS proves advantageous in significantly improving the effectiveness of hailstorm detection. This study introduces an algorithm that synergizes Jensen–Shannon divergence (JSD) and support vector machine (SVM) for rapid TBSS detection in two decades' worth of single-polarization radar data across China. The algorithm, tested on data from 50 S-band China Next Generation Weather Radar (CINRAD) in central and eastern China, utilized reflectivity factor images for sample extraction. An application in Chenzhou, China, demonstrates the algorithm's efficacy in improving hailstorm detection resolution. Significance Statement: In recent years, China's hail recordkeeping, primarily based on manual observations at national surface meteorological stations, has suffered from limited spatial and temporal detail. However, the advent of the China Next Generation Weather Radar (CINRAD) network offers a new avenue for hailstorm detection. TBSS, a secondary but significant indicator for large hail in S-band radar, presents an opportunity for enhanced hail warning capabilities. By automating TBSS detection in radar archives spanning two decades, this research significantly enhances the resolution of hailstorm climatology, contributing to more effective hail disaster mitigation and management. [ABSTRACT FROM AUTHOR]

Every night during spring and autumn, the mass movement of migratory birds redistributes bird abundances found on the ground during the day. However, the connection between the magnitude of nocturnal migration and the resulting change in diurnal abundance remains poorly quantified. If departures and landings at the same location are balanced throughout the night, we expect high bird turnover but little change in diurnal abundance (stream‐like migration). Alternatively, migrants may move simultaneously in spatial pulses, with well‐separated areas of departure and landing that cause significant changes in the abundance of birds on the ground during the day (wave‐like migration). Here, we apply a flow model to data from weather surveillance radars (WSR) to quantify the daily fluxes of nocturnally migrating birds landing and departing from the ground, characterizing the movement and stopover of birds in a comprehensive synoptic scale framework. We corroborate our results with independent observations of the diurnal abundances of birds on the ground from eBird. Furthermore, we estimate the abundance turnover, defined as the proportion of birds replaced overnight. We find that seasonal bird migration chiefly resembles a stream where bird populations on the ground are continuously replaced by new individuals. Large areas show similar magnitudes of take‐off and landing, coupled with relatively small distances flown by birds each night, resulting in little change in bird densities on the ground. We further show that WSR‐inferred landing and take‐off fluxes predict changes in eBird‐derived abundance turnover rate and turnover in species composition. We find that the daily turnover rate of birds is 13% on average but can reach up to 50% on peak migration nights. Our results highlight that WSR networks can provide real‐time information on rapidly changing bird distributions on the ground. The flow model applied to WSR data can be a valuable tool for real‐time conservation and public engagement focused on migratory birds' daytime stopovers. [ABSTRACT FROM AUTHOR]

Lightning is a rapidly evolving phenomenon, exhibiting both mesoscale and microscale characteristics. Its prediction significantly relies on timely and accurate data observation. With the implementation of new generation weather radar systems and lightning detection networks, radar reflectivity image products, and lightning observation data are becoming increasingly abundant. Research focus has shifted towards lightning nowcasting (prediction of imminent events), utilizing deep learning (DL) methods to extract lightning features from very large data sets. In this paper, we propose a novel spatio-temporal fusion deep learning lightning nowcasting network (STF-LightNet) for lightning nowcasting. The network is based on a 3-dimensional U-Net architecture with encoder-decoder blocks and adopts a structure of multiple branches as well as the main path for the encoder block. To address the challenges of feature extraction and fusion of multi-source data, multiple branches are used to extract different data features independently, and the main path fuses these features. Additionally, a spatial attention (SA) module is added to each branch and the main path to automatically identify lightning areas and enhance their features. The main path fusion is conducted in two steps: the first step fuses features from the branches, and the second fuses features from the previous and current levels of the main path using two different methodsthe weighted summation fusion method and the attention gate fusion method. To overcome the sparsity of lightning observations, we employ an inverse frequency weighted cross-entropy loss function. Finally, STF-LightNet is trained using observations from the previous half hour to predict lightning in the next hour. The outcomes illustrate that the fusion of both the multi-branch and main path structures enhances the network's ability to effectively integrate features from diverse data sources. Attention mechanisms and fusion modules allow the network to capture more detailed features in the images. [ABSTRACT FROM AUTHOR]

In recent years, the application of compact and cost-effective deployable X-band polarimetric radars has gained in popularity, particularly in regions with complex terrain. The deployable radars generally use a radome constructed by joining multiple panels using metallic threads to facilitate easy transportation. As a part of the ESPOIRS project, Laboratoire de l'Atmosphεave;re et des Cyclones has acquired an X-band meteorological radar with four panel radome configuration. In this study, we investigated the effect of the radome on the measured polarimetric variables, particularly differential reflectivity and differential phase. Our observations reveal that the metallic threads connecting the radome panels introduce power loss at vertical polarization, leading to a positive bias in the differential reflectivity values. To address the spatial variability bias observed in differential reflectivity and differential phase, we have developed a novel algorithm based on the Discrete Fourier Transform. The algorithm's performance was tested during an intense heavy rainfall event caused by the Batsirai cyclone on Reunion Island. The comparative and joint histogram analysis demonstrates the algorithm's effectiveness in correcting the spatial bias in the polarimetric variables. [ABSTRACT FROM AUTHOR]

Accurate predictions are crucial for addressing the challenges posed by climate change. Given South Korea's location within the East Asian summer monsoon domain, characterized by high spatiotemporal variability, enhancing prediction accuracy for regions experiencing heavy rainfall during the summer monsoon is essential. This study aims to derive temporal weighting functions using hybrid surface rainfall radar-observation data as the target, with input from two forecast datasets: the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) and the KLAPS Forecast System. The results indicated that the variability in the optimized parameters closely mirrored the variability in the rainfall events, demonstrating a consistent pattern. Comparison with previous blending results, which employed event-type-based weighting functions, showed significant deviation in the average AUC (0.076) and the least deviation (0.029). The optimized temporal weighting function effectively mitigated the limitations associated with varying forecast lead times in individual datasets, with RMSE values of 0.884 for the 1 h lead time of KLFS and 2.295 for the 4–6 h lead time of MAPLE. This blending methodology, incorporating temporal weighting functions, considers the temporal patterns in various forecast datasets, markedly reducing computational cost while addressing the temporal challenges of existing forecast data. [ABSTRACT FROM AUTHOR]

In Indonesia, the monitoring of rainfall requires an estimation system with a high resolution and wide spatial coverage because of the complexities of the rainfall patterns. This study built a rainfall estimation model for Indonesia through the integration of data from various instruments, namely, rain gauges, weather radars, and weather satellites. An ensemble learning technique, specifically, extreme gradient boosting (XGBoost), was applied to overcome the sparse data due to the limited number of rain gauge points, limited weather radar coverage, and imbalanced rain data. The model includes bias correction of the satellite data to increase the estimation accuracy. In addition, the data from several weather radars installed in Indonesia were also combined. This research handled rainfall estimates in various rain patterns in Indonesia, such as seasonal, equatorial, and local patterns, with a high temporal resolution, close to real time. The validation was carried out at six points, namely, Bandar Lampung, Banjarmasin, Pontianak, Deli Serdang, Gorontalo, and Biak. The research results show good estimation accuracy, with respective values of 0.89, 0.91, 0.89, 0.9, 0.92, and 0.9, and root mean square error (RMSE) values of 2.75 mm/h, 2.57 mm/h, 3.08 mm/h, 2.64 mm/h, 1.85 mm/h, and 2.48 mm/h. Our research highlights the potential of this model to accurately capture diverse rainfall patterns in Indonesia at high spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Clear-air echo studies are usually based on isotropic turbulence theory. But the theory has been considered incomplete by modern turbulence theory. The intermittence of turbulence can reveal obvious shortcomings in the existing studies of clear-air echoes. The mechanism of clear-air echo scattering needs to be supplemented. This paper introduces the troposcatter theory, normally used in over-the-horizon communication, to fill the gap left by Bragg scattering. By treating radar as a self-transmitted and self-received device, the equivalent transmission loss of weather radar is established and compared with the recommendations of the Radiocommunication Sector of the International Telecommunication Union (ITU-R). The results show that the S-band radar transmission loss aligns with ITU-R recommendations. There is also a linear regression relationship between the radar transmission loss and height, which conforms to the troposcatter theory. This means that the theory of troposcatter scattering is a supplement to the theory of Bragg scattering. Tropospheric scattering can be thought of as general Bragg scattering. Meanwhile, based on ITU-R recommendations, this study also provides a new way for the recognition of biological echoes. [ABSTRACT FROM AUTHOR]

