Weather Radar Research Articles

Recent Advances in Phased Array Weather Radar

Recent Advances in Phased Array Weather Radar

Analysis of a Case of a Classical Supercell Storm in Bihar, India: Observation and Tracking

In this study, we use the S-band Doppler Weather Radar to analyze the weather of a prolonged classical supercell storm that occurred on April 7, 2018, across the Indian state of Bihar. In the early morning hours of April 7th, 2018, a supercell storm with its origins in a colliding cloud mass produced in the Himalayan foothills invaded the Himalayan foothills from the North-West of Bihar via East Uttar Pradesh. The echo top increased to over 14 kilometers as it moved through the Bihar districts of Siwan and Gopalganj. As it followed, the storm shifted direction, heading northwest. Maximum radar reflectivity reached up to 61 dBz at 11: 32 IST observation (seen from Doppler Weather Radar, Patna), which may be the highest reflectivity ever recorded at DWR (Doppler Weather Radar) Station, Patna. It stopped being a supercell at 13: 30 IST and convert into a multicellular storm. A strange hook echo could be seen off the Storm's back in the Vertical Integrated Liquid profile. The supercell had two linked outflows, one to the northeast and one to the southeast. The Supercell's inverted "V"-shaped front flank was another characteristic feature. Within a small vertical band along the echo wall, reflectance reached over 60 dB at its highest. There was significant divergence near the peak of the supercell storm, with the speed differential between the updraft and downdraft reaching around 60 m/s. The system lasted for about 7 to 8 hours, damaging hailstorms occurred often along the path. Hail stones larger than 6-7 mm in diameter were spotted (as per the observed report). This particular cell was determined to be a supercell based on its internal structure, reflectivity, duration, and ground-level weather pattern. The incidence, timing, and development of the storms were all accurately predicted by the convective outlook products 2 to 3 hours in advance.

The Effect of Time Display Format on Cognitive Performance of Integrated Meteorological Radar Information.

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.

Assessment of ISRO’s S-band polarimetric Doppler Weather Radar (DWR) at SHAR with IMD-DWR and GPM-Ku radar

ABSTRACT 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.

Estimation of design precipitation using weather radar in Germany: A comparison of statistical methods

Study region:Germany. Study focus:Estimation of precipitation return levels on various temporal scales and with high spatial resolution is crucial for risk management and hydrological applications. Weather radars may provide this information, but design precipitation estimates from these instruments suffer from estimation biases and from the limited length of the available records. Here, the performance of two statistical methods for deriving design precipitation from 20 years of radar data for Germany is investigated: (a) a method based on peaks-over-threshold and an exponential distribution, operationally used for decades to compute design precipitation in Germany (DWA); and (b) a non-asymptotic approach that was recently shown to reduce estimation uncertainties related to short records (SMEV). The most recent official design precipitation for Germany derived from station data (KOSTRA-DWD-2020) are used as a benchmark. New Hydrological Insights for the RegionDesign precipitation from radar data tends to be lower than those derived from stations, due to the scale mismatch (point scale versus ∼1km2 of radar) and to biases in radar estimates of extremes. SMEV tends to underestimate more than DWA, especially for short durations. Larger uncertainties are reported for the DWA method, while SMEV estimates tend to be more stable and less influenced by statistical outliers. Application of the KOSTRA-DWD-2020 method on radar data leads to results closer to SMEV.

Personalized federated learning for improving radar based precipitation nowcasting on heterogeneous areas

AbstractThe 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.

