Thunderstorm Identification, Tracking, Analysis, And Nowcasting Research Articles

An early warning approach for the rapid identification of extreme weather disasters based on phased array dual polarization radar cooperative network data.

In recent years, X-band phase array dual polarization weather radar technology has matured. The cooperative networking data from X-band phase array dual polarization weather radar have many advantages compared with traditional methods, namely, high spatial and temporal resolution (approximately 70 seconds in one scan, 30 m in radial distance resolution), wide coverages that can compensate for the observation blind spots, and data fusion technology that is used in the observation overlap area to ensure that the observed precipitation data have spatial continuity. Based on the above radar systems, this study proposes an improved hail and lightning weather disaster rapid identification and early warning algorithm. The improved thunderstorm identification, tracking, analysis, and nowcasting (TITAN) algorithm is used to quickly identify three-dimensional strong convective storm cells. Large sample observation experiment data are used to invert the localized hail index (HDR) to identify the hail position. The fuzzy logic method is used to comprehensively determine the probability of lightning occurrence. The comparative analysis experiment shows that, compared with the live observation data from the ground-based automatic station, the hail and lightning disaster weather warning algorithm developed by this study can increase warning times by approximately 7 minutes over the traditional algorithm, and its critical success index (CSI), false alarm ratio (FAR) and omission alarm ratio (OAR) scores are better than those of the traditional method. The average root mean square error (ARMSE) for identifying hail and lightning locations by this improved method is also significantly better than that of traditional methods. We show that our method can provide probabilistic predictions that improve hail and lightning identification, improve the precision of early warning and support operational utility at higher resolutions and with greater lead times that traditional methods struggle to achieve.

Development and Application of a Real-Time Flood Forecasting System (RTFlood System) in a Tropical Urban Area: A Case Study of Ramkhamhaeng Polder, Bangkok, Thailand

In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system’s accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations.

Research on Thunderstorm Identification Based on Discrete Wavelet Transform

Lightning has been one of the most talked-about natural disasters worldwide in recent years, as it poses a great threat to all industries and can cause huge economic losses. Thunderstorms are often accompanied by natural phenomena such as lightning strikes and lightning, and many scholars have studied deeply the regulations of thunderstorm generation, movement and dissipation to reduce the risk of lightning damage. Most of the current methods for studying thunderstorms focus on using more complex algorithms based on radar or lightning data, which increases the computational burden and reduces the computational efficiency to some extent. This paper proposes a raster-based DWT (discrete wavelet transform) method for thunderstorm identification, this method uses DWT, CFSFD (clustering algorithm for fast search and finding density peaks) algorithm and ADTD (active divectory topology diagrammer) lightning location data for thunderstorm identification. The advantage of this method is that it supports different spatial resolutions and can identify any shape and number of thunderstorms at the same time and in the same area. It is effective in eliminating some of cluttered, scattered lightning data and extracting dense areas of thunderstorms. Furthermore, the method has a time complexity of O(n), and the computational efficiency is significantly better than the current TITAN (thunderstorm identification, tracking, analysis, and nowcasting) algorithm, which provides a good basis for subsequent extrapolation studies of thunderstorms.

Understanding the relationship of storm‐ to large‐scale environment in the monsoon trough region: results inferred from long‐term radar and reanalysis datasets

AbstractMonsoon trough (MT) convection exhibits multiscale spatial variability ranging from cellular storms to large organized convective systems. Understanding the relationships between storm‐scale convection and the large‐scale environment is crucial for accurately representing the properties and effect of convection in climate models. We investigate these relationships using nine wet seasons (June–September of 2009–2017) of S‐band radar observations over a tropical location (Kolkata) in the Indian summer monsoon trough region. A Lagrangian‐based objective cell‐tracking method, Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN), is applied to the volumetric reflectivity data to identify and track the convective storms. ERA5 hourly reanalysis products from the European Centre for Medium‐Range Weather Forecasts (ECMWF) are utilized for the field variables representing the large‐scale environment. We focus on the relationships of radar‐derived storm properties with large‐scale dynamic, thermodynamic, and stability variables such as convergence, mid‐level humidity, convective available potential energy (CAPE), and convective inhibition (CIN). Storm‐scale convection shows a selective nature, characterized as embedded cells in their unique environment. Furthermore, storms are classified as storm cells (simple and intense) and multicellular (linear and nonlinear) based on their geometrical properties and intensity. MT convection is predominantly composed of storm cells and characterized by an environment of moist mid‐level, low‐to‐moderate CAPE, low CIN, and strong dynamic forcing, that is, vertical ascent. In dry and weak dynamic forcing (i.e., subsiding atmosphere) with high CAPE and CIN, the highest rain intensity is associated with a few linear and nonlinear multicellular storms. In the development of such major linear and nonlinear multicellular storms over the eastern flank of the MT, the mid‐troposphere acts as a dry capping inversion. It is observed that the storm‐scale convection varies with the large‐scale dynamic forcing. During weak (strong) dynamic forcing conditions, the CAPE (mid‐level humidity) relates well with the observed storm‐scale convection.

