An Algorithmic Approach for Detecting Bolides with the Geostationary Lightning Mapper

sensors

Article An Algorithmic Approach for Detecting Bolides with the Geostationary Lightning Mapper

Clemens M. Rumpf 1,2,*, Randolph S. Longenbaugh 1, Christopher E. Henze 1, Joseph C. Chavez 3 and Donovan L. Mathias 1

1 NASA Advanced Supercomputing Division, NASA Ames Research Center, Moffett Field, CA 94035, USA; randolph.s.longenbaugh@nasa.gov (R.S.L.); [email protected] (C.E.H.); [email protected] (D.L.M.) 2 NASA Postdoctoral Program, USRA, Mountain View, CA 94043, USA 3 Independent Researcher, Albuquerque, NM 87111, USA; [email protected] * Correspondence: [email protected]

Received: 31 December 2018; Accepted: 19 February 2019; Published: 27 February 2019

Abstract: The Geostationary Lightning Mapper (GLM) instrument onboard the GOES 16 and 17 satellites can be used to detect bolides in the atmosphere. This capacity is unique because GLM provides semi-global, continuous coverage and releases its measurements publicly. Here, six filters are developed that are aggregated into an automatic algorithm to extract bolide signatures from the GLM level 2 data product. The filters exploit unique bolide characteristics to distinguish bolide signatures from lightning and other noise. Typical lightning and bolide signatures are introduced and the filter functions are presented. The filter performance is assessed on 144845 GLM L2 files (equivalent to 34 days-worth of data) and the algorithm selected 2252 filtered files (corresponding to a pass rate of 1.44%) with bolide-similar signatures. The challenge of identifying frequent but small, decimeter-sized bolide signatures is discussed as GLM reaches its resolution limit for these meteors. The effectiveness of the algorithm is demonstrated by its ability to extract confirmed and new bolide discoveries. We provide discovery numbers for November 2018 when seven likely bolides were discovered of which four are confirmed by secondary observations. The Cuban meteor on Feb 1st 2019 serves as an additional example to demonstrate the algorithms capability and the first light curve as well as correct ground track was available within 8.5 hours based on GLM data for this event. The combination of the automatic bolide extraction algorithm with GLM can provide a wealth of new measurements of bolides in Earth’s atmosphere to enhance the study of asteroids and meteors.

Keywords: meteor; asteroid; GLM; GOES; lightning; Geostationary Lightning Mapper; bolide; light curve; asteroid; Cuba

1. Introduction A new space-based remote sensing system designed to detect terrestrial lightning activity called the Geostationary Lighting Mapper (GLM) aboard the GOES 16 and GOES 17 satellites [1], operated by the National Oceanic and Atmospheric Administration (NOAA), has recently become operational. Together, the GLM sensors on GOES 16 and GOES 17 cover about half of the Earth’s surface with overlapping viewing ﬁelds over the Americas. Although the GLM system is originally designed to capture natural lightning activity, it is well capable of detecting large meteors, called bolides [2,3]. In addition to its large coverage area, which potentially allows it to capture unprecedented numbers of meteors, its data is freely accessible to the public.

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Given that the system was originally designed to capture lightning activity in the atmosphere and produces about 1.2 GB of data each day, a new algorithm is required that can sieve through the large quantity of data to automatically extract meteor signatures. Here, we present an algorithm that uses the GLM Level 2 data product as input and filters through the data to extract bolide candidate signatures. Signatures are comprised of time-stamped ground track information as well as light curve intensity. Such data is valuable for future analysis such as orbit determination [4] and compositional investigation of the body [5]. In addition, meteor observations, such as delivered by GLM, may help to analyze the flow regimes [6] encountered by the meteoroid as it travels through the atmosphere and to reconstruct their entry trajectory. The latter application is particularly useful to the recovery meteorites for further analysis [7,8]. Section 1.1 provides a short introduction into meteors and places GLM in context to some of the systems that are in use today to detect them. Section 1.2 introduces the sensor itself while Section 1.3 outlines the size range of meteoroids detectable by GLM. The filters and the architecture of the automatic algorithm are described in Section2. In addition, this section details the differences between lightning and bolide flashes. The algorithm’s performance and its sensitivities are discussed in Section3. This section highlights bolide discoveries made with the algorithm as well as the challenge of extracting small bolides. The Cuban meteor case study is noteworthy as a performance demonstration and it is discussed in Section 3.5.

1.1. Meteors and Meteor Detection Systems It has been estimated that more than 50 t of meteoroidal material collides with the Earth daily [5,9]. The asteroid size – impact frequency relationship indicates that most colliding objects are as small as dust grains and remain undetected [10,11]. However, 1 m-sized objects are expected to collide with the Earth every two weeks on average and these objects release sufficient kinetic energy when entering the atmosphere to trigger sensors that monitor transient occurrences in the atmosphere. In fact, the kinetic energy of these objects quickly reaches levels comparable to nuclear weapons because their impact speeds range between 11–72 km s−1 [12,13]. A recent example is the 2013 Chelyabinsk superbolide that registered at 550 kilotons Trinitrotoluene (kt TNT) equivalent (comparable to about 36 times the energy yield of the Hiroshima bomb) when it disintegrated over northern Russia. This superbolide, estimated at about 18 m in diameter, injured over 1500 people [14,15]. Most of the large bolides are thus recorded by a network of ground-based sensors that is intended to enforce the nuclear test ban treaty (operated by the Comprehensive Nuclear-Test-Ban Treaty Organization, CTBTO) and is capable of detecting such occurrences globally [16–18]. The sensor network is designed for national security applications and therefore only publishes a significantly reduced data set it deems safe to present to the public. Published detections include the geolocated position and time of peak brightness, as well as cumulative energy release, but no light curve or ground track information. Of similarly confidential nature as the CTBTO sensor network are space-borne, military United States Government (USG) sensors [3]. Occasionally, researchers may gain access to portions of the collected data by these sensors but the data products are generally not available to the public. Local ground camera systems that usually use fisheye lenses to record the entire, locally visible sky (e.g. European Fireball Network, Desert Fireball Network, NASA All Sky Fireball Network, Sky Sentinel, Cameras for Allsky Meteor Surveillance, Spanish fireball network, Fireball Recovery and Interplanetary Observation Network - FRIPON) can detect smaller meteors and provide open data access [19–22]. That data regularly includes flight path and lightcurve information. However, these systems only have a limited detection range around their installation sites on the Earth. The total sky coverage of these systems remains sparse compared to Earth-viewing satellite systems. In addition, most ground based camera systems can easily saturate for larger events and do not produce accurate disintegration recordings. Sensors 2019, 19, 1008 3 of 24

With GLM, the scientiﬁc community gains access to a system that is open to the public, has semi-global coverage, and provides ground track as well as light curve information. GLM will provide signiﬁcant value to the meteor detection community when it is used in conjunction with the systems mentioned above. The algorithm presented here, enables access to this data which would otherwiseSensors 2019, by19, x treated as noise. 3 of 24

1.2. Geostationary Lightning Mapper Instrument The GOES Geostationary Lightning Mapper [1] [1] recordsrecords from geostationary orbit transient light events at a rate of 500500 framesframes perper secondsecond (2(2 ms).ms). GLMGLM hashas aa staringstaring CCDCCD imagerimager (1372(1372 ×× 1300 pixels) with aa narrownarrow 1.1 1.1 nm nm pass-band pass-band centered centered at 777.4at 777.4 nm, nm, a wavelength a wavelength associated associated with the with neutral the neutral atomic oxygenatomic oxygen emission emission (OI) line (OI) of the line lightning of the lightn spectrum.ing spectrum. Figure1 shows Figure the 1 shows GLM system the GLM being system assembled being atassembled the Lockheed at the MartinLockheed Advanced Martin TechnologyAdvanced Technology Center in Palo Center Alto, in CAPalo with Alto, a workerCA with in a the worker picture in forthe scale.picture for scale.

Figure 1. GLM assembly with sun shade pre-launch [[23].23].

Although GLM has been designed to detect lightning,lightning, it detects bolides as well. For a detailed analysis of how GLM responds to energyenergy emittedemitted byby bolidesbolides thethe readerreader isis referredreferred toto [[2].2]. GLM provides continuous coverage from geostationary orbit and covers approximately half of the Earth with the AmericasAmericas as thethe primaryprimary land-mass focus. Its spatial resolution is in the range of 8–14 km. Currently, twotwo GLMGLM sensors sensors are are in in space: space: GOES GOES 16 16 occupies occupies the the geostationary geostationary orbital orbital slot slot at 75.2at 75.2 degree degree west west and and GOES GOES 17 is located17 is located at 137.2 at degree137.2 degree west. Figure west.2 Figure shows the2 shows geographical the geographical coverage coverage for both GLM sensors over a map of lightning intensity. Parts of the United States and the Pacific have overlapping fields of view of the two GLM sensors potentially enabling stereo bolide detections.

