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The heterogeneous land surface spanning the Yellow River irrigated oasis and the adjacent Kubuqi and Ulan Buh Desert (Hetao area) in Inner Mongolia, China, has been noted to frequently generate planetary boundary layer convergence line (BLCL), providing an important source of low-level lifting for convection initiation (CI). As the first field experiment to collect comprehensive observations of vegetation-contrast-resulting thermal circulations that consistently generate BLCLs and lead to CI, the Desert-Oasis Convergence Line and Deep Convection Experiment (DECODE) was conducted from 5 July to 9 August 2022 in the Hetao area. Two oasis and four desert observation sites were set up in the region that exhibits the highest frequency of BLCL and CI occurrences, equipped with a suite of advanced instruments probing land-atmosphere interactions, planetary boundary layer processes, and evolution of BLCLs and their associated CI, including Doppler lidars, microwave radiometers, soil temperature and moisture sensors, eddy covariance systems, portable radiosondes, C-band polarimetric Doppler radar, aircraft, and Geostationary High-speed Imager onboard FY-4B satellite. DECODE captured 29 BLCLs (16 with CI), 66 gust fronts, 12 horizontal convective rolls, and one tornado. The observations unveiled full thermal circulations spanning the desert-oasis boundary characterized by a horizontal width of ~25 km, a convergence height of ~1 km above ground level (AGL), and divergence from 2 to ~3.5 km AGL, with vertical wind speeds of up to 2 m [s.sup.-1]. Future publications stemming from DECODE will delve into a spectrum of scientific inquiries, including but not limited to land surface and boundary layer processes, BLCL dynamics, CI mechanisms, convective organization, predictability, and model evaluation. SIGNIFICANCE STATEMENT: The purpose of Desert-Oasis Convergence Line and Deep Convection Experiment (DECODE) is to collect intensive observations around desert-oasis divide to better understand the physical processes of boundary layer convergence lines (BLCLs), their associated thermal circulations, convection initiation (CI), and organization over heterogeneous land surfaces. This is important because deep convection initiation poses a major challenge for weather forecasting over heterogeneous land surfaces due to limited understanding of land-atmosphere interactions. In addition, the targeting divide between a desert and an irrigated oasis facilitates a unique situation where human activities can change the weather. This campaign captured 29 BLCLs (16 with CI), which provided a rich dataset for studies not only on convection dynamics and microphysics but also on land-atmosphere interactions, boundary layer processes, and even interdisciplinary studies such as wind farm impact and convective dust storms. KEYWORDS: Complex terrain; Convection lines; Convective-scale processes; Boundary layer; Vegetation; Orographic effects, 1. Background In early October 2016, by fortuitously clicking on a radar image folder labeled 'Linhe' on a computer at the Training Center of the China Meteorological Administration, the first [...]

This study improved the first lightning flash (FLF) warning method of isolated thunderstorm cells in the warm season over South China. The S-band polarimetric radar and three independent lightning location systems provided 57 thunderstorm and 39 nonthunderstorm cells. The results indicated the median value of horizontal reflectivity below the melting layer when the cloud detected by the radar first was a good indicator for forecasting the FLF with the longest lead time (threshold value ≤ 15 dBZ in this study). The algorithm produced 0.96–1 probability of detection (POD), 0.32–0.38 false alarm ratio (FAR), 0.63–0.66 critical skill index (CSI), and 12–21-min average lead time. Methods of minimum echo (20 dBZ) top height extending to −10°C and the 35 dBZ at −10°C were more accurately for warning the FLF but with a shorter lead time; the former algorithm resulted in 1 POD, 0.08–0.29 FAR, 0.71–0.92 CSI, and 5–9-min average lead time. The latter algorithm resulted in 1 POD, 0–0.06 FAR, 0.94–1 CSI, and 3–5.5-min average lead time. The fluctuation of lead time depended on the FLF occurrence time. The differential reflectivity ZDR column and the height of graupel were not as effective as the reflectivity information on warning the FLF in this study. The results are expected to improve the FLF forecasting in considering accuracy and lead time within warm-season thunderstorm cells over South China based on the radar. Significance Statement: It is difficult to forecast the first lightning flash (FLF) occurrence. While many methods are provided based on weather radars, the threshold values for hitting the start of lightning activity are dominated by mixed-phase ice microphysics, especially associated with graupel particles; therefore, to improve the lead time of the FLF nowcasting, usage of knowledge of warm-phase microphysics is possible. Fortunately, the latest discovery indicates the difference is recognizable in warm-phase microphysics during the first radar echo between thunderstorms and nonthunderstorms based on the weather radar. This study utilized the finding to construct a method for the FLF warning. The early lead time and accuracy are considered comprehensively in this method. It is important for the FLF warning. [ABSTRACT FROM AUTHOR]

Accurate and timely precipitation nowcasting is essential for numerous applications including emergency services, infrastructure management, and agriculture. Recently, deep learning (DL) techniques have shown promise in enhancing nowcasting capabilities. This study introduces a novel Physical‐Driven Diffusion Network (PDDN) model that leverages both radar and numerical weather prediction (NWP) data to improve the accuracy and physical consistency of precipitation nowcasts. Our approach integrates the strengths of data‐driven DL techniques with physics‐based NWP models. The PDDN model utilizes latent diffusion models and autoencoders within a two‐stage architecture to predict future radar images, incorporating the Weather Research and Forecasting (WRF) model data to enhance understanding of atmospheric dynamics. Our results demonstrate significant improvements over traditional models, particularly in short‐term forecasting up to 6 hr. This research highlights the potential of combining advanced machine learning techniques with conventional meteorological data, offering new directions for enhancing the accuracy and reliability of weather forecasting. Plain Language Summary: Traditional methods for predicting rainfall often lose accuracy over time, especially in fast‐changing weather conditions. Deep learning techniques have recently shown promise in improving these predictions. To enhance rainfall nowcasting, we developed the Physical‐Driven Diffusion Network (PDDN). This new model combines radar data and numerical weather prediction (NWP) data to improve the accuracy and consistency of forecasts. By integrating advanced machine learning with physics‐based models, the PDDN excels in predicting rainfall for up to 6 hr. Our results show that the PDDN significantly outperforms traditional methods, providing more accurate and reliable short‐term weather forecasts. Key Points: Our PDDN model outperforms baseline models, exhibiting high proficiency in predicting precipitation up to 6 hr aheadThe integration of physical data from WRF enhances the model's understanding of atmospheric dynamics and improves forecasting accuracyThe novel design of latent diffusion models in a two‐stage architecture enables more accurate and robust precipitation nowcasts [ABSTRACT FROM AUTHOR]

This paper presents how the concept of opportunistic integrated sensing and communication (ISAC), focusing on weather sensing, is incorporated into wireless smart cities' networks. The concept, first introduced in 2006, utilized standard signal level measurements from wireless backhaul cellular networks for rain monitoring. Since then, it has expanded to include technologies like satellite communication and smart cities' networks. Opportunistic ISAC (OISAC) for weather involves transforming communication networks into virtual sensors by interpreting the signal attenuation caused by environmental factors, such as rain. These virtual sensors form the sensing layer of an IoT system, with built-in connectivity. In this paper, we present the recent advancements in the field, emphasizing the potential of current and future smart cities' wireless networks for accurate rainfall monitoring. We also demonstrate a test case in the city of Rehovot in Israel, where high spatiotemporal resolution rain maps produced via the OISAC paradigm significantly outperform the spatial resolution achieved by modern weather radars. We also discuss the challenges and opportunities in applying this concept. [ABSTRACT FROM AUTHOR]