Object-based analysis of simulated thunderstorms in Switzerland: application and validation of automated thunderstorm tracking with simulation data

Abstract. We present a feasibility study for an object-based method to characterise thunderstorm properties in simulation data from convection-permitting weather models. An existing thunderstorm tracker, the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) algorithm, was applied to thunderstorms simulated by the Advanced Research Weather Research and Forecasting (AR-WRF) weather model at convection-permitting resolution for a domain centred on Switzerland. Three WRF microphysics parameterisations were tested. The results are compared to independent radar-based observations of thunderstorms derived using the MeteoSwiss Thunderstorms Radar Tracking (TRT) algorithm. TRT was specifically designed to track thunderstorms over the complex Alpine topography of Switzerland. The object-based approach produces statistics on the simulated thunderstorms that can be compared to object-based observation data. The results indicate that the simulations underestimated the occurrence of severe and very large hail compared to the observations. Other properties, including the number of storm cells per day, geographical storm hotspots, thunderstorm diurnal cycles, and storm movement directions and velocities, provide a reasonable match to the observations, which shows the feasibility of the technique for characterisation of simulated thunderstorms over complex terrain.

A Case Study of Rural Area Hail Storm in Yomra, Trabzon, on August 31, 2017

On August 31, 2017, a hail storm occurred in rural area in Yomra District of Trabzon Province. The hail storm occurred during the harvest season, and it did not cause big losses on hazelnut production because hazelnuts had been fully grown. The hail storm was recorded, and some of hail accumulations had been photographed by a mobile phone. Diameters of hail were approximately 1.5 cm. In this study, the hail storm event is analysed by satellite and weather radar products that obtained from Turkish State Meteorological Service. Also, the radar data analysed by Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) software which is used for the first time in the analysis of meteorological events in Turkey. Here, it was discussed that the storm cell with 62.5 dBZ reflectivity value is whether a “supercell” or not.

A rapid identification and warning method for severe weather via Doppler radar based on an improved TITAN algorithm

An adaptive fast-recognition warning algorithm for severe weather using ground-based Doppler radar and Tropical Rainfall Measuring Mission (TRMM) sensor data is proposed for different seasons, regions and topography conditions in the East and South China Seas. An improved regional segmentation method and the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) algorithm were applied to identify three-dimensional strong storm cells and their physical features. Multiple logistic linear regression was used to establish a probabilistic warning model for strong convective weather, such as hail and lightning. Doppler radar identification and warning experiments were carried out for a strong squall line near the East China Sea and a supercell storm near the South China Sea. The experimental results showed that the proposed method has higher identification accuracy based on the azimuth and distance than the traditional algorithm. Moreover, the false negative and false report rates of the proposed method are low, which helps to quickly identify and warn against severe weather and protect lives and property.

Predicting Microbursts in the Northeastern U.S. Using Lightning Flash Rates and Simple Radar Parameters