A workshop at MIT discussed the importance of data assimilation and Earth system reanalysis in improving climate models. The workshop emphasized the need to bring models and observations into consistency to reduce uncertainties in climate projections. Recommendations were made for actions and funding in areas such as exploiting Earth observations, developing infrastructure for data assimilation and machine learning, and performing Earth system reanalysis. The document highlights the significance of using observations to improve models and calls for collaboration between different scientific communities to achieve these goals. The recommendations aim to enhance the quality and usability of climate information. [Extracted from the article]

The last few decades have presented a significant increase in hydrological disasters, such as floods. In some countries, most of the environmental, socioeconomic, and biodiversity losses are caused by floods. Thus, flood forecasting is crucial to support an efficient disaster warning system. This work proposes a model for hydrological forecasting based on a neural network with a geographically aligned input named GeoNN. It employs weather radar data to obtain accumulated rainfall in each grid cell of the watershed and make 15‐ and 120‐min predictions of the outlet river level. An uncertainty propagation analysis was performed for GeoNN from a collection of test cases obtained by either using different schemes of the dataset partitioning or introducing different additive‐noise rates to the input data to provide a probability of flood occurrence and also an ensemble prediction. Both this probability and the ensemble were able to detect occurrences of river levels exceeding a given flood threshold. [ABSTRACT FROM AUTHOR]

Jiangsu is a province in China and has the highest frequency of tornado occurrences. Studying the meteorological background and mechanisms of tornado formation is crucial for predicting tornado events and preventing the resulting disasters. This paper analyzed the meteorological background, instability mechanisms, and lifting conditions of the two Enhanced Fujita Scale level 2 (EF2) and above tornadoes that occurred in southern Jiangsu on 14 May 2021 ("5.14") and 6 July 2020 ("7.06") using ERA5 reanalysis data. Detailed analyses of the internal structure of tornado storms were conducted using Changzhou and Qingpu radar data. The results showed that (1) both tornadoes occurred in warm and moist areas ahead of upper-level troughs with significant dry air transport following the cold troughs. The continuous strengthening of low-level warm and moist advection was crucial in maintaining potential instability and triggering tornado vortices. The 14 May tornado formed within a low-level shear line and a warm area of a surface trough, while the 6 July tornado occurred at the end of a low-level jet stream, north of the eastern section of a quasi-stationary front. (2) The convective available potential energy (CAPE) and K indices for both tornado processes were very close (391 for "5.14" and 378 for "7.06"), with the lifting condensation level (LCL) near the ground. The "5.14" showed greater instability and more favorable thermodynamic conditions, with deep southwesterly jets at the mid-level shear line producing rotation under strong convergent action (convergence center value exceeding −1 ×   10 − 4 s − 1 ). In contrast, the "7.06" was driven by super-low-level jet stream pulsations and wind direction convergence under the influence of the Meiyu Front (convergence center value exceeding −1.5 ×   10 − 4   s − 1 ), resulting in intense lifting and vertical vorticity triggered by a surface convergence line. (3) The "5.14" tornado process involved a supercell storm over a surface dry line experiencing tilting due to strong vertical wind shear, which led to the formation of smaller cyclonic vortices near a hook echo that developed into a tornado. The "7.06" developed on a bow echo structure within a mesoscale convective system formed over the Meiyu Front, where dry air subsidence, entrainment, and convergence of the southeast jet stream triggered a "miniature" supercell. The relevant research results provide a reference for the prediction and early warning of tornadoes. [ABSTRACT FROM AUTHOR]

This study investigates the characteristics of the raindrop size distribution (DSD) for five typhoons that made landfall or passed by Zhejiang on the eastern coast of China, from 2019 to 2022. Additionally, it examines the raindrop shape–slope (µ-Λ) relationship, as well as the local Z-R relationship for these typhoons. The DSD datasets were collected by the DSG1 disdrometer located in Ningbo, Zhejiang Province. Based on rainfall rate (R), the DSD can be categorized into convective and stratiform rainfall types. Some rainfall parameters can also be derived from the DSDs to further analyze the specific characteristics of rainfall. The histograms of the generalized intercept parameter (log10Nw) exhibit negative skewness in both convective and stratiform rainfall, whereas the histograms of the mass-weighted mean diameter (Dm) of raindrops display positive skewness. During typhoon periods on the eastern coast of China, the DSD characteristic was composed of a lower number concentration of small and midsize raindrops (3.42 for log10Nw, 1.43 mm for Dm in the whole dataset) as compared to Jiangsu in eastern China, Tokyo, in Japan, Miryang, in South Korea, and Thiruvananthapuram in south India, respectively. At the same time, the scatter plots of Dm and log10Nw indicate that the convective rain during typhoon periods exhibits characteristics that are intermediate between "maritime-like" and "continental-like" clusters. Additionally, the raindrop spectra of convective rainfall and midsize raindrops in stratiform rainfall are well-represented by a three-parameter gamma distribution. The µ-Λ relation in this region is similar to Taiwan and Fujian, located along the southeastern coast of China. The Z-R relationship for eastern coastal China during typhoons based on filtered disdrometer data is Z = 175.04R1.53. These results could offer deeper insights into the microphysical characteristics of different rainfall types along the eastern coast of China and potentially improve the accuracy of precipitation estimates from weather radar observations. [ABSTRACT FROM AUTHOR]

Based on C-band weather radar and ground precipitation data from the Helan Mountain area in Yinchuan between 2017 to 2020, we evaluated the forecasting performances of 15 mainstream deep learning models used in recent years, including recurrent-based and recurrent-free models. The critical success index (CSI), probability of detection (POD), false alarm rate (FAR), mean square error (MSE), mean absolute error (MAE), and learned perceptual image patch similarity (LPIPS), were used to evaluate the forecasting abilities. The results showed that (1) recurrent-free models have significant parameter quantity and computing power advantages, especially the SimVP model. Among the recurrent-based models, PredRNN and PredRNN++ demonstrate good predictive capabilities for changes in echolocation and intensity, PredRNN++ performs better in predicting long sequences (1 h); (2) SimVP uses Inception to extract temporal features, which cannot capture the complex physical changes in radar echo images and fails to extract spatial–temporal correlations and accurately predict heavy rainfall areas effectively. Therefore, we constructed the SimVP-GMA model, replacing the temporal prediction module in SimVP and modifying the spatial encoder part. Compared with SimVP, the MSE and LPIPS indicators were improved by 0.55 and 0.0193, respectively. It can be seen from the forecast images that the forecast details have been significantly improved, especially in the forecasting of heavy rainfall weather. [ABSTRACT FROM AUTHOR]

Lightning protection is important for weather radars to prevent critical damage or outages, but this can have negative effects on data quality. The existing lightning protection of the German Meteorological Service (Deutscher Wetterdienst, DWD) polarimetric C-band weather radar network consists of four vertical poles with a maximum diameter of 10 cm. During radar operation, these rods cause local scattering in the near field of the antenna, resulting in negative impacts on radar products. One effect is the removal of significant transmission power from the main beam axis and its addition to other areas or the side lobes. This results in wrongly localised precipitation fields in a radial direction. The second effect is the loss of transmitted and received power, appearing as a decrease in system gain, and subsequently an underestimation of all power-based radar moments in the vicinity of the rods. The underestimation in radar reflectivity Z then leads to a negative bias of approximately 20 % in the actual rain rate if a Z – R relationship is applied. These detrimental effects on data quality led to the requirement of developing a new lightning protection concept. The new concept must minimise the effect on data quality but also provide sufficient protection from lightning strikes according to the existing regulations and requirements. Three possible lighting protection concepts are described in this paper: two using vertical rods of different diameters (16 and 40 mm) and one with horizontally placed rods outside the antenna aperture. Their possible influence on data quality is quantified through a dedicated measurement campaign by analysing resulting antenna patterns and precipitation sum products. Antenna patterns are analysed with respect to the side-lobe levels compared to antenna patterns without lightning protection and the original lightning protection. With the newly tested lightning rods, the apparent side-lobe levels are slightly increased compared to an antenna pattern taken without lightning protection but are within the accepted antenna specifications. Compared to the original lightning protection, a decrease of up to - 15 dB in apparent side-lobe levels is found for all tested lightning protection options. Beam blockage is substantially reduced compared to the existing lightning protection, as shown by the evaluation of quantitative precipitation estimation (QPE) sums. These results and some structural considerations are a solid basis to recommend the installation of four rods with a maximum 40 mm diameter for all 17 radar systems of the DWD weather radar network. [ABSTRACT FROM AUTHOR]

