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).

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 Kalman Filtering–Based Probabilistic Nowcasting of Object-Oriented Tracked Convective Storms

2015 ◽  
Vol 32 (3) ◽  
pp. 461-477 ◽  
Author(s):  
Pekka J. Rossi ◽  
V. Chandrasekar ◽  
Vesa Hasu ◽  
Dmitri Moisseev
Keyword(s):  
Kalman Filtering ◽  
Object Oriented ◽  
Weather Radar ◽  
Radar Data ◽  
Skill Score ◽  
Convective Storms ◽  
Error Covariance ◽  
Storm Tracking ◽  
Noisy Measurements ◽  
Improved Accuracy

AbstractThe weather radar–based object-oriented convective storm tracking is a standard approach for analyzing and nowcasting convective storms. However, the majority of current storm-tracking algorithms provide nowcasts only in a deterministic fashion with limited ability to estimate the related uncertainties.This paper proposes a method for probabilistic nowcasting of convective storms that addresses the issue of uncertainty of nowcasts. The approach first utilizes a two-dimensional radar-based storm identification and tracking algorithm in conjunction with the Kalman filtering of noisy measurements of storm centroid with the continuous white noise acceleration model. The resulting smoothed estimates of storm centroid and velocity components and their error covariance values are then applied to nowcast the probability of storm occurrence.To verify the approach, 20–60-min nowcasts were computed every 5 min using composite weather radar data in Finland including approximately 22 000 tracked storms. The verification shows that the algorithm is applicable in both deterministic and probabilistic manner. Moreover, the forecast probabilities are consistent with observed frequencies of the storms, especially with 20- and 30-min nowcasts. The accuracy of the probabilistic nowcasts was evaluated through the Brier skill score with respect to the deterministic nowcasts and nowcasts based on observation persistence and sample climatology. The results show that the proposed nowcasting method has an improved accuracy over all of the reference forecast types.

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.

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 A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

2019 ◽  
Vol 12 (9) ◽  
pp. 4031-4051 ◽  
Author(s):  
Shizhang Wang ◽  
Zhiquan Liu
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Radar Data ◽  
Poor Quality ◽  
Variational Data Assimilation ◽  
Radar Reflectivity ◽  
Background Error ◽  
Background Fields ◽  
Ice Phase

Abstract. A reflectivity forward operator and its associated tangent linear and adjoint operators (together named RadarVar) were developed for variational data assimilation (DA). RadarVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3D-Var) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadarVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50–100 iterations in each loop were needed to obtain a converged 3-D analysis of reflectivity, rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. It is shown that the deficiencies in the analysis using this operator, caused by the poor quality of the background fields and the use of the static background error covariance, can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using RadarVar also improved the short-term (2–5 h) precipitation forecasts compared to those of the experiment without DA.

Fire ignitions related to radar reflectivity patterns in Arizona and New Mexico

2008 ◽  
Vol 17 (3) ◽  
pp. 317 ◽  
Author(s):  
Beth L. Hall
Keyword(s):  
New Mexico ◽  
Leading Edge ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Lightning Activity ◽  
Fire Season ◽  
Lightning Strike ◽  
Radar Echo ◽  
Cloud To Ground Lightning ◽  
Storm Cells

Over 5400 lightning-ignited wildfires were detected on federal land in Arizona and New Mexico from 1996 through 1998 during the fire season of May through September. The non-uniform and sporadic spatial nature of precipitation events in this region makes the use of rain gauge data a limited means of assessing when and where a cloud-to-ground lightning strike might have ignited a wildfire due to dry lightning. By analysing weather radar reflectivity data with lightning and wildfire data, characteristics of radar reflectivity can be used by fire weather forecasters to identify regions of increased ignition potential. Critical ranges of reflectivity, life span of a reflectivity cell, and storm movement are characteristics of radar reflectivity that are examined in this analysis. The results of this type of analysis can help focus attention of wildfire personnel to particular locations where there is known to be cloud-to-ground lightning in conjunction with radar reflectivity patterns that have been historically associated with wildfire ignition. Results from the analysis show that wildfire ignitions typically occur near the perimeter of a radar echo. The reflectivity values at the ignition location are less than the highest reflectivity located within the echo, and often magnitudes are sufficiently low to suggest that the precipitation is not reaching the ground in this dry region with high cloud bases. Interpretation of the duration, size and level of lightning activity of the radar echo associated with the ignition indicate that ignitions tend to occur in the early stages of a radar echo. However, there are often multiple storm cells having isolated areas of higher reflectivity within a radar echo at the time of ignition. Nearly two-thirds of radar echoes associated with wildfire ignitions moved more than 50 km throughout the echo’s lifetime. These moving storm systems often propagated in a northerly or easterly direction, and ignitions occurred on the leading edge of the storm in over half of the cases that propagated in the same direction. Forecasters can use results from this study to determine where there is an increased potential of wildfire ignitions when similar radar patterns appear in conjunction with lightning activity in the future.

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