Convective storms that produce microburst winds are difficult to predict because the strong surface winds arise in a short time period. Previous research suggests that timing and patterns in cloud height, echo top height, vertical integrated liquid (VIL), intracloud (IC) lightning, and cloud-to-ground (CG) lightning may identify and predict microbursts. Eleven quasi-cellular microburst cases and eight non-microburst severe wind cases were identified from New York, Pennsylvania, and New Jersey between 2012 and 2016. Total lightning data (IC + CG) were obtained from Vaisala’s National Lightning Detection Network (NLDN), and radar parameters were obtained from the Thunderstorm Identification Tracking Analysis and Nowcasting (TITAN) software. Values of VIL, echo top height, and cloud height were tracked through time along with total lightning strikes within a 15 km radius of the storm center. These parameters were plotted with respect to their mean and standard deviation for the 45 minutes leading up to event occurrence. Six of eleven cases featured peaks in total and IC lightning within 25 minutes prior to the microburst. These were the only variables among those examined to peak more than half the time for either the microburst cases or the null cases. The results suggest that microbursts behave somewhat differently than severe wind events, particularly in terms of lightning and VIL timing. The results dispute previous research that suggests that microbursts are highly predictable by the behavior of lightning and radar parameters.

Enhanced object-based tracking algorithm for convective rain storms and cells

This paper proposes a new object-based storm tracking algorithm, based upon TITAN (Thunderstorm Identification, Tracking, Analysis and Nowcasting). TITAN is a widely-used convective storm tracking algorithm but has limitations in handling small-scale yet high-intensity storm entities due to its single-threshold identification approach. It also has difficulties to effectively track fast-moving storms because of the employed matching approach that largely relies on the overlapping areas between successive storm entities. To address these deficiencies, a number of modifications are proposed and tested in this paper. These include a two-stage multi-threshold storm identification, a new formulation for characterizing storm's physical features, and an enhanced matching technique in synergy with an optical-flow storm field tracker, as well as, according to these modifications, a more complex merging and splitting scheme. High-resolution (5-min and 529-m) radar reflectivity data for 18 storm events over Belgium are used to calibrate and evaluate the algorithm. The performance of the proposed algorithm is compared with that of the original TITAN. The results suggest that the proposed algorithm can better isolate and match convective rainfall entities, as well as to provide more reliable and detailed motion estimates. Furthermore, the improvement is found to be more significant for higher rainfall intensities. The new algorithm has the potential to serve as a basis for further applications, such as storm nowcasting and long-term stochastic spatial and temporal rainfall generation.

Flash Flood–Producing Storm Properties in a Small Urban Watershed

Abstract The structure and evolution of flash flood–producing storms over a small urban watershed in the mid-Atlantic United States with a prototypical flash flood response is examined. Lagrangian storm properties are investigated through analyses of the 32 storms that produced the largest peak discharges in Moores Run between January 2000 and May 2014. The Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) algorithm is used to track storm characteristics over their life cycle with a focus on storm size, movement, intensity, and location. First, the 13 June 2003 and 1 June 2006 storms, which produced the two largest peak discharges for the study period, are analyzed. Heavy rainfall for the 13 June 2003 and 1 June 2006 storms were caused by a collapsing thunderstorm cell and a slow-moving, low-echo centroid storm. Analyses of the 32 storms show that collapsing storm cells play an important role in peak rainfall rate production and flash flooding. Storm motion is predominantly southwest-to-northeast, and approximately half of the storms exhibited some linear organization. Mean storm total rainfall for the 32 storms displayed an asymmetric distribution around Moores Run, with sharply decreasing gradients southwest of the watershed (upwind and into the city) and increased rainfall to the northeast (downwind and away from the city). Results indicate urban modification of rainfall in flash flood–producing storms. There was no evidence that the storms split around Baltimore. Flood-producing rainfall was highly concentrated in time; on average, approximately 21% of the storm total rainfall fell within 15 min.

Radar‐based cell tracking with fuzzy logic approach

ABSTRACTThis study presents a new algorithm Fuzzy logic Algorithm for Storm Tracking (FAST) for tracking convective cells in 3D Cartesian co‐ordinates. A fuzzy logic approach was applied to track cells with objectively determined membership functions (MFs) and weights that were derived from a large set of convective cell tracks (2326 pairs of convective cells). Three feature parameters (cell motion speed, area change ratio and axis transformation ratio) were selected by Bayesian information criterion and combined through fuzzy logic. FAST was evaluated with three skill scores using 2561 (2456) pairs of convective cells with (without) mergers and splits. The performance was tested in comparison with Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) as a baseline method. For reference tracks without merger and split, the critical success index (CSI) of FAST reaches about 0.89. FAST shows better skill scores (CSI = 0.92 and 0.89 at speed limitations of 16.1 m s−1 and 25.0 m s−1, respectively) than those of TITAN (CSI = 0.89 and 0.82 at the same speed limitations) and is independent of the number of convective cells and the speed limitation. When mergers and splits are considered in the reference, the tracking performance of FAST (FASTMS) with a merger and split process is always better than that of TITAN (TITANMS) with a merger and split process and is independent of speed limitations. When FAST (=FASTnoMS) does not include a merger and split process, its tracking performance is better than that of TITANMS at speed limitation of > 20 m s−1.