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for both GLM sensors over a map of lightning intensity. Parts of the United States and the Paciﬁc have Sensorsoverlapping 2019, 19, ﬁeldsx of view of the two GLM sensors potentially enabling stereo bolide detections.4 of 24

FigureFigure 2. GLM coveragecoverage showingshowing both both GOES-16 GOES-16 (green) (green) and and GOES-17 GOES-17 (cyan) (cyan) on mapon map of lightning of lightning flash flashrate [rate24]. [24]. 1.3. GLM’s Meteoroid Detection Size Range 1.3. GLM’s Meteoroid Detection Size Range Brightness determines GLM’s meteor detection range. Towards the small end of the detection Brightness determines GLM’s meteor detection range. Towards the small end of the detection range, GLM is limited by its minimum required brightness level and it has been reported in [2] that range, GLM is limited by its minimum required brightness level and it has been reported in [2] that the detection threshold is about −14 visual magnitude. At visual magnitude −14, comparable to twice the detection threshold is about −14 visual magnitude. At visual magnitude −14, comparable to twice as bright as the full moon, a meteor is considered a bolide [25]. It is expected that a meteor needs to as bright as the full moon, a meteor is considered a bolide [25]. It is expected that a meteor needs to be at least decimeter-sized (in diameter) to reach bolide brightness levels and, thus, to be detectable be at least decimeter-sized (in diameter) to reach bolide brightness levels and, thus, to be detectable by GLM [5]. On the other hand, GLM’s sensor will saturate for objects measuring multiple meters in by GLM [5]. On the other hand, GLM’s sensor will saturate for objects measuring multiple meters in diameter due to their excessive energy release [2]. A saturated sensor will reduce data quality of light diameter due to their excessive energy release [2]. A saturated sensor will reduce data quality of light curves and ground tracks. As such, GLM’s target range will be for objects ranging from decimeter to curves and ground tracks. As such, GLM’s target range will be for objects ranging from decimeter to meter size in diameter. meter size in diameter. Figure3 presents size and impact frequency for bolides. It is estimated that a 1 m diameter small Figure 3 presents size and impact frequency for bolides. It is estimated that a 1 m diameter small asteroid collides with the Earth every two weeks, while a 4 m diameter object hits the Earth roughly asteroid collides with the Earth every two weeks, while a 4 m diameter object hits the Earth roughly once per year [26]. The shaded blue area in Figure3 approximates the usable bolide detection regime once per year [26]. The shaded blue area in Figure 3 approximates the usable bolide detection regime expected of GLM. It extends from 0.1 m (outside the figure) to 3 m. The axis conversion of impact expected of GLM. It extends from 0.1 m (outside the figure) to 3 m. The axis conversion of impact energy and object diameter fits the assumption of a meteoroid density of 2300 kg m−3, and an impact energy and object diameter fits the assumption of a meteoroid density of 2300 kg m-3, and an impact speed of 21 km s−1. The data points in the figure correspond to other sensor systems such as CTBTO speed of 21 km s-1. The data points in the figure correspond to other sensor systems such as CTBTO and space based USG sensors. The expected GLM detection regime complements the detection range and space based USG sensors. The expected GLM detection regime complements the detection range provided by these remote sensing systems towards smaller sized meteors. provided by these remote sensing systems towards smaller sized meteors.

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FigureFigure 3. Impact 3. Impact frequency frequency over over mean mean impact impact energy energy (and estimated(and estimated diameter). diameter). The grey The line grey represents line a ﬁtrepresents through actually a fit through recorded actually bolide recorded events bolide presented events presented in several in publications several publications (see legend) (see legend) based on severalbased sensor on several systems. sensor The systems. light The blue light shaded blue areashaded is thearea expected is the expected detection detection range range of GLM of GLM and it complementsand it complements the available the available data towards data towards smaller smaller sizes [ 27sizes]. [27].

2. Bolide2. Bolide Extraction Extraction Algorithm Algorithm ThisThis section section highlights highlights data data signatures signatures producedproduced by by GLM GLM and and presents presents the the bolide bolide extraction extraction algorithmalgorithm which which takes takes advantage advantage of the of differencesthe differen betweences between bolide bolide and lightningand lightning signatures signatures to identify to thoseidentify signatures those thatsignatures are bolide-like. that are Sixbolide-like. ﬁlters have Six beenfilters devised have been to extract devised bolides to extract signatures bolides and signatures and they are presented in the following. they are presented in the following.

2.1.2.1. Definition Definition of Termsof Terms Transient optical events are detected by the GLM. The data that is produced on-board is labeled Transient optical events are detected by the GLM. The data that is produced on-board is labeled as as the Level 0 (L0) data product. This data is subsequently sent to the ground where it is post- the Level 0 (L0) data product. This data is subsequently sent to the ground where it is post-processed processed into the Level 2 (L2) data product, which is published and available to the public. It is intoworth the Levelmentioning 2 (L2) that data the product, algorithm which presented is published here relies andpurely available on the publicly to the available public. It L2 is data worth mentioningproduct. thatThe processing the algorithm from presented L0 to L2 aims here to relies decrease purely the onamount the publicly of noise availablein the data L2 and data clusters product. Thethe processing optical events from into L0 tological L2 aims entities to decreasesuch as groups the amount and flashes of noise based in on the spatial data andand clusterstemporal the opticalthreshold events parameters into logical as entities described such in the as groups Product and Definition flashes and based Users’ on spatialGuide Volume and temporal 5 [1,28,29]. threshold parametersThere as are described three data in thestructures Product in Definitionthe GLM level and 2 Users’ relevant Guide to this Volume document. 5 [1 ,28 They,29]. are events, groupsThere and are flashes. three data Although structures conceived in the for GLM applicatio levelns 2 relevantin a lightning to this context, document. these data They structures are events, groupsare not and lightning flashes. specific Although and conceived can be understood for applications as a general in aGLM lightning data concept. context, An these event data represents structures are nota light lightning emission specific signal and that can stood be understoodout against th ase abackground general GLM image data seen concept. by the An GLM event sensor. represents An a lightevent emission occurs signal in an thatindividual stood pixel out against over a 2 the ms background integration period. image A seen group by therepresents GLM sensor. the collection An event occurs in an individual pixel over a 2 ms integration period. A group represents the collection of events Sensors 2019, 19, 1008 6 of 24

Sensors 2019, 19, x 6 of 24 detected in adjacent sensor pixels over the 2 ms integration period. A group’s energy is equal to the sum of its event’sof events energies detected and in adjacent the location sensor of pixels a group over is thethe energy2 ms integration weighted period. average A group’s location energy of its events.is A ﬂashequal represents to the sum a series of its of event’s measurements energies and constrained the location by of temporal a group is and the spatial energy extentweighted thresholds average that are associatedlocation of with its events. one orA flash more repr groups.esents a In series a hierarchical of measurements sense, co anstrained ﬂash is by at temporal the top, and followed spatial by a group,extent with thresholds events representing that are associated the smallest with one entity or more as visualized groups. In ina hierarchical Figure4. The sense, algorithm a flash is presented at the here mainlytop, followed utilizes by the a group, group with structure. events representing the smallest entity as visualized in Figure 4. The algorithm presented here mainly utilizes the group structure.

FigureFigure 4. Hierarchical 4. Hierarchical relationship relationship between between a flash, ﬂash, groups groups and and events events [28]. [28 ].

2.2. Collecting2.2. Collecting GLM GLM Groups Groups Into Into Flashes Flashes The GLMThe GLM L2 data L2 data can can be readbe read using using a a netCDF4 netCDF4 filefile reader. Even Even though though the the GLM GLM L2 data L2 data product product alreadyalready provides provides groups groups that were that clusteredwere clustered into flashes, into flashes, we employed we employed an in-house an in-house clustering clustering algorithm to maintainalgorithm flexibility to maintain in case flexibility the original, in case lightning-focused the original, lightning-focused clustering would clustering prove would detrimental prove to groupdetrimental clustering to for group bolides. clustering The literaturefor bolides. provides The literature a thorough provides description a thorough ofdescription the original of the GLM clusteringoriginal algorithm GLM clustering [1,29]. In algorithm a first step, [1,29]. the algorithmIn a first step, sifts the through algorithm the datasifts through and collects the data groups, and that collects groups, that are spatially and temporally close, into flashes. The algorithm compares each are spatially and temporally close, into flashes. The algorithm compares each group to its following group to its following group and assesses if its location and timestamp falls within the threshold group and assesses if its location and timestamp falls within the threshold values listed in Table1. values listed in Table 1. All groups for which the threshold is met successively without interruption All groupsare collected for which intothe one threshold flash. These is threshold met successively values ha withoutve been selected interruption early arein this collected analysis into on a one trial flash. Thesebasis threshold and were values sufficient have for been the selected purpose earlyof bolide in this detection. analysis on a trial basis and were sufficient for the purpose of bolide detection. Table 1. Spatial and temporal threshold values to meet closeness requirement collecting groups into Tableflashes. 1. Spatial and temporal threshold values to meet closeness requirement collecting groups into flashes. Closeness Aspect Value Spatial separation in longitude ≤ 0.05° Closeness Aspect Value Spatial separation in latitude ≤ 0.05° ◦ Spatial separationTemporal separation in longitude ≤≤0.05 0.2 s Spatial separation in latitude ≤0.05◦ Temporal separation ≤0.2 s 2.3. Flashes After collecting groups into flashes, the flash objects are assessed for their resemblance to 2.3. Flashes expected bolide behavior. This section presents a recorded lightning and a real bolide flash Afteremphasizing collecting distinguishing groups into features flashes, to the inform flash the objects subsequent are assessed filtering for process. their resemblance to expected bolide behavior. This section presents a recorded lightning and a real bolide flash emphasizing 2.3.1. Lightning Behavior distinguishing features to inform the subsequent filtering process. The most common data entity found in the GLM L2 data are lightning flashes as shown in Figure 2.3.1.5. Lightning This figure Behavior presents the ground track of the flash in plot (a) on a latitude/longitude coordinate grid (in degrees). Each ground track point is a group location as assigned in the GLM L2 data product. The most common data entity found in the GLM L2 data are lightning flashes as shown in Figure5. The distance between the two furthest ground track points is annotated in the top left corner This figureconveying presents a sense the of groundscale. Below track the of distance, the flash speed in plot is annotated (a) on a latitude/longitudewhich is calculated as coordinate the division grid (in degrees).of the ground Each groundtrack extent track by pointthe signature is a group duration. location Note as that assigned the ground in the track GLM only L2 shows data product. the The distancehorizontal between component the two of the furthest trajectory ground (more track relevant points for is meteors), annotated equivalent in the top to lefta projection cornerconveying of the a sense of scale. Below the distance, speed is annotated which is calculated as the division of the ground track extent by the signature duration. Note that the ground track only shows the horizontal Sensors 2019, 19, 1008 7 of 24

