scholarly journals Relationships between Deep Convection Updraft Characteristics and Satellite-Based Super Rapid Scan Mesoscale Atmospheric Motion Vector–Derived Flow

2018 ◽  
Vol 146 (10) ◽  
pp. 3461-3480 ◽  
Author(s):  
Jason M. Apke ◽  
John R. Mecikalski ◽  
Kristopher Bedka ◽  
Eugene W. McCaul ◽  
Cameron R. Homeyer ◽  
...  
Keyword(s):  
Doppler Radar ◽  
Deep Convection ◽  
Motion Vector ◽  
Three Dimensional ◽  
Radar Data ◽  
Severe Weather ◽  
Lightning Flash ◽  
Rapid Scan ◽  
Total Lightning ◽  
The Relationship

Abstract Rapid acceleration of cloud-top outflow near vigorous storm updrafts can be readily observed in Geostationary Operational Environmental Satellite-14 (GOES-14) super rapid scan (SRS; 60 s) mode data. Conventional wisdom implies that this outflow is related to the intensity of updrafts and the formation of severe weather. However, from an SRS satellite perspective, the pairing of observed expansion and updraft intensity has not been objectively derived and documented. The goal of this study is to relate GOES-14 SRS-derived cloud-top horizontal divergence (CTD) over deep convection to internal updraft characteristics, and document evolution for severe and nonsevere thunderstorms. A new SRS flow derivation system is presented here to estimate storm-scale (<20 km) CTD. This CTD field is coupled with other proxies for storm updraft location and intensity such as overshooting tops (OTs), total lightning flash rates, and three-dimensional flow fields derived from dual-Doppler radar data. Objectively identified OTs with (without) matching CTD maxima were more (less) likely to be associated with radar-observed deep convection and severe weather reports at the ground, suggesting that some OTs were incorrectly identified. The correlation between CTD magnitude, maximum updraft speed, and total lightning was strongly positive for a nonsupercell pulse storm, and weakly positive for a supercell with multiple updraft pulses present. The relationship for the supercell was nonlinear, though larger flash rates are found during periods of larger CTD. Analysis here suggests that combining CTD with OTs and total lightning could have severe weather nowcasting value.

scholarly journals The Above-Anvil Cirrus Plume: An Important Severe Weather Indicator in Visible and Infrared Satellite Imagery

2018 ◽  
Vol 33 (5) ◽  
pp. 1159-1181 ◽  
Author(s):  
Kristopher Bedka ◽  
Elisa M. Murillo ◽  
Cameron R. Homeyer ◽  
Benjamin Scarino ◽  
Haiden Mersiovsky
Keyword(s):  
Satellite Imagery ◽  
Wave Breaking ◽  
Lead Time ◽  
Doppler Radar ◽  
Severe Weather ◽  
Rapid Scan ◽  
The World ◽  
Total Lightning ◽  
Anvil Cirrus ◽  
Rapid Scanning

Abstract Intense tropopause-penetrating updrafts and gravity wave breaking generate cirrus plumes that reside above the primary anvil. These “above anvil cirrus plumes” (AACPs) exhibit unique temperature and reflectance patterns in satellite imagery, best recognized within 1-min “super rapid scan” observations. AACPs are often evident during severe weather outbreaks and, due to their importance, have been studied for 35+ years. Despite this research, there is uncertainty regarding why some storms produce AACPs but other nearby storms do not, exactly how severe are storms with AACPs, and how AACP identification can assist with severe weather warning. These uncertainties are addressed through analysis of severe weather reports, NOAA/National Weather Service (NWS) severe weather warnings, metrics of updraft cloud height, intensity, and rotation derived from Doppler radars, as well as ground-based total lightning observations for 4583 storms observed by GOES super rapid scanning, 405 of which produced an AACP. Datasets are accumulated throughout storm lifetimes through radar object tracking. It is found that 1) AACP storms generated 14 times the number of reports per storm compared to non-AACP storms; 2) AACPs appeared, on average, 31 min in advance of severe weather; 3) 73% of significant severe weather reports were produced by AACP storms; 4) AACP recognition can provide comparable warning lead time to that provided by a forecaster; and 5) the presence of an AACP can increase forecaster confidence that large hail will occur. Given that AACPs occur throughout the world, and most of the world is not observed by Doppler radar, AACP-based severe storm identification and warning would be extremely helpful for protecting lives and property.

