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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Correction and calibration of atmospheric impact observations in GOES GLM data

The Earth's atmosphere is impacted daily by both meteoroids and artificial objects. Calibrated observations of the emitted light at sufficiently high sampling rates can enable or improve the estimation of impactor attributes such as size, cohesion, trajectory, and composition, but are difficult to obtain owing to the unpredictability, brevity, and high dynamic (brightness) range of impacts. Ground-based camera systems have successfully monitored small regions of the atmosphere at video frame rates and with limited radiometric capabilities, but most impacts occur over the 70% of the Earth's surface covered by water and are therefore missed by these networks. The Geostationary Lightning Mapper (GLM) instruments aboard Geostationary Operational Environmental Satellites 16 and 17 provide near-hemispherical coverage at 500 frames per second. These data have been shown to contain the signatures of many independently confirmed impacts, often from both viewing angles simultaneously, and constitute an observational resource that is currently unparalleled in the public domain. NASA's Asteroid Threat Assessment Project has implemented an automated impact detection pipeline that processes data from GLM daily. Given a detected impact, the GLM data contain a wealth of information for use in quantitative follow-up analyses. However, impact events differ from lightning in ways that violate key assumptions built into GLM's design. The result is that GLM's onboard processing introduces errors into pixel observations of impact events and the calibrated energies near the periphery of the detector may be substantially overestimated. We present methods for mitigating these and other issues to produce a data product more suitable for impact analyses than the existing GLM lightning product.

58 GEOSCIENCES↗
DOE OSTI2020

GLM Observations of the Brightest Lightning in the Americas

Two years of Geostationary Lightning Mapper (GLM) science data are used to document the brightest lighting flashes observed on the Americas continent. The most radiant optical lightning emissions—termed “superbolts”—were first identified by the Vela satellite constellation in the 1970s (Turman, 1977) and are defined in terms of peak optical power. GLM is an integrating sensor that, instead, measures the total optical energy from a lightning pulse. Furthermore, while GLM might not correctly classify short-duration superbolts, its top lightning cases certainly fall in the superbolt category, and the wealth of GLM measurements over its stationary hemispheric field of view provide an unmatched sample of extraordinarily bright lightning. While radiant bolts in excess of 100× the optical energy of typical lightning are ubiquitous across the Americas and result from many types of lightning processes, we find the most radiant cases (>1,000×) are concentrated in the central United States and in the La Plata basin in South America. Coincident Earth Networks Global Lightning Network (ENGLN) observations reveal that these extremely bright emissions usually result from + CG strokes with high peak currents in long horizontal flashes outside of the convective core. Single cases of these megaflashes might produce multiple superbolts over their durations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Illumination of Thunderclouds by Lightning: 2. The Effect of GLM Instrument Threshold on Detection and Clustering

Abstract Lightning is measured from space using optical instruments that detect transient changes in the illumination of the cloud top. How much of the flash (if any) is recorded by the instrument depends on the instrument detection threshold. NOAA's Geostationary Lightning Mapper (GLM) employs a dynamic threshold that varies across the imaging array and changes over time. This causes flashes in certain regions and at night to be recorded in greater detail than other flashes, and threshold inconsistencies will impose biases on all levels of GLM data products. In this study, we quantify the impact of the varying GLM threshold on event/group/flash detection, flash clustering, and gridded product generation by imposing artificial thresholds on the event data taken from a thunderstorm with a low instrument threshold (∼0.7 fJ). We find that even modest increases in threshold severely impact event (60% loss by 2 fJ, 90% loss by 10 fJ) and group (25% loss by 2 fJ, 81% loss by 10 fJ) detection by suppressing faint illumination of the cloud top from weak sources and scattering. Flash detection is impacted less by threshold increases (4% loss by 2 fJ), but reductions are still significant at higher thresholds (35% loss by 10 fJ, or 44% if single‐group flashes are removed). Undetected pulses cause individual flashes to be split and severely impact the construction of gridded products. All of these factors complicate the interpretation of GLM data, particularly when trended over time under a changing threshold.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Illumination of Thunderclouds by Lightning: 3. Retrieving Optical Source Altitude