Sensors 2019, 19, x 7 of 24 component of the trajectory (more relevant for meteors), equivalent to a projection of the trajectory ontotrajectory the ground. onto Speedthe ground. estimates Speed are therefeforeestimates onlyare therefefore indicative andonly may indicative differ significantly and may differ from the truesignificantly speed. For from illustration, the true considerspeed. For a meteor illustration, that enters consider on aa nearmeteor vertical that enters trajectory on a in near which vertical case the groundtrajectory track in extentwhich willcase bethe small ground and track the speedextent estimatewill be small is low. and Hence, the speed the estimate speed tag is onlylow. showsHence, the horizontalthe speedcomponent tag only shows of the the meteor horizontal velocity. component The signature of the meteor duration velocity. is noted The insignature the top leftduration of plot is (b). Plotnoted (b) in presents the top theleft lightof plot curve (b). Plot in terms (b) presents of received the light luminous curve in energy terms of at received the aperture luminous of the energy GLM in Joulesat the over aperture time. of This the isGLM not in the Joules total over energy time. emitted This is by not the the bolide total energy and further emitted scaling by the is bolide necessary and for suchfurther an estimate scaling is [2 necessary,3,26,30]. Forfor such formatting an estimate reasons [2,3,26,30]. only the For date formatting and time reasons stamp correspondingonly the date and to the signature’stime stamp middle corresponding group is to shown. the signature’s Together withmiddl thee group signature is shown. duration, Together these with coordinates the signature provide adequateduration, information these coordinates about theprovide flash adequate occurrence info timermation and itsabout persistence. the flash occurrence time and its persistence.Common lightning flashes are characterized by a seemingly random ground path (Figure5a). In contrast,Common bolides lightning are expected flashes are to characterized travel along by straight a seemingly lines. random Hence, ground a first step path for (Figure the algorithm 5a). In presentedcontrast, herebolides is to are perform expected a line to fittravel to the along ground stra pathight datalines. and Hence, this fita first is represented step for the by algorithm a thin, black, dashedpresented line here in Figure is to 5performa. The average a line fit residual to the ground of such path a fit data (average and this residual fit is representedR = 4.425 × by10 a− 4thin,in this black, dashed line in Figure 5a. The average residual of such a fit (average residual 𝑅 = 4.425 10 case) will be used to assess if a flash could be a bolide or not (Section 2.4.2). We assume further, based in this case) will be used to assess if a flash could be a bolide or not (section 2.4.2). We assume further, on our experience having seen numerous lightning profiles in L2 data, that a typical lightning flash based on our experience having seen numerous lightning profiles in L2 data, that a typical lightning deposits its energy randomly over time without a temporal preference for peak energy deposition flash deposits its energy randomly over time without a temporal preference for peak energy as demonstrated in Figure5b. Particularly, there is no predominant and extended energy deposition deposition as demonstrated in Figure 5b. Particularly, there is no predominant and extended energy featuredeposition which feature would which be characteristic would be characteristic for a bolide. for a bolide.

Figure 5. A lighting ﬂash with its ground path (made up of groups) in plot (a) on a latitude/longitude Figure 5. A lighting flash with its ground path (made up of groups) in plot (a) on a latitude/longitude grid and its light curve in plot (b). The light curve provides the luminous energy at the bafﬂe of the grid and its light curve in plot (b). The light curve provides the luminous energy at the baffle of the GLM instrument (before passing the lens) in Joules. The abscissa-axis provides the date and time GLM instrument (before passing the lens) in Joules. The abscissa-axis provides the date and time (format: YY-MM-DD HH:MM:SS in UT) of the signature and the annotation in the top left of plot (b) (format: YY-MM-DD HH:MM:SS in UT) of the signature and the annotation in the top left of plot (b) yields the signature duration. The annotation in in the top left of plot (a) shows the largest distance yields the signature duration. The annotation in in the top left of plot (a) shows the largest distance between two points of the ground track and the speed is calculated based on the division of that between two points of the ground track and the speed is calculated based on the division of that distance and the signature duration. The color coding of the dots help to correlate ground track points distance and the signature duration. The color coding of the dots help to correlate ground track points withwith light light curve curve datadata (dark points points indicate indicate earlier earlier groups groups while while yellow yellow points points indicate indicate the end the of endthe of thesignature). signature). In addition, In addition, the size the of size the of ground the ground track points track pointsis proportional is proportional to the energy to the value energy of the value ofcorresponding the corresponding light lightcurve curve point, point, meaning meaning that thatlarger larger points points correspond correspond to a tolarger a larger amount amount of of recordedrecorded energy. energy.

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Sensors 2019, 19, x 8 of 24 2.3.2. Confirmed Bolide 2.3.2. Confirmed Bolide To illustrate the appearance of a typical bolide signature, a bolide that occurred at 02:27:12 UT on To illustrate the appearance of a typical bolide signature, a bolide that occurred at 02:27:12 UT May 8th 2018 over the Atlantic Ocean and was confirmed by USG sensors [31] is shown in Figure6. on May 8th 2018 over the Atlantic Ocean and was confirmed by USG sensors [31] is shown in Figure Plot (a) contains the ground track and plot (b) shows the light curve. Bolide confirmations in this 6. Plot (a) contains the ground track and plot (b) shows the light curve. Bolide confirmations in this manuscript rely on detections of the same flash from multiple sources and the corraborating source is manuscript rely on detections of the same flash from multiple sources and the corraborating source mentioned or cited in all presented cases. The typical confirmation process involved checking if other is mentioned or cited in all presented cases. The typical confirmation process involved checking if systems (such as data published on the JPL fireball website [31], or ground based camera systems) other systems (such as data published on the JPL fireball website [31], or ground based camera had a detection at the same location and time as identified by GLM. In some cases, such as shown in systems) had a detection at the same location and time as identified by GLM. In some cases, such as Figure6, we were able to perform a more comprehensive comparison of GLM detections with USG shown in Figure 6, we were able to perform a more comprehensive comparison of GLM detections sensor data which was available to us. The bolide’s signature contrasts to the lightning data shown with USG sensor data which was available to us. The bolide’s signature contrasts to the lightning in Figure5. The ground path is significantly more line-like (average residual R = 4.938 × 10−9) than data shown in Figure 5. The ground path is significantly more line-like (average residual 𝑅 = 4.938 the lightning flash example and the dashed fit line in Figure6a is congruent with the actual bolide 10) than the lightning flash example and the dashed fit line in Figure 6a is congruent with the actual ground path. Furthermore, as the meteoroid enters the atmosphere and begins to ablate and fragment, bolide ground path. Furthermore, as the meteoroid enters the atmosphere and begins to ablate and a limited amount of its kinetic energy is deposited in the early phase of the signature. Subsequently, fragment, a limited amount of its kinetic energy is deposited in the early phase of the signature. the fragmentation cascades into an explosion-like energy deposition which is represented by a clear Subsequently, the fragmentation cascades into an explosion-like energy deposition which is peak in the last third of the energy deposition timeline shown in Figure6b. Compared to Figure5b, represented by a clear peak in the last third of the energy deposition timeline shown in Figure 6b. the light curve appears smoother and with a more defined and pronounced peak than the lightning Compared to Figure 5b, the light curve appears smoother and with a more defined and pronounced flash. For illustration purposes, a vertical dashed line in Figure6b indicates the point in time when peak than the lightning flash. For illustration purposes, a vertical dashed line in Figure 6b indicates half of the total energy of that bolide is emmitted and here that value is after 73% of the signature time the point in time when half of the total energy of that bolide is emmitted and here that value is after has passed. It is expected that bolides show a clear bias towards late energy deposition in contrast to 73% of the signature time has passed. It is expected that bolides show a clear bias towards late energy lightning which is not expected to have a general bias. deposition in contrast to lightning which is not expected to have a general bias.

Figure 6. A bolide recording (true positive) conﬁrmed by USG sensors. The ground track in plot (a) can beFigure well approximated6. A bolide recording by a straight (true line.positive) Plot confirmed (b) shows theby lightUSG curvesensors. over The time. ground The track dashed in energyplot (a) balancecan be linewell indicates approximated that half by of a thestraight energy line. was Plot deposited (b) shows only the in the light last curve 27%of over total time. bolide The duration. dashed energy balance line indicates that half of the energy was deposited only in the last 27% of total bolide duration.

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2.4. Individual Filter Design This section describes the design of the six filters that comprise the bolide extraction algorithm. Two of the six selected filters work on characteristics that are inherent in ground track information. They are the “Line Fit Filter” (Section 2.4.2) and the “Group to Line Distance Filter” (Section 2.4.4). Another two filters work on light curve characteristics and those include the “Energy Balance Filter” (Section 2.4.3) as well as the “Polynomial to Energy Moving Window Filter” (Section 2.4.5). The last two filters utilize metadata for the “Group Count Filter” (Section 2.4.1) and the “Signature Duration Filter” (Section 2.4.6). The individual filters are combined to form the extraction algorithm and the corresponding architecture is described in Section 2.5. The six presented filters are the result of an extensive trial and error process that included adjustments to the filter metrics and exploring filter concepts that proved impractical or ineffective. One example of an unused filter concept includes extracting patterns from latitude and longitude time histories before settling on the combination of the two in the form of ground track plots. Another example of positive filter development is the switch from raw data to normalized light curve and ground track data to increase the robustness of some filters against variations in bolide flash magnitudes. The filters operate on specific signature metrics and sigmoid functions were used to transform individual metric values into bolide scores ranging from zero to one. A larger score value reflects better agreement with behavior that is expected for bolides. The family of sigmoid functions is defined with:

1 S(x) = (1) 1 + e−a(x−b) where S denotes the individual filter score, x is the filter metric or free variable, a determines the steepness of the transition region of the function, and b assigns the free variable value for which Si(x = b) = 0.5. In the cases of the line fit residual filter, the line distance ratio filter, the polynomial fit filter, and the signature duration filter, the sigmoid function needed to provide smaller values for larger input values. In those cases, the structure of the sigmoid function was changed to:

1 S(x) = 1 − (2) 1 + e−a(x−b)

Figure7 visualizes the six ﬁlters used in the algorithm presented here. The following sections detail the derivation of each ﬁlter and the baseline parameters are listed in Table2.

Table 2. Filter parameter set as they apply to the sigmoid function of the ﬁlters.