The Relationship between Total Cloud Lightning Behavior and Radar-Derived Thunderstorm Structure

2013 ◽  
Vol 28 (1) ◽  
pp. 237-253 ◽  
Author(s):  
Eric Metzger ◽  
Wendell A. Nuss
Keyword(s):  
Weather Type ◽  
Severe Weather ◽  
Characteristic Change ◽  
Lightning Flash ◽  
Fort Worth ◽  
Detection Systems ◽  
Lightning Detection ◽  
Rapid Changes ◽  
Total Lightning ◽  
The Relationship

Abstract Total lightning detection systems have been in development since the mid-1980s and deployed in several areas around the world. Previous studies on total lightning found intra- and intercloud lightning (IC) activity tends to fluctuate significantly during the lifetime of thunderstorms and have indicated that lightning jumps or rapid changes in lightning flash rates are closely linked to changes in the vertical integrated liquid (VIL) reading on the National Weather Service’s Weather Surveillance Radar-1988 Doppler (WSR-88D) systems. This study examines the total lightning and its relationship to WSR-88D signatures used operationally to determine thunderstorm severity to highlight the potential benefit of a combined forecast approach. Lightning and thunderstorm data from the Dallas–Fort Worth, Texas, and Tucson, Arizona, areas from 2006 to 2009, were used to relate total lightning behavior and radar interrogation techniques. The results indicate that lightning jumps can be classified into severe wind, hail, or mixed-type jumps based on the behavior of various radar-based parameters. In 25 of 34 hail-type jumps and in 18 of 20 wind-type jumps, a characteristic change in cloud-to-ground (CG) versus IC lightning flash rates occurred prior to the report of severe weather. For hail-type jumps, IC flash rates increased, while CG flash rates were steady or decreased. For wind-type jumps, CG flash rates increased, while IC flash rates either increased (12 of 18) or were steady or decreased (6 of 18). Although not every lightning jump resulted in a severe weather report, the characteristic behavior in flash rates adds information to radar-based approaches for nowcasting the severe weather type.

Random Forest Model to Assess Predictor Importance and Nowcast Severe Storms using High-Resolution Radar–GOES Satellite–Lightning Observations

2021 ◽  
Author(s):  
John R. Mecikalski ◽  
Thea N. Sandmæl ◽  
Elisa M. Murillo ◽  
Cameron R. Homeyer ◽  
Kristopher M. Bedka ◽  
...  
Keyword(s):  
Great Plains ◽  
Random Forests ◽  
Motion Vector ◽  
Radar Data ◽  
Lightning Flash ◽  
Severe Storms ◽  
Tornado Warnings ◽  
Predictor Importance ◽  
Scan Data ◽  
Total Lightning

AbstractFew studies have assessed combined satellite, lightning, and radar databases to diagnose severe storm potential. The research goal here is to evaluate next-generation, 60-second update frequency geostationary satellite and lightning information with ground-based radar to isolate which variables, when used in concert, provide skillful discriminatory information for identifying severe (hail ≥2.5 cm in diameter, winds ≥25 m s–1, tornadoes) versus non-severe storms. The focus of this study is predicting severe thunderstorm and tornado warnings. A total of 2,004 storms in 2014–2015 were objectively tracked with 49 potential predictor fields related to May, daytime Great Plains convective storms. All storms occurred when 1-min Geostationary Operational Environmental Satellite (GOES)–14 “super rapid scan” data were available. The study used three importance methods to assess predictor importance related to severe warnings, and random forests to provide a model and skill evaluation measuring the ability to predict severe storms. Three predictor importance methods show that GOES mesoscale atmospheric motion vector derived cloud-top divergence and above anvil cirrus plume presence provide the most satellite-based discriminatory power for diagnosing severe warnings. Other important fields include Earth Networks Total Lightning flash density, GOES estimated cloud-top vorticity, and overshooting-top presence. Severe warning predictions are significantly improved at the 95% confidence level when a few important satellite and lightning fields are combined with radar fields, versus when only radar data are used in the random forests model. This study provides a basis for including satellite and lightning fields within machine-learning models to help forecast severe weather.