Study of low-level wind shear at a Qinghai-Tibetan Plateau airport

The Qinghai-Tibetan Plateau (QTP) is recognized as the world's largest and highest plateau, characterized by intricate topography and underlying surfaces. Within this region, volatile meteorological conditions and severe synoptic systems, including thunderstorms, turbulence, sandstorms, and notably low-level wind shear (LLWS), present safety hazards to aviation operations. Therefore, a comprehensive examination of wind shear at typical QTP airports is essential. Xining Caojiapu International Airport (ZLXN) serves as a crucial transportation hub in the northeastern QTP. The frequent incidence of LLWS events due to the airport's unique geographical and meteorological features makes it an ideal location for investigating LLWS on the plateau. This study analyzed 80 pilot reports collected from 2016 to 2021 to elucidate the spatial and temporal characteristics of LLWS events at this plateau-valley airport. Subsequently, reanalysis data and observations from ground automated weather observing systems (AWOS), a geostationary satellite, a Doppler weather radar (DWR), and a Doppler wind lidar (DWL) were comprehensively employed to investigate the underlying meteorological factors and weather patterns associated with these LLWS events. The findings revealed that LLWS events at ZLXN are predominantly triggered by convective systems, followed by cold fronts and downward momentum transportation, with a smaller proportion being induced by orographic winds and turbulence. Analysis of four representative LLWS events utilizing high-resolution DWL measurements provided insights into the types of LLWS generated and their potential impacts on aircraft operations. Conceptual models, aimed at establishing a foundation for the forecasting and warning of local LLWS, were also proposed based on multiple data sources.

Precipitation measurements experiment using microwave links in Eastern China from October 2020 to March 2022

Precipitation is an important piece of information needed in many areas such as transportation, military and agriculture, and microwave links have proven to be an effective means of acquiring it and can be used as a complementary means for professional precipitation measurement instruments such as rain gauges, weather radar, rain measuring satellites, etc. In this paper, two microwave links (at 26 GHz, both vertically polarised) located across a river in eastern China and a nearby OTT PARSIVEL disdrometer were used to carry out a precipitation observation experiment from October 2020 to March 2022 (March to November 2021 for liquid precipitation, others for possible non-liquid precipitation). The applicability of the dry and rainy period identification methods (standard deviation method and correlation coefficient method) based on this link data is analyzed first. In addition, a wet antenna attenuation correction model based on a Long Short-Term Memory (LSTM) neural network was applied to non-winter precipitation inversion. The results show that the rain rate and cumulative rainfall calculated from the two links are in good agreement with that calculated by the disdrometer data. Furthermore, quantitative inversion of winter precipitation using two microwave links is performed, and the variation characteristics of the link levels during non-liquid precipitation periods are also analyzed.

Automatic Identification of Three-Body Scatter Spike Based on Jensen–Shannon Divergence and Support Vector Machine

Abstract 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.

A Localized Quantitative Precipitation Estimation for S-band Polarimetric Radar in Taiwan

Abstract A polarimetric radar quantitative precipitation estimation to estimate rain rate (R) from specific attenuation (A) has been applied in Taiwan's operational Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system since 2016. A 3-yrs' (2016-2018) drop size distribution dataset from an operational Parsivel network was used to derive a localized coefficient as well as the α(K) function in the R(A) scheme for S-band radar, where α is a key parameter in the estimation of A and K is the linear fitted slope of differential reflectivity (ZDR) versus reflectivity (Z). The local DSD data was also used to derive localized R(Z) and R(KDP) relationships, and the relationships were evaluated using radar observations in heavy rain cases. A synthetic QPE combining the localized R(A), R(Z), and R(KDP) relationships is compared to its operational counterpart and showed about 8 % reduction in normalized mean error for the Mei-Yu cases. Typhoon cases exhibited similar improvements by the localized QPE relationships, but showed higher uncertainties than in the Mei-Yu cases. The higher uncertainties in the typhoon QPE verification was likely due to the stronger winds in typhoons than in the Mei-Yu events that caused greater mismatches between the radar observations at an altitude and the gauges at the ground. Overall, the results demonstrated advantages of localized radar rainfall relationships derived from the disdrometer data to improve the accuracy of the operational rainfall estimation products.

