scholarly journals Climatology of Convective Storms in Estonia from Radar Data and Severe Convective Environments

2021 ◽  
Vol 13 (11) ◽  
pp. 2178
Author(s):  
Tanel Voormansik ◽  
Tuule Müürsepp ◽  
Piia Post
Keyword(s):  
Convective Available Potential Energy ◽  
Grid Cell ◽  
Radar Data ◽  
Detection Algorithm ◽  
Radar Reflectivity ◽  
Convective Storm ◽  
Convective Storms ◽  
Base Level ◽  
Circulation Types ◽  
System Data

Data from the C-band weather radar located in central Estonia in conjunction with the latest reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5, and Nordic Lightning Information System (NORDLIS) lightning location system data are used to investigate the climatology of convective storms for nine summer periods (2010–2019, 2017 excluded). First, an automated 35-dBZ reflectivity threshold-based storm area detection algorithm is used to derive initial individual convective cells from the base level radar reflectivity. Those detected cells are used as a basis combined with convective available potential energy (CAPE) values from ERA5 reanalysis to find thresholds for a severe convective storm in Estonia. A severe convective storm is defined as an area with radar reflectivity at least 51 dBZ and CAPE at least 80 J/kg. Verification of those severe convective storm areas with lightning data reveals a good correlation on various temporal scales from hourly to yearly distributions. The probability of a severe convective storm day in the study area during the summer period is 45%, and the probability of a thunderstorm day is 54%. Jenkinson Collison’ circulation types are calculated from ERA5 reanalysis to find the probability of a severe convective storm depending on the circulation direction and the representativeness of the investigated period by comparing it against 1979–2019. The prevailing airflow direction is from SW and W, whereas the probability of the convective storm to be severe is in the case of SE and S airflow. Finally, the spatial distribution of the severe convective storms shows that the yearly mean number of severe convective days for the 100 km2 grid cell is mostly between 3 and 8 in the distance up to 150 km from radar. Severe convective storms are most frequent in W and SW parts of continental Estonia.

scholarly journals Constructing Three-Dimensional Multiple-Radar Reflectivity Mosaics: Examples of Convective Storms and Stratiform Rain Echoes

2005 ◽  
Vol 22 (1) ◽  
pp. 30-42 ◽  
Author(s):  
Jian Zhang ◽  
Kenneth Howard ◽  
J. J. Gourley
Keyword(s):  
High Resolution ◽  
Coordinate System ◽  
Real Time ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Raw Data ◽  
Spherical Coordinate ◽  
Base Level ◽  
Analysis Scheme ◽  
Reflectivity Data

Abstract The advent of Internet-2 and effective data compression techniques facilitates the economic transmission of base-level radar data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network to users in real time. The native radar spherical coordinate system and large volume of data make the radar data processing a nontrivial task, especially when data from several radars are required to produce composite radar products. This paper investigates several approaches to remapping and combining multiple-radar reflectivity fields onto a unified 3D Cartesian grid with high spatial (≤1 km) and temporal (≤5 min) resolutions. The purpose of the study is to find an analysis approach that retains physical characteristics of the raw reflectivity data with minimum smoothing or introduction of analysis artifacts. Moreover, the approach needs to be highly efficient computationally for potential operational applications. The appropriate analysis can provide users with high-resolution reflectivity data that preserve the important features of the raw data, but in a manageable size with the advantage of a Cartesian coordinate system. Various interpolation schemes were evaluated and the results are presented here. It was found that a scheme combining a nearest-neighbor mapping on the range and azimuth plane and a linear interpolation in the elevation direction provides an efficient analysis scheme that retains high-resolution structure comparable to the raw data. A vertical interpolation is suited for analyses of convective-type echoes, while vertical and horizontal interpolations are needed for analyses of stratiform echoes, especially when large vertical reflectivity gradients exist. An automated brightband identification scheme is used to recognize stratiform echoes. When mosaicking multiple radars onto a common grid, a distance-weighted mean scheme can smooth possible discontinuities among radars due to calibration differences and can provide spatially consistent reflectivity mosaics. These schemes are computationally efficient due to their mathematical simplicity. Therefore, the 3D multiradar mosaic scheme can serve as a good candidate for providing high-spatial- and high-temporal-resolution base-level radar data in a Cartesian framework in real time.