Lightning Data Assimilation Scheme in a 4DVAR System and its Impact on Very-Short-Term Convective Forecasting

2020 ◽  
Author(s):  
Xian Xiao ◽  
Juanzhen Sun ◽  
Xiushu Qie ◽  
Zhuming Ying ◽  
Lei Ji ◽  
...  
Keyword(s):  
Vertical Velocity ◽  
Doppler Radar ◽  
Analysis Data ◽  
Radar Data ◽  
Lightning Flash ◽  
Short Term ◽  
Distance Weighted ◽  
Dynamical Consistency ◽  
Analysis System ◽  
Total Lightning

AbstractA proof-of-concept method for the assimilation of total lightning observations in the 4DVAR framework is proposed and implemented into the Variational Doppler Radar Analysis System (VDRAS). Its performance is evaluated for the very-short-term precipitation forecasts of a localized convective event over northeastern China. The lightning DA scheme assimilated pseudo observations for vertical velocity fields derived from observed total lightning rates and statistically computed vertical velocity profile from VDRAS analysis data. To reduce representative errors of the derived vertical velocity, a distance-weighted horizontal interpolation is applied to the input data prior to the DA. The case study reveals that although 0–2 hour precipitation nowcasts are improved by assimilating lightning data alone compared to CTRL (no radar or lightning) and RAD (radar only), better results are obtained when the lightning data are assimilated with radar data simultaneously. The assimilation of both data sources results in improved dynamical consistency with enhanced updraft and latent heat as well as improved moisture distributions. Additional experiments are conducted to evaluate the sensitivity of the combined DA scheme to varied vertical velocity profiles, radii of horizontal interpolation, binning time intervals, and relationships used to estimate the maximum vertical velocity from lightning flash rates. It is shown that the scheme is robust to these variations with both radar and lightning assimilated data.

scholarly journals Examining Deep Convective Cloud Evolution Using Total Lightning, WSR-88D, and GOES-14 Super Rapid Scan Datasets*

2015 ◽  
Vol 30 (3) ◽  
pp. 571-590 ◽  
Author(s):  
Kristopher M. Bedka ◽  
Cecilia Wang ◽  
Ryan Rogers ◽  
Lawrence D. Carey ◽  
Wayne Feltz ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Convective Cloud ◽  
Severe Weather ◽  
High Temporal Resolution ◽  
Lightning Flash ◽  
Rapid Scan ◽  
Wind Events ◽  
Total Lightning ◽  
Cloud Tops ◽  
Anvil Cirrus

Abstract The Geostationary Operational Environmental Satellite-14 (GOES-14) Imager operated in 1-min Super Rapid Scan Operations for GOES-R (SRSOR) mode during summer and fall of 2012 to emulate the high temporal resolution sampling of the GOES-R Advanced Baseline Imager (ABI). The current GOES operational scan interval is 15–30 min, which is too coarse to capture details important for severe convective storm forecasting including 1) when indicators of a severe storm such as rapid cloud-top cooling, overshooting tops, and above-anvil cirrus plumes first appear; 2) how satellite-observed cloud tops truly evolve over time; and 3) how satellite cloud-top observations compare with radar and lightning observations at high temporal resolution. In this paper, SRSOR data, radar, and lightning observations are used to analyze five convective storms, four of which were severe, to address these uncertainties. GOES cloud-top cooling, increased lightning flash rates, and peak precipitation echo tops often preceded severe weather, signaling rapid intensification of the storm updraft. Near the time of several severe hail or damaging wind events, GOES cloud-top temperatures and radar echo tops were warming rapidly, which indicated variability in the storm updraft that could have allowed the hail and wind gusts to reach the surface. Above-anvil cirrus plumes were another prominent indicator of impending severe weather. Detailed analysis of storms throughout the 2012 SRSOR period indicates that 57% of the plume-producing storms were severe and 85% of plumes from severe storms appeared before a severe weather report with an average lead time of 18 min, 9 min earlier than what would be observed by GOES operational scanning.