Accurate estimations of extreme precipitation return levels are critical for many hydrological applications. Extreme precipitation is highly variable in both space and time; therefore, to better understand and manage the related risks, knowledge of their probability at different spatial–temporal scales is crucial. We employ a novel non-asymptotic framework to estimate extreme return levels (up to 100 years) at multiple spatial–temporal scales from weather radar precipitation estimates. The approach reduces uncertainties and enables the use of relatively short archives typical of weather radar data (12 years in this case). We focus on the eastern Mediterranean, an area of high interest due to its sharp climatic gradient, containing Mediterranean, semi-arid, and arid areas across a few tens of kilometres, and its susceptibility to flash flood. At-site intensity–duration–area–frequency relations are derived from radar precipitation data at various scales (10 min–24 h, 0.25–500 km 2) across the study area, using ellipses of varying axes and orientations to account for the spatial component of storms. We evaluate our analysis using daily rain gauge data over areas for which sufficiently dense gauge networks are available. We show that extreme return levels derived from radar precipitation data for 24 h and 100 km 2 are generally comparable to those derived from averaging daily rain gauge data over a similar areal scale. We then analyse differences in multi-scale extreme precipitation over coastal, mountainous, and desert regions. Our study reveals that the power-law scaling relationship between precipitation and duration (simple scaling) weakens for increasing area sizes. This finding has implications for temporal downscaling. Additionally, precipitation intensity varies significantly for different area sizes at short durations but becomes more similar at long durations, suggesting that, in the region, areal reduction factors may not be necessary for computing return levels over long durations. Furthermore, the reverse orographic effect, which causes decreased precipitation for hourly and sub-hourly durations, diminishes for larger areas. Finally, we discuss the effects of orography and coastline proximity on extreme precipitation intensity over different spatial–temporal scales. [ABSTRACT FROM AUTHOR]

Weather radar is an active remote sensing device used to monitor the full lifecycle changes in severe convective weather with high spatial and temporal resolution. Effective radar calibration is a crucial foundation for ensuring the high-quality application of observational data. This paper utilizes a UAV platform equipped with a high-precision RTK system and standard metal spheres to study the principles and methods of metal sphere calibration, constructing a complete calibration process and calibration accuracy evaluation metrics. Additionally, a collocated radar comparison observation experiment was conducted for cross-validation, and metal sphere calibration tests were performed on problematic radars. The experimental results indicate the following: (1) The combined application of a high-precision RTK system and a laser range camera can provide real-time position information on the metal sphere, improving the efficiency of radar target acquisition. (2) The calibration method based on UAV-suspended metal spheres can periodically conduct the quantitative calibration of Z and ZDR, achieving calibration accuracies within 0.5 dB and 0.2 dB, respectively, and supports the qualitative inspection of key parameters such as beamwidth and pulse width. (3) During field tests, a high success rate "coarse adjustment + fine adjustment + staring" sphere-finding technique was established, based on automatic switching between RHI, PPI, and FIX scanning modes. This method directs the UAV to adjust the metal sphere to the center of the radar distance bin, reducing the impact of uneven beam filling and bin crossing, ensuring the accuracy of scattering characteristic measurements. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting plays an important role in mitigating the damage caused by severe weather. The objective of precipitation nowcasting is to forecast the weather conditions 0–2 h ahead. Traditional models based on numerical weather prediction and radar echo extrapolation obtain relatively better results. In recent years, models based on deep learning have also been applied to precipitation nowcasting and have shown improvement. However, the forecast accuracy is decreased with longer forecast times and higher intensities. To mitigate the shortcomings of existing models for precipitation nowcasting, we propose a novel model that fuses spatiotemporal features for precipitation nowcasting. The proposed model uses an encoder–forecaster framework that is similar to U-Net. First, in the encoder, we propose a spatial and temporal multi-head squared attention module based on MaxPool and AveragePool to capture every independent sequence feature, as well as a global spatial and temporal feedforward network, to learn the global and long-distance relationships between whole spatiotemporal sequences. Second, we propose a cross-feature fusion strategy to enhance the interactions between features. This strategy is applied to the components of the forecaster. Based on the cross-feature fusion strategy, we constructed a novel multi-head squared cross-feature fusion attention module and cross-feature fusion feedforward network in the forecaster. Comprehensive experimental results demonstrated that the proposed model more effectively forecasted high-intensity levels than other models. These results prove the effectiveness of the proposed model in terms of predicting convective weather. This indicates that our proposed model provides a feasible solution for precipitation nowcasting. Extensive experiments also proved the effectiveness of the components of the proposed model. [ABSTRACT FROM AUTHOR]

Winter continues to witness numerous automobile accidents attributed to graupel and hail precipitation in Japan. Detecting these weather phenomena using radar technology holds promise for reducing the impact of such accidents and improving road maintenance operations. Weather radars operating at different frequencies, such as C- and X-band, prove effective in graupel detection by analyzing variations in backscattered signals within the same radar volume. When particle diameters exceed 5 mm, the study of Mie scattering characteristics across different melting ratios reveals insights. The dual frequency ratio (DFR) shows potential for graupel detection. The DFR presents wider variations with ten-times difference in melting ratios with increased density, offering opportunities for precise detection. Additionally, the DFR amplitude rises with temperature changes. However, for hydrometeor diameters below approximately 3 mm, and within the Rayleigh region, the DFR exhibits minimal fluctuations. Hence, this technique is best suited for diameters exceeding 3 mm for optimal efficacy. Additionally, a "detection alert" for graupel/hail has been proposed. Based on this alert, and with realistic rain/graupel size distributions, graupel/hail can be detected with an approximate probability of 70%. [ABSTRACT FROM AUTHOR]

Leveraging weather radar technology for environmental monitoring, particularly the detection of biometeors like birds, bats, and insects, presents a significant challenge due to the dynamic nature of their behavior. Unlike hydrometeor targets, biometeor targets exhibit arbitrary changes in direction and position, which significantly alter radar wave polarization upon scattering. This study addresses this challenge by introducing a novel methodology utilizing Rwanda's C-Band Polarization Radar. Our approach exploits the capabilities of dual-polarization radar by analyzing parameters such as differential reflectivity (ZDR) and correlation coefficient (RHOHV) to derive the Depolarization Ratio (DR). While existing radar metrics offer valuable insights, they have limitations in fully capturing depolarization effects. To address this, we propose an advanced fuzzy logic algorithm (FL_DR) integrating the DR parameter. The FL_DR's performance was rigorously evaluated against a standard FL algorithm. Leveraging a substantial dataset comprising nocturnal clear air radar echoes collected during a Fall Armyworm (FAW) outbreak in maize fields from September 2020 to January 2021, the FL_DR demonstrated a notable improvement in accuracy compared to the existing FL algorithm. This improvement is evident in the Fraction of Echoes Correctly Identified (FEI), which increased from 98.42% to 98.93% for biological radar echoes and from 87.02% to 95.81% for meteorological radar echoes. This enhanced detection capability positions FL_DR as a valuable system for monitoring, identification, and warning of environmental phenomena in regions similar to tropical areas facing FAW outbreaks. Additionally, it could be tested and further refined for other migrating biological targets such as birds, insects, or bats. [ABSTRACT FROM AUTHOR]