Abstract Optical space‐based lightning sensors such as the Geostationary Lightning Mapper (GLM) detect and geolocate lightning by recording rapid changes in cloud top illumination. While lightning locations can be determined to within a pixel on the GLM imaging array, these instruments are not individually able to natively report lightning altitude. It has previously been shown that thunderclouds are illuminated differently based on the altitude of the optical source. In this study, we examine how altitude information can be extracted from the spatial distributions of GLM energy recorded from each optical pulse. We match GLM “groups” with Lightning Mapping Array (LMA) source data that accurately report the 3‐D positions of coincident Radio‐Frequency (RF) emitters. We then use machine learning methods to predict the mean LMA source altitudes matched to GLM groups using metrics from the optical data that describe the amplitude, breadth, and texture of the group spatial energy distribution. The resulting model can predict the LMA mean source altitude from GLM group data with a median absolute error of <1.5 km, which is sufficient to determine the location of the charge layer where the optical energy originated. This model is able to capture changes to the source altitude distribution in response to convective processes in the thunderstorm, and the GLM predictions can reveal the vertical structure of individual flashes ‐ enabling 3‐D flash geolocation with GLM for the first time. Future work will account for differences in thunderstorm charge/precipitation structures and viewing angle across the GLM Field of View.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Illumination of Thunderclouds by Lightning: 1. The Extent and Altitude of Optical Lightning Sources

Abstract Optical space‐based lightning sensors including NOAA's Geostationary Lightning Mapper (GLM) detect lightning though its transient illumination of the surrounding clouds. What space‐based optical lightning sensors measure is influenced by the physical attributes of the light source, the location of the source within the cloud scene, and the spatial variations in cloud composition. We focus on the lightning channels that serve as optical sources for GLM groups and flashes in this first part of our thundercloud illumination study. We match Lightning Mapping Array (LMA) sources with GLM groups and flashes during two thunderstorms to examine channel segments that are active during optical emission. We find that in each storm, the LMA sources matched with GLM groups are small (median: 3 km) compared to GLM pixels (nominal: 8 km), and preferentially come from high altitudes in the cloud (>8–10 km). The detection advantage for high‐altitude sources permits GLM to resolve faint optical pulses near the cloud top that might be missed from lower altitudes. However, the most energetic groups can be detected from all altitudes, and the largest groups largely originate at low altitudes. The relationship between group energy and illuminated area depends on flash development within the cloud medium, and flash development into different cloud regions can be identified by tracking GLM metrics of cloud illumination over time.

58 GEOSCIENCES↗
DOE OSTI2019

Research Applications for the Geostationary Lightning Mapper Operational Lightning Flash Data Product

The Lightning Cluster Filter Algorithm in the Geostationary Lightning Mapper (GLM) ground system identifies lightning flashes from the stream of event detections. It excels at clustering simple flashes but experiences anomalies with complex flashes that last longer than 3 s or contain more than 100 groups, leading to flashes being artificially split. In this work, we develop a technique that corrects these anomalies and apply it to the 2018 GLM data to document all lightning across the Americas. We produce statistics describing the characteristics and frequencies of reclustered GLM flashes and thunderstorm area features. The average GLM Americas flash rate in 2018 was 11.7 flashes per second with the greatest flash rate densities occurring over Lake Maracaibo (157 flashes per km 2 /year). Lloró, Chocó, Colombia had the most thunderstorm activity with 256 thunder days. The longest GLM flash spanned 673 km, the largest flash covered 114,997 km 2 , and the longest-lasting flash had a 13.496-s duration. The first case occurred over Rio Grande do Sul in Brazil, while the other two cases occurred in the central United States. All three extreme flashes are located in the stratiform regions of Mesoscale Convective Systems. The highest flash rate for a thunderstorm area feature was 17.6 flashes per second, while the largest thunderstorm was 216,865 km 2 in size. Both storms occurred in South America. In conclusion, these initial results demonstrate the value that the development of a reprocessed GLM science product would offer and how such a product might be created at a reduced computational cost.