Radar-Derived Statistics of Convective Storms in Southeast Queensland

AbstractThe aim of this study is to examine the statistics of convective storms and their concomitant changes with thermodynamic variability. The thermodynamic variability is analyzed by performing a cluster analysis on variables derived from radiosonde releases at Brisbane Airport in Australia. Three objectively defined regimes are found: a dry, stable regime with mainly westerly surface winds, a moist northerly regime, and a moist trade wind regime. S-band radar data are analyzed and storms are identified using objective tracking software [Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN)]. Storm statistics are then investigated, stratified by the regime subperiods. Convective storms are found to form and maintain along elevated topography. Probability distributions of convective storm size and rain rate are found to follow lognormal distributions with differing mean and variance among the regimes. There was some evidence of trimodal storm-top heights, located at the trade inversion (1.5–2 km), freezing level (3.6–4 km), and near 6 km, but it was dependent on the presence of the trade inversion. On average, storm volume and height are smallest in the trade regime and rain rate is largest in the westerly regime. However, westerly regime storms occur less frequently and have shorter lifetimes, which were attributed to the enhanced stability and decreased humidity profiles. Furthermore, time series of diurnal rain rate exhibited early morning and midafternoon maxima for the northerly and trade regimes but were absent for the westerly regime. The observations indicate that westerly regime storms are primarily driven by large-scale forcing, whereas northerly and trade wind regime storms are more responsive to surface characteristics.

Numerical simulation of airborne cloud seeding over Greece, using a convective cloud model

An extensive work has been done by the Department of Meteorology and Climatology at Aristotle University of Thessaloniki and others using a three-dimensional cloud resolving model to simulate AgI seeding by aircraft of three distinct hailstorm cases occurred over Greece in period 2007-2009. The seeding criterion for silver iodide glaciogenic seeding from air is based on the beneficial competition mechanism. According to thermodynamic analysis and classification proposed by Marwitz (1972a, b, and c) and based on their structural and evolutionary properties we classified them in three groups as singlecell, multicell and supercell hailstorms. The seeding optimization for each selected case is conducted by analysis of the thermodynamic characteristics of the meteorological environment as well as radar reflectivity fields observed by the state of the art Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) software applied in the Greek National Hail Suppression Program (GNHSP). Results of this comprehensive study have shown positive effects with respect to hailfall decrease after successful seeding as our primarily objective. All three cases have illustrated 15-20% decrease in accumulated hailfall at the ground Seeded clouds have exhibited earlier development of precipitation and slight dynamical enhancement of the updraft and rainfall increase of ~10- 12.5%. The results have emphasized a strong interaction between cloud dynamics and microphysics, especially the subgrid scale processes that have impact on agent transport and diffusion in a complex environment. Comparisons between modelled and observed radar reflectivity also show a relatively good agreement. Simulated cloud seeding follows the operational aircraft seeding for hail suppression. The ability of silver-iodide particles to act as ice nuclei has been used to perform airborne cloud seeding, under controlled conditions of temperature and humidity. The seeding effects depend upon applying the seeding methodology in proper seeding time, right placement and agent dose rate.