Remote hail detection and hail size estimation using weather radar observations has the advantage of wide spatial coverage and high spatial and temporal resolution. Switzerland's National Weather Service (MeteoSwiss) uses two radar-based hail metrics: the probability of hail on the ground (POH) to assess the presence of hail and the maximum expected severe hailstone size (MESHS) to estimate the largest hailstone diameter. However, radar-based metrics are not direct measurements of hail and have to be calibrated with and verified against ground-based observations of hail, such as crowdsourced hail reports. Switzerland benefits from a particularly rich and dense dataset of crowdsourced hail reports from the MeteoSwiss app. We combine a new spatiotemporal clustering method (Density-Based Spatial Clustering of Applications with Noise, ST-DBSCAN) with radar reflectivity to filter the reports and use the filtered reports to verify POH and MESHS in terms of the hit rate, false-alarm ratio (FAR), critical success index (CSI), and Heidke skill score (HSS). Using a 4 km × 4 km maximum upscaling approach, we find FAR values between 0.3 and 0.7 for POH and FAR > 0.6 for MESHS. For POH, the highest CSI (0.37) and HSS (0.52) are obtained using a 60 % threshold, while for MESHS the highest CSI (0.25) and HSS (0.4) are obtained using a 2 cm threshold. We find that the current calibration of POH does not correspond to a probability and suggest a recalibration based on the filtered reports. [ABSTRACT FROM AUTHOR]

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China, and it is of great importance to correctly forecast them. At present, the forecasting of thunderstorm gusts is mainly based on traditional subjective methods, which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources. In this paper, we propose a deep learning method called Thunderstorm Gusts TransU-net (TG-TransUnet) to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology (IUM) with a lead time of 1 to 6 h. To determine the specific range of thunderstorm gusts, we combine three meteorological variables: radar reflectivity factor, lightning location, and 1-h maximum instantaneous wind speed from automatic weather stations (AWSs), and obtain a reasonable ground truth of thunderstorm gusts. Then, we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture, which is based on convolutional neural networks and a transformer. The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training, validation, and testing datasets. Finally, the performance of TG-TransUnet is compared with other methods. The results show that TG-TransUnet has the best prediction results at 1–6 h. The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China. [ABSTRACT FROM AUTHOR]

Areal rainfall is routinely estimated based on the observed rainfall data using distributed point rainfall gauges. However, the data collected are sparse and cannot represent the continuous rainfall distribution (or field) over a large watershed due to the limitations of weather station networks. Recent improvements in remote-sensing-based rainfall estimation facilitate more accurate and effective hydrological modeling with a continuous spatial representation of rainfall over a watershed of interest. In this study, we conducted a systematic stepwise comparison of the areal rainfalls estimated by a synoptic weather station and radar station networks throughout South Korea. The bias in the areal rainfalls computed by the automated synoptic observing system and automatic weather system networks was analyzed on an hourly basis for the year 2021. The results showed that the bias increased significantly for hydrological analysis; more importantly, the identified bias exhibited a magnitude comparable to that of the low flow. This discrepancy could potentially mislead the overall rainfall-runoff modeling process. Moreover, the areal rainfall estimated by the radar-based approach significantly differed from that estimated by the existing Thiessen Weighting approach by 4%–100%, indicating that areal rainfalls from a limited number of weather stations are problematic for hydrologic studies. Our case study demonstrated that the gauging station density must be within 10 km2 on average for accurate areal rainfall estimation. This study recommends the use of radar rainfall networks to reduce uncertainties in the measurement and prediction of areal rainfalls with a limited number of ground weather station networks. [ABSTRACT FROM AUTHOR]

In recent years, synthetic aperture radar (SAR) interferometry (InSAR) has emerged as a valuable tool for measuring water level change (WLC) to study hydrodynamic processes in coastal wetlands. However, the highly dynamic wet atmosphere conditions common in these areas have a significant impact on InSAR observations, producing errors in the derived values. Standard methods for estimating atmospheric noise in InSAR time series lack the spatial or temporal resolution needed to adequately correct for wet tropospheric delays. In this study, we utilize the Independent Component Analysis (ICA) signal decomposition technique to identify the likely WLC signal and eliminate atmospheric noise in a time series derived from rapid repeat measurements made with the L‐band uninhabited aerial vehicle synthetic aperture radar airborne instrument. The method compares in‐situ water level measurements with the independent components (IC) to identify the ICA components corresponding to WLC. The signal‐to‐noise ratio between the WLC after the ICA‐based filtering and in situ water level gauges used for validation reaches 16 dB compared to an average of 2.6 dB before filtering. The excluded IC are used to generate maps showing a time series of likely atmospheric features. The identified features in the maps generally correspond to atmospheric features identifiable in Next Generation Weather Radar (NEXRAD) S‐band ground weather radar reflectivity maps collected during the SAR acquisitions. The method is sufficiently general to be applied to any InSAR‐derived surface displacement time series. Key Points: Independent Component Analysis (ICA) is used to remove the wet troposphere noise in an InSAR‐derived time series of water level change in wetlandsIn‐situ data are used to identify components showing water level change and the other components used to generate tropospheric delay mapsThe filtered water level change values have improved accuracy based on measurements from water gauges located throughout the wetlands [ABSTRACT FROM AUTHOR]

The annual, seasonal, and diurnal spatiotemporal heavy convective precipitation patterns over a pan-European domain are analyzed in this study using a combination of datasets, including the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) precipitation rate product, E-OBS ground-based precipitation gauge data, European climatological gauge-adjusted radar precipitation dataset (EURADCLIM), Operational Programme for the Exchange of Weather Radar Information (OPERA) ground-based radar-derived precipitation rates, and fifth major global reanalysis produced by ECMWF (ERA5) total and convective precipitation products. Arrival Time Difference Network (ATDnet) lightning data are used in conjunction with IMERG and EURADCLIM precipitation rates, with an imposed threshold of 10 mm h−1 to classify precipitation as convective. Annually, the largest convective precipitation accumulations are over the European seas and coastlines. In summer, convective precipitation is more common over the European continent, though relatively large accumulations exist over the northern coastal waters and the southern seas, with a seasonal localized maximum over the northern Adriatic Sea. Activity shifts southward to the Mediterranean and its coastlines in autumn and winter, with maxima over the Ionian Sea, the eastern Adriatic Sea, and the adjacent coastline. Over the continent, 1%–10% of the total precipitation accumulated is classified as convective, increasing to 10%–40% over the surrounding seas. In contrast, 30%–50% of ERA5 precipitation accumulations over land is produced by the convective parameterization scheme and 40%–60% over the seas; however, only 1% of ERA5 convective precipitation accumulations are from rain rates exceeding 10 mm h−1. Regional analyses indicate that convective precipitation rates over the inland mountains follow diurnal heating, though little to no diurnal pattern exists in convective precipitation rates over the seas and coastal mountains. [ABSTRACT FROM AUTHOR]

Extreme precipitation events in small alpine catchments trigger hazardous hydro‐geomorphic processes that cause considerable damage to settlements and infrastructure. In summer 2011 and 2017, two flood events mobilizing large amounts of sediments struck the town of Oberwölz (Styria, Austria) located at the outlet of the Schöttlbach catchment. We used data from local weather stations and precipitation radar to analyse the meteorological settings that caused the flooding. We compiled a consistent sediment budget for the 2017 event by combining geomorphic mapping, connectivity analysis, high‐resolution airborne LiDAR (ALS) and uncrewed aerial vehicle (UAV)‐borne LiDAR (ULS), data from other authors for the 2011 event and external information (e.g., event analysis and excavation data). The 2017 event mobilized higher sediment volumes than the 2011 event (131 000 m3 vs 90 000 m3) even though 24‐h precipitation and peak discharges were lower in 2017. First assumptions that the larger sediment output was caused by the reworking of the 2011 flood deposits proved to be incorrect. The impacts of the 2011 event affected the resilience of the geomorphic system resulting in a significantly higher hillslope sediment supply. We conclude that sediment transport in alpine catchments can increase disproportionately when recurrence intervals fall below a critical level. [ABSTRACT FROM AUTHOR]