Hail climatology and impacts for the Netherlands

2020 ◽  
Author(s):  
Hans de Moel ◽  
Lucas Wouters ◽  
Maaike Boon ◽  
Demi van Putten ◽  
Bram Van 't Veen ◽  
...  
Keyword(s):  
The Netherlands ◽  
Urban Areas ◽  
Management Strategies ◽  
Radar Data ◽  
Detection Algorithm ◽  
Solar Panels ◽  
Convective Storms ◽  
Time Period ◽  
Risk Management Strategies ◽  
Spatial Differences

<p><span>Convective storms that produce large hail are among the most damaging natural hazards and globally losses due to these events are increasing. To evaluate and quantify the potential risk associated with these storms, hail climatologies are created from historical records. Unfortunately, a comprehensive analysis of the Netherlands does not exist. </span></p><p><span>The aim of this study is to create a hail climatology of the Netherlands and report on spatial and temporal hail risk by combining two approaches. The first approach relies on written documents containing information on historic events collected from <em>Weerspiegel</em>-magazine and the <em>European Severe Weather Database</em> (ESWD), from the time period 1974-2019. The second approach utilizes radar-data from the time period 2008-2019 and implements a radar-based Hail Detection Algorithm (HDA) to estimate hailstone sizes.</span></p><p><span>Using these sources of hail observations, return periods are estimated for hail storms with various hail sizes in the Netherlands. Moreover, spatial differences within the Netherlands are explored based on both the written documents as well as the radar-based observations. Using this climatology, probabilities of being hit by hail with a certain size are calculated, differentiated by province. Such probabilities are of direct use for developing and evaluating risk management strategies for both the public (municipalities) and private sector (such as insurance). This becomes evident when looking, for instance, at solar panels, which serve an important role in the transition towards climate-neutral urban areas, but are also vulnerable to an (increasing) hail risk.</span></p>

History of Convective Storm Science

2016 ◽  
Author(s):  
Charles A. Doswell
Keyword(s):  
United States ◽  
World War Ii ◽  
Convective Available Potential Energy ◽  
Vertical Wind Shear ◽  
The United States ◽  
World War ◽  
Convective Storm ◽  
Severe Storms ◽  
Convective Storms ◽  
Modern Era

Convective storms are the result of a disequilibrium created by solar heating in the presence of abundant low-level moisture, resulting in the development of buoyancy in ascending air. Buoyancy typically is measured by the Convective Available Potential Energy (CAPE) associated with air parcels. When CAPE is present in an environment with strong vertical wind shear (winds changing speed and/or direction with height), convective storms become increasingly organized and more likely to produce hazardous weather: strong winds, large hail, heavy precipitation, and tornadoes. Because of their associated hazards and their impact on society, in some nations (notably, the United States), there arose a need to have forecasts of convective storms. Pre-20th-century efforts to forecast the weather were hampered by a lack of timely weather observations and by the mathematical impossibility of direct solution of the equations governing the weather. The first severe convective storm forecaster was J. P. Finley, who was an Army officer, and he was ordered to cease his efforts at forecasting in 1887. Some Europeans like Alfred Wegener studied tornadoes as a research topic, but there was no effort to develop convective storm forecasting. World War II aircraft observations led to the recognition of limited storm science in the topic of convective storms, leading to a research program called the Thunderstorm Product that concentrated diverse observing systems to learn more about the structure and evolution of convective storms. Two Air Force officers, E. J. Fawbush and R. C. Miller, issued the first tornado forecasts in the modern era, and by 1953 the U.S. Weather Bureau formed a Severe Local Storms forecasting unit (SELS, now designated the Storm Prediction Center of the National Weather Service). From the outset of the forecasting efforts, it was evident that more convective storm research was needed. SELS had an affiliated research unit called the National Severe Storms Project, which became the National Severe Storms Laboratory in 1963. Thus, research and operational forecasting have been partners from the outset of the forecasting efforts in the United States—with major scientific contributions from the late T. T. Fujita (originally from Japan), K. A. Browning (from the United Kingdom), R. A. Maddox, J. M. Fritsch, C. F. Chappell, J. B. Klemp, L. R. Lemon, R. B. Wilhelmson, R. Rotunno, M. Weisman, and numerous others. This has resulted in the growth of considerable scientific understanding about convective storms, feeding back into the improvement in convective storm forecasting since it began in the modern era. In Europe, interest in both convective storm forecasting and research has produced a European Severe Storms Laboratory and an experimental severe convective storm forecasting group. The development of computers in World War II created the ability to make numerical simulations of convective storms and numerical weather forecast models. These have been major elements in the growth of both understanding and forecast accuracy. This will continue indefinitely.

scholarly journals Storm Tracking via Tree Structure Representation of Radar Data

2017 ◽  
Vol 34 (4) ◽  
pp. 729-747 ◽  
Author(s):  
Jinyi Hou ◽  
Ping Wang
Keyword(s):  
Radar Data ◽  
Radar Reflectivity ◽  
Tree Structure ◽  
Spatial Relationships ◽  
Structure Representation ◽  
Convective Storms ◽  
Critical Success ◽  
Storm Tracking ◽  
Internal Structures ◽  
Storm Cells