Estudo dos eventos extremos de precipitação ocorridos em 2009 no estado do Pará

A importância da previsão meteorológica para períodos muito curtos de tempo (nowcasting) tem crescido nos últimos anos, devido ao aumento considerável de desastres naturaiscomo enchentes, deslizamentos de terra, rajadas de vento, etc. Nesse contexto, o uso do equipamento radar meteorológico tem grande utilidade operacional, na detecção e acompanhamento de fenômenos meteorológicos adversos de curto prazo. A Região Metropolitana de Belém (RMB) é uma das regiões mais populosas da Amazônia. Nos últimos anos vem apresentando uma ocupação urbana desordenada, o que talvez possa ser uma das causas dos problemas que a população sofre com os alagamentos e inundações. Os eventos extremos ocorridos durante o ano de 2009 foram identificados e analisados com o uso do TITAN (Thunderstorm Identification, Tracking, Analysis and Nowcasting). Os resultados mostram que 66% dos eventos extremos ocorreram na estação chuvosa, sendo devido a três principais sistemas meteorológicos, a Zona de Convergência Intertropical (ZCIT), a interação desta com a Zona de convergência do Atlântico Sul (ZCAS) e as Linhas de Instabilidade (LI) em sua maioria originadas na brisa marítima. Os resultados mostram também que os sistemas de menor escala, como as LI, apresentam severidade maior por provocar altas taxas de precipitação horária.

Statistical Characteristics of Convective Storms in Belgium Derived from Volumetric Weather Radar Observations

AbstractHigh-resolution volumetric reflectivity measurements from a C-band weather radar are used to study the characteristics of convective storms in Belgium. After clutter filtering, the data are processed by the storm-tracking system Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) using a 40-dBZ reflectivity threshold. The 10-yr period of 5-min data includes more than 1 million identified storms, mostly organized in clusters. A storm is observed at a given point 6 h yr−1 on average. Regions of slightly higher probability are generally correlated with orographic variations. The probability of at least one storm in the study area is 15%, with a maximum of 35% for July and August. The number of storms, their coverage, and their water mass are limited most of the time. The probability to observe a high number of storms reaches a maximum in June and in the early afternoon in phase with solar heating. The probability of large storm coverage and large water mass is highest in July and in the late afternoon. Convective storms are mostly small and weak. Deeper ones are found mainly in the afternoon whereas bigger and more intense ones also appear in the evening. The occurrence of the most intense storms does not vary along the day. Simple tracks have a mean duration of 25 min. Complex tracks, involving splitting or merging, last 70 min on average. Most convective storms move in the northeast direction, with a median speed of 30 km h−1. Their motion is slower in summer and in the afternoon. Regions with slightly higher convective initiation are related to orography.

Statistical Assessment of Tropical Convection-Permitting Model Simulations Using a Cell-Tracking Algorithm

Abstract This study presents a method for comparing convection-permitting model simulations to radar observations using an innovative object-based approach. The method uses the automated cell-tracking algorithm, Thunderstorm Identification Tracking Analysis and Nowcasting (TITAN), to identify individual convective cells and determine their properties. Cell properties are identified in the same way for model and radar data, facilitating comparison of their statistical distributions. The method is applied to simulations of tropical convection during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) using the Weather Research and Forecasting Model, and compared to data from a ground-based radar. Simulations with different microphysics and model resolution are also conducted. Among other things, the comparisons between the model and the radar elucidate model errors in the depth and size of convective cells. On average, simulated convective cells reached higher altitudes than the observations. Also, when using a low reflectivity (25 dBZ) threshold to define convective cells, the model underestimates the size of the largest cells in the observed population. Some of these differences are alleviated with a change of microphysics scheme and higher model resolution, demonstrating the utility of this method for assessing model changes.

Thunderstorm Strike Probability Nowcasting

Abstract To assist in thunderstorm warning, automated nowcasting systems have been developed that detect thunderstorm cells in radar images and propagate them forward in time to generate forecasted threat areas. Current methods, however, fail to quantify the probabilistic nature of the error structure of such forecasts. This paper introduces the Thunderstorm Environment Strike Probability Algorithm (THESPA), which forecasters can use to provide probabilistic thunderstorm nowcasts for risk assessment and emergency decision making. This method accounts for the prediction error by transforming thunderstorm nowcasts into a strike probability, or the probability that a given location will be impacted by a thunderstorm in a given period, by specifying a bivariate Gaussian distribution of speed and direction errors. This paper presents the development and analysis of the THESPA method and verifies performance using experimental data. Results from a statistical analysis of Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) tracking errors of nowcasts made near Sydney, Australia, were used to specify the distribution, which was then applied to data collected from the World Weather Research Programme (WWRP) Beijing 2008 Forecast Demonstration Project. The results are encouraging and show Brier skill scores between 0.36 and 0.44 with respect to a deterministic advected threat area forecast.