Climate change in the Mediterranean region is manifesting itself as an increase in average air temperature and a change in the rainfall regime: the value of cumulative annual rainfall generally appears to be constant, but the intensity of annual rainfall maxima, between 1 and 24 h , is increasing, especially in the period between late summer and early autumn. The associated ground effects in urban areas consist of flash floods and pluvial floods, often in very small areas, depending on the physical-geographical layout of the region. In the context of global warming, it is therefore important to have an adequate monitoring network for rain events that are highly concentrated in space and time. This research analyses the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in the city of Genoa, Italy, just 4 d after the record maximum air temperature was recorded: between 19:00 and 02:00 UTC almost 400 mm of rainfall was recorded in the eastern sector of the historic centre of Genoa, with significant ground effects such as flooding episodes and the overflowing of pressurised culverts. Rainfall observations and estimates were made using both official or "authoritative" networks (rain gauges and meteorological radar) and rain gauge networks inspired by citizen science principles. The combined analysis of observations from authoritative and citizen science networks reveals, for the event analysed, a spatial variability of the precipitation field at an hourly and a sub-hourly timescale that cannot be captured by the current spatial density of the authoritative measurement stations (which have one of the highest densities in Italy). Monthly total rainfall and short-duration annual maximum time series recorded by the authoritative rain gauge network of the Genoa area are then analysed. The results show significant variation even at distances of less than 2 km in the average rainfall depth accumulated over sub-hourly duration. Extreme weather monitoring activity is confirmed as one of the most important aspects in terms of flood prevention and protection in urban areas. The integration between authoritative and citizen science networks can prove to be a valid contribution to the monitoring of extreme events. [ABSTRACT FROM AUTHOR]

Spectral polarization filtering technology is widely used in weather radar clutter suppression, but when ground clutter is mixed with precipitation, these methods will lose precipitation while filtering out clutter. To address this issue, a polarization-Doppler weather radar signal reconstruction method based on spectral continuity is proposed. This method utilizes the characteristic differences between precipitation and clutter on the range-Doppler map to achieve the preservation of precipitation and the filtering of clutter. Specifically, the spatial continuity of Doppler velocity and spectral width of precipitation is utilized to compensate for precipitation using polynomial fitting and Gaussian fitting method. The effectiveness of the proposed method is verified by the measured data of the operational weather radar. [ABSTRACT FROM AUTHOR]

The use of meteorological radars in monitoring current weather conditions is crucial regarding the observation of the evolution and dissipation of thunderstorms. Thus, Doppler velocities being measured in each radar scan and velocity vectors derived from Numerical Weather Prediction (NWP) models—that are usually not as highly resolving as radar scans—are combined, as a monitoring utility during the evolution of a convective weather event. The objective of this study is to develop a new method that allows the implementation of a thunderstorm movement velocity estimation technique combining block matching and optical flow techniques. This new method constitutes a nowcasting (NWC) application that enables the use of a single Doppler radar without the need of using NWPs. The method relies on the estimation of the thunderstorm movement vector velocities (Doppler velocity) for each constant altitude plan position indicator (CAPPI) and through correction for aliasing errors to obtain 3D vector velocity fields for convective systems. The performance of the method is evaluated for selected case studies of convective thunderstorms under different synoptic scale conditions over Greece, a geographical area with challenges in forecasting due to its sharp relief and the need for optimization of the use of radar products. [ABSTRACT FROM AUTHOR]

Earth – satellite microwave links such as TV-SAT can help for rainfall monitoring and could be a complement or an alternative to ground-based weather radars, rain gauges or satellites dedicated to Earth observation. Rain induced attenuation which is harmful for telecommunication is exploited here as an opportunistic way to estimate rain rate along the path link. This technique makes it possible to obtain rain measurements at a fine temporal resolution (a few tens of seconds) and with a spatial resolution of few kilometers, which is a good compromise for human activities such as civil security (watershed monitoring, flash flood), agriculture or transport. Among the advantages of this technique, one can note the low cost of the hardware used (which can be commercial) as well as that of its maintenance on site. However, measured attenuation does not directly provide rain intensity and some parameters have to be estimated. Among these, it is necessary to take into account the contribution of the natural radiation of the atmosphere. In this paper, we detail a theoretical framework allowing to estimate rainfall from the measurements of a low-cost sensor operating simultaneously over two parts of the Ku-frenquency band. Then this framework is assessed in a densely instrumented area in south of France, where it is shown that very good results are obtained when compared to rain gauges measurements, both in terms of overall rain accumulation and in terms of rainfall rate distribution. Then we apply this dual channel method in Ivory Coast, in the metropolitan area of Abidjan, where such an approach is very promising. It is shown that this technique when compared to rain gauge measurements give results far better than a single-channel naive approach neglecting natural radiation of atmosphere, but that there are still significant errors in the rain assessment, leading to a persistent underestimation of rain accumulation. Finally we discuss various effects that could lead to this remaining underestimation, opening the door to further studies. [ABSTRACT FROM AUTHOR]

Tornadoes, as highly destructive weather events, require accurate detection for effective decision‐making. Traditional radar‐based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high false alarm rates and low detection probabilities. This study introduces the Multi‐Task Identification Network (MTI‐Net), leveraging Doppler radar data to enhance tornado recognition. MTI‐Net integrates tornado detection and estimation tasks to acquire comprehensive spatial and locational information. As part of MTI‐Net, we introduce a novel backbone network of Multi‐Head Convolutional Block (MHCB), which incorporates Spatial and Channel Attention Units (SAU and CAU). SAU optimizes local tornado feature extraction, while CAU reduces false alarms by enhancing dependencies among input variables. Experiments demonstrate the superiority of MTI‐Net over TDA, with a decrease in false alarm rates from 0.94 to 0.46 and an increase in hit rates from 0.23 to 0.81, highlighting the effectiveness of MTI‐Net in handling small‐scale tornado events. Plain Language Summary: Tornadoes, highly destructive small‐scale weather phenomena, demand accurate detection for informed decision‐making. Although meteorological radars are commonly utilized for tornado identification, current methods often suffer from false alarms or missed detections due to radar noise. In this study, we introduce the multi‐task learning‐based identification network (MTI‐Net), which not only enables tornado detection but also estimates tornado counts within radar data. We integrate Convolutional Neural Networks (CNNs) with Transformer techniques to enhance the model's ability to capture tornado information. CNNs detect local details using filters, while Transformers manage global connections through attention mechanisms. A series of experiments demonstrate significant improvements in tornado detection with MTI‐Net compared to traditional methods. Key Points: A tornado dataset with detailed radar features was created over China from 2017 to 2023Multi‐task learning was designed to simultaneously infer tornado detection and tornado number estimationIntegration of spatial and channel attention units can better extract tornado features from radar data [ABSTRACT FROM AUTHOR]

In this study, the Extreme Hourly Precipitation Areas (EHPAs) of three extreme levels (i.e., between the 95th and 99th percentiles, between the 99th and 99.9th percentiles, beyond the 99.9th percentile) in the Pearl River Delta over South China are identified; then the related events and associated Convective Cores (CCs) are tracked, and their macro‐and‐microphysical characteristics are analyzed using multi‐year dual‐polarization radar observations. Results show that >90% of EHPAs are smaller than 10 km2, and 65%–75% of EHPA events last only one hour. They tend to be more localized and persist longer with increasing hourly‐precipitation extremity. The EHPAs overlap with the CCs during 50%–64% of the EHPAs' life span. Their occurrence frequencies are nearly quadrupled after the monsoon onset over South China Sea (SCS), with a major (secondary) peak at about 1400 LST (0600 LST) in the diurnal variations. The CCs are non‐linear shaped with about 65% being meso‐γ‐scale and embedded within mostly meso‐β or α‐scale 20 dBZ regions. The CCs generally contain active warm‐rain processes and about 70% possess moderate‐to‐intense mixed‐phase microphysical processes. The ratios of ice water path to liquid water path are about 0.37, and coalescence dominates (about 68%) the liquid‐phase processes. The average size of raindrop is slightly larger than the "maritime‐like" regime and the average concentration is much higher than the "continental‐like" regime. These CCs' characteristics roughly resemble those of the convection producing extreme instantaneous precipitation, except for a larger horizontal scale and less evident variations with the increasing hourly‐precipitation extremity. Plain Language Summary: Extreme precipitation is of global concern, but macro‐ and microphysical characteristics of its associated convection remain elusive. Recent studies have analyzed the characteristics of convection producing extreme instantaneous precipitation in a monsoon coastal region of South China. We would like to know the characteristics of extreme hourly precipitation, which is potentially more disastrous, and how they vary with increasing extremity. For this purpose, we use 5‐year dual‐polarization radar observations combined with distrometer observations over the region to compare among three different extreme levels, and with those of extreme instantaneous precipitation. Results show that the extreme hourly precipitation mostly covers a small area, lasts only 1 hour, and overlaps with strong echoes during more than half of its life span. After the onset of summer monsoon over the South China Sea, the extreme hourly precipitation occurs much more frequently with a bimodal feature in the diurnal variation of occurrence frequency, consistent with those of extreme instantaneous precipitation. The microphysical characteristics of convection overlapping with the extreme hourly precipitation area resemble those of the extreme instantaneous precipitation‐producing convection. However, the former vary less evidently with the increasing hourly precipitation extremity likely due to smaller variations in probability distribution of convective intensity. Key Points: The Extreme Hourly Precipitation Areas (EHPAs) overlap with non‐linear shaped Convective Cores (CCs) during 50%–64% of their life spanThe CCs have active coalescence with abundant small‐to‐medium size raindrops and ∼70% have moderate‐to‐intense mixed‐phase processesThe CCs' characteristics generally resemble extreme instantaneous rain‐producing convection but change less evidently with the extremity [ABSTRACT FROM AUTHOR]