Above anvil cirrus plumes (AACPs) form atop deep convective storm anvils. Storms containing AACPs are closely associated with surface severe weather. AACPs often reside in the lower stratosphere, and their microphysics are thought to be characterized by smaller ice particles with increased reflectance in shortwave infrared (SWIR) satellite imagery. This study aimed to determine which SWIR channels exhibit the strongest unique signatures over AACP lifetimes, assess how NEXRAD GridRad radar‐derived fields associated with severe weather relate to AACP presence and evolution, and take steps toward a red‐green‐blue (RGB) recipe to identify AACPs. AACPs and non‐plume anvil regions were labeled and analyzed using 5‐min resolution Geostationary Operational Environmental Satellites (GOES)‐16 Advanced Baseline Imager observations for five storms between 2019 and 2021. Considered were 0.64, 1.37, 1.6, 2.24, 3.9 μm reflectances, and 10.3 μm brightness temperatures (BTs). Results show: (a) higher median reflectance of accumulated AACPs in the 0.64, 1.37, and 2.24 μm channels compared to non‐plumes, (b) the 60−79° solar zenith angle bin contained the most data, corresponded with an afternoon peak in storm intensity, and contained a large plume to non‐plume pixel ratio, (c) increased lower stratosphere water vapor associated with AACPs impacted the 1.37 μm reflectance, leading to smaller, more reflective cloud ice particles than non‐plume regions, (d) GridRad radar 10‐ and 40‐dBZ echo heights and column maximum reflectivity (CMR) show AACPs coinciding with a secondary maximum in CMR, and (e) an RGB combining 1.37 μm, 0.64 μm + 3.9 μm reflectance, and 10.3 μm BT visually revealed AACPs. Plain Language Summary: This study was done to examine geostationary satellite reflectance signatures related to cloud microphysics of above anvil cirrus plumes (AACPs). AACPs are commonly found on top of anvil clouds of severe thunderstorms, and identifying them can improve forecaster situational awareness of high impact weather. The results of this study helped to identify which satellite channels best show AACPs, and this information was put into an RGB recipe specific for AACP identification and visualization. Key Points: Shortwave IR GOES‐16 satellite observations and weather radar signatures tied with above anvil cirrus plumes are analyzed every 5 minutesResults show 0.64, 1.37, and 3.9 μm reflectance signatures can improve recognition of strong convection likely to produce severe weatherThe unique characteristics of AACP reflectance in GOES‐16 data allow for the formation of specific AACP red‐green‐blue imagery [ABSTRACT FROM AUTHOR]

A rare overnight thunderstorm with hail on 12/13 December 2019 over Delhi was driven by a Westerly Disturbance (WD) and analysed with the aid of multi‐platform observations. The Doppler weather radar (DWR) detected high reflectivity values (>50dBZ), indicating hail within towering convective clouds. In‐situ measurements showed intense rainfall (totalling ~134mm), accompanied by strong wind gusts and a rapid pressure drop. Satellite data revealed widespread cold cloud tops (<−50°C) associated with the deep convection, while radiosonde profiles indicated atmospheric instability and veering winds during its passage. Lightning observations confirmed energetic convective updrafts. The event highlights the impact of WDs in triggering severe and rare winter convective storms over northern India. [ABSTRACT FROM AUTHOR]

Based on previously released data, this paper first presented the criteria for recognizing bow echoes and divided their life cycle into three stages: the development stage, the mature stage, and the attenuation stage. Based on Doppler weather radar data during 2011-2020, 43 bow echo events (including 54 individual bow echoes) in western South China were identified. The spatial and temporal distributions, formation and dissipation modes of these bow echoes, and the severe weather they caused were statistically analyzed. The results show that: (1) The bow echo events were unevenly distributed year-to-year, but all occurred from March to July, with the highest in April and May and the lowest in July. The period from night to early morning was found to be the main period for bow echo generation and intensification. (2) A banded area from Hechi City on the southeastern edge of the Yunnan--Guizhou Plateau to Wuzhou City in southeast Guangxi was identified as a high-incidence area of bow echoes. The length of bow echoes was correlated with their life cycle. (3) The origins of the bow echoes could be divided into five locations, most of which were in the eastern Yunnan-- Guizhou Plateau. After entering western South China, their moving paths were categorized into three types, among which most bow echoes moved southeastward, generally because of the effect of cold air. Specifically, bow echoes generally moved eastward when cold air was weak or in the warm zone. Meanwhile, the fewest bow echoes moved northeastward. (4) Four modes of bow echo formation were identified: linearly organized, broken areal, linearly merging, and broken line. Dissipation could also be classified into four types. (5) The probability of convective weather generated by a bow echo was largest in the mature stage. [ABSTRACT FROM AUTHOR]

A new method is proposed for identifying ground clutter in single-polarization radar data based on the gated recurrent unit (GRU) neural network. This method needs five independent input variables related to radar reflectivity structure, which are the reflectivity at current tilt, the reflectivity at the upper tilt, the reflectivity at 3.5 km, the echo top height, and the texture of reflectivity at current tilt, respectively. The performance of the new method is compared with that of the traditional method used in the Weather Surveillance Radar 1988-Doppler system in four cases with different scenarios. The results show that the GRU method is more effective than the traditional method in capturing ground clutter, particularly in situations where ground clutter exists at two adjacent elevation angles. Furthermore, in order to assess the new method more comprehensively, 709 radar scans from Nanchang radar in July 2019 and 708 scans from Jingdezhen radar in June 2019 were collected and processed by the two methods, and the frequency map of radar reflectivity exceeding 20 dBZ was analyzed. The results indicate that the GRU method has a stronger ability than the traditional method to identify and remove ground clutter. Meanwhile, the GRU method can also preserve meteorological echoes well. [ABSTRACT FROM AUTHOR]

The velocity/distance trade-off poses a fundamental challenge in pulsed Doppler weather radar systems and is known as the velocity/distance dilemma. Techniques such as multiple-pulse repetition frequency, staggered pulse repetition time (PRT), and pulse phase coding are commonly used to mitigate this issue. The current study evaluates the adaptability/capability of a specific type of low-capture signal called the non-uniform PRT (NPRT) through analyzing the weather target characteristics of typical velocity distributions. The spectral moments estimation (SME) signal-processing algorithm of the NPRT weather echo is designed to calculate the average power, velocity, and spectrum width of the target. A comprehensive error analysis is conducted to ascertain the efficacy of the NPRT processing algorithm under influencing factors. The results demonstrate that the spectral parameters of weather target echo with a velocity of [ − 50 , 50 ] m/s through random-jitter NPRT signals align with radar functionality requirements (RFRs). Notably, the NPRT waveform resolves the inherent conflicts between the maximum unambiguous distance and velocity and elevates the upper limit of the maximal observation velocity. The evaluation results confirm that nonlinear radar signal processing technology can improve a radar's detection performance and provide a new method for realizing the multifunctional observation of radar in different applications. [ABSTRACT FROM AUTHOR]