Filter Parameter a Parameter b Group Count 0.07 25 Line Fit 3 −5 Energy Balance 25 0.3 Max Group to Line Distance 80 0.4 Polynomial Fit 3 −2 Duration 2 6

2.4.1. Group Count Filter The smallest measurement entity that is utilized by the algorithm in Level 2 data is the GLM group (Section 2.1). With this basis, the algorithm searches the data sequentially for other groups that are spatially and temporally close (Table1) and collects those into flashes which then can become bolide candidates after passing the filters. The first filter for a bolide flash to pass in order to be considered a bolide candidate is the minimum number of groups that constitute a bolide flash. Individual light emissions in typical lightning storms, are short and distinct, triggering only a limited number of group Sensors 2019, 19, x 10 of 24 are spatially and temporally close (Table 1) and collects those into flashes which then can become bolideSensors 2019 candidates, 19, 1008 after passing the filters. The first filter for a bolide flash to pass in order10 to of be 24 considered a bolide candidate is the minimum number of groups that constitute a bolide flash. Individual light emissions in typical lightning storms, are short and distinct, triggering only a limited numberrecordings, of resultinggroup recordings, in a low group resulting count in (compare a low group number count of data (compare points innumber Figure5 ofb withdata Figure points6b). in FigureConversely, 5b with bolides Figure emit 6b). light Conversely, continuously bolides and emit over light longer continuously periods than and a number over longer of light periods emissions than ain number a lightning of light storm. emissions It is expected in a lightning that bolide storm. flashes It is inexpected GLM data that distinguishbolide flashes themselves in GLM fromdata distinguishlightning data themselves by a larger from group lightning count per data flash. by To a support larger thisgroup observation, count per the flash. histogram To support in Figure this8 observation,shows the distribution the histogram of group in Figure count 8 shows per flash the based distribution on three of minutes group count and twenty per flash seconds based worthon three of minutesrandomly and selected twenty data. seconds Only worth 9% of of the randomly recorded select flashesed havedata. moreOnly than9% of 20 the groups. recorded For flashes comparison, have morethe bolide than portrayed20 groups. inFor Figure comparison,6 corresponds the bolide to a port flashrayed with in 519 Figure groups. 6 corresponds In fact, flashes to a withflash large with 519group groups. counts In are fact, outliers flashes and with represent large group an effective counts filteringare outliers opportunity and represent for bolides. an effective It is therefore filtering opportunityprudent to filter for bolides. out flashes It is withtherefore a small prudent number to f ofilter groups out flashes as those with are a likely small lightningnumber of and groups to pass as b = those are with likely large lightning group counts. and to Here,pass those the b-parameter with large group of the groupcounts. count Here,filter the b-parameter was set to of the25. groupBased oncount extensive filter was algorithm set to 𝑏=25 sensitivity. Based analysis on extensive baseline algorithm filter parameters sensitivity were analysis chosen baseline and they filter are parameterslisted in Table were2. chosen and they are listed in Table 2.

Figure 7. Filter functions providing the passing score in the range [0,1] for each test. A larger score is Figure 7. Filter functions providing the passing score in the range [0,1] for each test. A larger score is returned when: Plot (a)—more groups constitute a flash; Plot (b)—the line fit returns a low residual; returned when: Plot (a) - more groups constitute a flash; Plot (b) - the line fit returns a low residual; Plot (c)—the flash energy balance is skewed to later times; Plot (d)—the maximum observed distance Plot (c) - the flash energy balance is skewed to later times; Plot (d) - the maximum observed distance between any group and the fitted line is small; Plot (e)—the polynomial fits return smaller residuals; between any group and the fitted line is small; Plot (e) - the polynomial fits return smaller residuals; Plot (f)—the signature’s temporal duration is shorter. Plot (f) - the signature’s temporal duration is shorter.

Sensors 2019, 19, 1008 11 of 24 Sensors 2019, 19, x 11 of 24

Figure 8. Histogram showing the occurrence rate of flashes with a given number of groups. The random Figuresample 8. of Histogram GLM data showsshowing 8864 the flashes occurrence from 3 rate minutes of fl andashes 20 with seconds a given worth number of data. of Over groups. 91% The of the randomflashes in sample the sample of GLM consist data ofshows less than8864 20flashes groups. from 3 minutes and 20 seconds worth of data. Over 91% of the flashes in the sample consist of less than 20 groups. 2.4.2. Line Fit Filter 2.4.2. Line Fit Filter Meteors enter the atmosphere with a speed of at least 11 km s−1 and remain on an approximately straightMeteors trajectory enter alongthe atmosphere their short with visible a speed arcs asof theyat least burn 11 andkm s ablate.-1 and remain Hence, on a linean approximately is well suited to approximatestraight trajectory the ground along their path short of the visible flash. Inarcs contrast, as they a burn flash and caused ablate. by lighting,Hence, aexhibits line is well a substantially suited to higherapproximate degree the of randomnessground path in itsof groundthe flash. track. In Figurecontrast,9 provides a flash ancaused example by forlighting, such random exhibits walk a insubstantially plot (a). Even higher though degree the of overall randomness motion in has its aground tendency track. to followFigure the9 provides fitted dashed an example line, individualfor such trackrandom points walk may in plot move (a). inEven every though direction the overall away motion from or has across a tendency the line. to Figurefollow 5thea showcases fitted dashed this randomnessline, individual yielding track nopoints directional may move trend in inevery this example.direction Inaway comparison, from or across the bolide the line. ground Figure track 5a of Figureshowcases6a follows this randomness the expected yielding straight no path. directional This clear trend distinction in this ex inample. the ground In comparison, paths of lighting the bolide and bolidesground facilitatestrack of Figure filtering 6a throughfollows linethe fitting.expected Specifically, straight path. a line This is fitted clear to distinction the ground in track the ground of a flash andpaths the of residuallighting isand evaluated bolides facilitates subsequently filtering to assess through the line quality fitting. of the Specifically, fit. In Equation a line is (3), fitted the averageto the lineground residual track ( Rof )a isflash defined and asthe the residual sum of is squared evaluated deviations subsequently (d2) of to the assess group th pointse quality (j) fromof the the fit. fitted In L j line,Equation divided (3), bythe the aver totalage numberline residual of groups (𝑅) is (N defined): as the sum of squared deviations (𝑑 ) of the group points (𝑗) from the fitted line, divided by the total number of groups (𝑁): N 2 ∑j dj R =∑ 𝑑 (3) 𝑅 =L N (3) 𝑁 Because a lower residual indicates a closer fit and should therefore receive a larger filter score, Because a lower residual indicates a closer fit and should therefore receive a larger filter score, the parent sigmoid function of Equation (1) was changed to reflect this behavior and the adapted the parent sigmoid function of Equation (1) was changed to reflect this behavior and the adapted sigmoid function of Equation (2) was used instead. Furthermore, because the range of residuals sigmoid function of Equation (2) was used instead. Furthermore, because the range of residuals was was large, the base 10 logarithmic value of the residual value was chosen as the filter parameter large, the base 10 logarithmic value of the residual value was chosen as the filter parameter 𝑥= x = log10 RL. With these adaptations, the line filter function is plotted in Figure7b using the parameters log 𝑅. With these adaptations, the line filter function is plotted in Figure 7b using the parameters listed in Table2. listed in Table 2. 2.4.3. Energy Balance Filter 2.4.3. Energy Balance Filter The process of a meteor burning up in the atmosphere begins with a single body ablating at The process of a meteor burning up in the atmosphere begins with a single body ablating at high high altitude in low density atmosphere. These conditions yield a low energy deposition rate. As the altitude in low density atmosphere. These conditions yield a low energy deposition rate. As the meteor descends, fragments break off and contribute to the ablative energy deposition. In addition, meteor descends, fragments break off and contribute to the ablative energy deposition. In addition, increasing atmospheric density results in larger heating which promotes fragmentation. This process increasing atmospheric density results in larger heating which promotes fragmentation. This process is self-reinforcing and leads to one or more explosion like flares. Figure6b shows an example of such is self-reinforcing and leads to one or more explosion like flares. Figure 6b shows an example of such

Sensors 2019, 19, 1008 12 of 24

Sensors 2019, 19, x 12 of 24 an energy deposition profile. The vertical dashed line in that figure annotates the time of half energy depositionan energy deposition for that profile. profile. For The bolides, vertical the dashed pre-half-energy line in that duration figure annotates tends to the be time long of accounting half energy for overdeposition 50% (or for a that fraction profile. of >0.5)For bolides, of total the profile pre-half-energy duration. Asduration an example, tends to for be thelong case accounting in Figure for6b thisover fraction 50% (or is a 0.72fraction because of >0.5) the of pronounced total profile flare duration. peak emitsAs an theexample, remaining for the half case of in the Figure energy 6b in this the lastfraction 28% ofis 0.72 the signaturebecause the time. pronounced flare peak emits the remaining half of the energy in the last 28%In of contrast,the signature lighting time. emits energy sporadically without an apparent temporal bias towards the earlyIn or contrast, late profile lighting duration. emits In energy fact, the sporadically energy half without time in thean apparent lighting example temporal shown bias towards in Figure the9b wasearly reached or late afterprofile exactly duration. half ofInthe fact, profile the energy duration half had time passed. in the lighting In this case, example the half shown energy in Figure deposition 9b fractionwas reached is 0.5 asafter equal exactly amounts half ofof energythe profile are released duration before had passed. and after In half this of case, the profilethe half period energy has passed.deposition While fraction the fraction is 0.5 as might equal varyamounts for individual of energy are lightning released cases, before it isand expected after half that of thethe meanprofile of period has passed. While the fraction might vary for individual lightning cases, it is expected that the lightning energy deposition fractions is 0.5. Figure7c visualizes the energy balance filter function mean of lightning energy deposition fractions is 0.5. Figure 7c visualizes the energy balance filter which uses Equation (1) with the parameters of Table2. function which uses Equation (1) with the parameters of Table 2.