Radar signatures of rain over the ocean have a complex structure since they receive contributions from surface scattering and volume scattering and attenuation by hydrometeors in the atmosphere. These contributions overlap and are often difficult to detangle. While most of the mechanisms contributing to radar signatures of rain over the ocean are well understood, there is one remaining issue that has been discussed controversially in the literature for a long time. It is the question what scattering mechanism causes the areas of strongly enhanced radar backscatter, also called 'bright blobs' or 'bright patches', which are frequently observed on spaceborne C-band SAR images acquired over tropical and subtropical oceans in the presence of convective rain. Recently, papers have been published in which it is hypothesized that they are caused by radar backscattering at hydrometeors in the melting layer (ML). Although many observational facts seem to support this hypothesis, there exists one strong argument against this hypothesis: It is the observation that the position of the ML radar signatures (bright blob) in the SAR image is not shifted in anti-range from the position, where the rain column hits the sea surface. This absence of a shift is observed when 1) comparing Sentinel-1 SAR images on which rain cells are visible with quasi-concurrently acquired weather radar images and 2) when inter-comparing of SAR images of rain cells acquired concurrently at different frequencies and polarizations. Based on these observations, we discard the hypothesis that the bright blobs are due to volume scattering at hydrometeors in the ML and hypothesize instead that they are due to scattering at splash products at the sea surface. This hypothesis is supported by radar backscattering measurements carried out in the laboratory and from a shore-based platform, which show that, at C-and X-band, strong rain can give rise to strong radar returns also at cross-polarization. [ABSTRACT FROM AUTHOR]

A unipolar ground-based weather radar is a widely-used instrument for rainfall measurement. These radar measurements, however, need to be calibrated for more accurate rainfall estimation. This article introduces a new calibration approach using time-stepwise processing of reflectivity–rainfall rate () relationship. Based on the previous work utilizing the radar–rainfall relationship, this article hypothesizes that the rainfall measurement from a ground based radar are affected by the distance from radar, altitudes and rainfall time duration. Unipolar ground-based radar data sets for two consecutive years with 77 occurrences of rainfall from 39 stations in calibration window of three hours as well as the corresponding rainfall measured from registered rain gauges were used in this study. The results indicated that it is better to use the radar–rainfall relationship in view of the altitude effect () and empirical coefficient (), such that . The changes in the distance from the radar and duration of precipitation were evidently significant. For radar–rainfall relationship individually, the values of the determination coefficient made up from 0.88 to 0.97, and those for this relationship in view of the altitude effect () were from 0.70 to 0.97. It is therefore concluded that the use of other effective parameters (distance from radar, altitude and rainfall time duration) leads to optimum accuracy of the relationship. [ABSTRACT FROM AUTHOR]

In the development of meteorological detection technology and services, weather radar undoubtedly plays a pivotal role, especially in the monitoring and early warning of severe convective weather events, where it serves an irreplaceable function. This research delves into the landscape of weather radar research from 1945 to 2024, employing scientometric methods to investigate 13,981 publications from the Web of Science (WoS) core collection database. This study aims to unravel, for the first time, the foundational structures shaping the knowledge domain of weather radar over an 80-year period, exploring general features, collaboration, co-citation, and keyword co-occurrence. Key findings reveal a significant surge in both publications and citations post-1990, peaking in 2022 with 1083 publications and 13832 citations, signaling sustained growth and interest in the field after a period of stagnation. The United States, China, and European countries emerge as key drivers of weather radar research, with robust international collaboration playing a pivotal role in the field's rapid evolution. Analysis uncovers 30 distinct co-citation clusters, showcasing the progression of weather radar knowledge structures. Notably, deep learning emerges as a dynamic cluster, garnering attention and yielding substantial outcomes in contemporary research efforts. Over eight decades, the focus of weather radar investigations has transitioned from hardware and software enhancements to Artificial Intelligence (AI) technology integration and multifunctional applications across diverse scenarios. This study identifies four key areas for future research: leveraging AI technology, advancing all-weather observation techniques, enhancing system refinement, and fostering networked collaborative observation technologies. This research endeavors to support academics by offering an in-depth comprehension of the progression of weather radar research. The findings can be a valuable resource for scholars in efficiently locating pertinent publications and journals. Furthermore, policymakers can rely on the insights gleaned from this study as a well-organized reference point. [ABSTRACT FROM AUTHOR]

Sea level monitoring is an essential foundational project for studying global climate change and the rise in sea levels. Satellite radar altimeters, which can sometimes provide inaccurate sea surface height data near the coast, are affected by both the instrument itself and geophysical factors. Buoys equipped with GNSS receivers offer a relatively flexible deployment at sea, allowing for long-term, high-precision measurements of sea surface heights. When operating at sea, GNSS buoys undergo complex movements with multiple degrees of freedom. Attitude measurements are a crucial source of information for understanding the motion state of the buoy at sea, which is related to the buoy's stability and reliability during its development. In this study, we designed and deployed a four-antenna GNSS buoy with both position and attitude measurement capabilities near Jimiya Wharf in Qingdao, China, to conduct offshore sea surface monitoring activities. The GNSS data were processed using the Precise Point Positioning (PPK) method to obtain a time series of sea surface heights, and the accuracy was evaluated using synchronous observation data from a small sea surface height radar. The difference between the GNSS buoy and the full-time radar was calculated, resulting in a root-mean-square error (RMSE) of 1.15 cm. Concurrently, the attitude of the GNSS buoy was calculated using multi-antenna technology, and the vertical elevation of the GNSS buoy antenna was corrected using the obtained attitude data. The RMSE between the corrected GNSS buoy data and the high ground radar was 1.12 cm, indicating that the four-antenna GNSS buoy can not only acquire high-precision coastal sea level data but also achieve synchronous measurement of the buoy's attitude. Furthermore, the data accuracy was also improved after the sea level attitude correction. [ABSTRACT FROM AUTHOR]

In addressing the challenges of quantitative precipitation estimation (QPE) using weather radar, the importance of enhancing the rainfall estimates for applications such as flash flood forecasting and hydropower generation management is recognized. This study employed dual-polarization weather radar data to refine the traditional Z–R relationship, which often needs higher accuracy in areas with complex meteorological phenomena. Utilizing tree-based machine learning algorithms, such as random forest and gradient boosting, this research analyzed polarimetric variables to capture the intricate patterns within the Z–R relationship. The results highlight machine learning's potential to improve the precision of precipitation estimation, especially under challenging weather conditions. Integrating meteorological insights with advanced machine learning techniques is a remarkable achievement toward a more precise and adaptable precipitation estimation method. [ABSTRACT FROM AUTHOR]

The article discusses the importance of accurate monitoring and early warning of heavy rainfall induced by strong convection for preventing natural disasters such as floods and landslides. It highlights the use of weather radars as the most powerful technique for monitoring natural hazards caused by heavy rainstorms. The article also covers various topics related to radar signal processing methods, error characterization, new sensing techniques, and the application of radar data in weather analysis and forecasting. It emphasizes the advancements in weather radar technology and its potential for revolutionizing meteorological forecasting and disaster preparedness. [Extracted from the article]

This paper considers theoretical and observed uncertainties in the estimates of ZDR and ρHV(0) using data from an operational S-band radar and a mobile X-band radar. Cases of widespread uniform precipitation including bright-band, clear air, and ash echoes from forest fires are all considered in order to obtain a wide range of ρHV(0) values as this along with the radar frequency and spectrum width determines the uncertainties. The theoretical uncertainties in these parameters provide a good estimate of the lower bound of the standard deviations of the observed values where these have been estimated using the adjacent data to the target pixel. The implications for the accuracy of precipitation estimation, particle identification, and estimates of drop-size distributions are discussed. Significance Statement: High-quality quantitative precipitation and particle size/classification retrievals using weather radar are strongly dependent on the accuracy of ZDR and ρHV(0). This paper examines the theoretical limits to the measurement accuracy and verifies these limits with radar data at 10- and 3-cm wavelengths. [ABSTRACT FROM AUTHOR]

