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NASA NTRS2010

A Ground Flash Fraction Retrieval Algorithm for GLM

A Bayesian inversion method is introduced for retrieving the fraction of ground flashes in a set of N lightning observed by a satellite lightning imager (such as the Geostationary Lightning Mapper, GLM). An exponential model is applied as a physically reasonable constraint to describe the measured lightning optical parameter distributions. Population statistics (i.e., the mean and variance) are invoked to add additional constraints to the retrieval process. The Maximum A Posteriori (MAP) solution is employed. The approach is tested by performing simulated retrievals, and retrieval error statistics are provided. The approach is feasible for N greater than 2000, and retrieval errors decrease as N is increased.

Koshak, William J.↗

NASA NTRS2010

The Sao Paulo Lightning Mapping Array (SPLMA): Prospects to GOES-R GLM and CHUVA

This paper presents the characteristics and prospects of a Lightning Mapping Array to be deployed at the city of S o Paulo (SPLMA). This LMA network will provide CHUVA campaign with total lightning, lightning channel mapping and detailed information on the locations of cloud charge regions for the thunderstorms investigated during one of its IOP. The real-time availability of LMA observations will also contribute to and support improved weather situational awareness and mission execution. For GOES-R program it will form the basis of generating unique and valuable proxy data sets for both GLM and ABI sensors in support of several on-going research investigations

Albrecht, Rachel I.↗

NASA NTRS2011

Advancements in the Development of an Operational Lightning Jump Algorithm for GOES-R GLM

Rapid increases in total lightning have been shown to precede the manifestation of severe weather at the surface. These rapid increases have been termed lightning jumps, and are the current focus of algorithm development for the GOES-R Geostationary Lightning Mapper (GLM). Recent lightning jump algorithm work has focused on evaluation of algorithms in three additional regions of the country, as well as, markedly increasing the number of thunderstorms in order to evaluate the each algorithm s performance on a larger population of storms. Lightning characteristics of just over 600 thunderstorms have been studied over the past four years. The 2 lightning jump algorithm continues to show the most promise for an operational lightning jump algorithm, with a probability of detection of 82%, a false alarm rate of 35%, a critical success index of 57%, and a Heidke Skill Score of 0.73 on the entire population of thunderstorms. Average lead time for the 2 algorithm on all severe weather is 21.15 minutes, with a standard deviation of +/- 14.68 minutes. Looking at tornadoes alone, the average lead time is 18.71 minutes, with a standard deviation of +/-14.88 minutes. Moreover, removing the 2 lightning jumps that occur after a jump has been detected, and before severe weather is detected at the ground, the 2 lightning jump algorithm s false alarm rate drops from 35% to 21%. Cold season, low topped, and tropical environments cause problems for the 2 lightning jump algorithm, due to their relative dearth in lightning as compared to a supercellular or summertime airmass thunderstorm environment.

Shultz, Chris↗

NASA NTRS2013

Assessing the Lifetime Performance of the Lightning Imaging Sensor (LIS): Implications for the Geostationary Lightning Mapper (GLM)

Project motivation is to analyze the performance of the Lightning Imaging Sensor (LIS) over its 13 years in orbit and examine implications for the Geostationary Lightning Mapper (GLM) .

Buechler, Dennis E.↗

NASA NTRS2017

Image Navigation and Registration Performance Assessment Evaluation Tools for GOES-R ABI and GLM

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. This paper describes the software design and implementation of IPATS and provides preliminary test results.

Image registration↗

NASA NTRS2017

Image Navigation and Registration Performance Assessment Evaluation Tools for GOES-R ABI and GLM

image registration↗

NASA NTRS2017

The Extratropical Transition of Tropical Storm Cindy From a GLM, ISS LIS and GPM Perspective

The distribution of lightning with respect to tropical convective precipitation systems has been well established in previous studies and more recently by the successful Tropical Rainfall Measuring Mission (TRMM). However, TRMM did not provide information about precipitation features poleward of +/-38 deg latitude. Hence we focus on the evolution of lightning within extra-tropical cyclones traversing the mid-latitudes, especially its oceans. To facilitate such studies, lightning data from the Geostationary Lightning Mapper (GLM) onboard GOES-16 was combined with precipitation features obtained from the Global Precipitation Measurement (GPM) mission constellation of satellites.

Precipitation↗

NASA NTRS2018

Examining Sub-Flash Properties of Lightning from GLM for Tracking and Intensification Characterization of Thunderstorms

Current methodologies for operational use of lightning are developed using ground-based networks. Lightning detectors measure different characteristics of the flash, thus they don't observe the same lightning event in the same manner: i.e., flash rates from NDLN (National Lightning Detection Network (R)) will typically not match flash rates from GLM (Geostationary Lightning Mapper) because each sensor is measuring different characteristics (EM (Electromagnetic) radiation vs. optical). Resolution/timeliness of space-based sensor data will change our "rules of thumb" for operational use: Lightning safety - how does the 2D mapping of lightning enhance lightning safety metrics; Is the super-fast input of data (20s) useful for decision-makers, including (non-AWIPS (Advanced Weather Interactive Processing System) -users) non-mets?

lightning jump↗

NASA NTRS2019

The Evolution and Extratropical Transition of Tropical Cyclones from a GPM, ISS LIS and GLM Perspective

Not much is known about the evolution of lightning within extra-tropical cyclones traversing the mid-latitudes, especially its oceans. To facilitate such studies we combine a recently constructed precipitation features (PF) database obtained from the Global Precipitation Measurement (GPM) mission constellation of satellites with lightning observations from the Geostationary Lightning Mapper (GLM) onboard GOES-16 and the Lightning Imaging Sensor (LIS) onboard the International Space Station (ISS). The goal of this study is to provide a new observationally-based view of the tropical to extra-tropical transition and its impact on lightning production. Such data fusion approaches, as presented here, will also be important in future satellite studies of convective precipitation.