Figure 9. A false positive detected by the algorithm. Ground track is shown in plot (a) together with a least-squaresFigure 9. A false fit line. positive The grounddetected track by the exhibits algorithm. considerable Ground track jitter is relative shown to in the plot line (a) approximation.together with Plota least-squares (b) shows thefit line. energy The deposition ground track profile exhibits over co time.nsiderable jitter relative to the line approximation. Plot (b) shows the energy deposition profile over time. 2.4.4. Group to Line Distance Filter 2.4.4. Group to Line Distance Filter While the residual of the line fit is computationally cheap early in the filtering process to discard unsuitableWhile signatures, the residual it allows of the for line a largefit is numbercomputationally of false positives cheap early to pass. in Thethe reasonfiltering is thatprocess the lineto fitdiscard filter relies unsuitable on the averagesignatures, discrepancy it allows offor all a grouplarge number points from of false the fitpositives line. As to a result, pass. The signatures reason that is onlythat havethe line few fit points filter far relies away on from the theaverage line pass discrepancy the filter becauseof all group their points overall from average the isfit low. line. In As other a words,result, thesignatures line fit residual that only provides have few an aggregatepoints far valueaway forfrom the the entirety line pass of the the ground filter trackbecause and their does notoverall check average the fit qualityis low. ofIn eachother individual words, the point. line fit Rather residual than provides considering an aggregate the aggregate value fit for quality, the theentirety maximum of the distanceground track filter and described does not in check this section the fit considersquality of each individual point in the point. ground Rather track than and calculatesconsidering its distancethe aggregate to the previouslyfit quality, obtained the maximum best fit line. distance Because filter the described ground track in followsthis section a well predictableconsiders each straight point line in forthe bolides,ground theretrack isand high calculates confidence its distance in the observation to the previously that each obtained point should best befit closeline. toBecause the center the lineground and track no single follows point a well is allowed predictable to deviate straight farfrom line for it. Therefore, bolides, there this filteris high is a stricterconfidence version in the of theobservation line fit filter that which each comespoint should at the cost be close of larger to the computational center line and expense. no single The point larger computationalis allowed to deviate cost is acceptable far from it. at Therefore, this point becausethis filter the is seta stricter of remaining version flashes of the isline small fit filter compared which to comes at the cost of larger computational expense. The larger computational cost is acceptable at the starting set as earlier filters have discarded most flashes. this point because the set of remaining flashes is small compared to the starting set as earlier filters have discarded most flashes.

Sensors 2019, 19, x 13 of 24 Sensors 2019, 19, 1008 13 of 24 The angular distance in km is calculated between each group and the best fit line and the distance value of the group with maximum separation from the line is selected. This value is normalized to increaseThe angularrobustness distance against in km variations is calculated in boli betweende magnitude. each group The and normal the bestization fit line is and accomplished the distance valuethrough of division the group of withthe angular maximum distance separation value by from thethe overall line angular is selected. span This of the value ground is normalized track. For tothe increase overall angular robustness span, against we use variations simply the in larger bolide value magnitude. of the overall The normalization latitude or longitude is accomplished span. The throughnormalized division distance of allows the angular for larger distance point-line value dist byances the overallfor large angular bolide events span of as thethe groundoverall lat/lon track. For the overall angular span, we use simply the larger value of the overall latitude or longitude span. range increases. Equation (4) formalizes the distance ratio 𝑇 for group 𝑗 where 𝑑_𝑗 is the angular The normalized distance allows for larger point-line distances for large bolide events as the overall distance between the group and the line,  denotes maximum longitudinal span of the ground lat/lon range increases. Equation (4) formalizes the distance ratio Tj for group j where d_j is the track, and  denotes maximum latitudinal span of the ground track: angular distance between the group and the line, λ denotes maximum longitudinal span of the 𝑑 max ground track, and Φ denotes maximum⎧ latitudinal span of the ground track: max ⎪    𝑇 = ( 𝑑dj (4) ⎨ , λmax ≥ Φmax λmax   Tj =⎪ d (4) ⎩j , λ < Φ Φmax max max Given that smaller point-line distance values should correspond to larger filter scores, the Given that smaller point-line distance values should correspond to larger filter scores, the sigmoid sigmoid function structure of Equation (2) is used where 𝑥=max(𝑇). The filter function is plotted function structure of Equation (2) is used where x = max Tj . The filter function is plotted in Figure7d in Figure 7d using the parameters shown in Table 2. j using the parameters shown in Table2.

2.4.5.2.4.5. Polynomial toto EnergyEnergy MovingMoving WindowWindow FilterFilter TheThe lightlight curve curve of of a bolidea bolide is generally is generally smoother smoother than thatthan of that lightning. of lightning. This difference This difference is exploited is rd usingexploited a filter using that a fitsfilter a 3rdthat order fits a polynomial 3 order polynomial to a small subrangeto a small of subrange the light of curve. the Specifically,light curve. theSpecifically, subrange the is defined subrange here is todefined be five here groups to be wide. five groups Figure 10wide. compares Figure the10 compares light curve the of light a bolide curve in plotof a (bolidea) with in a plot lightning (a) with light a lightning curve in plotlight ( bcurve). in plot (b).

FigureFigure 10.10. VisualizationVisualization ofof thethe polynomialpolynomial fitfit filter.filter. PlotPlot ((aa)) showsshows aa bolidebolide lightlight curve.curve. TheThe movingmoving windowwindow span is indicated indicated by by the the clear clear portion portion of of the the plot. plot. A polynomial A polynomial represented represented by a by dashed a dashed line line is notionally fitted to the light curve data. Plot (b) shows a non-bolide light curve and the fitted is notionally fitted to the light curve data. Plot (b) shows a non-bolide light curve and the fitted polynomial represented by the dashed line. The polynomial fits better the data of the bolide (a) than polynomial represented by the dashed line. The polynomial fits better the data of the bolide (a) than for the non-bolide (b). for the non-bolide (b). A polynomial is notionally fitted to the highlighted subrange of the light curve data as a dashed A polynomial is notionally fitted to the highlighted subrange of the light curve data as a dashed black line. It is clear that in the case of the bolide (Figure 10a), the polynomial yields a closer fit than a black line. It is clear that in the case of the bolide (Figure 10a), the polynomial yields a closer fit than

Sensors 2019, 19, 1008 14 of 24 similar fit to the lightning lightcurve (Figure 10b). The metric of the fit is the least-squares residual for the polynomial normalized by the energy range of the signature. Thus, the normalized polynomial k+5 2 residual RP is the sum of squared residuals ∑i=k di for the current subrange of the lightcurve divided 2 by the squared energy range of the signature (Emax − Emin) as shown in Equation (5). The residual normalization with the energy range is necessary because flashes with large energy releases (such as bolides) would otherwise yield large residuals even though the fit appears adequate:

+ ∑k 5 d2 = i=k i RP 2 (5) (Emax − Emin)

The five group-wide lightcurve subrange moves across the entire signature and the polynomial fit is computed for each location. The input for the filter function visualized in Figure7e is finally formed as the mean normalized residual of all subrange fits. The base ten logarithmic value of that h i mean serves as input to the filter function (Equation (2)) such that x = log meanRP if there are m 10 m m subfits in a signature. Figure7e visualizes the corresponding filter function based on the parameters listed in Table2.

2.4.6. Signature Duration Filter A substantial amount of artifacts that appear in L2 data are reflections of the Sun on the surface of the Earth (glints). These tend to endure for longer than bolides could possibly last. Some instances of glints lasted for longer than 30 s. For comparison, the large Chelyabinsk super-bolide lasted for about 13s. To discard long duration artifacts, a signature duration filter has been implemented which is visualized in Figure7f based on Equation (2) and the parameters listed in Table2. In its baseline setting, it filters out signatures that last for longer than approximately 6 s. Even though bolides may last longer than 6 s, this value was deemed appropriate because very large bolides are rare and would be noticed by other systems with global coverage (such as USG sensors) and could be extracted from GLM data manually.

2.5. Algorithm Architecture Our initial algorithm architecture was driven by sequential filtering steps with distinct threshold values. Flash signatures had to pass all to be classified as bolide candidates. This approach did not prove adequate for the diversity of signatures present in the GLM L2 data. Tuning the thresholds in such a way that they would consistently allow true positives to pass while filtering out obvious lightning and other artifact signatures was impractical and resulted in either excessively restrictive (not passing true positive signatures) or too relaxed filters (passing too many false positives). With this insight, we improved the procedure by replacing the distinct threshold values with an overall score aggregated from the individual filter scores in the range [0,1]. Continuous sigmoid functions (Figure7) represent each filter and they assign a value between zero and one based on the signature’s similarity with bolide characteristics. Following each filter step, the cumulative bolide score is calculated through multiplication of all previous filter scores. Hence, the overall bolide score for a signature after passing six filters is given with: 6 Θ = ∏ Si (6) i=1 th where Θ is the cumulative filter score, and Si denotes the individual filter score by the i filter. The overall filter score Θ needs to pass a minimum threshold score to be considered a bolide candidate. This cumulative threshold score is a tuning parameter of the filter and a value of 0.5 was found to be a suitable first guess. Figure 11 illustrates this algorithm architecture. Sensors 2019, 19, 1008 15 of 24 Sensors 2019, 19, x 15 of 24

Figure 11. AlgorithmAlgorithm architecture. architecture. The The L2 L2 group group data data pass passeses through through each each filter ﬁlter sequentially sequentially andand the theproduct product of the of theindividual individual filter ﬁlter scores scores forms forms the the cumulative cumulative candidate candidate score. score. If If the cumulativecumulative candidate score falls below the threshold valuevalue (here(here 0.5),0.5), thethe signaturesignature isis discarded.discarded.

