Nonsevere Storms Research Articles

The Utility of ProbSevere v2.0 for Predicting Pulse Severe Thunderstorms

Abstract Pulse severe storms are single-cell thunderstorms that produce severe wind and/or severe hail for a brief period of time. These storms pose a major warm season forecasting problem since forecasters presently do not have sufficient guidance to know which, if any, of the cells that are observed will become severe. The empirical Probability of Severe (ProbSevere) model, developed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS), fuses real-time data to produce short-term (0–60 min), statistically derived probabilistic forecasts of thunderstorm intensity. This study evaluates the ability of ProbSevere to predict pulse severe storms in the southeast United States. ProbSevere objects fitting the usual definition of a pulse severe environment were matched with severe events from Storm Data to create a dataset of ProbSevere objects that corresponded to pulse severe thunderstorms. A null dataset consisted of objects in pulse severe environments that did not match with a severe event. Results reveal that ProbSevere’s probabilities are small to moderate at the times corresponding to pulse severe events. While probabilities of nonsevere storms are generally smaller, there are a large number of outliers. Lightning flash rate is the only predictor relevant to this study that correlates strongly with increasingly favorable pulse storm probabilities. We conclude that ProbSevere provides forecasters only limited guidance as to whether a pulse severe event will soon occur. Developing a version of ProbSevere specifically for pulse severe storms would likely lead to better predictability for this mode of convection.

Early History of Using Total Lightning Data at NWS Melbourne, Florida

This forecaster’s note documents the early history (1989–1998) of the use of total lightning data within an operational forecast and warning environment. As early as 1989, the Melbourne field office of the National Weather Service had access to real-time cloud-to-ground lightning data. In 1993, the Lightning Detection and Ranging system (capable of detecting all types of lightning flashes) became available. In 1996, these two lightning data sets, along with radar data, were incorporated into the Lightning Imaging Sensor Data Applications Display (LISDAD) system. During a 3-y period (1996–1998 inclusive), the LISDAD permitted forecasters to observe relationships of total lightning with a variety of convective events, including pulse-severe thunderstorms in the warm season, cool-season tornadic supercells, tornadic mini-supercells in tropical cyclones, and non-severe storms. Major findings included: 1) “lightning jumps” with warm-season pulse-severe storms several minutes prior to reported severe weather; 2) cool-season tornadic supercell storms with very large total flash rates; and 3) tornadic mini-supercells in tropical cyclones produced only small amounts of lightning, however this sporadic activity benefited forecasters by implying stronger updraft development in a favorably sheared environment, in turn implying possible storm rotation and potential tornadogenesis. Finally, given the availability of total lightning datasets to operational forecasters, local forecast products could more effectively provide the public information about the overall lightning threat.

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

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.

Using ZDR Columns in Forecaster Conceptual Models and Warning Decision-Making

ABSTRACTResearch has shown that dual-polarization (dual-pol) data currently available to National Weather Service forecasters could provide important information about changes in a storm’s structure and intensity. Despite these new data being used gradually by forecasters more over time, they are still not used extensively to inform warning decisions because it is unclear how to apply dual-pol radar data to specific warning decisions. To address this knowledge gap, rapid-update (i.e., volumetric update time of 2.3 min or less) radar data of 45 storms in Oklahoma are used to examine one dual-pol signature, known as the differential reflectivity (ZDR) column, to relate this signature to warning decisions. Base data (i.e., ZDR, reflectivity, velocity) are used to relate ZDR columns to storm intensity, radar signatures such as upper-level reflectivity cores, and scientific conceptual models used by forecasters during the warning decision process. Analysis shows that 1) differences exist between the ZDR columns of severe and nonsevere storms, 2) ZDR columns develop and evolve prior to upper-level reflectivity cores, 3) rapid-update radar data provide a more complete picture of ZDR column evolution than traditional-update radar data (i.e., volumetric update time of about 5 min), and 4) ZDR columns provide a clearer and earlier indication of changes in updraft strength compared to reflectivity signatures. These findings suggest that ZDR columns can be used to inform warning decisions, increase warning confidence, and potentially increase warning lead time especially when they are integrated into existing conceptual models about a storm’s updraft and intensity.