58 GEOSCIENCES↗
DOE OSTI2021

The Hazards Posed by Mesoscale Lightning Megaflashes

Lightning megaflashes extending over >100-km distances have been observed by the Geostationary Lightning Mappers (GLMs) on NOAA’s R-series Geostationary Operational Environmental Satellites (GOES). The hazards posed by megaflashes are unclear, however, because of limitations in the GLM data. We address these by reprocessing GOES-16 GLM measurements from 1 January 2018 to 15 January 2020 and integrating them with Earth Networks Global Lightning Network (ENGLN) observations. ENGLN verified 194 880 GLM megaflashes as natural lightning. Of these, 127 479 flashes occurred following the October 2018 GLM software update that standardized GLM timing. Reprocessed GLM/ENGLN lightning maps from these postupdate cases provide a comprehensive view of how individual megaflashes evolve. This megaflash dataset is used to generate statistics that describe their hazards. The average megaflash produces 5–7 cloud-to-ground (CG) strokes that are spread across 40%–50% of the flash extent. As flash extent increases beyond 100 km, megaflashes become concentrated in key hot-spot regions in North and South America while the number of CG and intracloud events per flash and the overall peak current increase. CGs in the larger megaflashes occur over 80% of the flash extent measured by GLM, and the majority contain regions where the megaflash is the only lightning activity in the preceding hour. These statistics demonstrate that there is no safe location below an electrified cloud that is producing megaflashes, and current lightning safety guidance is not always sufficient to mitigate megaflash hazards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Illumination of Thunderclouds by Lightning: 4. Volumetric Thunderstorm Imagery

Optical instruments such as the Geostationary Lightning Mapper (GLM) detect lightning based on transient changes in cloud illumination. The horizontal location of lightning is determined from the coordinates of the pixels on the imaging array illuminated during the flash. However, the vertical position of the lightning pulses (approximated by GLM “groups”) below the cloud top cannot be routinely measured from a single space-based instrument. In our prior work, we have developed a machine learning algorithm that can infer optical source altitude for a given pulse based on how the optical energy is distributed across the group footprint and the local Advanced Baseline Imager Cloud-Top Height (CTH). In this fourth part of our thundercloud illumination study, we leverage these source altitudes to generate volumetric GLM imagery of a Colombia thunderstorm. We find that 3D versions of the current GLM meteorological imagery products (that describe thunderstorm kinematics) and thundercloud imagery products (that depict how the flashes appear from space) provide additional insights into lightning activity in the thunderstorm that are lost in the vertical integration used to generate the current 2D GLM gridded products. This new volumetric imaging capability provides a more comprehensive picture of where lightning occurs in the storm, how its physical characteristics vary across three-dimensional space, and how its optical emissions interact with surrounding the cloud medium.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

First Observations of Gigantic Jets from Geostationary Orbit

In this work, we present the first observations of gigantic jets (GJs) by the Geostationary Lightning Mapper (GLM) on board the Geostationary Operational Environmental Satellite-R series. Fourteen GJs produced by Tropical Storm Harvey on 19 August 2017 were observed by both GLM and a ground-based low-light-level camera system. The majority of the GJs produced distinguishable signatures in the GLM data, which include long continuous emissions, large peak flash optical energies, and small lateral propagation distances in comparison with other flashes observed by GLM. For two GJs with the best ground-based images, each have a single pixel that contains the largest optical energy throughout the duration of the GJ and also coincides with the azimuth of the GJ from the video images. The optical energy of the pixel increases as the GJ propagates upward, reaches its peak when the GJ connects to the ionosphere, and then fades away.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Thunderstorm Cloud-Type Classification from Space-Based Lightning Imagers

The organization and structure of thunderstorms determines the extent and severity of their hazards to the general public and their consequences for the Earth system. Distinguishing vigorous convective regions that produce heavy rain and hail from adjacent regions of stratiform clouds or overhanging anvil clouds that produce light to no rainfall is valuable in operations and physical research. Cloud-type algorithms that partition convection from stratiform regions have been developed for space-based radar, passive microwave, and now Geostationary Operational Environmental Satellites (GOES) Advanced Baseline Imager (ABI) multispectral products. However, there are limitations for each of these products including temporal availability, spatial coverage, and the degree to which they based on cloud microphysics. We report we have developed a cloud-type algorithm for GOES Geostationary Lightning Mapper (GLM) observations that identifies convective/nonconvective regions in thunderstorms based on signatures of interactions with nonconvective charge structures in the lightning flash data. The GLM sensor permits a rapid (20 s) update cycle over the combined GOES-16–GOES-17 domain across all hours of the day. Storm regions that do not produce lightning will not be classified by our algorithm, however. The GLM cloud-type product is intended to provide situational awareness of electrified nonconvective clouds and to complement other cloud-type retrievals by providing a contemporary assessment tied to lightning physics. We propose that a future combined ABI–GLM cloud-type algorithm would be a valuable product that could draw from the strengths of each instrument and approach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Removing solar artifacts from Geostationary Lightning Mapper data to document lightning extremes