Typically, aa realreal bolidebolide willwill scorescore closeclose toto oneone in all filtersfilters and, thus, receive a cumulative bolide score closeclose to to one. one. However, However, using using the score-basedthe score-ba approachsed approach also allows also forallows a signature for a withsignature deviations with fromdeviations an ideal from bolide an ideal signature bolide to signature be considered to be as co bolidensidered candidate as bolide when candidate it receives when a relatively it receives low a scorerelatively in one low or score two ofin theone filter or two metrics. of the Asfilter long, metrics. as the As cumulative long, as the score cumulative remains score above remains the threshold above forthe thethreshold cumulative for the score cumulative the flash canscore still the be flash considered can still a be candidate considered and a the candidate algorithm and is thusthe algorithm amenable tois thus natural amenable variations to natural in bolide variations signature in appearances. bolide signature appearances. On the other hand, an unwanted lightning flash flash might resemble bolide signatures in few of the metrics but it is unlikely that it scores sufficiently sufficiently high in enough metrics to pass the cumulative score threshold. ByBy formingforming thethe cumulativecumulative bolide bolide score score through through multiplication multiplication of of individual individual filter filter scores scores an overallan overall low low score score for for lightning lightning flash flash signatures signatures is enforced is enforced and and the the signature signature is discarded. is discarded. 3. Filter Sensitivity and Algorithm Performance Discussion 3. Filter Sensitivity and Algorithm Performance Discussion 3.1. Filtering Rate and Sensitivity Sample 3.1. Filtering Rate and Sensitivity Sample The bolide extraction algorithm has been analyzed for its overall performance. About 34 days- worthThe bolide of dataextraction or 144845 algorithm files has (each been accountinganalyzed for it fors overall 20 s worthperformance. of data) About were 34 days- processed worth withof data the or 144845 files (each accounting for 20 s worth of data) were processed with the algorithm using the baseline algorithm using the baseline parameter set shown in Table2. The yield was 2252 files which contained parameter set shown in Table 2. The yield was 2252 files which contained bolide candidate signatures. This bolideperformance candidate corresponds signatures. to a pass This rate performance of 1.55% of an correspondsalyzed files. The to aalgorith pass ratem has of been 1.55% implemented of analyzed in files.Python The and algorithmtypically runs has on been a desktop implemented computer using in Python seven andprocessing typically cores. runs With on such a desktopan arrangement computer one usingday worth seven of processingdata can be processed cores. With within such 2-3 an hours. arrangement As this is onea prototype, day worth there of are data multiple can be avenues processed for withinprocessing 2-3 speed hours. increases As this such is a as prototype, an implementation there are in multiple a faster language avenues (for for example processing C++) speedor to run increases it on an suchincreased as annumber implementation of processing incores. a faster Memory language usage is (forminimal example and is C++)not a challenge or to run for it a on common an increased desktop numberPC. To understand of processing algorithm cores. sensitivities, Memory usage an addition is minimalal downselection and is not was a challenge performed for yielding a common the evaluation desktop PC.subset. To The understand evaluation algorithm subset was sensitivities, generated in order an additional to facilitate downselection short runtimes for was each performed sensitivity yielding iteration. the To obtain a clear feedback signal about the performance of the algorithm given a combination of filter parameters, evaluation subset. The evaluation subset was generated in order to facilitate short runtimes for each the subset has an increased ratio of passing to rejected signatures relative to a generic set of files. The larger sensitivity iteration. To obtain a clear feedback signal about the performance of the algorithm given a number of passing signatures allowed us to assess how a change in filter parameters affected the number of combinationpassed signatures of filteras well parameters, as the characteristics the subset of signatur has an increasedes that were ratio filtered of passingout. Facilitated to rejected by the signatures Hyperwall relativefacility at to NASA a generic Ames set Research of files. Center The larger(ARC) number(the Hype ofrwall passing facility signatures uses a configuration allowed us of to8 x assess 16 screens how to a changevisualize in large filter quantities parameters of data), affected the 150 the file number counting of evaluation passed signatures subset was asderived well asby themanually characteristics selecting 122 of signaturesfiles with highly that bolide-similar were filtered out.signatures Facilitated from the by filt theered Hyperwall set and adding facility 6 confirmed at NASA Ames(confirmation Research was Center based (ARC)on USG (thesensors Hyperwall and/or meteor facility detection uses a camera configuration networks) of bolide 8 x 16 signatures, screens to as visualize well as 22 large random quantities data files. of data),The evaluation the 150 filesubset counting was used evaluation to assess th subsete effectiveness was derived of each by individual manually filter. selecting 122 files with highly bolide-similar signatures from the filtered set and adding 6 confirmed (confirmation was based on USG3.2. Filter sensors Performance and/or meteor detection camera networks) bolide signatures, as well as 22 random data files.The The filter evaluation effectiveness subset waswas usedtested to by assess varying the effectiveness one filter parameter of each individual at a time while filter. keeping the other parameters at the baseline setting (Table 2). Figure 12 presents the outcome of the performance analysis. The tested filter values are marked on the x-axis and they correspond to the b-parameter

Sensors 2019, 19, 1008 16 of 24

3.2. Filter Performance The filter effectiveness was tested by varying one filter parameter at a time while keeping the other parameters at the baseline setting (Table2). Figure 12 presents the outcome of the performance Sensors 2019, 19, x 16 of 24 analysis. The tested filter values are marked on the x-axis and they correspond to the b-parameter shown in Table2, while the y-axis denotes how many signatures of the evaluation subset were passed shown in Table 2, while the y-axis denotes how many signatures of the evaluation subset were passed withwith this this particular particular setting. setting.

Figure 12. Filter effect on number of bolide candidate passes. The plots are oriented such that the filter Figure 12. Filter effect on number of bolide candidate passes. The plots are oriented such that the filter parameters further to the right correspond to a more restrictive filter setting. The green line indicates parameters further to the right correspond to a more restrictive filter setting. The green line indicates the baseline filter setting. The red line marks the filter parameter step when a known true positive the baseline filter setting. The red line marks the filter parameter step when a known true positive bolide signature was filtered out from the evaluation subset corresponding to an overly restrictive bolide signature was filtered out from the evaluation subset corresponding to an overly restrictive filter setting. The color shading of the points corresponds to the mean bolide score in the passed filter setting. The color shading of the points corresponds to the mean bolide score in the passed instances (colorbar shows score values). The shown filters are group count (a), line fit (b), energy ratio instances (colorbar shows score values). The shown filters are group count (a), line fit (b), energy ratio (c), polynomial fit (d), distance ratio (e), and cumulative bolide score cutoff (f). (c), polynomial fit (d), distance ratio (e), and cumulative bolide score cutoff (f). The filter performance results in Figure 12 demonstrate that all filters are effective in The filter performance results in Figure 12 demonstrate that all filters are effective in downsampling signatures when assigning more restrictive filter parameters (going left to right in downsampling signatures when assigning more restrictive filter parameters (going left to right in the the plots). This is evident by the fact that the number of passes decreases for more restrictive filter plots). This is evident by the fact that the number of passes decreases for more restrictive filter settings. It is also important to note that each filter (except for the distance ratio filter) begins to be settings. It is also important to note that each filter (except for the distance ratio filter) begins to be effectiveeffective in in filtering filtering out out unwanted unwanted signatures signatures before be filteringfore filtering out a knownout a known true positive true positive bolide signature bolide (indicatedsignature by(indicated the red line).by the This red indicates line). This that indicate the filterss that work the asfilters intended work andas intended have the and positive have effect the ofpositive discarding effect unwanted of discarding signatures unwanted and signatures allowing bolide-similarand allowing bolide-similar signatures to signatures pass. The to main pass. reason The whymain the reason distance why ratio the distance filter seems ratio to filter be ineffective seems to beforebe ineffective it discards before confirmed it discards bolide confirmed cases is bolide due to thecases initial is due sample to the selection. initial sample The evaluation selection. The sample evaluation had been sample vetted had manually been vetted prior manually to the sensitivity prior to analysisthe sensitivity and candidates analysis which and candidates are obviously which not bolidesare obviously were already not bolides discarded. were Signatures already discarded. with large outliersSignatures are easilywith large identified outliers as are non-bolides easily identified and therefore as non-bolides did not makeand therefore it into the did evaluation not make sample.it into Furthermore,the evaluation it issample. likely thatFurthermore, the line fit it already is likely removed that the thoseline fit signatures already removed in the 22 those random signatures files in thein evaluationthe 22 random set which files in would the evaluation otherwise set be which discarded would by otherwise the distance be discarded filter. Hence, by the the distance distance filter. filter Hence, the distance filter appears ineffective for the evaluation subset even though it performs as intended. In other words, the evaluation subset is a challenging set for the filter algorithm as the majority of its content is already made up of bolide-like signatures rather than random signatures that are clearly not bolide-like. It is harder for the filter algorithm to discard the preselected candidate signatures because they are similar to bolides.

Sensors 2019, 19, 1008 17 of 24 appears ineffective for the evaluation subset even though it performs as intended. In other words, the evaluation subset is a challenging set for the filter algorithm as the majority of its content is already made up of bolide-like signatures rather than random signatures that are clearly not bolide-like. It is harder for the filter algorithm to discard the preselected candidate signatures because they are similar to bolides. Figure 12a shows the performance of the group count filter as the minimum number of groups per flash is increased (going left to right in the plot). The color shading of the points indicates the mean bolide score of the set that passes the filter. In the first three steps, this filter reduces the mean score of the passing set before the lowest scoring signatures are filtered out. Removing these low scorers raises the mean score of the remaining set. This trend continues to more restrictive filter values. This desirable behavior is also visible in Figure 12b, Figure 12d, Figure 12e, and Figure 12f representing the line fit, polynomial fit, distance ratio, and cutoff score parameters, respectively. The only filter that does not increase the mean score of the passed files for an increasingly restrictive filter setting is the energy ratio filter shown in Figure 12c. This observation indicates that, by tightening the filter, all signatures receive a lower bolide score rather than just the unwanted signatures. While still being an effective filter in general, it does not increase data quality as efficiently as the other filters. The reason for this behavior is that the assumption of a clear energy deposition bias in the light curve breaks down for very small bolides and this phenomenon is discussed further in Section 3.3. The polynomial fit filter is noteworthy because it shows the steepest transition region of all filters in the parameter range that reduces signature passage rate suggesting that it is highly effective. However, the point of overly restrictive filter setting is located in the steep transition region indicating that the filter is easy to overtune. All filters show a plateau-like behavior initially and this is connected to the biased evaluation subset. The majority of the files in the evaluation subset (122) were selected after they had already passed the baseline filter setting. Therefore, these files will not be filtered out until the individual filter becomes as restrictive as the baseline setting. Those files that are filtered out in the plateau region are the random data files which have not necessarily passed the baseline filter previously. The distance ratio filter shown in Figure 12e visualizes this concept clearly as no files are filtered out before the filter passes the baseline setting. The effect of raising the overall bolide score cutoff value is shown in Figure 12f. Increasing the cutoff value continuously reduces the pass count but, unsurprisingly, has the effect of increasing the mean bolide score of the set that passes the algorithm. It should be noted that for this test, the filter settings were tightened (for example polynomial = 2.2 and group count = 30) such that the pass count at the baseline setting (0.5) is lower than in the other filter plots. Finally, due to its late addition to the algorithm after the presented test, the signature duration filter is not present in Figure 12. The baseline parameter set (Table2) represents a suitable filter parameter selection for effective bolide extraction algorithm operations and this setting is used today at NASA ARC to assess GLM data for bolides on a daily basis. All parameters (green lines) are on the relaxed side of the red lines in Figure 12 that indicate the borders to an overly restrictive filter setting. It should be noted that it would be impractical to set all parameters equal to, or very close to, the red line setting as this would result in an overly restrictive filter algorithm. The individual filters are connected to each other through the multiplication that yields the overall bolide score. Hence, if all filters are borderline restrictive, the overall bolide score will certainly drop below the score threshold and good signatures could potentially be discarded. This is especially true for small bolide events that only produce a weak bolide signature which we discuss in the next section.