The Multiple-Vortex Structure of the El Reno, Oklahoma, Tornado on 31 May 2013

2018 ◽  
Vol 146 (8) ◽  
pp. 2483-2502 ◽  
Author(s):  
Howard B. Bluestein ◽  
Kyle J. Thiem ◽  
Jeffrey C. Snyder ◽  
Jana B. Houser
Keyword(s):  
Doppler Radar ◽  
Radar Data ◽  
Rapid Scan ◽  
X Band ◽  
Close Range ◽  
Formation And Evolution ◽  
Using Data ◽  
Secondary Vortices ◽  
The Right ◽  
Gust Front

Abstract This study documents the formation and evolution of secondary vortices associated within a large, violent tornado in Oklahoma based on data from a close-range, mobile, polarimetric, rapid-scan, X-band Doppler radar. Secondary vortices were tracked relative to the parent circulation using data collected every 2 s. It was found that most long-lived vortices (those that could be tracked for ≥15 s) formed within the radius of maximum wind (RMW), mainly in the left-rear quadrant (with respect to parent tornado motion), passing around the center of the parent tornado and dissipating closer to the center in the right-forward and left-forward quadrants. Some secondary vortices persisted for at least 1 min. When a Burgers–Rott vortex is fit to the Doppler radar data, and the vortex is assumed to be axisymmetric, the secondary vortices propagated slowly against the mean azimuthal flow; if the vortex is not assumed to be axisymmetric as a result of a strong rear-flank gust front on one side of it, then the secondary vortices moved along approximately with the wind.

scholarly journals Evaluation of Fengyun-4A Lightning Mapping Imager (LMI) Performance during Multiple Convective Episodes over Beijing

2021 ◽  
Vol 13 (9) ◽  
pp. 1746
Author(s):  
Zhixiong Chen ◽  
Xiushu Qie ◽  
Juanzhen Sun ◽  
Xian Xiao ◽  
Yuxin Zhang ◽  
...  
Keyword(s):  
Doppler Radar ◽  
Detection Efficiency ◽  
Ground Truth ◽  
Radar Data ◽  
Detection Capability ◽  
Cloud Properties ◽  
Order Of Magnitude ◽  
Total Lightning ◽  
Matching Criteria ◽  
Event Based

This study investigates the characteristics of space-borne Lightning Mapping Imager (LMI) lightning products and their relationships with cloud properties using ground-based total lightning observations from the Beijing Broadband Lightning Network (BLNET) and cloud information from S-band Doppler radar data. LMI showed generally consistent lightning spatial distributions with those of BLNET, and yielded a considerable lightning detection capability over regions with complex terrain. The ratios between the LMI events, groups and flashes were approximately 9:3:1, and the number of LMI-detected flashes was roughly one order of magnitude smaller than the number of BLNET-detected flashes. However, in different convective episodes, the LMI detection capability was likely to be affected by cloud properties, especially in strongly electrified convective episodes associated with frequent lightning discharging and thick cloud depth. As a result, LMI tended to detect lightning flashes located in weaker and shallower cloud portions associated with fewer cloud shielding effects. With reference to the BLNET total lightning data as the ground truth of observation (both intra-cloud lightning and cloud-to-ground lightning flashes), the LMI event-based detection efficiency (DE) was estimated to reach 28% under rational spatiotemporal matching criteria (1.5 s and 65 km) over Beijing. In terms of LMI flash-based DE, it was much reduced compared with event-based DE. The LMI flash-based ranged between 1.5% and 3.5% with 1.5 s and 35–65 km matching scales. For 330 ms and 35 km, the spatiotemporal matching criteria used to evaluate Geostationary Lightning Mapper (GLM), the LMI flash-based DE was smaller (<1%).

scholarly journals A Total Lightning Trending Algorithm to Identify Severe Thunderstorms