High-flying insects that exploit tropospheric winds can disperse over far greater distances in a single generation than species restricted to below-canopy flight. However, the ecological consequences of such long-range dispersal remain poorly understood. For example, high-altitude dispersal may facilitate more rapid range shifts in these species and reduce their sensitivity to habitat fragmentation, in contrast to low-flying insects that rely more on terrestrial patch networks. Previous studies have primarily used surface-level variables with limited spatial coverage to explore dispersal timing and movement. In this study, we introduce a novel application of niche modelling to insect aeroecology by examining the relationship between a comprehensive set of atmospheric conditions and high-flying insect activity in the troposphere, as detected by weather surveillance radars (WSRs). We reveal correlations between large-scale dispersal events and atmospheric conditions, identifying key variables that influence dispersal behaviour. By incorporating high-altitude atmospheric conditions into niche models, we achieve significantly higher predictive accuracy compared with models based solely on surface-level conditions. Key predictive factors include the proportion of arable land, altitude, temperature, and relative humidity. [ABSTRACT FROM AUTHOR]

The increasing generation of data in different areas of life, such as the environment, highlights the need to explore new techniques for processing and exploiting data for useful purposes. In this context, artificial intelligence techniques, especially through deep learning models, are key tools to be used on the large amount of data that can be obtained, for example, from weather radars. In many cases, the information collected by these radars is not open, or belongs to different institutions, thus needing to deal with the distributed nature of this data. In this work, the applicability of a personalized federated learning architecture, which has been called adapFL, on distributed weather radar images is addressed. To this end, given a single available radar covering 400 km in diameter, the captured images are divided in such a way that they are disjointly distributed into four different federated clients. The results obtained with adapFL are analyzed in each zone, as well as in a central area covering part of the surface of each of the previously distributed areas. The ultimate goal of this work is to study the generalization capability of this type of learning technique for its extrapolation to use cases in which a representative number of radars is available, whose data can not be centralized due to technical, legal or administrative concerns. The results of this preliminary study indicate that the performance obtained in each zone with the adapFL approach allows improving the results of the federated learning approach, the individual deep learning models and the classical Continuity Tracking Radar Echoes by Correlation approach. [ABSTRACT FROM AUTHOR]

The accuracy of wind speed estimation is an important factor affecting wind-shear detection in airborne weather radar. Aiming at the problem that dictionary mismatch in the sparse recovery-based wind speed estimation leads to the performance degradation, this paper proposes a wind speed estimation method based on atomic norm minimization for airborne array weather radar. The method first constructs joint sparse recovery measurements by compensating multiple array element data with wind-shear orientation information, and then the wind speed is estimated on continuous parameter domain using atomic norm minimization with multiple compensated measurements. Simulation experiments demonstrate that the proposed method can effectively improve the accuracy of wind speed estimation under dictionary mismatch, and the performance is better than that of the existing sparse recovery-based method of wind speed estimation with the pre-set discretized dictionary. [ABSTRACT FROM AUTHOR]

For helicopters and small civil airplanes, it is very critical to improve the accuracy of Doppler spectrum estimation which is used for extraction of weather phenomena parameters in detection of weather hazards such as microbursts and the abrupt shift of wind velocity that may cause the serious danger. However, the acquisition time of a received signal is very short and the strong ground clutter may remain frequently since the weather radar of this purpose are used for the low altitude and short range detection of abrupt weather change. Therefore, serious problems can occur in the accuracy of the conventional FFT spectrum estimation method. In this paper, the AR parameter and eigenvector spectrum estimation method is applied to improve the accuracy of Doppler spectrum estimates. The results are analyzed and compared among the conventional FFT estimation method, the AR parameter and eigenvector spectrum estimation method according to the various weather operation environment. [ABSTRACT FROM AUTHOR]

Two simple feedforward neural networks (multilayer perceptrons – MLPs) are trained to detect rainfall events using signal attenuation from commercial microwave links (CMLs) as predictors and high-temporal-resolution reference data as the target. MLPGA is trained against nearby rain gauges, and MLPRA is trained against gauge-adjusted weather radar. Both MLPs were trained on 26 CMLs and tested on 843 CMLs, all located within 5 km of a rain gauge. Our results suggest that these MLPs outperform existing methods, effectively capturing the intermittent behaviour of rainfall. This study is the first to use both radar and rain gauges for training and testing CML rainfall detection. While previous studies have mainly focused on hourly reference data, our findings show that it is possible to classify rainy and dry time steps with a higher temporal resolution. [ABSTRACT FROM AUTHOR]

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The article presents the design and investigation of a lotus-shaped triple-tuned wideband monopole-antenna for wireless communication services (WCS), satellite radio, and weather RADAR applications. The novel lotus-shaped antenna structure contains seven arms developed using arithmetic operations and loaded with SRR-quadruplets. Further, the effect of ring orientation position changes is analysed. Seven arms increase the electrical length of the patch which yields an improvement in broad bandwidth. The parasitic loading of an antenna with SRR quadruplets changes the nature of the antenna to triple-tuned at 2.04 GHz, 2.27 GHz, and 2.90 GHz with enhanced -10dB fractional bandwidth (FBW) 57.19% from initially designed single band (2.06-3.12 GHz) antenna. A wideband is achieved by reduced compressed ground. The proposed antenna achieves a peak gain of 5.2dBi and an efficiency of 92%. The RLC equivalent circuit of the designed antenna is presented for compatibility at low-frequency operations. The ADS and HFSS-generated electrical equivalent circuit reflection coefficients are found in excellent agreements and they validate the antenna. The wideband nature of the antenna from 1.61GHz to 3.24 GHz makes this antenna suitable for WCS, satellite digital audio radio services (SDARS), and weather RADAR applications. The modified antenna structure and quadruplet-SRR loading offer additional 5G bands, GSM, and PCS applications. [ABSTRACT FROM AUTHOR]

The US operates a system of 160 S‐band Doppler weather radars known as NEXRAD (NEXt generation weather RADar) that continuously monitors the airspace around the majority of the United States and outlying territories. These radars detect and track birds, insects, and bats. Free‐tailed bats (Genus Tadarida) provide considerable ecosystem services through their voracious insect consumption; but their movements and ecosystem service provision have historically been difficult to track/study in space and time.We introduce 'BATS', a Python toolkit that streamlines the process of downloading, classifying, and aggregating time series of free‐tailed bats across large landscapes. BATS retrieves data from NOAA's weather radar data repositories and classifies the processed radar data using a pre‐trained ML trained to detect and classify radar echoes associated with free‐tailed bats.We trained various machine learning approaches at classifying pixels containing free‐tailed bats and compared the effectiveness across approaches. With an AUC of 0.963, the neural network approach is highly effective in identifying free‐tailed bats in NEXRAD data over our study sites in California and Texas. Furthermore, BATS is capable of quickly distilling 6 months of radar data from a single tower (3.5 Tb) into a single 15 Mb‐sized map of bat occurrence, contingent on available computing resources.BATS will help scientists and stakeholders identify areas of high bat occupancy at the landscape level over long periods of time. This ability has the potential to increase our understanding of the economic and agricultural value of these species. [ABSTRACT FROM AUTHOR]