3.3. Small Bolides Small bolide events are particularly interesting because they are more common than large events and will, thus, provide the largest percentage of bolide signatures. Therefore, it is desirable to capture Sensors 2019, 19, 1008 18 of 24 small bolide occurrences. However, extracting small bolides is also more challenging because the noise level in these signatures increases as the GLM instrument reaches its resolution limits. The GLM pixel resolution is 8–14 km and small bolides might not traverse from one pixel to the next while burning up in the atmosphere. This causes challenges for the geolocation algorithm of GLM and yields meteor ground tracks that significantly deviate from line-like behavior. For example, the ground track of a small bolide presented in Figure 13a, features some ground track points that clearly deviate from the line fit. The deviation from a line-like ground track is likely caused by the fact that GLM operates at the lower end of its resolution for small bolide events. In addition, for groups that only trigger one pixel of the CCD, GLM tends to place those ground track points at the center of the illuminated pixel producing a cluster of points rather than a line. This behavior is apparent in Figure 13c where two ground point clusters form the start and end of the ground track for that bolide. A similar pattern is visible in Figure 13a. The clusters in that example occur at the beginning and the end because the energy released during those times only triggered a single pixel. Few ground track points are in between the clusters marking the time when the energy releasing bolide passed from one pixel to the other and activating both pixels. Activation of more than one pixel allows the geolocation algorithm of GLM to produce position estimates based on pixel intensity averaging. Even though no connecting points are visible between the clusters in the examples of Figure 13c, it was still possible for the algorithm to detect this bolide and to produce an adequate line fit. Unfortunately, this capability breaks down when the bolide does not traverse between pixels and only one pixel is triggered yielding one group cluster. At present, this situation presents a serious challenge to the algorithm which currently leads to the discarding of such signatures. An alternative approach might be necessary that purely focuses on the light curve data and this prospect constitutes future work. The speed estimates tagged in the top left corner of the ground track plots also suffer from resolution issues for small flashes. For example, the speed estimate for the bolide shown in Figure 13a is unrealistically high. Because of the resolution limitations of GLM it is assumed that the bolide travelled from the center of the first pixel to the second pixel center. However, it is likely that the bolide did not, in fact, travel from the center of one pixel to the next but only occurred on a smaller distance between these two points. The assumption of the longer ground track leads to an overstated speed estimate because the distance covered is longer in the well-known amount of light curve duration. It is important to be cognizant of this artifact behavior when interpreting speed estimates. The light curve of small bolides presents a challenge for the energy balance filter because the bias towards late peak energy deposition is less pronounced. In fact, the energy balance is calculated to be close to half signature duration in the examples provided in Figure 13. What is more, the received energy barely suffices to trigger the event recording and energy quantization is apparent in the light curves as the energy levels are at the low end of the dynamic range of the CCD (especially visible in Figure 13b,d). Energy quantization might cause poor fit results for the polynomial fit filter reducing the overall bolide score leading to the potential discarding of such signatures. Finally, small bolides contain only a limited number of groups and their signatures, thus, are close to the noisier regime of small group count flashes (see Figure8). Specifically, the bolide in Figure 13a,b contains 55 and the one in Figure 13c,d only 35 groups. Hence, these two bolides pass the current filter with relatively low scores (0.73 and 0.57 with baseline filter setting) but similar, small bolides might just fall below the threshold. Given that small bolides are frequent and the most numerous source of meteor data, future work should focus on extracting small events more robustly. Sensors 2019,, 19,, x 1008 1919 of of 24 24

Figure 13. Two small true positive bolide signatures confirmed by All Sky measurements [20]. Figure 13. Two small true positive bolide signatures confirmed by All Sky measurements [20]. Groundtrack (a) and light curve (b) correspond to the same flash. Similarly, (c) and (d) are one flash. Groundtrack (a) and light curve (b) correspond to the same flash. Similarly, (c) and (d) are one flash. 3.4. General Discoveries 3.4. General Discoveries The algorithm design will be a work in progress for some time. As such, it is a challenge to extract representativeThe algorithm discovery design statistics will be at a this work point. in progress Nonetheless, for some Table 3time. lists theAs algorithmsuch, it is performancea challenge into extractNovember representative 2018 and 7 highlydiscovery likely statistics bolide discoveriesat this point. have Nonetheless, been made inTable that month.3 lists the algorithm performance in November 2018 and 7 highly likely bolide discoveries have been made in that month. Table 3. Bolide extraction algorithm performance in November 2018. If more than one observation Tabledata entry 3. Bolide is shown, extraction the bolide algorithm can be performance considered confirmed.in November The 2018. entry If “all-sky”more than in one the Observationobservation dataData entry column is shown, refers to the ground bolide based can be camera considered observations confirmed. of that The bolide. entry “all-sky” in the Observation Data column refers to ground based camera observations of that bolide. Date (YY/MM/DD) Latitude Longitude Lightcurve Energy Duration (s) Observation Data Date (YY/MM/DD)Time (UT) (degree)Latitude (degree)Longitude Duration (J) Lightcurve Observation 18/11/1Time (UT) 18:36:44 (degree) 51.0N 58.9W(degree) 0.462(s) 1.05E-11Energy (J) GLM-16Data 18/11/3 12:36:21 5.0N 102.3W 0.155 1.49E-12 GLM-16 18/11/118/11/11 18:36:44 7:58:29 34.1N 51.0N 35.6W 58.9W 0.078 0.462 7.25E-13 1.05E-11 GLM-16GLM-16 18/11/318/11/12 12:36:21 4:58:15 29.1N 5.0N 85.9W 102.3W 0.837 0.155 2.09E-12 1.49E-12 GLM-16,GLM-16 all-sky 18/11/15 8:02:44 42.4N 52.8W 0.877 1.33E-10 USG, GLM-16 18/11/1118/11/20 7:58:29 12:17:52 34.9N 34.1N 118.4W 35.6W 0.36 0.078 9.55E-12 7.25E-13 GLM-16, GLM-17,GLM-16 all-sky 18/11/22 13:10:46 33.1N 122.2W 0.324 1.14E-11 GLM-16, GLM-17, all-sky 18/11/12 4:58:15 29.1N 85.9W 0.837 2.09E-12 GLM-16, all-sky

18/11/15 8:02:44 42.4N 52.8W 0.877 1.33E-10 USG, GLM-16 The first three discoveries are unconfirmed (meaning that they have not been observed by other means)18/11/20 and 12:17:52 are likely bolides 34.9N based on their 118.4W appearance. The 0.36 other 9.55E-12 four signatures wereGLM-16, observed GLM-17, by all-sky other sources such as USG sensors, all-sky cameras or the second GLM instrument on the GOES-17 18/11/22 13:10:46 33.1N 122.2W 0.324 1.14E-11 GLM-16, GLM-17, satellite and could, thus, be confirmed. As an example of an unconfirmed bolide, Figure 14 presents all-sky the ground track and light curve information of the first entry listed in Table3. This data demonstrates that the algorithm is capable of discovering new bolide-like signatures where other systems cannot The first three discoveries are unconfirmed (meaning that they have not been observed by other provide data. This new capability also presents a challenge as the lack of corroborating observations means) and are likely bolides based on their appearance. The other four signatures were observed by makes confirmations difficult. other sources such as USG sensors, all-sky cameras or the second GLM instrument on the GOES-17 satellite and could, thus, be confirmed. As an example of an unconfirmed bolide, Figure 14 presents

Sensors 2019, 19, x 20 of 24 the ground track and light curve information of the first entry listed in Table 3. This data demonstrates that the algorithm is capable of discovering new bolide-like signatures where other systemsSensors 2019 cannot, 19, 1008 provide data. This new capability also presents a challenge as the lack20 of of 24 corroborating observations makes confirmations difficult.

