An Analysis of the Lightning Jump Algorithm Using Geostationary Lightning Mapper Flashes

This project aims to implement the two-sigma lightning jump algorithm (LJA) developed using Lightning Mapping Arrays (LMAs), with GOES-16 Geostationary Lightning Mapper (GLM) flashes, evaluate its performance, and identify any needed adjustments to the algorithm to optimize operational skill. The GLM is projected to have lower detection efficiency (DE) (70-90 percent) than operational LMAs (95-99 percent). The reduced GLM DE coupled with the coarser spatial resolution of the GLM could have impacts on flash rates and trends that could affect the LJA in various ways. Deep dives are conducted on four separate cases. Three of four cases show LMAs seeing two to three times as many flashes as the GLM. Only fifteen of twenty five GLM jumps saw increases in radar intensity while fourteen of nineteen LMA jumps did. These results suggest a larger sample sized study must be conducted to determine how to implement the LJA with the GLM

An Analysis of the Lightning Jump Algorithm Using Geostationary Lightning Mapper Flashes

Lightning's relation to severe weather has been studied since the 1980's. The invention of the Lightning Mapping Array allowed for total lightning measurements in a 125 km operational range. This brought forth an automated lightning Jump Algorithm (LIA) that predicted severe weather based on two-sigma increases in total lightning. The LIA's biggest downfall is being restrained to the limited field of view (FOV) of LMA's. The launch of the Geostationary Lightning Mapper (GLM) aboard the GOES-16 satellite now gives us hemispheric total lightning measurements. The wide FOV makes the GLM a good candidate to apply the LIA to. However the GLM and LMA have some differences. One being the coarser spatial resolution of GLM. Another being that LMA measures very high frequency (VHF) electromagnetic radiation while GLM measures optical radiation. These differences suggest an extensive study must be done on using the LIA with GLM to understand potential differences in the LIA and to maximize its operational skill. Four deep dive cases are conducted showcasing the differences between the GLM and LMA and their jumps

Recent Advancements in Lightning Jump Algorithm Work

In the past year, the primary objectives were to show the usefulness of total lightning as compared to traditional cloud-to-ground (CG) networks, test the lightning jump algorithm configurations in other regions of the country, increase the number of thunderstorms within our thunderstorm database, and to pinpoint environments that could prove difficult for any lightning jump configuration. A total of 561 thunderstorms have been examined in the past year (409 non-severe, 152 severe) from four regions of the country (North Alabama, Washington D.C., High Plains of CO/KS, and Oklahoma). Results continue to indicate that the 2 lightning jump algorithm configuration holds the most promise in terms of prospective operational lightning jump algorithms, with a probability of detection (POD) at 81%, a false alarm rate (FAR) of 45%, a critical success index (CSI) of 49% and a Heidke Skill Score (HSS) of 0.66. The second best performing algorithm configuration was the Threshold 4 algorithm, which had a POD of 72%, FAR of 51%, a CSI of 41% and an HSS of 0.58. Because a more complex algorithm configuration shows the most promise in terms of prospective operational lightning jump algorithms, accurate thunderstorm cell tracking work must be undertaken to track lightning trends on an individual thunderstorm basis over time. While these numbers for the 2 configuration are impressive, the algorithm does have its weaknesses. Specifically, low-topped and tropical cyclone thunderstorm environments are present issues for the 2 lightning jump algorithm, because of the suppressed vertical depth impact on overall flash counts (i.e., a relative dearth in lightning). For example, in a sample of 120 thunderstorms from northern Alabama that contained 72 missed events by the 2 algorithm 36% of the misses were associated with these two environments (17 storms)

Preliminary Development and Evaluation of Lightning Jump Algorithms for the Real-Time Detection of Severe Weather

Previous studies have demonstrated that rapid increases in total lightning activity (intracloud + cloud-to-ground) are often observed tens of minutes in advance of the occurrence of severe weather at the ground. These rapid increases in lightning activity have been termed "lightning jumps." Herein, we document a positive correlation between lightning jumps and the manifestation of severe weather in thunderstorms occurring across the Tennessee Valley and Washington D.C. A total of 107 thunderstorms were examined in this study, with 69 of the 107 thunderstorms falling into the category of non-severe, and 38 into the category of severe. From the dataset of 69 isolated non-severe thunderstorms, an average peak 1 minute flash rate of 10 flashes/min was determined. A variety of severe thunderstorm types were examined for this study including an MCS, MCV, tornadic outer rainbands of tropical remnants, supercells, and pulse severe thunderstorms. Of the 107 thunderstorms, 85 thunderstorms (47 non-severe, 38 severe) from the Tennessee Valley and Washington D.C tested 6 lightning jump algorithm configurations (Gatlin, Gatlin 45, 2(sigma), 3(sigma), Threshold 10, and Threshold 8). Performance metrics for each algorithm were then calculated, yielding encouraging results from the limited sample of 85 thunderstorms. The 2(sigma) lightning jump algorithm had a high probability of detection (POD; 87%), a modest false alarm rate (FAR; 33%), and a solid Heidke Skill Score (HSS; 0.75). A second and more simplistic lightning jump algorithm named the Threshold 8 lightning jump algorithm also shows promise, with a POD of 81% and a FAR of 41%. Average lead times to severe weather occurrence for these two algorithms were 23 minutes and 20 minutes, respectively. The overall goal of this study is to advance the development of an operationally-applicable jump algorithm that can be used with either total lightning observations made from the ground, or in the near future from space using the GOES-R Geostationary Lightning Mapper

