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NASA NTRS2013

Tool for Automated Retrieval of Generic Event Tracks (TARGET)

Methods have been developed to identify and track tornado-producing mesoscale convective systems (MCSs) automatically over the continental United States, in order to facilitate systematic studies of these powerful and often destructive events. Several data sources were combined to ensure event identification accuracy. Records of watches and warnings issued by National Weather Service (NWS), and tornado locations and tracks from the Tornado History Project (THP) were used to locate MCSs in high-resolution precipitation observations and GOES infrared (11-micron) Rapid Scan Operation (RSO) imagery. Thresholds are then applied to the latter two data sets to define MCS events and track their developments. MCSs produce a broad range of severe convective weather events that are significantly affecting the living conditions of the populations exposed to them. Understanding how MCSs grow and develop could help scientists improve their weather prediction models, and also provide tools to decision-makers whose goals are to protect populations and their property. Associating storm cells across frames of remotely sensed images poses a difficult problem because storms evolve, split, and merge. Any storm-tracking method should include the following processes: storm identification, storm tracking, and quantification of storm intensity and activity. The spatiotemporal coordinates of the tracks will enable researchers to obtain other coincident observations to conduct more thorough studies of these events. In addition to their tracked locations, their areal extents, precipitation intensities, and accumulations all as functions of their evolutions in time were also obtained and recorded for these events. All parameters so derived can be catalogued into a moving object database (MODB) for custom queries. The purpose of this software is to provide a generalized, cross-platform, pluggable tool for identifying events within a set of scientific data based upon specified criteria with the possibility of storing identified events into a searchable database. The core of the application uses an implementation of the connected component labeling (CCL) algorithm to identify areas of interest, then uses a set of criteria to establish spatial and temporal relationships between identified components. The CCL algorithm is used for identifying objects within images for computer vision. This application applies it to scientific data sets using arbitrary criteria. The most novel concept was applying a generalized CCL implementation to scientific data sets for establishing events both spatially and temporally. The combination of several existing concepts (pluggable components, generalized CCL algorithm, etc.) into one application is also novel. In addition, how the system is designed, i.e., its extensibility with pluggable components, and its configurability with a simple configuration file, is innovative. This allows the system to be applied to new scenarios with ease.

Clune, Thomas↗

NASA NTRS2012

Properties of Ground Level Enhancement Events and the Associated Solar Eruptions During Solar Cycle 23

Solar cycle 23 witnessed the most complete set of observations of coronal mass ejections (CMEs) associated with the Ground Level Enhancement (GLE) events. We present an overview of the observed properties of the GLEs and those of the two associated phenomena, viz., flares and CMEs, both being potential sources of particle acceleration. Although we do not find a striking correlation between the GLE intensity and the parameters of flares and CMEs, the solar eruptions are very intense involving X-class flares and extreme CME speeds (average approx. 2000 km/s). An M7.1 flare and a 1200 km/s CME are the weakest events in the list of 16 GLE events. Most (80 %) of the CMEs are full halos with the three non-halos having widths in the range 167 to 212 degrees. The active regions in which the GLE events originate are generally large: 1290 msh (median 1010 msh) compared to 934 msh (median: 790 msh) for SEP-producing active regions. For accurate estimation of the CME height at the time of metric type II onset and GLE particle release, we estimated the initial acceleration of the CMEs using flare and CME observations. The initial acceleration of GLE-associated CMEs is much larger (by a factor of 2) than that of ordinary CMEs (2.3 km/sq s vs. 1 km/sq s). We confirmed the initial acceleration for two events for which CME measurements are available in the inner corona. The GLE particle release is delayed with respect to the onset of all electromagnetic signatures of the eruptions: type II bursts, low frequency type III bursts, soft X-ray flares and CMEs. The presence of metric type II radio bursts some 17 min (median: 16 min; range: 3 to 48 min) before the GLE onset indicates shock formation well before the particle release. The release of GLE particles occurs when the CMEs reach an average height of approx 3.09 R(sub s) (median: 3.18 R (sub s) ; range: 1.71 to 4.01 R (sub s) ) for well-connected events (source longitude in the range W20–W90). For poorly connected events, the average CME height at GLE particle release is ∼66 % larger (mean: 5.18 R (sub s) ; median: 4.61 R (sub s) ; range: 2.75–8.49 R (sub s) ). The longitudinal dependence is consistent with shock accelerations because the shocks from poorly connected events need to expand more to cross the field lines connecting to an Earth observer. On the other hand, the CME height at metric type II burst onset has no longitudinal dependence because electromagnetic signals do not require magnetic connectivity to the observer. For several events, the GLE particle release is very close to the time of first appearance of the CME in the coronagraphic field of view, so we independently confirmed the CME height at particle release. The CME height at metric type II burst onset is in the narrow range 1.29 to 1.8 R(sub s), with mean and median values of 1.53 and 1.47 R(sub s). The CME heights at metric type II burst onset and GLE particle release correspond to the minimum and maximum in the Alfven speed profile. The increase in CME speed between these two heights suggests an increase in Alfvenic Mach number from 2 to 3. The CME heights at GLE particle release are in good agreement with those obtained from the velocity dispersion analysis, including the source longitude dependence. We also discuss the implications of the delay of GLE particle release with respect to complex type III bursts by approx 18 min (median: 16 in; range: 2 to 44 min) for the flare acceleration mechanism. A similar analysis is also performed on the delay of particle release relative to the hard X-ray emission.