Recurring polarimetric radar signatures in supercells include deep and persistent differential reflectivity (ZDR) columns, hail inferred in low-level scans, and the ZDR arc signature. Prior investigations of supercell polarimetric signatures reveal positive correlations between the ZDR column depth and cross-sectional area and quantitative characteristics of the radar reflectivity field. This study expands upon prior work by examining temporal associations between supercell polarimetric radar signatures, incorporating a dataset of relatively discrete, right-moving supercells from the continental United States observed by the Weather Surveillance Radar 1988-Doppler (WSR-88D) network. Cross-correlation coefficients were calculated between the ZDR column area and depth and the base-scan hail area, ZDR arc area, and mean ZDR arc value. These correlation values were computed with a positive and negative lag time of up to 45 min. Results of the lag correlation analysis are consistent with prior observations indicative of storm cycling, including temporal associations between ZDR columns and inferred hail signatures/ZDR arcs in both tornadic and nontornadic supercells, but were most pronounced in tornadic storms. [ABSTRACT FROM AUTHOR]

Mediterranean tropical-like cyclones, or Medicanes, present unique challenges for precipitation estimations due to their rapid development and localized impacts. This study evaluates the performance of satellite precipitation products in capturing the precipitation associated with Medicane Daniel that struck Greece in early September 2023. Utilizing a combination of ground-based observations, reanalysis, and satellite-derived precipitation data, we assess the accuracy and spatial distribution of the satellite precipitation products GPM IMERG, GSMaP, and CMOPRH during the cyclone event, which formed in the Eastern Mediterranean from 4 to 7 September 2023, hitting with unprecedented, enormous amounts of rainfall, especially in the region of Thessaly in central Greece. The results indicate that, while satellite precipitation products demonstrate overall skill in capturing the broad-scale precipitation patterns associated with Medicane Daniel, discrepancies exist in estimating localized intense rainfall rates, particularly in convective cells within the cyclone's core. Indeed, most of the satellite precipitation products studied in this work showed a misplacement of the highest amounts of associated rainfall, a significant underestimation of the event, and large unbiased root mean square error in the areas of heavy precipitation. The total precipitation field from IMERG Late Run and CMORPH showed the smallest bias (but significant) and good temporal correlation against rain gauges and ERA5-Land reanalysis data as a reference, while IMERG Final Run and GSMaP showed the largest underestimation and overestimation, respectively. Further investigation is needed to improve the representation of extreme precipitation events associated with tropical-like cyclones in satellite precipitation products. [ABSTRACT FROM AUTHOR]

The aim of this study is the operational dynamical nowcasting application over different parts of India from Indian Meteorological Department (IMD) using Doppler Weather Radar (DWR) network of multiple radars and numerical weather prediction (NWP) model. The High Resolution Rapid Refresh (HRRR) approach has been adopted to achieve this objective in which the DWR data are hourly assimilated at convective-scale in the Weather Research and Forecasting (WRF) model. The designated NWP setup implemented for very short-range to nowcasting of weather is defined as the IMD-HRRR modelling system. Various quality controls are employed before assimilating DWR data in the IMD-HRRR system. Three different domains are specified over India that cover the entire Indian landmass, and next 12-h predictions are provided from hourly cyclic assimilation experiments. The results of all domains suggested that the IMD-HRRR predictions are not degraded with forecast lengths (up to 12 h) when compared against observations e.g. Synop, Metar, Buoy, total precipitable water (TPW) from GPS stations. Minimum errors are achieved when model predictions are compared against Buoy and TPW observations. The correlation values are higher than 0.9 for all domains. Furthermore, the IMD-HRRR model forecasts are also compared with observed DWR radial winds to demonstrate auxiliary applications of the DWR data for model verifications at high spatio-temporal resolution. [ABSTRACT FROM AUTHOR]

Typical temperature characteristic of cold air pool (CAP) in the main producing areas of Xihu Longjing plantation was analyzed using data from 29 automatic meteorological stations and radar sounding inversion data during spring tea budding period in 2021. A method was explored to automatically identify areas prone CAP under complex terrain. The results showed that the occurrence frequency of CAP day was 45% in typical prone CAP areas of Xihu Longjing plantation from February 20 to March 31, 2021. The occurrence frequency and intensity of CAP were related to weather types. CAP was more likely to occur on sunny and cloudy weather conditions, less on overcast weather conditions, and strong CAP days predominantly appearing under clear sky conditions. CAP increased the frequency and intensity of inversions, with an increase of 23% in inversion frequency, and an increase of 1.26℃ per 100m in average maximum inversion intensity compared to flat areas. A typical daily variation of CAP included three stages: formation and strengthening, maintenance, and weakening and dissipation, with the maximum hourly temperature increase at the valley during the weakening and dissipation stage reaching 11.3℃·h−1, which was 4.1℃·h−1 higher than the maximum hourly temperature decrease during the formation and strengthening stage (7.2℃·h−1 ). To identify the CAP prone areas, three terrain factors in DEM data including slope, percentile of height relative to surroundings, and terrain curvature were constructed as discriminant indicators showing good identification effectiveness. Coincidence rates of stations located in CAP prone and non prone areas were 80% and 78% respectively. About 26% of the tea plantation in study area was located in CAP prone areas, making them more susceptible to spring tea frost damage during the budding period due to extreme low temperatures and rapid warming processes. [ABSTRACT FROM AUTHOR]

This paper is devoted to the issue of using laser (lidar) sensing to determine wind speed and direction when solving practical problems during the analysis of meteorological conditions in the area around spaceports. This issue is relevant both for making decisions on the possibility of a safe launch of a launch vehicle and for conducting search and rescue operations using groups of manned and unmanned aerial vehicles. Based on numerical and experimental modeling, it is shown that lidars provide highly accurate measurements of wind speed profiles and allow for the determination of weak wind shear in vertical and horizontal directions. This paper proposes a method for determining the main parameters of lidar sensing (range, resolution, detectability, etc.), which allows for the capabilities of this technology in solving the practical problems of meteorological monitoring to be predicted. Of particular interest in this article are experimental modeling data verifying the proposed calculation methods and the experimental determination of the capabilities of lidar diagnostics. This paper summarizes the data from multi-month experiments measuring wind speed in clear weather conditions when other means of remote diagnostics are ineffective. As a result of the experiments, a statistical distribution of the maximum range of wind speed measurement in normal weather conditions with natural variation in the concentration of scattering particles in the atmosphere was obtained. This article also discusses the possibility of combining lidars and meteorological radars for the meteorological support of flights. [ABSTRACT FROM AUTHOR]