Evaluating the Ability of Remote Sensing Observations to Identify Significantly Severe and Potentially Tornadic Storms.

Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote sensing networks in the United States have provided unprecedented spatial and temporal sampling to study such storms. These networks help forecasters subjectively identify storms capable of producing severe weather at the ground; however, uncertainties remain in how to objectively identify severe thunderstorms using the same data. Here, three large-area datasets (geostationary satellite, ground-based radar, and ground-based lightning detection) are used over 28 recent events in an attempt to objectively discriminate between severe and nonsevere storms, with an additional focus on severe storms that produce tornadoes. Among these datasets, radar observations, specifically those at mid- and upper levels (altitudes at and above 4 km), are shown to provide the greatest objective discrimination. Physical and kinematic storm characteristics from all analyzed datasets imply that significantly severe [≥2-in. (5.08 cm) hail and/or ≥65-kt (33.4 ms-1) straight-line winds] and tornadic storms have stronger upward motion and rotation than nonsevere and less severe storms. In addition, these metrics are greatest in tornadic storms during the time in which tornadoes occur.

Rapid-Update Radar Observations of ZDR Column Depth and Its Use in the Warning Decision Process

Abstract The recent dual-polarization upgrade to the National Weather Service radar network provides forecasters with new information to use during operations, yet currently this information is not routinely used to explicitly make warning decisions. One potential way to increase operational use is to link new radar signatures and products to existing forecaster conceptual models and the warning decision process. Over the past several years, a unique dataset consisting of rapid-update (<2-min volumes) radar data of storms over central Oklahoma has been collected to examine possible links between ZDR columns and forecaster conceptual models. In total, over 1400 volume scans from 42 storms—ranging from tornadic supercells to nonsevere multicells—are used to relate ZDR column depth to storm reports and radar signatures typically used to issue warnings, such as −20°C reflectivity core and low-level mesocyclone evolution. After completing the analysis, the following key operational findings emerged: 1) no clear differences exist between the ZDR column depth of tornadic and nontornadic mesocyclones, but statistically significant differences do exist between severe and nonsevere storms, 2) the lead time in advance of severe hail and wind reports provided by peaks in ZDR column depth is greater than that provided by peaks in −20°C reflectivity cores, 3) increases in ZDR column size precede increases in −20°C reflectivity core size by about 3.5–9.0 min, and 4) rapid-update volumetric data captures signature evolution several minutes earlier than conventional-update data therefore providing forecasters more time to anticipate hazards and issue warnings.

Evaluation of geostationary satellite observations and the development of a 1–2 h prediction model for future storm intensity

AbstractA study was conducted to gain insights into the use of geostationary satellite‐based indicators for characterizing and identifying growing cumulus clouds that evolve into severe weather producing convective storms. Eleven convective initiation (CI), 41 cloud top temperature–effective radius (T‐re), and 9 additional fields were formed for 340 growing cumulus clouds that were manually tracked for 2 h and checked for association with severe weather to 2–3 h into the future. The geostationary satellite data were at 5 min resolution from Meteosat‐8 on six convectively active days in 2010, 2012, and 2013. The study's goals were to determine which satellite fields are useful to forecasting severe storms and to form a simple model for predicting future storm intensity. The CI fields were applied on 3 × 3 pixel regions, and the T‐re fields were analyzed on 9 × 9 and 51 × 51 pixel domains (needed when forming T‐re vertical profiles). Of the 340 growing cumulus clouds examined, 34 were later associated with severe weather (using European Severe Weather Database reports), with the remaining being nonsevere storms. Using a multivariate analysis, transforming predictors into their empirical posterior probability, and maximizing the Peirce skill score, the best predictors were T1451 (51 × 51 pixel T, where re exceeds 14 µm), TG9 (9 × 9 pixel glaciation T surrounding a growing cloud), and ReBRTG51 (51 × 51 pixel re at the breakpoint T in the T‐re profile). Rapid cloud growth prior to severe storm formation leads to delayed particle growth, colder temperatures of the first 14 µm particles, and lower TG values.