Remote Analysis of Grain Size Characteristic in Submarine Pyroclastic Deposits from Kolumbo Volcano, Greece

Grain size characteristics of pyroclastic deposits provide valuable information about source eruption energetics and depositional processes. Maximum size and sorting are often used to discriminate between fallout and sediment gravity flow processes during explosive eruptions. In the submarine environment the collection of such data in thick pyroclastic sequences is extremely challenging and potentially time consuming. A method has been developed to extract grain size information from stereo images collected by a remotely operated vehicle (ROV). In the summer of 2010 the ROV Hercules collected a suite of stereo images from a thick pumice sequence in the caldera walls of Kolumbo submarine volcano located about seven kilometers off the coast of Santorini, Greece. The highly stratified, pumice-rich deposit was likely created by the last explosive eruption of the volcano that took place in 1650 AD. Each image was taken from a distance of only a few meters from the outcrop in order to capture the outlines of individual clasts with relatively high resolution. Mosaics of individual images taken as the ROV transected approximately 150 meters of vertical outcrop were used to create large-scale vertical stratigraphic columns that proved useful for overall documentation of the eruption sequence and intracaldera correlations of distinct tephra units. Initial image processing techniques, including morphological operations, edge detection, shape and size estimation were implemented in MatLab and applied to a subset of individual images of the mosiacs. A large variety of algorithms were tested in order to best discriminate the outlines of individual pumices. This proved to be challenging owing to the close packing and overlapping of individual pumices. Preliminary success was achieved in discriminating the outlines of the large particles and measurements were carried out on the largest clasts present at different stratigraphic levels. In addition, semi-quantitative analysis of the size distribution could also be determined for individual images. Although a complete size distribution is not possible with this technique, information about the relative distribution of large and medium size clasts is likely to provide a reasonable proxy for the overall sorting of submarine deposits. Our preliminary work represents the first attempt to carry out an in situ granulometric analysis of a thick submarine pyroclastic sequence. This general technique is likely to be valuable in future studies of submarine explosive volcanism given the recent discoveries of extensive pumiceous deposits in many submarine calderas associated with subduction zone environments. AGU session number OS13A-150

An Analysis of the Lightning Jump Algorithm Using the GOES-16 Geostationary Lightning Mapper

No abstract availabl

Total Lightning Characteristics with Respect to Radar-Derived Mesocyclone Strength

Recent work investigating the microphysical and kinematic relationship between a storm's updraft, its total lightning production, and manifestations of severe weather has resulted in development of tools for improved nowcasting of storm intensity. The total lightning jump algorithm, which identifies rapid increases in total lightning flash rate that often precede severe events, has shown particular potential to benefit warning operations. Maximizing this capability of total lightning and its operational implementation via the lightning jump may best be done through its fusion with radar and radarderived intensity metrics. Identification of a mesocyclone, or quasisteady rotating updraft, in Doppler velocity is the predominant radarinferred early indicator of severe potential in a convective storm. Fused lightningradar tools that capitalize on the most robust intensity indicators would allow enhanced situational awareness for increased warning confidence. A foundational step toward such tools comes from a better understanding of the updraftcentric relationship between intensification of total lightning production and mesocyclone development and strength. The work presented here utilizes a sample of supercell case studies representing a spectrum of severity. These storms are analyzed with respect to total lightning flash rate and the lightning jump alongside mesocyclone strength derived objectively from the National Severe Storms Laboratory (NSSL) Mesocyclone Detection Algorithm (MDA) and maximum azimuthal shear through a layer. Early results indicate that temporal similarities exist in the trends between total lightning flash rate and low to midlevel rotation in supercells. Other characteristics such as polarimetric signatures of rotation, flash size, and cloudtoground flash ratio are explored for added insight into the significance of these trends with respect to the updraft and related processes of severe weather production

Lightning Jump Algorithm and Relation to Thunderstorm Cell Tracking, GLM Proxy and Other Meteorological Measurements

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

Investigation of linezolid resistance in staphylococci and enterococci

The objective of this study was to investigate an apparent increase in linezolid-nonsusceptible staphylococci and enterococci following a laboratory change in antimicrobial susceptibility testing from disk diffusion to an automated susceptibility testing system. Isolates with nonsusceptible results (n = 27) from Vitek2 were subjected to a battery of confirmatory testing which included disk diffusion, Microscan broth microdilution, Clinical and Laboratory Standards Institute (CLSI) reference broth microdilution, gradient diffusion (Etest), 23S rRNA gene sequencing, and cfr PCR. Our results show that there is poor correlation between methods and that only 70 to 75% of isolates were confirmed as linezolid resistant with alternative phenotypic testing methods (disk diffusion, Microscan broth microdilution, CLSI broth microdilution, and Etest). 23S rRNA gene sequencing identified mutations previously associated with linezolid resistance in 16 (59.3%) isolates, and the cfr gene was detected in 3 (11.1%) isolates. Mutations located at positions 2576 and 2534 of the 23S rRNA gene were most common. In addition, two previously undescribed variants (at positions 2083 and 2345 of the 23S rRNA gene) were also identified and may contribute to linezolid resistance

Total Lightning Observations within Electrified Snowfall using Polarimetric Radar, LMA, and NLDN Measurements

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