Accurate quantitative precipitation estimation (QPE) is fundamental for a large number of hydrometeorological applications, especially when addressing extreme rainfall phenomena. This paper presents a comprehensive comparison of various rainfall estimation methods, specifically those relying on weather radar data, rain gauge data, and their fusion. The study evaluates the accuracy and reliability of each method in estimating rainfall for a severe event that occurred in Tuscany, Italy. The results obtained confirm that merging radar and rain gauge data outperforms both individual approaches by reducing errors and improving the overall reliability of precipitation estimates. This study highlights the importance of data fusion in enhancing the accuracy of QPE and also supports its application in operational contexts, providing further evidence for the greater reliability of merging methods. [ABSTRACT FROM AUTHOR]

In previous studies, radar reflectivity is often directly assimilated using reflectivity operators based on a single-moment (SM) microphysics scheme, though the forecast model uses a double-moment (DM) microphysics scheme. With the fixed number concentrations, only the mixing ratios of hydrometeors are directly updated during the assimilation, which leads to a mismatch between the analyzed microphysical state variables and the microphysics scheme of the forecast model. In this study, the radar reflectivity is directly assimilated through an observation operator consistent with the DM Thompson microphysics scheme used in numerical integrations, and the impact of reflectivity operators based on SM and DM schemes on the analysis performance of the ensemble Kalman filter for typhoon Lekima on 9 August 2019 is evaluated. Reflectivity observations from a single operational weather radar in Wenzhou City, Zhejiang Province of China are assimilated. In addition, the dual-polarization observations from the same radar are used to evaluate the quality of the analysis. The analyzed reflectivity and dual-polarization characteristics obtained by different reflectivity operators are examined in detail. Compared with the experiments applying the reflectivity operator based on the SM Lin scheme, the use of a reflectivity operator consistent with the DM Thompson scheme adopted in the forecast model results in analyzed reflectivity and polarization characteristics that are more consistent with the observed characteristics in terms of general structure, location, and intensity. Forecasted reflectivity, 3 h accumulated precipitation, and typhoon intensity and track are also evaluated. The application of the reflectivity operator based on the DM scheme makes better forecasts of typhoon intensity, precipitation, and reflectivity, which also improves the forecast skills on typhoon tracks to a certain extent. [ABSTRACT FROM AUTHOR]

Composite sandwich structures, which are widely employed in engineering structures, require a multitude of inserts. In certain instances, the necessity for a specialized insert arises from the unique characteristics of a particular application. In applications such as weather radar radomes, double-curved sandwich panels should be designed with electromagnetic (EM) transparency as a primary objective. The use of metal inserts should be restricted to the absolute minimum. Given the limitations of using metal materials to protect against EM radiation and the need to enhance the load-bearing capacity of the joint against pull-out loads, a composite insert has developed as an innovative solution. In this study, a composite insert of a double-curved sandwich dome has been developed using silica nanoparticles, and its mechanical strength against pull-out load has been evaluated through both experimental and numerical analysis. The strength results obtained have been compared with analytical estimates. Additionally, the buckling of the double-curved sandwich dome against a wind speed of 220 km/h has been investigated numerically. The critical buckling load for wind loading for the full-scale sandwich radome was estimated to be 16,303 N. According to the numerical results obtained with the Abaqus finite element (FE) software, the maximum pull-out force applied to the connection area is approximately 10.7 kN. A parametric study of geometric variables and experimental results showed that it is possible to achieve a stronger composite insert (by 1 wt % nano silica particles) by 20.7% lighter and 102.65% more bearing capacity. [ABSTRACT FROM AUTHOR]

The urgency of understanding the intricate input–output relationships of the hydrologic cycle is amplified by the accelerating climate change impacts in mountain environments. This study focuses on optimising water resource management of a dammed valley in the Central Alps (Northern Italy). The research aims to integrate radar data and precipitation interpolation techniques (TIN, Copula, cumulative distribution function; CDF techniques, inverse distance weighting; IDW, thin plate spline; TPS, ordinary kriging; OK and detrended kriging; DK) into a semi‐distributed hydrologic model, by utilising hourly precipitation data from 22 rain gauges and a composite weather radar product spanning 2010–2020. Two main objectives were pursued: (i) to develop and evaluate various radar precipitation correction methods against a benchmark dataset and (ii) to calibrate and assess the performance of the GEOFrame hydrologic model forced with corrected precipitation input. Point‐based and spatial correction approaches were evaluated against ground measurements through leave‐one‐out tests. The former derives dependence functions between the biased radar series and those of the closest three rain gauges to the target point applying a triangular irregular network. The latter combines deterministic and geospatial interpolations to the rain gauge/radar residuals to derive a corrected surface by incorporating radar values as trends. Precipitation series exceeding the composite scaled score of the benchmark dataset were used as input for hydrologic modelling. The spatial method combining radar values with ordinary kriging provided the best results for both correction and modelling (hourly KGE > 0.75). The spatial approaches proved easier to apply than the point‐based methods. In addition, correcting precipitation significantly improved low‐flow simulation from negative hourly lnNSE to values greater than 0.25. As a further step, given the overall good performance of the spatial methods, they could be used operationally as an ensemble to analyse management scenarios. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of the interference-handling radio frequency direction finding (RFDF) monitoring system in detecting and localizing interference sources near the BMKG C-Band weather radar in West Kalimantan, Indonesia. We conducted field tests varying interference transmitter power levels (0-30 dBm) and distances (100, 500, and 1,000 m) from the radar. Results indicate the RFDF system's robust performance, consistently detecting interference within 5-20 minutes and accurately localizing sources with minimal deviation from actual positions. The findings confirm the system's superiority over traditional manual methods, offering a reliable solution for interference management in weather radar operations. However, limitations include controlled test conditions and a need for further exploration of the system's efficacy in diverse environmental settings. This research contributes to improving radar reliability and lays the groundwork for future studies to refine RFDF technology for broader meteorological applications. [ABSTRACT FROM AUTHOR]

Digital transformation in the architecture industry encounters challenges such as data silos within design stages, between design and construction, and across the entire industry chain, including BIM and project management. This study introduces a pioneering approach for seamlessly integrating building information modeling (BIM) and product lifecycle management (PLM) within the management framework and collaborative workflows of architectural design and construction. The proposed methodology was implemented in two major projects, namely, the Wuhan Next-Generation Weather Radar Construction Project and the Hubei Center for Disease Control and Prevention Comprehensive Capacity Enhancement Project (Phase I), with the former being China's first end-to-end digitally integrated construction project. Compared with traditional projects, these implementations achieved improvements of 24.39% and 38.04%, respectively, in objective function values. The findings highlight the unique advantages of BIM–PLM integration in optimizing lifecycle management, enhancing cross-disciplinary, cross-enterprise, and cross-regional collaboration, improving project design efficiency and quality, and reducing costs and waste. This integration provides valuable insights into the digital advancement of the construction industry. [ABSTRACT FROM AUTHOR]