Geomagnetic Storms↗

NASA NTRS2015

Large Solar Energetic Particle Events Associated With Filament Eruptions Outside Active Regions

We report on four large filament eruptions (FEs) from solar cycles 23 and 24 that were associated with large solar energetic particle (SEP) events and interplanetary type II radio bursts. The post-eruption arcades corresponded mostly to C-class soft X-ray enhancements, but an M1.0 flare was associated with one event. However, the associated coronal mass ejections (CMEs) were fast (speeds approx. 1000 km/s) and appeared as halo CMEs in the coronagraph field of view. The interplanetary type II radio bursts occurred over a wide wavelength range, indicating the existence of strong shocks throughout the inner heliosphere. No metric type II bursts were present in three events, indicating that the shocks formed beyond 2-3 Rs. In one case, there was a metric type II burst with low starting frequency, indicating a shock formation height of approx.2 Rs. The FE-associated SEP events did have softer spectra (spectral index >4) in the 10-100 MeV range, but there were other low-intensity SEP events with spectral indices ⩾4. Some of these events are likely FE-SEP events, but were not classified as such in the literature because they occurred close to active regions. Some were definitely associated with large active region flares, but the shock formation height was large. We definitely find a diminished role for flares and complex type III burst durations in these large SEP events. Fast CMEs and shock formation at larger distances from the Sun seem to be the primary characteristics of the FE-associated SEP events.

filaments↗

NASA NTRS2017

Classification of Meteorological Influences Surrounding Extreme Precipitation Events in the United States using the MERRA-2 Reanalysis

Extreme precipitation events can have a large impact on society through flooding that can result in property destruction, crop losses, economic losses, the spread of water-borne diseases, and fatalities. Observations indicate there has been a statistically significant increase in extreme precipitation events over the past 15 years in the Northeastern United States and other localized regions of the country have become crippled with record flooding events, for example, the flooding that occurred in the Southeast United States associated with Hurricane Matthew in October 2016. Extreme precipitation events in the United States can be caused by various meteorological influences such as extratropical cyclones, tropical cyclones, mesoscale convective complexes, general air mass thunderstorms, upslope flow, fronts, and the North American Monsoon. Reanalyses, such as the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), have become a pivotal tool to study the meteorology surrounding extreme precipitation events. Using days classified as an extreme precipitation events based on a combination of observational gauge and radar data, two techniques for the classification of these events are used to gather additional information that can be used to determine how events have changed over time using atmospheric data from MERRA-2. The first is self organizing maps, which is an artificial neural network that uses unsupervised learning to cluster like patterns and the second is an automated detection technique that searches for characteristics in the atmosphere that define a meteorological phenomena. For example, the automated detection for tropical cycles searches for a defined area of suppressed sea level pressure, alongside thickness anomalies aloft, indicating the presence of a warm core. These techniques are employed for extreme precipitation events in preselected regions that were chosen based an analysis of the climatology of precipitation.

Monsoon↗

NASA NTRS2018

A Statistical Study of Solar Particle Events in Flux and Dose

The high-energy protons from solar energetic particle (SEP) events present a hazard to space systems: damage to science instruments/electronics/materials or to astronauts. A reliable estimate of the high-energy proton environment is critical to assure mission success. Important characteristics of an SEP event are fluence, peak flux, energy spectrum, time to reach the peak flux, time to reach peak dose, and properties of the cumulative dose profile after an event starts. All of these characteristics are important to understand in order to design space missions properly for both robotic and human missions. Because of the unpredictable and sporadic nature of SEP events, statistical models are often used to represent the SEP parameters described above. In a study by Jun et al. (2007), the statistics of event fluences, durations, and time intervals between events were investigated using the then available historical SEP dataset obtained from the instruments onboard the IMP-8 spacecraft. Since then, a more comprehensive SEP dataset based off of IMP-8 and GOES called Reference Data Set Version 2.0 (RDSv2.0) has become available covering the SEP events up to Year 2015 under a framework of the European Space Agency's (ESA's) Solar Energetic Particle Environment Modelling (SEPEM) project (Jiggens et al., 2018). The main objectives of this statistical study of SEP events are two-fold: First, the statistics of peak fluxes, event fluences, durations, and time intervals will be re-visited by using RDSv2.0; Second, the statistical analyses of flux and dose timing will be performed using the same dataset RDSv2.0. The results of this study will address the statistical properties of all key parameters for designing a spacecraft or a human mission where the SEP environment is an important consideration.