The extension of lightning flashes from thunderstorms near Cape Canaveral, Florida

AbstractThe horizontal distance that intracloud (IC) and cloud to ground (CG) lightning flashes extend from thunderstorms is presented for warm season nonsevere storms near Cape Canaveral, FL. The Warning Decision Support System–Integrated Information (WDSS‐II) software allowed us to ingest, display, and make distance calculations using radar reflectivity data from nearby National Weather Service Offices and lightning information from the National Lightning Detection Network and the local Lightning Detection and Ranging network. The flash distances were determined manually using a measuring tool within WDSS‐II. Six categories of flashes were investigated: (a) CG flashes measured from the edge of the convective core (30 dBZ) to the ground strike location that is beneath the attached anvil, (b) CG “bolt from the blue” flashes measured from cloud edge (0 dBZ) to the ground strike point that contains only 0 dBZ overhead, (c) CG bolt from the blue flashes as in Category B but measured from the edge of the precipitation (18 dBZ), (d) IC flashes that exit a portion of the cloud that is not an anvil, measured from cloud edge (0 dBZ), (e) IC flashes within anvils measured from the edge of convective precipitation (30 dBZ) to the termination point within the anvil, and (f) IC flashes exiting the edge (0 dBZ) of anvils. Results for each category are presented in the forms of frequency distributions of distances and their cumulative frequency distributions.

Documenting Storm Severity in the Mid-Atlantic Region Using Lightning and Radar Information

Abstract Storm severity in the mid-Atlantic region of the United States is examined using lightning, radar, and model-derived information. Automated Warning Decision Support System (WDSS) procedures are developed to create grids of lightning and radar parameters, cluster individual storm features, and data mine the lightning and radar attributes of 1252 severe and nonsevere storms. The study first examines the influence of serial correlation and uses autocorrelation functions to document the persistence of lightning and radar parameters. Decorrelation times are found to vary by parameter, storm severity, and mathematical operator, but the great majority are between three and six lags, suggesting that consecutive 2-min storm samples (following a storm) are effectively independent after only 6–12 min. The study next describes the distribution of lightning jumps in severe and nonsevere storms, differences between various types of severe storms (e.g., severe wind only), and relationships between lightning and radar parameters. The 2σ lightning jump algorithm (with a 10 flashes min−1 activation threshold) yields 0.92 jumps h−1 for nonsevere storms and 1.44 jumps h−1 in severe storms. Applying a 10-mm maximum expected size of hail (MESH) threshold to the 2σ lightning jump algorithm reduces the frequency of lightning jumps in nonsevere storms to 0.61 jumps h−1. Although radar-derived parameters are comparable between storms that produce severe wind plus hail and those that produce tornadoes, tornadic storms exhibit much greater intracloud (IC) and cloud-to-ground (CG) flash rates. Correlations further illustrate that lightning data provide complementary storm-scale information to radar-derived measures of storm intensity.

A Total Lightning Trending Algorithm to Identify Severe Thunderstorms

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.

THE SEVERE HAZARDS ANALYSIS AND VERIFICATION EXPERIMENT

During the springs and summers of 2006 through 2008, scientists from the National Severe Storms Laboratory and students from the University of Oklahoma have conducted an enhanced severe-storm verification effort. The primary goal for the Severe Hazards Analysis and Verification Experiment (SHAVE) was the remote collection of high spatial and temporal resolution hail, wind (or wind damage), and flash-flooding reports from severe thunderstorms. This dataset has a much higher temporal and spatial resolution than the traditional storm reports collected by the National Weather Service and published in Storm Data (tens of square kilometers and 1–5 min versus thousands of square kilometers and 30–60 min) and also includes reports of nonsevere storms that are not included in Storm Data. The high resolution of the dataset makes it useful for validating high-resolution, gridded warning guidance applications. SHAVE is unique not only for the type of data collected and the resolution of that data but also for how the...