AbstractAn algorithm for automatic storm identification, tracking, and nowcasting using tree structure representation of radar reflectivity images is proposed. The algorithm aims to track and nowcast different kinds of storm objects (stratiform regions, convective storms, and storm cells) simultaneously and to preserve their spatial relationships in the tracking and nowcasting processes. The algorithm applies a region tree structure to represent intensity regions and their spatial relationships in radar reflectivity images. Storm objects are identified by clustering regions within the region tree structure. Storm tracking is accomplished using an iterative region tree matching algorithm. Storm nowcasting applies the tree structure to the nowcasting of the internal structures of storm objects. Using eight cases with different storm types, a comparative evaluation with the enhanced Thunderstorm Identification, Tracking, Analysis, and Nowcasting (ETITAN) method and the Storm Cell Identification and Tracking (SCIT) method has shown that the proposed tree-based storm-tracking algorithm achieves better performance in storm tracking and nowcasting. The critical success index (CSI) value of storm association is 78.16% for the tree-based method, as compared with 74.88% for SCIT and 74.71% for ETITAN. The CSI value of an 18-min nowcast is 29.02% for the tree-based method, as compared with 24.98% for SCIT and 24.44% for ETITAN. The evaluation also shows that the tree-based method is able to nowcast the internal structure of storms and therefore produces small mean absolute errors (MAE).

Initiation of Convective Storms at Low-Level Mesoscale Boundaries in Southwestern Ontario

2018 ◽  
Vol 33 (2) ◽  
pp. 583-598 ◽  
Author(s):  
Lisa S. Alexander ◽  
David M. L. Sills ◽  
Peter A. Taylor
Keyword(s):  
Radar Data ◽  
Moving Boundaries ◽  
Lake Breeze ◽  
Convective Storm ◽  
Convective Storms ◽  
Low Level ◽  
New Findings ◽  
Gust Fronts ◽  
Storm Cells ◽  
The Relationship

Abstract The relationship between low-level mesoscale boundaries and convective storm initiation was investigated in southwestern Ontario, Canada. The influence of lake-breeze fronts, a type of boundary that frequently affects this region of the Great Lakes watershed in summer, presented a particular interest. Radar data were processed using thunderstorm cell identification and tracking algorithms. The distances between the locations of storm cells reaching an intensity of 40 dBZ and the closest low-level mesoscale boundary were measured. Considering only days not influenced by a warm front, more than 75% of cells developed within 30 km of a low-level mesoscale boundary. Further examination by boundary type showed that cell initiations associated with moving boundaries and storm gust fronts occurred most often 0–5 km behind the boundaries. However, cell initiations associated with lake-breeze fronts most often occurred 0–5 km ahead of the boundaries. The analysis also suggested that lake-breeze fronts would often initiate the first storms of the day, which in turn generated gust fronts that could initiate subsequent storms. Overall, the results were similar to a previous study investigating storm initiation in the vicinity of low-level mesoscale boundaries in eastern Colorado and include some new findings in relation to lake-breeze fronts. The findings can be used by forecasters as well as automated nowcasting algorithms in order to improve predictions of storm initiation.

Variability of Updraft and Downdraft Characteristics in a Large Parameter Space Study of Convective Storms

2009 ◽  
Vol 137 (5) ◽  
pp. 1550-1561 ◽  
Author(s):  
Cody Kirkpatrick ◽  
Eugene W. McCaul ◽  
Charles Cohen
Keyword(s):  
Vertical Distribution ◽  
Wind Shear ◽  
Convective Available Potential Energy ◽  
Environmental Parameters ◽  
Cloud Base ◽  
Convective Storm ◽  
Convective Storms ◽  
Regression Methods ◽  
The Vertical Distribution ◽  
Large Parameter Space

Abstract Over 200 convective storm simulations are analyzed to examine the variability in storm vertical velocity and updraft area characteristics as a function of basic environmental parameters. While it is known that bulk properties of the troposphere such as convective available potential energy (CAPE) and deep-layer wind shear exert significant influence over updraft intensity and area, additional parameters such as the temperature at the cloud base, the height of the level of free convection (LFC), and the vertical distribution of buoyancy also have an effect. For example, at low CAPE, updraft strength is strongly related to the vertical distribution of buoyancy, and also to the bulk environmental wind shear. More generally, updraft area and its temporal variability both tend to increase in environments where the LFC is raised. Additionally, in environments with persistent storms, downdraft strength is sensitive to the bulk shear, environmental temperature, and LFC height. Using multiple linear regression methods, the best combinations of environmental parameters explain up to 81% of the interexperiment variance in second-hour mean peak updraft velocity, 74% for midlevel updraft area, and 64% for downdraft velocity. Downdraft variability is explained even less well (49%) when only persistent storms are considered. These idealized simulation results show that it is easier to predict storm updraft characteristics than those of the downdraft.

scholarly journals A ZDR Column Detection Algorithm to Examine Convective Storm Updrafts