Here, we compare the performance of three different filters that identify solar artifacts in the Geostationary Lightning Mapper (GLM) data. These filters differ in their complexity and intended use. We use the most appropriate filters to document the severity of GLM solar contamination and to compute the frequency of extreme Americas lightning. Solar artifacts occur at an average rate of one every ~8 min. Due to their continuous emissions across a portion of the GLM CCD array, these events disproportionately contribute to the GLM event and group rates. Up to 40% of all events recorded between 10:00 and 12:00 local time during 2018 were solar artifacts. The frequency-domain solar filter also reveals a carrier wave at 50 to 60 Hz in some lightning flashes and solar episodes that appears to be anthropogenic in origin. Removing the relatively frequent solar artifact cases reveals rare cases of extreme lightning flashes. Exceptional flashes on the order of 500+ km in length and 10+ s in duration only occur a few times per year and are produced by storms that are predisposed for exceptional lightning rather than as single random occurrences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A New CIERRA Gridded Product Climatology for LIS/OTD

The GLM-CIERRA processing generates two products: A level-2 cluster feature dataset that merges flashes that should have been a single flash following the clustering algorithm flash definition (for GLM, this is due to the LCFA thresholds); A set of level-3 gridded products including Flash Extent Density, Convective Probability, etc. The CIERRA Level-3 products are generated on a 0.1 degree grid. This is suboptimal given the spatial variations in GLM pixel size AND the size of gridpoints in the output grid. As a stepping-stone towards developing improved GLM-CIERRA grids, I am testing pixel matching techniques on the LIS/OTD data. The goal is to maintain accuracy while vastly improving computational efficiency. The CIERRA reclustering codes are also being applied to LIS / OTD to merge flashes split by the “first fit” clustering technique. The end result will be standardized grids at the nominal resolution of each instrument (5 km for LIS, 10 km for OTD) that take into account the pixel geometry for each event that comprises a given reclustered flash.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Survey of Thunderstorms That Produce Megaflashes Across the Americas

We previously observed that long-horizontal lightning flashes exceeding 100 km in length, known as “megaflashes,” occur preferentially in certain thunderstorms. In this study, we develop a cluster feature approach for automatically documenting the evolutions of thunderstorm systems from continuous lightning observations provided by the Geostationary Lightning Mapper (GLM) on NOAA's Geostationary Operational Environmental Satellites (GOES). We apply this methodology to GOES-16 GLM observations from 2018 to mid-2022 to improve our understanding of megaflash-producing storms. We find that megaflashes occur in long-lived (median: 14 hr) storms that grow to exceptional sizes (median: 11,984 km 2 ) while they propagate across long distances (median: 622 km) compared to ordinary storms. The first megaflashes are typically produced within 15 min of the storm reaching its peak intensity and extent. However, most megaflashes occur ≥13 hr after the initial megaflash activity, and are sufficiently close to convection to suggest initiation in the thunderstorm core (where GLM has difficulty detecting faint early light sources from megaflashes). Megaflashes generated outside of convection are rare, accounting for 2.7% of the sample using a 50 km convective distance threshold, but also tend to be larger than normal megaflashes, possibly due to having direct access to electrified stratiform clouds through which megaflashes propagate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Distinctive Signals in 1-min Observations of Overshooting Tops and Lightning Activity in a Severe Supercell Thunderstorm

This work examines a severe weather event that took place over central Argentina on December 11, 2018. The evolution of the storm from its initiation, rapid organization into a supercell, and eventual decay was analyzed with high-temporal resolution observations. This work provides insight into the spatio-temporal co-evolution of storm kinematics (updraft area and lifespan), cloud-top cooling rates, and lightning production that led to severe weather. The analyzed storm presented two convective periods with associated severe weather. An overall decrease in cloud-top local minima IR brightness temperature (MinIR10.3), increase in overshooting top area, and lightning jump (LJ) preceded both periods. LJs provided the highest lead time to the occurrence of severe weather, with the ground-based lightning networks providing the maximum warning time of around 30 min. Lightning flash counts from the Geostationary Lightning Mapper (GLM) were underestimated when compared to detections from ground-based lightning networks. Among the possible reasons for GLM’s lower detection efficiency were an optically dense medium located above lightning sources and the occurrence of flashes smaller than GLM’s footprint. The minimum MinIR provided the shorter warning time to severe weather occurrence. However, secondary minima in MinIR10.3 that preceded the absolute minima improved this warning time by more than 10 min. Furthermore, trends in MinIR10.3 for time scales shorter than 6 min revealed shorter cycles of fast cooling and warming, which provided information about the lifecycle of updrafts within the storm. The advantages of using observations with high-temporal resolution to analyze the evolution and intensity of convective storms are discussed.

54 ENVIRONMENTAL SCIENCES↗