Indonesia, a country located in the equatorial region with hilly and valley lands surrounded by vast oceans, has complex rainfall patterns that can generally be classified into three types: equatorial, monsoon, and local. Rainfall estimates have only been derived based on local data and characteristics so far, and have not yet been developed based on universal data for all of Indonesia. This study aimed to develop a rainfall estimation model based on weather radar data throughout Indonesia using ensemble machine learning with the gradient boosting algorithm. The proposed rainfall estimation model is universal, can be applied to different rainfall pattern areas, and has a temporal resolution of 10 min. It is based on determining the root mean square error (RMSE) and R-squared (R2) values. Research was conducted in six areas with different rainfall patterns: Bandar Lampung and Banjarmasin with monsoon rain patterns, Pontianak and Deli Serdang with equatorial rain patterns, and the Gorontalo and Biak areas with local rain patterns. The analysis of the proposed model reveals that the best hyperparameters for the learning rate, maximum depth, and number of trees are 0.7, 3, and 50, respectively. The results demonstrate that the estimated rainfall in the six areas was very accurate, with RMSE < 2 mm/h and R2 > 0.7. [ABSTRACT FROM AUTHOR]

The X-band phased array radar offers faster scanning speed and higher spatial resolution compared to the S-band radar, making it capable of enhancing tornado monitoring and early warning capabilities. This study analyzed the characteristics and nowcasting signals of a tornado case that occurred on June 16, 2022 in the Guangzhou region. Our findings indicate that the violent contraction of rotation radius and the dramatic increase in rotation speed were important signal characteristics associated with tornado formation. The X-band phased array radar, with its high temporal and spatial resolution, provided an opportunity to capture early warning signals from polarimetric characteristics. The X-band phased array radar demonstrated noteworthy ability to identify apparent tornado vortex signature (TVS) features in a 10-minute lead time, surpassing the capabilities of the CINRAD/SA radar. Additionally, due to its higher scanning frequency, the Xband phased-array radar was capable of consistently identifying TVS with shorter intervals, enabling a more precise tracking of the tornado's path. The application of professional radars, in this case, provides valuable insights for the monitoring of evolutions of severe local storms and even tornadoes and the issuance of early warning signals. [ABSTRACT FROM AUTHOR]

Information on the spatial distribution of rainfall is required for many applications, including water and flood management. Weather radars can provide quantitative rainfall estimation over an area. Taking the example of an X-band radar installed at Mandhardev in the Western Ghats, we show that radar reflectivity data can have significant bias despite following standard calibration procedures. We report a technique to identify bias in the X-band radar reflectivity factor using collocated disdrometer observations and propose a methodology to correct it. Simultaneous data collected on 83 days during June–September of 2018 are used. Our results show that bias in the radar reflectivity factor reduced from –8.3 to –0.8 dB after correction. The estimated 83-day accumulated rainfall using uncorrected radar reflectivity data is 80% less compared to that of the disdrometer and the difference between the two reduces to 2% with correction. Bias in the estimated rainfall reduces from –30 to –0.7 mm day–1 and the RMSE reduces by 42%. There are days where daily rainfall from the two instruments agrees well with each other, while large differences exist on some days. We show that the sampling issue is one of the major sources of error, and its contribution depends on the nature/type of precipitating clouds, whereas uncertainties in the Z–R relationships account for about 15% difference. [ABSTRACT FROM AUTHOR]

Hail can cause extensive damage to property and livestock all over the globe. Given the predicted rise in hail strength due to global warming, it is critical to comprehend the characteristics of hail-producing convective storms. This study uses the S-band Doppler Weather Radar (DWR) deployed at Kolkata, India, to comprehensively assess the characteristics of convective storms producing hail over eastern India during premonsoon months from 2007 to 2017. The Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) algorithm is employed to track storms with a minimum volume of 50 km3 and a threshold of 35 dBZ. The spatiotemporal variability of the radar-derived storm properties such as storm area, eccentricity, convective volume greater than 40 dBZ, 50 dBZ, maximum reflectivity, and 35 dBZ echo top height are examined for 32 hail days. Additionally, the hail metrics such as Foote-Krauss criteria for hail (FOKR), Probability of Severity Hail (POH), Vertical Integral of Liquid (VIL), and Vertical Integral Mass of Hail (VIHM) were extracted for all days. The analysis revealed that storms mostly occur between 9 and 12 UTC, with median storm areas in the range of 100–1000 km2, and eccentricity values between 0.5 and 0.85. The storms with hail are non-linear multicellular, with VIL between 60 and 150 kg/m2 and VIHM from 2 to 10 kg/m2. The storm top extends above 12 km with maximum reflectivity exceeding 55 dBZ exemplifying the nature of intense storms. The storms with non-hail showed an area less than 500 km2 with a maximum reflectivity of 45 dBZ. The storm top is observed to be 8 km with VIL and VIHM to be less than 50 kg/m2 and 2 kg/m2 respectively. We observed that 2 h before hail, the convective available potential energy, convective inhibition, lower and mid-tropospheric humidity, temperature, and vertical wind showed a conducive environment for intense storms. The magnitude of the background meteorological parameters was considerably less for the storms with no hail. [ABSTRACT FROM AUTHOR]

X‐band marine radar observations and a hindcast simulation from a 3D hydrostatic model are used to provide an overview of Connecticut River (USA) ebb plume front expansion into the strong tidal crossflow of eastern Long Island Sound. The model performance is evaluated against in situ and remote sensing observations and demonstrates dominant control of the front by semidiurnal tides. The recurring frontal evolution is classified into three dynamical stages of arrest, propagation, and advection. A conceptual model that follows this progressing balance between outflow buoyancy and crossflow momentum qualitatively reproduces frontal evolution in both the radar observations and the hindcast. The majority of the residual, intertidal variability of front timing and geometry is explained by co‐varying tidal amplitude, freshwater discharge, and wind stress using a multi‐linear regression analysis of the radar observation record. Intrinsic front speeds in the modeled frontal propagation are compared with the analytical model of Benjamin (1968, https://doi.org/10.1017/s0022112068000133), with better agreement achieved after accounting for ambient near‐surface shear associated with wind forcing. Plain Language Summary: The fresh buoyant water that exits the Connecticut River mouth (USA) during each ebb tide expands as a plume, and floats above the denser waters of Long Island Sound. Currents in the Sound flow back and forth along the coast each tide, pushing the plume along the shore first to the east and then to the west. The location of the plume boundary (front) goes through a similar cycle almost every tide, initially being held stationary by eastward tidal currents, then expanding freely around slack tide, and subsequently being aided as it moves to the west by the ambient flow. Small changes to the arrival time and shape of the front are linked to changes in river discharge, tidal strength, and winds. Realistic simulations using a type of numerical model that aids coastal decision making are able to reproduce the tidal progression of front movement that was observed in 6 weeks of marine radar images of the water surface. Front movement in the numerical model also fits a simple analytical theory to a degree not previously seen on such a large scale, despite the complicating effects of wind. Key Points: Buoyant ebb plume front kinematics in a tidal cross‐flow are characterized by stages of arrest, propagation, and advectionWind, tide, and discharge variations explain modifications to the dominant semidiurnal control of the Connecticut River plume frontFront propagation in a hydrostatic numerical model reproduces both radar remote sensing observations and gravity current theory [ABSTRACT FROM AUTHOR]

