ALBERT

Description: Many studies over the past several decades have attempted to correlate trends in lightning (e.g., rates, polarity) to severe weather occurrence. These studies mainly used cloud-to-ground (CG) lightning information due to the ease of data availability, high detection efficiency, and broad coverage across the United States, with somewhat inconclusive results. Conversely, it has been demonstrated that trends in total lightning are more robustly correlated to severe weather occurrence, with rapid increases in total lightning observed 10s of minutes prior to the onset of severe weather. Unfortunately, total lightning observations are not as numerous, or available over the same areal coverage domain, as provided by CG networks. Relatively few studies have examined concurrent trends in both total and CG lightning within the same severe thunderstorm, or even large sets of thunderstorms using an objective lightning jump algorithm. Multiple studies have shown that the total flash rate rapidly increases prior to the onset of severe weather. What is untested within the same framework is the use of CG information to perform the same task. Herein, total and CG lightning trends for 711 thunderstorms occurring in four regions of the country were examined to demonstrate the increased utility that total lightning provides over CG lightning, specifically within the framework of developing a useful lightning-based severe weather warning decision support tool. Results indicate that while both lightning datasets demonstrate the presence of increased lightning activity prior to the onset of severe weather, the use of total lightning trends was more effective than CG trends [probability of detection (POD), 79% versus 66%; false alarm rate (FAR), 36% versus 53%; critical success index (CSI), 55% versus 38%; Heidke skill score (HSS), 0.71 versus 0.55]. Moreover, 40% of false alarms associated with total lightning, and 16% of false alarms with CG lightning trends, occurred when a lightning jump associated with a severe weather “warning” was already in effect. If these false alarms are removed, the FAR drops from 36% to 22% for total lightning and from 53% to 44% for CG lightning. Importantly, average lead times prior to severe weather occurrence were higher using total lightning as compared with CG lightning (20.65 versus 13.54 min). The ultimate goal of this study was to demonstrate the increased utility of total lightning information that the Geostationary Lightning Mapper (GLM) will provide to operational meteorology in anticipation of severe convective weather on a hemispheric scale once Geostationary Operational Environmental Satellite-R (GOES-R) is deployed in the next decade.

Description: Four electrified snowfall cases are examined using total lightning measurements from lightning mapping arrays (LMAs), and the National Lightning Detection Network (NLDN) from Huntsville, AL and Washington D.C. In each of these events, electrical activity was in conjunction with heavy snowfall rates, sometimes exceeding 5-8 cm hr-1. A combination of LMA, and NLDN data also indicate that many of these flashes initiated from tall communications towers and traveled over large horizontal distances. During events near Huntsville, AL, the Advanced Radar for Meteorological and Operational Research (ARMOR) C-band polarimetric radar was collecting range height indicators (RHIs) through regions of heavy snowfall. The combination of ARMOR polarimetric radar and VHF LMA observations suggested contiguous layer changes in height between sloping aggregate-dominated layers and horizontally-oriented crystals. These layers may have provided ideal conditions for the development of extensive regions of charge and resultant horizontal propagation of the lightning flashes over large distances.