2010 ◽  
Vol 27 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Patrick N. Gatlin ◽  
Steven J. Goodman
Keyword(s):  
Severe Weather ◽  
Lightning Flash ◽  
Severe Thunderstorm ◽  
Critical Success ◽  
Severe Thunderstorms ◽  
Weather Events ◽  
Total Lightning ◽  
Jump Algorithm ◽  
Early Indication ◽  
Critical Success Index

Abstract An algorithm that provides an early indication of impending severe weather from observed trends in thunderstorm total lightning flash rates has been developed. The algorithm framework has been tested on 20 thunderstorms, including 1 nonsevere storm, which occurred over the course of six separate days during the spring months of 2002 and 2003. The identified surges in lightning rate (or jumps) are compared against 110 documented severe weather events produced by these thunderstorms as they moved across portions of northern Alabama and southern Tennessee. Lightning jumps precede 90% of these severe weather events, with as much as a 27-min advance notification of impending severe weather on the ground. However, 37% of lightning jumps are not followed by severe weather reports. Various configurations of the algorithm are tested, and the highest critical success index attained is 0.49. Results suggest that this lightning jump algorithm may be a useful operational diagnostic tool for severe thunderstorm potential.

scholarly journals Retrieval of three-dimensional wind fields from Doppler radar data using an efficient two-step approach

2010 ◽  
Vol 3 (5) ◽  
pp. 4459-4495 ◽  
Author(s):  
C. López Carrillo ◽  
D. J. Raymond
Keyword(s):  
Variational Approach ◽  
Continuity Equation ◽  
Doppler Radar ◽  
Three Dimensional ◽  
Relevant Information ◽  
Radar Data ◽  
Wind Fields ◽  
Wind Retrieval ◽  
Complex Sampling ◽  
Doppler Radar Data

Abstract. In this work, we describe an efficient approach for wind retrieval from dual Doppler radar data. The approach produces a gridded field that not only satisfies the observations, but also satisfies the anelastic mass continuity equation. The method is based on the so-called three-dimensional variational approach to the retrieval of wind fields from radar data. The novelty consists in separating the task into steps that reduce the amount of data processed by the global minimization algorithm, while keeping the most relevant information from the radar observations. The method is flexible enough to incorporate observations from several radars, accommodate complex sampling geometries, and readily include dropsonde or sounding observations in the analysis. We demonstrate the usefulness of our method by analyzing a real case with data collected during the TPARC/TCS-08 field campaign.

Investigation of Extreme Rainfall Patterns around Antalya and Muğla Cities in Türkiye by Using C Band Doppler Radar Data

2021 ◽  
Author(s):  
Emir Yapıcı ◽  
Ahmet Öztopal ◽  
Erdem Erdi
Keyword(s):  
Doppler Radar ◽  
Three Dimensional ◽  
Extreme Rainfall ◽  
Radar Data ◽  
Mediterranean Coast ◽  
Extreme Rainfall Events ◽  
Weather Radars ◽  
Hourly Rainfall ◽  
Raingauge Data ◽  
Rainfall Patterns

&lt;p&gt;As is known, rainfall varies spatially and temporally with regard to intensity and frequency. Floods, related to extreme rainfall cases, cause stress on geophysical system and community if climate change is considered. For this reason determining of extreme rainfall patterns is very important. While obtaining three dimensional status of hydrometors in atmosphere is not possible only by using ground station networks, it is possible by using weather radars. Therefore, weather radars provide significant contribution to studies about getting cloud and rainfall patterns. The aim of this study is to investigate spatial patterns of extreme rainfall events in Antalya and Mu&amp;#287;la cities which are located on the Mediterranean coast of T&amp;#252;rkiye. Firstly, hourly rainfall (RN1) and rain rate (SRI) products of 2 C band doppler radars and raingauge data between 2015 and 2020 will be processed by a software named MeteoRadar which is developed by &amp;#304;stanbul Technical University. It is capable of reading, decoding, parallel processing and visualization. Secondly, extreme rainfall patterns will be obtained over 2 study areas. Finally, after validation by using raingauge data, results will be discussed in detail.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Key Words&lt;/strong&gt;: Antalya, Extreme rainfall, MeteoRadar, Mu&amp;#287;la, RN1, SRI, Weather radar.&lt;/p&gt;

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