Gatlin, Patrick↗

NASA NTRSDate not supplied

The Convective Nature of the Tropical Cyclone Lifecycle via GLM and GPM Observations

This study examines the convective nature of the tropical cyclone (TC) lifecycle from tropical storm through extratropical transition. We analyze lightning observations collected from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in combination with coincident passive microwave and Ku-band radar observations collected from Global Precipitation Measurement (GPM) mission satellites. A unique aspect of this study, which spans the Atlantic basin hurricane seasons 2018-2020, is that it provides the first known total lightning observations in TCs throughout their extratropical (ET) transition. Analysis of Tropical Storm (TS), Category 1-2 hurricanes (CAT12), Category 3-5 hurricanes (CAT35), and ET time periods, which are grouped by storm-motion and shear-relative characteristics, show that lightning maxima generally occur regimes of down-motion (up-shear), consistent (inconsistent) with previous studies. Further analysis also breaks down time periods by geographic location (e.g., land, coast, ocean) and shear strength; shifting lightning patterns are observed with increasing shear. The lightning maxima are also generally collocated with minima in 37-GHz brightness temperature observations, which is indicative of precipitation-sized ice. DPR Ku-band reflectivity profiles from the GPM Precipitation Feature (PF) database exhibit distinct differences in depth and intensity for electrically active PFs vs those that are not. On average, PFs defined by a rain rate threshold are larger for hurricane strength ITPs (CAT12, CAT35) as compared to either TS or ET ITPs. This indicates that during the hurricane strength ITPs, PFs may be capturing the entire, symmetric rain shield.

hurricane↗

NASA NTRSDate not supplied

GOES-16 GLM Observations for National Climate Assessments

The heating of air by a lightning discharge, followed by rapid cooling, leads to the production of lightning nitrogen oxides (“LNO x ” for brevity, where NO x = NO + NO 2 ). The LNO x are important trace gases because they control the concentration of the greenhouse gas ozone (O 3 ) and the highly reactive hydroxyl radicals (OH). In fact, LNO x are the most important source of NO x in the upper troposphere at tropical and subtropical latitudes, and climate is most sensitive to O 3 in the upper troposphere. Since lightning indirectly influences the Earth's climate, it is a key variable to monitor in National Climate Assessment (NCA) studies. In this work, GOES-16 Geostationary Lightning Mapper (GLM) flash counts and flash optical properties (i.e., optical energy, optical area, …) that are linked to LNO x production are examined in detail for the continental United States analysis region, and are compared with ground-based National Lightning Detection Network (NLDN) observations.

Lightning↗

NASA NTRSDate not supplied

Reanalysis of Fly's Eye GLM Simulator (FEGS) Optical Pulse Detections from the 2017 GOES-R Post Launch Test (GOES-R PLT) Field Campaign

In 2017, the GOES-R PLT field campaign was conducted to validate new instruments on board GOES-16, including the Geostationary Lightning Mapper (GLM). A NASA ER-2 high altitude aircraft was equipped with a sensor suite including FEGS and an electric field change meter (EFCM) for complementary observations of lightning. As part of a modern reanalysis, we plan to combine aircraft and ground observations from the lightning instrumentation and polarimetric Doppler radars for a more complete characterization of optical lightning measurements in the context of convective properties. This presentation focuses on our initial efforts which include a reanalysis of the FEGS multi-spectral optical waveforms using an updated pulse detection algorithm and a comparison of detected pulses with coincident EFCM and Lightning Mapping Array (LMA) data. We will present on characteristics of discharge processes including leaders, strokes, and continuing current signatures as observed by the suite of aircraft and ground-based lightning instrumentation.

T Daniel Walker↗

NASA NTRS2016

Automated Storm Tracking and the Lightning Jump Algorithm Using GOES-R Geostationary Lightning Mapper (GLM) Proxy Data

This study develops a fully automated lightning jump system encompassing objective storm tracking, Geostationary Lightning Mapper proxy data, and the lightning jump algorithm (LJA), which are important elements in the transition of the LJA concept from a research to an operational based algorithm. Storm cluster tracking is based on a product created from the combination of a radar parameter (vertically integrated liquid, VIL), and lightning information (flash rate density). Evaluations showed that the spatial scale of tracked features or storm clusters had a large impact on the lightning jump system performance, where increasing spatial scale size resulted in decreased dynamic range of the system's performance. This framework will also serve as a means to refine the LJA itself to enhance its operational applicability. Parameters within the system are isolated and the system's performance is evaluated with adjustments to parameter sensitivity. The system's performance is evaluated using the probability of detection (POD) and false alarm ratio (FAR) statistics. Of the algorithm parameters tested, sigma-level (metric of lightning jump strength) and flash rate threshold influenced the system's performance the most. Finally, verification methodologies are investigated. It is discovered that minor changes in verification methodology can dramatically impact the evaluation of the lightning jump system.

lightning jump↗

NASA NTRS2016