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In this study, we present radar polarimetric characterizations of the storm producing 201.9 mm hourly rainfall on 20 July 2021 in Zhengzhou, China. We employed the separation signatures of enhanced polarimetric observations to investigate hydrometeor size sorting processes, and developed an algorithm to quantify the size sorting directions. Analysis of coupled polarimetric observations unraveled multi‐directional size sorting (MSS) occurred as a low‐level differential reflectivity ZDR backwards arc signature encompassing the rainfall center during the most intensive rainfall period. The rainfall intensification is in step with the increase of size sorting directions. Model simulations with two‐moment microphysics scheme suggest that the presence of arc‐shaped updrafts is conducive to MSS and increased rain rates around the rainfall center. This work sheds novel insights into the kinematics‐driven microphysics in extreme rainfall storms, warranting the potential of using coupled polarimetric signatures for warning catastrophic extreme rainfall events. Plain Language Summary: Raindrops of varying sizes have different fall speeds. In presence of storm‐relative winds, the differential sedimentation between large and small raindrops leads to different sorting distances, redistributing the regional characteristic sizes of raindrops. In radar polarimetric measurements, differential reflectivity ZDR is representative of the characteristic size of raindrops and specific differential phase KDP is proportional to the rain water content. The coupled low‐level polarimetric separation signatures in rainfall storms are indicative of the raindrop size sorting as driven by the storm kinematics. In this study, we identified unusual polarimetric separation signatures characterized by an unprecedented ZDRbackwards arc in the storm producing 201.9 mm hourly rainfall accumulation. Different from previously observed ZDR arc in a single inflow, ZDRbackwards arc is suggestive of multi‐directional size sorting as driven by arc‐shaped updrafts. We found that the intensification of rain rates is associated with increased size sorting directions. Detection of this novel radar polarimetric signature in operational services is recommended for warning such extreme rainfall events in the future. Key Points: Radar polarimetric separation signatures were used to characterize size sorting in the extreme rainfall stormMSS as driven by arc‐shaped updrafts in the storm is evidenced by an unprecedented ZDR backwards arcIncrease of size sorting directions is correlated to rainfall intensification [ABSTRACT FROM AUTHOR]

To improve short‐term severe weather forecasts through assimilation of polarimetric radar data (PRD), the use of accurate and efficient forward operators for polarimetric radar variables is required. In this study, a new melting model is proposed to estimate the mixing ratio and number concentration of melting hydrometeor species and incorporated in a set of parameterized polarimetric radar forward operators. The new melting model depends only on the mixing ratio and number concentration of rain and ice species and is characterized by its independence from ambient temperature and its simplicity and ease of linearization. To assess the impact of this newly proposed melting model on the simulated polarimetric radar variables, a real mesoscale convective system is simulated using three double‐moment microphysics schemes. Compared with the output of the original implementation of the parameterized forward operators (PFO_Old) that rely on an "old" melting model which only estimates the mixing ratio of the melting species, the updated implementation with the new melting model (PFO_New) that estimates both the mixing ratio and number concentration of melting species eliminates the very large mass/volume‐weighted mean diameter (Dm) at the bottom of the melting layer and produces more reasonable melting layer signatures for all three double‐moment microphysics schemes that more closely match the corresponding radar observations. This suggests that the new melting model has more reasonable implicit estimates of mixing ratios and number concentrations of melting hydrometeor species than the "old" melting model. Plain Language Summary: Weather radars can provide information about observed precipitation that can be used to improve weather forecast models. Making use of this information requires linking the radar observations with the variables these models predict in an efficient and accurate way. One of the most pronounced signatures in weather radar observations occurs as precipitation melts from ice into rain. However, current operational forecast models do not explicitly predict melting ice as a separate category of precipitation. Therefore, existing methods for comparing radar observations to forecast models use an empirical melting model to estimate the mass of melting ice and assume melting ice has the same concentration as the model‐predicted dry ice. These assumptions result in a simulated radar melting layer signature that is unrealistically thick and low. In this study, a new melting model is proposed for estimating both the mass and concentration of melting ice, and its effects on simulated radar variables are evaluated for a severe thunderstorm case. For all of the microphysics schemes investigated, the newly proposed melting model results in improved simulations of the size of melting ice particles and simulated radar variables that are closer to those observed. Key Points: A new melting model is proposed and introduced into forward operators for simulation of polarimetric radar dataMore reasonable polarimetric signatures of the melting layer are simulated using different double‐moment microphysics schemesThese polarimetric forward operators have potential to be used in data assimilation for improving high‐resolution severe weather forecasts [ABSTRACT FROM AUTHOR]

The vertical distribution of wildfire smoke aerosols is important in determining its environmental impacts but existing observations of smoke heights generally do not possess the temporal resolution required to fully resolve the diurnal behavior of wildfire smoke injection. We use Weather Surveillance Radar‐1988 Doppler (WSR‐88D) dual polarization data to estimate injection heights of Biomass Burning Debris (BBD) generated by fires. We detect BBD as a surrogate for smoke aerosols, which are often collocated with BBD near the fire but are not within the size range detectable by these radars. Injection heights of BBD are derived for 2–10 August 2019, using WSR‐88D reflectivity (Z ≥ 10 dBZ) and dual polarization correlation coefficients (0.2 < C.C < 0.9) to study the Williams Flats fire. Results show the expected diurnal cycles with maximum injection heights present during the late afternoon period when the fire's intensity and convective mixing are maximized. WSR‐88D and airborne lidar injection height comparisons reveal that this method is sensitive to outliers and generally overpredicts maximum heights by 40%, though mean and median heights are better captured (<20% mean error). WSR‐88D heights between the 75th and 90th percentile seem to accurately represent the maximum heights, with the exception of heights estimated during the occurrence of a pyro‐cumulonimbus. Location specific mapping of WSR‐88D and lidar injection heights reveal that they diverge further away from the fire as expected due to BBD settling. Most importantly, WSR‐88D‐derived injection height estimates provide near continuous smoke height information, allowing for the study of diurnal variability of smoke injections. Plain Language Summary: Wildfire smoke aerosols injected into the atmosphere pose a serious threat to human health and the environment. Once in the atmosphere, these aerosols can be transported downwind, affecting air quality regionally. Aerosols advected downwind travel distances that are strongly correlated with the maximum heights that aerosols can reach near their source, making it important to observe these 'injection heights'. However, existing observations of injection heights are limited temporally, making it difficult to study their diurnal and day‐to‐day variability. Here, we use weather radar data to estimate the injection heights of Biomass Burning Debris (BBD), which is assumed to be collocated with aerosols that are too small to be detected by these radars. Injection heights are estimated for the Williams Flats Fire event in Washington for 2–10 August 2019. Results show that daily maximum injection heights occur in the late afternoon, when the wildfire's intensity is strongest. Further, weather radar‐derived heights are compared to airborne lidar‐derived heights for the same fire, revealing that the maximums are overpredicted but intermediate values like the mean are well represented. Weather radar‐derived injection height estimates allow for near continuous smoke heights, making them relevant for future studies. Key Points: Weather radar estimates of biomass burning debris injection heights are evaluated against aerosol heights from airborne lidarRadar maximum injection heights tend to be overpredicted while mean, median, 75th and 90th percentiles perform betterThe maximum injection height can be predicted generally well by the 75th to 90th percentiles of the radar estimates [ABSTRACT FROM AUTHOR]

This paper presents an open-access data set of reanalysed radar reflectivities and rainfall rates at sub-kilometre spatial and minute temporal scales. Variability at these scales is a blind spot for both operational rain gauge networks and operational radar networks. In the urban area of Hamburg, precipitation measurements of a single-polarized X-band weather radar operating at high temporal (30s), range (60m), and azimuthal sampling (1°) resolutions are made available for a period of more than 8 years. We describe in detail the reanalysis of the raw radar data, outline the radar performance for the years 2013 to 2021, and discuss open issues and limitations of the data set. Several sources of radar-based errors were adjusted gradually, affecting the radar reflectivity and rainfall measurements, e.g. noise, alignment, non-meteorological echoes, radar calibration, and attenuation. The deployment of additional vertically pointing micro rain radars yields drop size distributions at the radar beam height, which effectively reduces errors concerning the radar calibration and attenuation correction and monitors the radar data quality. A statistical evaluation revealed that X-band radar reflectivities and rainfall rates are in very good agreement with the micro rain radar measurements. Moreover, the analyses of rainfall patterns shown for an event and accumulated rainfall of several months prove the quality of the data set. The provided radar reflectivities facilitate studies on attenuation correction and the derivation of further weather radar products, like an improved rainfall rate. The rainfall rates themselves can be used for studies on the spatial and temporal scales of precipitation and hydrological research, e.g. input data for high-resolution modelling, in an urban area. The radar reflectivities and rainfall rates are available at https://doi.org/10.26050/WDCC/LAWR_UHH_HHG_v2. [ABSTRACT FROM AUTHOR]