Upward Terrestrial Gamma‐Ray Flashes (TGFs) are mainly produced during the upward propagating negative leaders inside thunderclouds. The exact source position of TGFs, which is crucial to understanding TGF source properties, is still unclear. The link between positive energetic in‐cloud pulses (+EIPs) and TGFs provides us with a potential target to aim at. In this study, the low‐frequency radio emissions of 75 +EIPs are analyzed to retrieve the source altitudes with an improved ray theory model. Furthermore, the meteorology contexts of +EIPs derived from the ground‐based weather radars and satellite‐based infrared cloud top temperature measurements are investigated. +EIPs are produced at 8.8–13.7 km, with an average of 11.3 km inside thunderclouds, and at an average of ∼2.5 km below cloud tops. These altitudes indicate that a total number of 1.7 × 1016 to 2.6 × 1018 gamma ray photons with energy greater than 1 MeV are required for an EIP‐TGF to be measured by spaceborne detectors. Plain Language Summary: Terrestrial gamma‐ray flashes (TGFs) are high‐energy photon emissions generated during thunderstorms and related to the initial development of intra‐cloud discharges. How TGFs are produced inside thunderclouds is still an open question, and one crucial issue is where TGFs are produced. Till now, it has been challenging to obtain the TGF positions directly. Recently, a distinct type of high‐peak current events, which are named energetic in‐cloud pulses (EIPs), are found to be closely linked to TGFs. The radio emissions of EIPs can be measured by ground‐based radio sensors deployed hundreds of kilometers away from the source. In this study, a ray theory model is improved to retrieve the signature of very low‐frequency/low‐frequency radio signals of EIPs propagating in the Earth‐ionosphere waveguide to obtain the source altitudes of EIPs. A total of 75 EIPs were found to be produced at 8.8–13.7 km, with an average of 11.3 km inside thunderclouds, and at an average of ∼2.5 km below cloud tops. With the source position information and combining the previously reported method to estimate the total number of source electrons, we suggest a total number of about 1017 is needed for TGFs being detected by spaceborne gamma ray detectors. Key Points: An improved ray theory model is developed to retrieve energetic in‐cloud pulse (EIP) positionsSeventy‐five +EIPs are located at 8.8–13.7 km, with a mean altitude of 11.3 kmA mean gap of ∼2.5 km between EIPs and cloud tops was estimated [ABSTRACT FROM AUTHOR]

Highlights In this novel work, we fabricated the multiwalled carbon nanotubes (MWCNTs)/polypyrrole (PPy) nanocomposite by employing the novel ultrasonic dual mixing (UDM) method. Subsequently, the atomistics and structural characterizations of the fabricated MWCNT/PPy nanocomposite (PM) were performed using SEM, Raman, and XRD. Furthermore, we experimentally evaluated the mechanical properties of the fabricated PM by reinforcing the PPy matrix with MWCNTs at different wt.%. The experimental study of PM shows that the mechanical properties depend upon the amount and dispersion quality of MWCNTs in the PPy matrix. In order to validate the experimental outcomes, the conservative fully coupled finite element (FE) models were developed using the ANSYS Workbench and novel FEAST software. The electromagnetic interference (EMI) shielding effectiveness (SE) of developed PM was also calculated at different thicknesses of PM by taking the effect of SE due to absorption (SEA) and SE reflection (SER) in the microwave frequency range of the C‐band (5.37–8.2 GHz) into consideration. The total SE (SET), the cumulative sum of SEA and SER, is the strong function of the thickness of the PM. Additionally, our study's outcomes reveal that as the thickness of the PM increased, the SET also increased by dominating the SEA rather than the SER. The SET value of 75 dB was observed for a sample with a thickness of 3 mm, indicating a high level of shielding. Thus, material becomes a highly attractive option for achieving high‐performance EMI SE in the C‐band for satellite communications, Wi‐Fi devices, weather radar systems, surveillance, and cordless telephones. A complex permittivity and permeability were also studied using the Nicolson–Ross–Weir (NRW) method to understand the mechanism behind the EMI SE. Nanocomposite fabricated using the novel ultrasonic dual mixing method. FE models were developed to validate experimental results. The EC and EMI SE are improved with the increase in the sample thickness. Absorption, rather than reflection, dominated the shielding mechanism. SE was observed ~75$$ \sim 75 $$ dB with good mechanical properties. [ABSTRACT FROM AUTHOR]

Passive‐microwave hail detection methods rely on the scattering signal produced by large ice hydrometeors. Recently, passive‐microwave hail retrieval methods have been trained by pairing Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Global Precipitation Measurement (GPM) Microwave Imager (GMI) data with United States surface hail reports to present a global hail climatology. In particular, Southeastern South America (SESA) stands out as a hotspot for hail occurrence, more specifically northeastern Argentina. However, the analysis performed with the available ground hail reports in Argentina shows some differences from the available ground hail reports in Argentina, as it locates the most frequent hail storms in the southwestern (Mendoza and Córdoba) and northeastern (Misiones) regions of SESA. To assess the retrieval from a regional perspective, we take a case study approach and focus on polarization‐corrected temperature features (PCTFs) with a detected Phail>50%. We mainly rely on polarimetric weather radars and a hydrometeor identification algorithm (HID) as a proxy for ground truth. Results indicate that the hail retrieval responds mainly to low 37‐GHz polarized corrected (brightness) temperatures (PCTs) and large PCTF area, while HID outputs show that high probabilities of hail also correspond to abundantly suspended graupel. The observed passive‐microwave hail detection hotspot could be influenced by mesoscale convective systems with deep columns of graupel‐sized frozen hydrometeors, common in northeastern Argentina. [ABSTRACT FROM AUTHOR]

Millimeter-wave radars are widely used in various environments due to their excellent detection capabilities. However, the detection performance in severe weather environments is still an important research challenge. In this paper, the propagation characteristics of millimeter-wave radar in a rainfall environment are thoroughly investigated, and the modeling of the millimeter-wave radar echo signal in a rainfall environment is completed. The effect of rainfall on radar detection performance is verified through experiments, and an anti-rain clutter interference method based on a convolutional neural network is proposed. The method combines image recognition and classification techniques to effectively distinguish target signals from rain clutter in radar echo signals based on feature differences. In addition, this paper compares the recognition results of the proposed method with VGGnet and Resnet. The experimental results show that the proposed convolutional neural network method significantly improves the target detection capability of the radar system in a rainfall environment, verifying the method's effectiveness and accuracy. This study provides a new solution for the application of millimeter-wave radar in severe weather conditions. [ABSTRACT FROM AUTHOR]

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies. [ABSTRACT FROM AUTHOR]

Weather radar is a crucial tool for rainfall observation and forecasting, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods and imposing path-integrated attenuation effects, a stochastic drop-size-distribution field, and sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field and the corrected "best-guess" rainfall field which would be obtained if they were observed in a real-world case. The structure of these errors is then investigated, with a focus on the frequency and behaviour of "rainfall shadows". Half of the simulated weather radar images have at least 3 % of their significant rainfall rates shadowed, and 25 % have at least 45 km2 containing rainfall shadows, resulting in underestimation of the potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well in generating realistic weather radar images visually for a large range of event types. [ABSTRACT FROM AUTHOR]