Description: The presence and rates of total lightning are both correlated to and physically dependent upon storm updraft strength, mixed phase precipitation volume and the size of the charging zone. The updraft modulates the ingredients necessary for electrification within a thunderstorm, while the updraft also plays a critical role in the development of severe and hazardous weather. Therefore utilizing this relationship, the monitoring of lightning rates and jumps provides an additional piece of information on the evolution of a thunderstorm, more often than not, at higher temporal resolution than current operational radar systems. This correlation is the basis for the total lightning jump algorithm that has been developed in recent years. In order to become a viable option for operational forecasters to incorporate into their severe storm monitoring process, the total lightning jump must be placed into the framework of several severe storm conceptual models (e.g., radar evolution, storm morphology) which forecasters have built through training and experience. Thus, one of the goals of this study is to examine and relate the lightning jump concept to often used radar parameters (e.g., dBZ vertical structure, VIL, MESH, MESO/shear) in the warning environment. Tying lightning trends and lightning jump occurrences to these radar based parameters will provide forecasters with an additional tool that they can use to build an accurate realtime depiction as to what is going on in a given environment. Furthermore, relating the lightning jump concept to these parameters could also increase confidence in a warning decision they have already made, help tip the scales on whether or not to warn on a given storm, or to draw the forecaster s attention to a particular storm that is rapidly developing. Furthermore the lightning information will add vital storm scale information in regions that are not well covered by radar, or when radar failures occur. The physical basis for the lightning jump algorithm in relation to severe storm dynamics and microphysics is a key component that must be further explored. Many radar studies have examined flash rates and their relation to updraft strength, updraft volume, precipitation -sized ice mass, etc.; however, very few have related the concept of the lightning jump and manifestation of severe weather to storm dynamics and microphysics using multi -Doppler and polarimetric radar techniques. Therefore, the second half of this study will combine the lightning jump algorithm and these radar techniques in order to place the lightning jump concept into a physical and dynamical framework. This analysis includes examining such parameters as mixed phase precipitation volume, charging zone, updraft strength and updraft volume. Such a study should provide increased understanding of and confidence in the strengths and limitations of the lightning jump algorithm in the storm warning process.

Description: The lightning jump algorithm has a robust history in correlating upward trends in lightning to severe and hazardous weather occurrence. The algorithm uses the correlation between the physical principles that govern an updraft's ability to produce microphysical and kinematic conditions conducive for electrification and its role in the development of severe weather conditions. Recent work has demonstrated that the lightning jump algorithm concept holds significant promise in the operational realm, aiding in the identification of thunderstorms that have potential to produce severe or hazardous weather. However, a large amount of work still needs to be completed in spite of these positive results. The total lightning jump algorithm is not a stand-alone concept that can be used independent of other meteorological measurements, parameters, and techniques. For example, the algorithm is highly dependent upon thunderstorm tracking to build lightning histories on convective cells. Current tracking methods show that thunderstorm cell tracking is most reliable and cell histories are most accurate when radar information is incorporated with lightning data. In the absence of radar data, the cell tracking is a bit less reliable but the value added by the lightning information is much greater. For optimal application, the algorithm should be integrated with other measurements that assess storm scale properties (e.g., satellite, radar). Therefore, the recent focus of this research effort has been assessing the lightning jump's relation to thunderstorm tracking, meteorological parameters, and its potential uses in operational meteorology. Furthermore, the algorithm must be tailored for the optically-based GOES-R Geostationary Lightning Mapper (GLM), as what has been observed using Very High Frequency Lightning Mapping Array (VHF LMA) measurements will not exactly translate to what will be observed by GLM due to resolution and other instrument differences. Herein, we present some of the promising aspects and challenges encountered in utilizing objective tracking and GLM proxy data, as well as recent results that demonstrate the value added information gained by combining the lightning jump concept with traditional meteorological measurements.

Description: In order to produce useful proxy data for the GOES-R Geostationary Lightning Mapper (GLM) in regions not covered by VLF lightning mapping systems, we intend to employ data produced by ground-based (regional or global) VLF/LF lightning detection networks. Before using these data in GLM Risk Reduction tasks, it is necessary to have a quantitative understanding of the performance of these networks, in terms of CG flash/stroke DE, cloud flash/pulse DE, location accuracy, and CLD/CG classification error. This information is being obtained through inter-comparison with LMAs and well-quantified VLF/LF lightning networks. One of our approaches is to compare "bulk" counting statistics on the spatial scale of convective cells, in order to both quantify relative performance and observe variations in cell-based temporal trends provided by each network. In addition, we are using microsecond-level stroke/pulse time correlation to facilitate detailed inter-comparisons at a more-fundamental level. The current development status of our ground-based inter-comparison and evaluation tools will be presented, and performance metrics will be discussed through a comparison of Vaisala s Global Lightning Dataset (GLD360) with the NLDN at locations within and outside the U.S.