Figure 14. Unconfirmed bolide discovery corresponding to the first entry listed in Table3. The flash Figureoccurred 14. in Unconfirmed a remote location bolide over discovery the far corresponding eastern shore ofto Quebec,the first entry Canada. listed Plot in (Tablea) shows 3. The ground flash occurredtrack and in plot a remote (b) shows location light curve.over the far eastern shore of Quebec, Canada. Plot (a) shows ground track and plot (b) shows light curve. 3.5. Cuban Meteor 3.5. CubanThe valueMeteor of GLM as a public, and semi-global coverage meteor detection capability in combination with the automatic signature extraction functionality provided by this algorithm is further The value of GLM as a public, and semi-global coverage meteor detection capability in exemplified on the meteor that disintegrated over western Cuba on February 1st 2019. Figure 15 combination with the automatic signature extraction functionality provided by this algorithm is presents the ground track and light curve information for that flash. This meteor was widely observed further exemplified on the meteor that disintegrated over western Cuba on February 1st 2019. Figure and initial amateur observations collected by the American Meteor Society and through video uploads 15 presents the ground track and light curve information for that flash. This meteor was widely observedto the video and sharing initial amateur platform observations YouTube supported collected a by shallow the American entry trajectory Meteor overSociety Florida and towardsthrough videoCuba [uploads32]. After to thethe authorsvideo sharing of this paperplatform were YouT madeube aware supported of the a meteor, shallow and entry ran trajectory the algorithm, over Floridathe meteor towards was immediatelyCuba [32]. After recovered the authors in the of data. this Thepaper subsequent were made publication aware of the of themeteor, ground and track ran theon socialalgorithm, media the was meteor the first was available immediately data recovere that correctedd in the the data. initial The misconception subsequent publication about the of entry the groundtrajectory track [33] on that social actually media only was extended the first few availa kilometersble data over that Cuba corrected and headed the initial from Southmisconception to North. aboutIt was the also entry the firsttrajectory source [33] of athat light actually curve only at the extended same time. few Between kilometers the over meteor Cuba occurrence and headed and from the Southdiscovery to North. in GLM It data,was 8.5also hours the first had source passed. of It isa light conceivable curve at that the the same algorithm time. canBetween run constantly the meteor in occurrencethe future in and which the casediscovery this time in GLM could data, be reduced 8.5 hours significantly. had passed. This It is bolide conceivable has also that been the observed algorithm by cannumerous run constantly other sources in the such future as infrasound,in which case radar, this andtime USG could sensors. be reduced significantly. This bolide has also been observed by numerous other sources such as infrasound, radar, and USG sensors.

Sensors 2019, 19, x 1008 21 of 24

Figure 15. (a) Ground track and (b) light curve information of the Cuba meteor on 1 February 2019. Figure 15. (a) Ground track and (b) light curve information of the Cuba meteor on 1 February 2019. 3.6. Data Limitations and Future Work 3.6. DataThe Limitations GLM instrument and Future was designedWork to detect lightning in the atmosphere. As such, its assumptions and parameters are selected for that purpose. The geolocation procedure of GLM assumes that the The GLM instrument was designed to detect lightning in the atmosphere. As such, its energy emitting event is on top of the cloud cover (assumed to be 6 km at the poles to 14 km at the assumptions and parameters are selected for that purpose. The geolocation procedure of GLM equator [34]) because this is where lightning emissions occur. However, bolides may occur in altitude assumes that the energy emitting event is on top of the cloud cover (assumed to be 6 km at the poles bands ranging from the ground to about 100 km and typically they occur higher than the cloud cover to 14 km at the equator [34]) because this is where lightning emissions occur. However, bolides may level. As such, the geolocation of bolide signatures suffers from parallax effects and will generally be occur in altitude bands ranging from the ground to about 100 km and typically they occur higher inaccurate depending on the viewing angle from nadir. In the future, it could be possible to constrain than the cloud cover level. As such, the geolocation of bolide signatures suffers from parallax effects the bolide altitude for those discoveries that were detected by both GLM sensors on GOES 16 and and will generally be inaccurate depending on the viewing angle from nadir. In the future, it could GOES 17 enabling stereo vision. be possible to constrain the bolide altitude for those discoveries that were detected by both GLM Better understanding of the narrow bandwidth spectral ﬁlter is necessary in order to use GLM sensors on GOES 16 and GOES 17 enabling stereo vision. bolide light curves to calculate total radiant energy. It has been shown by [2] that calculated total optical Better understanding of the narrow bandwidth spectral filter is necessary in order to use GLM radiant energies in GLM correspond well to those reported from broad-band USG sensor data which bolide light curves to calculate total radiant energy. It has been shown by [2] that calculated total suggests that during the meteoroid’s peak brightness the GLM pass-band is dominated by continuum optical radiant energies in GLM correspond well to those reported from broad-band USG sensor data emission, rather than O I line emission [2]. However, more detailed analysis on how bolide emissions which suggests that during the meteoroid’s peak brightness the GLM pass-band is dominated by registered by GLM relate to the actually released energy by the bolide is necessary and is currently continuum emission, rather than O I line emission [2]. However, more detailed analysis on how under way at NASA Ames Research Center. Due to their increased occurrence rate, small bolides are bolide emissions registered by GLM relate to the actually released energy by the bolide is necessary more likely to be seen by multiple observatories. Small bolides are therefore particularly useful to and is currently under way at NASA Ames Research Center. Due to their increased occurrence rate, calibrate GLM-based energy estimates with independent observations. small bolides are more likely to be seen by multiple observatories. Small bolides are therefore The GLM algorithm that transforms level 0 to the level 2 data product removes knowledge about particularly useful to calibrate GLM-based energy estimates with independent observations. the accuracy and uncertainty associated, especially, with the bolide light curve. GLM could provide The GLM algorithm that transforms level 0 to the level 2 data product removes knowledge about value in the future as a large-quantity source for meteor observations. To increase science value of the accuracy and uncertainty associated, especially, with the bolide light curve. GLM could provide such an output, the uncertainties associated with GLM measurements will be vital information that is value in the future as a large-quantity source for meteor observations. To increase science value of currently not publicly available. such an output, the uncertainties associated with GLM measurements will be vital information that Terrestrial glint is a phenomenon where sunlight is reﬂected off of calm water surfaces and directly is currently not publicly available. enters the GLM sensor. This high energy input causes unwanted triggering of the GLM CCD and Terrestrial glint is a phenomenon where sunlight is reflected off of calm water surfaces and directly enters the GLM sensor. This high energy input causes unwanted triggering of the GLM CCD

Sensors 2019, 19, 1008 22 of 24 produces a considerable fraction of false positive bolide flashes. This is a known problem that is being addressed by the GLM ground segment. In the future, the addition of a glint filter would be a valuable contribution to this work that will reduce the amount of false positive detections. Figure2 shows the coverage area of GLM and it is clear that parts of the Americas are covered by two GLM sensors enabling stereo detections. In the future, this stereo detection of meteors enables a multitude additional benefits such as flight path reconstruction of meteoroids, burst altitude determination, and increased precision in the light curve signal. The clustering parameters for the collection of groups into flashes (see Table1) were selected on a trial basis. The authors recommend the usage of the official GLM clustering parameters [1,29] to maintain consistency in the GLM community and it is expected that changing the parameters should not have a detrimental effect on bolide detection. As the bolide extraction algorithm reaches maturity and enters routine operations, reliable statistics on its extraction efficiency will be gathered. This data can be used in the future to quantify the algorithm’s efficiency by comparing the detection rate to the statistical meteor flux. The GLM extraction algorithm presented here is a first step and leaves room for improvement. Improvements can be realized through further modification and tuning of the filters. However, as large quantities of data are produced by GLM, machine learning approaches appear to be a promising application to process and classify GLM data. An algorithm such as presented in this paper can be a useful first step to extract and classify training data for such an approach.

4. Conclusions The GLM instrument on NOAA GOES 16 and 17 satellites is originally intended to record lightning events. Here a new algorithm has been presented that utilizes publicly available L2 data from the GLM instrument to recover meteor signatures in the GLM data product. We highlight the signature differences between lightning and bolide ground tracks as well as light curves. These differences are exploited by filter functions to identify bolide signatures. The algorithm architecture and the derivation of the individual filters is presented. The algorithm discards 98.45% of GLM produced files in its baseline setting, thus, condensing the data effectively to the remaining 1.55% of the original data that include bolide-like signatures. Each filter’s performance was assessed and the filter baseline setting is shown to yield a practical parameter combination that preserves the 6 known true positive bolide signatures (for example, Figures6 and 13) while discarding the vast majority of generic GLM L2 files. Several bolides have been identified in L2 data using the algorithm and these signatures have been confirmed as real bolides with US government data or with ground based camera systems. Several more unconfirmed discoveries have been made of which three are listed in Table3 and one is presented in Figure 14. The case of the Cuban meteor of Feb 1st 2019, for which the algorithm in combination with GLM could quickly provide the first light curve and correct ground track, demonstrated nicely the new capability. This outcome demonstrates the value of GLM in combination with the automatic bolide algorithm as a source of meteor measurements drawing on semi-global coverage and providing publicly available data of meteor ground tracks and light curves. The key contribution of this algorithm is that it extracts bolide-like signatures from the vast amount of GLM data and therefore makes the GLM product manageable for bolide discoveries. It is a first step and it is a virtual certainty that more unflagged bolide signatures, especially smaller ones, are present in the data. Small bolide flashes remain a challenge as the signatures reside at the resolution limit of the GLM instrument. Future work should build on this contribution and increase discovery efficiency by tuning and modifying filters, as well as through alternative approaches such as machine learning. Additional work needs to focus on the processing and qualification of GLM derived bolide data to increase utility for scientific purposes. In summary, it has been demonstrated that this algorithm serves its intended purpose and can be used to extract bolide signatures from GLM data. Sensors 2019, 19, 1008 23 of 24

Author Contributions: C.M.R. is the lead author of this manuscript. He is responsible for the majority of the design and analysis work presented here. R.S.L. closely collaborated with C.M.R. on the analysis work and authored parts of the introduction of this manuscript. C.E.H. was instrumental and facilitating for the analysis work especially in relation to analyzing large data quantities with the Hyperwall. J.C.C. provided an early version of the GLM L2 data reader algorithm which this work built on and extended. D.L.M. has contributed through discussions and by building a network that facilitated collaboration for this work. Funding: Clemens Rumpf’s research was supported by an appointment to the NASA Postdoctoral Program at the NASA Ames Research Center, administered by Universities Space Research Association under contract with NASA. Acknowledgments: The authors would like to thank the Hyperwall team at NASA ARC who provided instrumental support in the development of the ﬁlter algorithm. Furthermore, Clem Tillier and Samantha Edgington of Lockheed Martin Corporation were of great assistance discussing the nature of data signatures. We would also like to thank Marian Nemec and Eric Stern of NASA ARC for helpful feedback as well as Peter M. Jenniskens, Jessie L. Dotson, Jeffrey L. Smith, and Robert L. Morris and for insightful discussions. The feedback of three anonymous reviewers was of great help to improve the quality of this paper. Conﬂicts of Interest: The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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