This research work focuses on the development and application of a storm-tracking algorithm for identifying and tracking storm cells. The algorithm first identifies storm cells on the basis of reflectivity thresholds and then matches the cells in the tracking procedure on the basis of their geometrical characteristics and the distance within the weather radar image. A sensitivity analysis was performed to evaluate the preferable thresholds for each case and test the algorithm's ability to perform in different time step resolutions. Following this, we applied the algorithm to 54 rainfall events recorded by the National Technical University X-Band weather radar, the rainscanner system, from 2018 to 2023 in the Attica region of Greece. Testing of the algorithm demonstrated its efficiency in tracking storm cells over various time intervals and reflecting changes such as merging or dissipation. The results reveal the predominant southwest-to-east storm directions in 40% of cases examined, followed by northwest-to-east and south-to-north patterns. Additionally, stratiform storms showed slower north-to-west trajectories, while convective storms exhibited faster west-to-east movement. These findings provide valuable insights into storm behavior in Athens and highlight the algorithm's potential for integration into nowcasting systems, particularly for flood early warning systems. [ABSTRACT FROM AUTHOR]

The electrical alignment signatures of ice crystals in the middle and upper parts of a thunderstorm have a physical connection with the strong electric fields within the cloud. By taking advantage of the high spatio‐temporal resolution of an X‐band dual‐polarized multiparameter phased array radar, this paper employs negative KDP signatures to investigate the variation of electric fields in an isolated thunderstorm within its evolution, and the relationship between the negative KDP signatures and intracloud (IC) lightning activity. The results show that the radar‐inferred strong electric fields distributed in a region of about 8.5–10 km during the developing stage, then extended from about 10 km to the cloud top during the mature stage, with an increasing IC lightning flash rate before the end of the mature stage. Subsequently, the electric fields weakened associated with a large amount of IC lightning discharges. The qualitative analysis results indicate that the evolution of the radar‐inferred electric fields associated with the upper charge regions is consistent with the electrification process in the isolated thunderstorm, inferred by the IC lighting activity. In addition, there is a tendency for the increased IC lightning rate as the decrease in average composite KDP in the middle and upper layers of the thunderstorm, with a time lag of about 5.5 min between the minimum average composite KDP and the peak IC lightning rate, while a good correlation between the negative KDP volume and the IC lightning flash rate at a lag of approximately 9 min. Plain Language Summary: By using an X‐band dual‐polarized multiparameter phased array radar with high temporal and spatial resolution, this study investigates the electrical alignment signatures of ice particles related to the strong electric fields in the middle and upper parts of an isolated thunderstorm. The results indicate the potential use of electrical alignment signatures of ice crystals for evaluating the evolution of radar‐inferred electric fields within the thunderstorm lifecycle. Moreover, well correlations are observed between negative KDP volume, average composite KDP, and intracloud lightning flash rate. Key Points: The composite KDP could be used as an indicator of intracloud (IC) lightning potential in isolated thunderstormsThe negative KDP volume and average composite KDP show well correlations with the IC lightning flash rate in an isolated thunderstormThe vertical structure of total electric field in the upper parts of an isolated thunderstorm could be explored by negative KDP signatures [ABSTRACT FROM AUTHOR]

This paper presents an outline of short-term regional forecast system based on the COSMO-Ru2By configuration (with a mesh size of 2.2 km) of the COSMO model, which provides numerical weather forecasts up to 48 hours for the European part of Russia and for the Republic of Belarus. The description is accompanied by analysis of verification results. The COSMO-Ru2By system has been implemented at the Hydrometcenter of Russia through the Collaboration Program with Belhydromet and now it is an integral part of the COSMO-Ru operational limited-area numerical weather prediction system running on the CRAY XC40-LC supercomputer of Roshydromet. The COSMO-Ru2By technology has several features: (1) an explicit simulation of deep convection, (2) a vast integration domain, (3) integrated assimilation of the Doppler meteorological radar data and (4) a coupled visualization system that provides a multitude of charts, including maps for different regions that cascadingly increase the details of images. All these aspects are important for forecasting rapidly developing weather processes over the coming hours. The operational trials conducted in 2020–2021 under the supervision of the Roshydromet Central Methodological Commission on Forecasts demonstrated high quality of near-surface weather parameter forecasts with the COSMO-Ru2By, which were significantly more accurate than products from other numerical weather forecast models available to forecasters at the Hydrometcenter of Russia. The comparison with non-hydrostatic models with coarser resolution (e.g., the COSMO-Ru6ENA with a mesh size of 6.6 km) as well with configurations with the same mesh size running in other technologic scenario was made. This analysis was focused on the model capacity of predicting of highly variable weather parameters (such as wind gusts, precipitation, and weather parameters in mountainous areas). For this purpose, the authors employed several additional approaches: assessments over geographically homogeneous areas, the use of special metrics for verifying forecasts of rare events, and comparisons of forecasts with radar data. [ABSTRACT FROM AUTHOR]

The stratiform and convective precipitation regions are examined in three phases (Phase 1 to Phase 3) during the Indian summer monsoon season 2022 using C-band radar observations over Thiruvananthapuram, India. Nearly 72% of the pixels are identified with stratiform precipitation region and the radar reflectivity value peaks near the melting layer in all phases of the monsoon. The convective precipitation accounts for ∼ 28% of the precipitating area, and radar reflectivity profiles peak near 2 km height. An estimation of convective and stratiform precipitation amounts using Doppler Weather Radar (DWR) and Global Precipitation Mission (GPM) measurements shows the ratio is 32–68% over the study region. A fuzzy-logic-based hydrometeor identification method is implemented using the radar polarimetric variables, which shows stratiform (convective) precipitation is dominated by drizzle (rain) hydrometers near the surface levels. Above the melting layer, the aggregates and crystals are the dominant hydrometeors types in both stratiform and convective precipitation. The WRF simulation using the Thompson and SBM scheme for a heavy precipitating event (70 mm) on 17th October 2022 shows that the graupel and snow mixing ratio above the melting layer contributed to the precipitation. The simulated hydrometeor properties differ considerably between the SBM and bulk microphysics, partly due to variations in their ice particle size distributions, as revealed by comparisons with polarimetric measurements. [ABSTRACT FROM AUTHOR]

Out of the 45 radars composing the Terminal Doppler Weather Radar (TDWR) network, 21 are located in areas of complex terrain. Their mission to observe low-level wind shear at major airports prone to strong shear-induced accidents puts them in an ideal position to fill critical boundary layer observation gaps within the NEXRAD network in these regions. Retrievals such as velocity azimuth display and velocity volume processing (VVP) are used to create time-height profiles of the boundary layer from radar conical scans, but assume that a wide area around the radar is horizontally homogeneous. This assumption is rarely met in regions of complex terrain. This paper introduces a VVP retrieval with limited radius to make these profiling techniques informative for flows affected by topography. These retrievals can be applied to any operational radar to help examine critical boundary layer processes. VVP retrievals were derived from the TDWR for Salt Lake City International Airport, TSLC, during a summertime high ozone period. These observations highlighted thermally driven circulations and variations in boundary layer depth at high vertical and temporal resolution and provided insight on their influence on air quality. Significance Statement: Residents in many urban areas of the United States are exposed to elevated ozone concentrations during the summer months. In complex terrain, thermally driven circulations and terrain-forced flows affect chemical processes by modulating mixing and transport. A novel technique to monitor local boundary layer conditions on small horizontal length scales was applied to data from the Terminal Doppler Weather Radar located near Salt Lake City International Airport during a multiday high ozone event, and effects of these flows on ozone concentrations are illustrated. This technique can be applied to other operational weather radars to create long-term and real-time records of near-surface processes at high vertical and temporal resolution. [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]