2015 ◽  
Vol 30 (6) ◽  
pp. 1819-1844 ◽  
Author(s):  
Jeffrey C. Snyder ◽  
Alexander V. Ryzhkov ◽  
Matthew R. Kumjian ◽  
Alexander P. Khain ◽  
Joseph Picca
Keyword(s):  
High Resolution ◽  
Spatial Association ◽  
Detection Algorithm ◽  
Latent Heating ◽  
Convective Storm ◽  
Prognostic Information ◽  
Convective Storms ◽  
Bin Microphysics ◽  
Scanning Strategies ◽  
Differential Reflectivity

Abstract Observations and recent high-resolution numerical model simulations indicate that liquid water and partially frozen hydrometeors can be lofted considerably above the environmental 0°C level in the updrafts of convective storms owing to the warm thermal perturbation from latent heating within the updraft and to the noninstantaneous nature of drop freezing. Consequently, upward extensions of positive differential reflectivity (i.e., ZDR ≥ 1 dB)—called ZDR columns—may be a useful proxy for detecting the initiation of new convective storms and examining the evolution of convective storm updrafts. High-resolution numerical simulations with spectral bin microphysics and a polarimetric forward operator reveal a strong spatial association between updrafts and ZDR columns and show the utility of examining the structure and evolution of ZDR columns for assessing updraft evolution. This paper introduces an automated ZDR column algorithm designed to provide additional diagnostic and prognostic information pertinent to convective storm nowcasting. Although suboptimal vertical resolution above the 0°C level and limitations imposed by commonly used scanning strategies in the operational WSR-88D network can complicate ZDR column detection, examples provided herein show that the algorithm can provide operational and research-focused meteorologists with valuable information about the evolution of convective storms.

Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

2014 ◽  
Vol 53 (8) ◽  
pp. 2017-2033 ◽  
Author(s):  
Vivek N. Mahale ◽  
Guifu Zhang ◽  
Ming Xue
Keyword(s):  
Fuzzy Logic ◽  
Standard Deviation ◽  
Radar Data ◽  
Classification Algorithm ◽  
Radar Reflectivity ◽  
Membership Functions ◽  
Polarimetric Radar ◽  
Radar Echo ◽  
Three Body ◽  
Reflectivity Factor

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

scholarly journals Mobile Radar Observations of the Evolving Debris Field Compared with a Damage Survey of the Shawnee, Oklahoma, Tornado of 19 May 2013

2020 ◽  
Vol 148 (5) ◽  
pp. 1779-1803 ◽  
Author(s):  
Roger M. Wakimoto ◽  
Zachary Wienhoff ◽  
Howard B. Bluestein ◽  
David J. Bodine ◽  
James M. Kurdzo
Keyword(s):  
Cross Correlation ◽  
Vertical Structure ◽  
Leading Edge ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Vertical Profiles ◽  
Damage Survey ◽  
X Band ◽  
First Time ◽  
Rapid Scanning

Abstract A detailed damage survey is combined with high-resolution mobile, rapid-scanning X-band polarimetric radar data collected on the Shawnee, Oklahoma, tornado of 19 May 2013. The focus of this study is the radar data collected during a period when the tornado was producing damage rated EF3. Vertical profiles of mobile radar data, centered on the tornado, revealed that the radar reflectivity was approximately uniform with height and increased in magnitude as more debris was lofted. There was a large decrease in both the cross-correlation coefficient (ρhv) and differential radar reflectivity (ZDR) immediately after the tornado exited the damaged area rated EF3. Low ρhv and ZDR occurred near the surface where debris loading was the greatest. The 10th percentile of ρhv decreased markedly after large amounts of debris were lofted after the tornado leveled a number of structures. Subsequently, ρhv quickly recovered to higher values. This recovery suggests that the largest debris had been centrifuged or fallen out whereas light debris remained or continued to be lofted. Range–height profiles of the dual-Doppler analyses that were azimuthally averaged around the tornado revealed a zone of maximum radial convergence at a smaller radius relative to the leading edge of lofted debris. Low-level inflow into the tornado encountering a positive bias in the tornado-relative radial velocities could explain the existence of the zone. The vertical structure of the convergence zone was shown for the first time.

scholarly journals On the Introduction of Canny Operator in an Advanced Imaging Algorithm for Real-Time Detection of Hyperbolas in Ground-Penetrating Radar Data

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 541 ◽  
Author(s):  
Željko Bugarinović ◽  
Lara Pajewski ◽  
Aleksandar Ristić ◽  
Milan Vrtunski ◽  
Miro Govedarica ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Penetrating Radar ◽  
Urban Areas ◽  
Radar Data ◽  
Detection Algorithm ◽  
Advanced Imaging ◽  
Canny Operator ◽  
Imaging Algorithm ◽  
Canny Edge ◽  
Ground Penetrating

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB.

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