A study of the total lightning activity in two hailstorms

Two thunderstorms which developed in the northeastern region of Spain on 17 August 2004 are analyzed. According to radar and ground observations, one of these storms produced large hail (larger than 19 mm) and therefore could be severe. Both thunderstorms exhibited strong vertical developments with cloud tops reaching 17.5 and 14 km for the severe and the nonsevere ones, respectively. The total lightning activity was monitored, thanks to the Spanish magnetic‐direction‐finding and time‐of‐arrival (MDF/TOA) network and thanks to a regional very‐high‐frequency (VHF) interferometer network. Both storms presented low cloud‐to‐ground (CG) flash rates (lower than 2 min−1) during their mature phase, while the intracloud (IC) flash rate reached 92 min−1 for the severe one. The IC/CG flash ratio was very high for both storms: 35 and 60 in average value for the severe and the nonsevere ones, respectively. The lightning characteristics exhibited low values in terms of peak current and multiplicity for negative CG (−CG) flashes and in terms of VHF source number per flash for IC flashes. The average multiplicity of the −CG flashes was 1.74 and 1.17 for the severe and the nonsevere storms, respectively, and their average peak current was −11.7 and −10.65 kA, respectively. When the presence of hail is suspected from high radar reflectivity values in both cells, the flash rates tended to substantially decrease. The severe storm produced positive CG (+CG) flashes during 1 hour, and at that moment, the multiplicity and peak currents of −CG flashes were very low. Lightning activity for both storms ended with a substantial increase of the −CG flash rate. The elevated charge hypothesis is consistent with the observations, especially if we consider two aspects of this hypothesis, the one about charge separation, which could be limited by the strong updrafts, and the one about the lightning initiation, which could be favored by low pressures at high altitude.

A Damaging Downburst Prediction and Detection Algorithm for the WSR-88D

Abstract The problem of predicting the onset of damaging downburst winds from high-reflectivity storm cells that develop in an environment of weak vertical shear with Weather Surveillance Radar-1988 Doppler (WSR-88D) is examined. Ninety-one storm cells that produced damaging outflows are analyzed with data from the WSR- 88D network, along with 1247 nonsevere storm cells that developed in the same environments. Twenty-six reflectivity and radial velocity–based parameters are calculated for each cell, and a linear discriminant analysis was performed on 65% of the dataset in order to develop prediction equations that would discriminate between severe downburst-producing cells and cells that did not produce a strong outflow. These prediction equations are evaluated on the remaining 35% of the dataset. The datasets were resampled 100 times to determine the range of possible results. The resulting automated algorithm has a median Heidke skill score (HSS) of 0.40 in the 20–45-km range with a median lead time of ...

Characteristics of cloud‐to‐ground lightning in severe and nonsevere storms over the central United States from 1989–1998

Over a region from the Kansas/Colorado border to Minnesota, previously associated with positive anomalies in the mean annual positive cloud‐to‐ground (CG) lightning percentage and positive peak current, we describe significant differences in the properties of CG lightning flashes between warm‐season severe (i.e., large hail or tornado producing) and nonsevere storms. The percentage of CG lightning flashes lowering positive charge to ground was substantially higher in severe storms (i.e., up to three times higher). The median positive peak current in severe storms was significantly larger (i.e., by about 25%). Finally, the median negative peak current in severe storms was very low (i.e., as low as 12–16 kA) and was noticeably smaller than in nonsevere storms (i.e., by at least 10%). Severe storms appeared to be significant producers of positive CG lightning in the anomaly region (AR). Up to 30%–70% of warm season positive flashes in the AR were associated with severe storms. Only 2%–30% of negative flashes were associated with severe storms in the AR. As a result, the mean warm season (and likely annual) positive anomalies in positive CG percentage and peak current are likely associated with a unique class of severe, organized storms that occur preferentially in this geographical region. We found substantial differences in the properties of CG lightning flashes associated with severe storms in other regions. For example, the mean warm season properties of CG lightning associated with severe storms in the region of peak large hail and tornado activity (e.g., Oklahoma) were fundamentally different (i.e., significantly lower positive CG percentage, lower positive peak currents and higher negative peak currents) compared to severe storms over the AR from western Kansas to Minnesota.