The time and spatial variability of hail events limit the capability of diagnosing the occurrence and stones' size in thunderstorms using weather radars. The bibliography presents multiple variables and methods with different pros and cons. The studied area, the Lleida Plain, is annually hit by different hailstorms, which have a high impact on the agricultural sector. A rectangular distributed hail pad network in this plain has worked operationally since 2000 to provide information regarding different aspects of hail impact. Since 2002, the Servei Meteorològic de Catalunya (SMC) has operated a single-pol C-band weather radar network that volumetrically covers the region of interest. During these years, the SMC staff has been working on improving the capability of detecting hail, adapting some parameters and searching for thresholds that help to identify the occurrence and size of the stones in thunderstorms. The current research analyzes a twenty-year period (2004–2023) to provide a good picture of the hailstorms occurring in the region of interest. The main research result is that VIL (Vertically Integrated Liquid) density is a better indicator for hailstone size than VIL, which presents more uncertainty in discriminating different hail categories. [ABSTRACT FROM AUTHOR]

The exodus of flying animals from their roosting locations is often visible as expanding ring‐shaped patterns in weather radar data. The NEXRAD network, for example, archives more than 25 years of data across 143 contiguous US radar stations, providing opportunities to study roosting locations and times and the ecosystems of birds and bats. However, access to this information is limited by the cost of manually annotating millions of radar scans. We develop and deploy an AI‐assisted system to annotate roosts in radar data. We build datasets with roost annotations to support the training and evaluation of automated detection models. Roosts are detected, tracked, and incorporated into our developed web‐based interface for human screening to produce research‐grade annotations. We deploy the system to collect swallow and martin roost information from 12 radar stations around the Great Lakes spanning 21 years. After verifying the practical value of the system, we propose to improve the detector by incorporating both spatial and temporal channels from volumetric radar scans. The deployment on Great Lakes radar scans allows accelerated annotation of 15 628 roost signatures in 612 786 radar scans with 183.6 human screening hours, or 1.08 s per radar scan. We estimate that the deployed system reduces human annotation time by ~7×. The temporal detector model improves the average precision at intersection‐over‐union threshold 0.5 (APIoU =.50) by 8% over the previous model (48%→56%), further reducing human screening time by 2.3× in its pilot deployment. These data contain critical information about phenology and population trends of swallows and martins, aerial insectivore species experiencing acute declines, and have enabled novel research. We present error analyses, lay the groundwork for continent‐scale historical investigation about these species, and provide a starting point for automating the detection of other family‐specific phenomena in radar data, such as bat roosts and mayfly hatches. [ABSTRACT FROM AUTHOR]

Influenced by the Northeast China cold vortex, Jiangsu Province experienced strong thunderstorm and gale weather on April 30, 2021, which caused 17 deaths and direct economic losses of 164 million yuan. In particular, a rare extreme gale of 47.9 m s− 1 occurred in the Tongzhou Bay area. To gain insights into the mechanisms of the discharge and lightning activity in the thunderstorm, the lightning characteristics during the evolution of the storm and their relationship with microphysical parameters are analyzed using the satellite-based and ground-based lightning detection data and dual-polarization Doppler weather radar data. The results show that the extreme gale occurred during the weakening stage of lightning activity and was located between the bow echo and gust front, where the lightning activity was scarce. The intracloud (IC) lightning flashes always played a major role throughout the development, maturation and weakening of the storm, with an average percentage of approximately 80.0%. During the 30 min before the extreme gale, the average percentage of the IC lightning flashes was 83.6%. The proportions of the positive total lightning flashes and positive IC lightning flashes were large, whereas the proportion of the positive cloud-to-ground (CG) lightning flashes remained small (5.5% on average). Additionally, four lightning jumps passing the 2σ threshold test occurred during this strong storm. The total lightning jump appeared approximately 34 min before the extreme gale. When lightning jumps are considered as a strong weather warning indicator, the total lightning jump has advantages over the CG and IC flashes in terms of accuracy and timeliness. The Linear fitting indicates that the volume of ice-phase precipitation particles at or above the altitude of the − 10 °C isotherm, with radar reflectivity above 35dBZ, is the best indicator of the CG and total lightning activities, and the correlation coefficients of it with the CG and total flash rates are 0.77 and 0.67, respectively. [ABSTRACT FROM AUTHOR]

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]

High spatio‐temporal variability is a characteristic of extreme rainfall. In mountainous regions like the Tropical Andes, where intricate orography and mesoscale atmospheric dynamics greatly impact rainfall systems, this particularly holds for mountain areas like the Tropical Andes. Thus, the absence of operational rainfall monitoring networks with high spatio‐temporal resolution has imposed difficulties for a proper analysis of extreme rainfall events in the Ecuadorian Andes. Nowhere, we present our improved knowledge on rainfall extremes based on newly available rainfall radar data of this region. In our study we employ a clustering approach to identify types of extreme rainfall events and analyze their spatio‐temporal characteristics. Based on 3 years of data obtained from an X‐band scanning weather radar data, the study was conducted in the southern Ecuadorian Tropical Andes at 4450 m a.s.l. By applying a rainfall threshold, 67 extreme rainfall events were selected. The rainfall characteristics of each extreme rainfall event, such as the amount of rain, its duration, its hour, and month of occurrence were determined and used as input variables of a k‐means clustering analysis to group the events into different classes. The result revealed three main classes of extreme rainfall events. The first class is characterized by highest rain intensity and lowest duration. The second class is characterized by its month of occurrence, during the first 5 months of the year. The third class showed lowest rain intensity and highest duration mainly occurred at higher elevations. The typology of events advances our understanding of the spatio‐temporal characteristics of extreme rainfall in the Tropical Andes. [ABSTRACT FROM AUTHOR]

A proper time display format is essential for pilots to understand integrated meteorological radar information, thereby making informed flying decisions and steering clear of hazardous weather. Previous studies on time display format supported the advantages of digital format, while some studies found that analog clock format is superior to digital format. This study explored the effect of time display format on the cognitive performance of integrated meteorological radar information through two experiments. Experiment 1 first examined the effects of digital and analog clock displays on the timing of individual processing advance or delay changes in a general scenario. Then, Experiment 2 was conducted in a simulated flight scenario to investigate the advantages and disadvantages of digital and analog clock display in delay time processing with and without time pressure. The results showed the following: (1) Analog clock has more advantages than digital display format in processing the varying time difference. (2) Whether with or without time pressure, analog clock is more conducive to individual cognition of integrated meteorological radar information than digital time display. (3) The length of delay time is an important factor affecting individual time cognition, and it can also affect the cognition of radar information. The longer the delay time, the more difficult it is to identify the time and understand the information. These findings provide a certain reference for the design of the integrated meteorological radar information display interface. [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]