Description: The presence and rates of total lightning are both correlated to and physically dependent upon storm updraft strength, mixed phase precipitation volume and the size of the charging zone. The updraft modulates the ingredients necessary for electrification within a thunderstorm, while the updraft also plays a critical role in the development of severe and hazardous weather. Therefore utilizing this relationship, the monitoring of lightning rates and jumps provides an additional piece of information on the evolution of a thunderstorm, more often than not, at higher temporal resolution than current operational radar systems. This correlation is the basis for the total lightning jump algorithm that has been developed in recent years. Currently, the lightning jump algorithm is being tested in two separate but important efforts. Schultz et al. (2014; this conference) is exploring the transition of the algorithm from its research based formulation to a fully objective algorithm that includes storm tracking, Geostationary Lightning Mapper (GLM) Proxy data and the lightning jump algorithm. Chronis et al. (2014) provides context for the transition to current operational forecasting using lightning mapping array based products. However, what remains is an end-to-end physical and dynamical basis for coupling total lightning flash rates to severe storm manifestation, so the forecaster has a reason beyond simple correlation to utilize the lightning jump algorithm within their severe storm conceptual models. Therefore, the physical basis for the lightning jump algorithm in relation to severe storm dynamics and microphysics is a key component that must be further explored. Many radar studies have examined flash rates and their relationship to updraft strength, updraft volume, precipitation-sized ice mass, etc.; however, their relationship specifically to lightning jumps is fragmented within the literature. Thus the goal of this study is to use multiple Doppler and polarimetric radar techniques to resolve the physical and dynamical storm characteristics specifically around the time of the lightning jump. This information will help forecasters anticipate lightning jump occurrence, or even be of use to determine future characteristics of a given storm (e.g., development of a mesocyclone, downdraft, or hail signature on radar), providing additional lead time/confidence in the severe storm warning paradigm.

Description: Tall structures play and important role in development of winter time lightning flashes.To what extent still needs to be assessed. Tower initiated flashes typically occur as banded structures pass near/overhead. Hi resolution RHI s from polarimetric radar show that the lightning has a tendency to propagate through layered structures within these snowstorms.

Description: Objective: To investigate the kinematic and microphysical control of lightning properties, particularly those that may govern the production of nitrogen oxides (NOx) in thunderstorms, such as flash rate, type (intracloud [IC] vs. cloud-to-ground [CG] ) and extent. Data and Methodology: a) NASA MSFC Lightning Nitrogen Oxides Model (LNOM) is applied to North Alabama Lightning Mapping Array (NALMA) and Vaisala National Lightning Detection Network(TradeMark) (NLDN) observations following ordinary convective cells through their lifecycle. b) LNOM provides estimates of flash type, channel length distributions, lightning segment altitude distributions (SADs) and lightning NOx production profiles (Koshak et al. 2012). c) LNOM lightning characteristics are compared to the evolution of updraft and precipitation properties inferred from dual-Doppler (DD) and polarimetric radar analyses of UAHuntsville Advanced Radar for Meteorological and Operational Research (ARMOR, Cband, polarimetric) and KHTX (S-band, Doppler).

Description: Key points that this analysis will begin to address are: 1)What physically is going on in the cloud when there is a jump in lightning? - Updraft variations, ice fluxes. 2)How do these processes fit in with severe storm conceptual models? 3)What would this information provide an end user (i.e., the forecaster)? - Relate LJA to radar observations, like changes in reflectivity, MESH, VIL, etc. based multi-Doppler derived physical relationships 4) How do we best transistionthis algorithm into the warning decision process. The known relationship between lightning updraft strength/volume and precipitation ice mass production can be extended to the concept of the lightning jump. Examination of the first lightning jump times from 329 storms in Schultz et al. shows an increase in the mean reflectivity profile and mixed phase echo volume during the 10 minutes prior to the lightning jump. Limited dual-Doppler results show that the largest lightning jumps are well correlated in time with increases in updraft strength/volume and precipitation ice mass production; however, the smaller magnitude lightning jumps appear to have more subtle relationships to updraft and ice mass characteristics.