Raindrop size distribution (DSD) retrieval from polarimetric radar observations using neural networks

As a key component of rainfall estimation, the understanding of raindrop size distribution (DSD) is a long-standing goal in meteorology and hydrology. Given that weather radar can observe the precipitation microphysics over large spatial and temporal scales, it has been broadly applied in DSD estimation. Traditional polynomial regression algorithms that correlate DSD parameters and radar signatures are still widely applied due to their simple structure and acceptable accuracy. This study proposes a new DSD retrieval model using dual-polarization radar observations based on long short-term memory (LSTM) network techniques. Three schemes able to retrieve the parameters of a normalized gamma DSD (LSTM-D0, LSTM-Nw, and LSTM-μ) are proposed with different combinations of polarimetric radar measurement inputs. All LSTM estimators exhibit better performance than the polynomial regression method. The Nash-Sutcliffe efficiency coefficient for estimates of drop median diameter (D0) and intercept parameter (Nw) increases from 0.93 and 0.70 to 0.95 and 0.93 respectively at Chilbolton station. Poor estimates of the shape parameter (μ) using the polynomial regression estimator complicate real applications, whereas the remarkable improvement of LSTM model estimation facilitates practical applications. The temporal and spatial predictability is then estimated to investigate long-term estimator performance for various radars, or at least for all radar pixels of a single radar. The predictability, measured by the Nash coefficient, increases temporally by 0.08, 0.31, and 0.39 and spatially by 0.03, 0.19, and 0.23 for the parameters D0, log10Nw, and μ respectively. This study contributes to improving quantitative precipitation estimates from radar polarimetry, enabling a better understanding of precipitation microphysics.

High-Resolution Rainfall Estimation Using Ensemble Learning Techniques and Multisensor Data Integration.

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.

Evolution characteristics and mechanism of meso-γ-scale vortex in an atypical bow echo in the North China Plain

On June 25, 2020, an atypical bow echo (ATBE) accompanied by a meso-γ-scale vortex (MV) incurred the strongest gust (41.4 m s−1) recorded since 1957 in the North China Plain. In this article, we investigate the evolution characteristics of MV and its mechanism for the extreme wind by using the observation data of Doppler weather radar and automatic weather stations as well as the four-dimensional Variational Doppler Radar Analysis System (VDRAS). The results show that the MV in this process was initially born at a height of 0.9–2.4 km, and the contracting and stretching vertical vortex rapidly descended to surface from the 2.0 km height with the rotation speed increasing rapidly. Six minutes later, the extreme wind occurred. The generation mechanism of the MV is that, under the strong vertical wind shear and downdraft in the lower troposphere, the vertical vortex tube of MV was transformed from the horizontal vortex tube generated in the zone of perturbation temperature gradient. The influence mechanism of MV on the extreme wind is that, as the MV continued to intensify and extend downward, a negative perturbation low-pressure center formed in the lower troposphere near the MV. Meanwhile, the descending rear-inflow jet (RIJ) was blocked by the prefrontal upward motion and a positive perturbation high-pressure center formed at around 1 km height. The violent positive-negative couplets of perturbation pressure gradient produced a downward nonhydrostatic perturbation pressure gradient force (NPPGF) that further intensified the vertical downward velocity. In addition to the strong downdraft velocity, the Coriolis force also enhanced the formation of the extreme wind through the action of ageostrophic winds. Besides, this paper also compares the mechanism of this process with that of a typical bow echo MV and the extreme wind it affected.

Quasi-Linear Convective Systems and Derechos across Europe: Climatology, Accompanying Hazards, and Societal Impacts

Abstract In this work, we use 8 years (2014–21) of Operational Programme for the Exchange of Weather Radar Information (OPERA) radar data, ESWD severe weather reports, and arrival time difference (ATD) lightning detection network (ATDnet) data to create a climatology of quasi-linear convective systems (QLCSs) across Europe. In the first step, 15-min radar scans were used to identify 1475 QLCS polygons. Severe weather reports, lightning data, and morphological properties were used to classify QLCSs according to their intensity into 1151 marginal (78.0%), 272 moderate (18.5%), and 52 derecho (3.5%) events. The manual evaluation led to the recognition of QLCS morphological and precipitation archetypes, areal extent, duration, speed, forward motion, width, length, accompanying hazards, injuries, and fatalities. Results indicate that QLCSs are the most frequent during summer in central Europe, while in southern Europe, their occurrence is extended to late autumn. A bow echo feature occurred in around 29% of QLCS cases, while a mesoscale convective vortex occurred in almost 9%. Among precipitation modes, trailing and embedded stratiform types accounted for around 50% of QLCSs. The most frequent hazard accompanying QLCSs was lightning (taking up on average 94.4% of the area impacted by QLCS), followed by severe wind gusts (7.9%), excessive precipitation (6.1%), large hail (2.9%), and tornadoes (0.5%). Derechos had the largest coverage of severe wind reports (49.8%), while back-building QLCSs were the most prone to excessive precipitation events (13.5%). QLCSs caused 104 fatalities and 886 injuries. Severe wind gusts were responsible for 87.6% of fatalities and 73.6% of injuries. Nearly half of all fatalities and injuries were associated with only the 10 most impactful QLCS events, mostly warm-season derechos.

Nocturnal avian migration drives high daily turnover but limited change in abundance on the ground

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.

A New Perspective on the Scattering Mechanism of S-Band Weather Radar Clear-Air Echoes Based on Communication Models

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.

The role of citizen science in assessing the spatiotemporal pattern of rainfall events in urban areas: a case study in the city of Genoa, Italy

Abstract. 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.

Potential Precursory Signals of Localized Torrential Rainfall From Geostationary Satellite and Radar Observations: A Case Study of the 2022 Seoul Flood

AbstractThe Korean Peninsula frequently experiences localized torrential rainfall (LTR) in the summer. However, on August 8, 2022, a peculiar LTR occurred by the continuous generation of convective clouds within a few hours, numerical weather prediction model was hard to forecast such a high intensity of LTR. This study explores the possibility of uncovering potential precursory signals using remote sensing techniques in both Geostationary Korea Multi-Purpose Satellite 2A (GK2A) and the operational RKSG (Camp Humphreys) Weather Surveillance Radar 88 Doppler (WSR-88D). Using cloud properties from GK2A, cloud top temperature showed a decrease and maintained low values below 220 K 1–1.5 h before the LTR events. However, discerning the exact onset of LTR in already mature stage clouds using only GK2A variables proved challenging. Instead, liquid water content from RKSG sharply increased before the LTR started. Our calculation of the LTR potential from a combination of GK2A and RKSG cloud properties shows a more accurate precursory signal of LTR than from GK2A cloud properties solely or RKSG either. This study highlights the synergistic benefits of combining geostationary satellite and radar observations to understand and predict early precursors of LTR events.

Heavy rainfall and thunderstorms in south interior Karnataka: a case study on august 31, 2023

Study aims to conduct a synoptic and thermodynamic analysis of significant rainfall events that happened during the break monsoon season. During the investigation of meteorological events, it was observed that the monsoon trough at mean sea level passed along the foothills of the Himalaya. On August 31, 2023, there was a cyclonic circulation observed in the northeast Bay of Bengal as well as adjacent areas, reaching an altitude of 5.8 km above mean sea level (AMSL). This circulation tilted southwestward with height. During the period of August 31-September 2, 2023, there was a cyclonic circulation persistent that occurred inside a particular geographical area. This phenomenon extended up to a height of 4.5 km AMSL and exhibited a south-westward tilt as the altitude increased. On August 31, 2023, the Convective Available Potential Energy (CAPE) was measured to be 2153 J/kg, whereas the Convective Inhibition Energy (CINE) was recorded as 0 J/kg. INSAT-3D satellite image showed a cloud top temperature of – 40 °C to – 60 °C. The Doppler Weather Radar (DWR) pictures also showed convective clouds to support this phenomenon. The synoptic and thermodynamic indices indicate a high likelihood of significant precipitation occurring over Bengaluru and its surrounding regions.

Weather Radar Calibration Method Based on UAV-Suspended Metal Sphere.

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.