Whitman, K.↗

NASA NTRS2021

Effect of volcanic emissions on clouds during the 2008 and 2018 Kilauea degassing events

Volcanic eruptions in otherwise clean environments are “natural experiments” wherein the effects of aerosol emissions on clouds and climate can be partitioned from meteorological variability and anthropogenic activities. In this work, we combined satellite retrievals, reanalysis products, and atmospheric modeling to analyze the mechanisms of aerosol–cloud interactions during two degassing events at the Kilauea volcano in 2008 and 2018. The eruptive nature of the 2008 and 2018 degassing events was distinct from long-term volcanic activity for Kilauea. Although previous studies assessed the modulation of cloud properties from the 2008 event, this is the first time such an analysis has been reported for the 2018 event and that multiple degassing events have been analyzed and compared at this location. Both events resulted in significant changes in cloud effective radius and cloud droplet number concentration that were decoupled from local meteorology and in line with an enhanced cloud albedo. However, it is likely that the effects of volcanic emissions on liquid water path and cloud fraction were largely offset by meteorological variability. Comparison of cloud anomalies between the two events suggested a threshold response of aerosol–cloud interactions to overcome meteorological effects, largely controlled by aerosol loading. In both events, the ingestion of aerosols within convective parcels enhanced the detrainment of condensate in the upper troposphere, resulting in deeper clouds than observed under pristine conditions. Accounting for ice nucleation on ash particles led to enhanced ice crystal concentrations at cirrus levels and a slight decrease in ice water content, improving the correlation of the model results with the satellite retrievals. Overall, aerosol loading, plume characteristics, and meteorology contributed to changes in cloud properties during the Kilauea degassing events.

Katherine H. Breen↗

NASA NTRS2023

On-Board Deployment Event Verification for GOES-R Spacecraft

As is common with many spacecraft designs, the GOES-R vehicles require a series of deployment events to transition from the launch configuration to the operational configuration. Rather than implementing additional sensors to verify various deployments, the GOES-R program developed an alternate approach that uses existing gyro rate sensing. The approach includes two pieces: the first is a new onboard shock detection capability to confirm initiation of individual deployment events, and the second is a ground-based dynamics verification step to confirm completion of deployment events. We first present the algorithm that detects shock events for various deployment devices along with the parameter tuning performed during ground tests. We then show inflight performance of the shock detection algorithm. While shock detection is useful for observing initiation of deployment events, completion of some deployment events cannot be determined by shock detection alone. For these events, such as solar panel latch-up and deployable boom extension, the program developed dynamics models for the deployment transient responses. High-rate gyro data were recorded for these events, which allow the ground team to verify that the appendages were fully deployed. We show the predictive models for these events and corresponding flight results.

GeoXO↗

NASA NTRS2023

The 17 April 2021 Widespread Solar Energetic Particle Event

A complex and long-lasting solar eruption on 17 April 2021 produced a widespread solar energetic particle (SEP) event that was observed by five longitudinally well-separated observers in the inner heliosphere that covered distances to the Sun from 0.42 to 1 au: BepiColombo, Parker Solar Probe, Solar Orbiter, STEREO A, and near-Earth spacecraft. The event was the second widespread SEP event detected in solar cycle 25, and it produced relativistic electrons and protons. It was associated with a long-lasting solar hard X-ray flare that showed multiple hard X-ray peaks over a duration of one hour. The event was further accompanied by a medium-fast coronal mass ejection (CME) with a speed of 880 km s -1 that drove a shock, an extreme ultraviolet wave, and long-lasting and complex radio burst activity that showed four distinct type III burst groups over a period of 40 min. We aim to understand the reason for the wide spread of elevated SEP intensities in the inner heliosphere as well as identify the underlying source regions of the observed energetic electrons and protons. We applied a comprehensive multi-spacecraft analysis of remote-sensing observations and in situ measurements of the energetic particles and interplanetary context to attribute the SEP observations at the different locations to the various potential source regions at the Sun. We used an ENLIL simulation to characterize the complex interplanetary state and its role in the energetic particle transport. The magnetic connection between each spacecraft and the Sun was determined using ballistic backmapping in combination with potential field source surface extrapolations in the lower corona. Using also a reconstruction of the coronal shock front, we then determined the times when the shock establishes magnetic connections with the different observers. Radio observations were used to characterize the directivity of the four main injection episodes, which were then employed in a 2D SEP transport simulation to test the importance of these different injection episodes. A comprehensive timing analysis of the inferred solar injection times of the SEPs observed at each spacecraft suggests different source processes being important for the electron and proton events. Comparison among the characteristics and timing of the potential particle sources, such as the CME-driven shock or the flare, suggests a stronger shock contribution for the proton event and a more likely flare-related source for the electron event. In contrast to earlier studies on widespread SEP events, we find that in this event an important ingredient for the wide SEP spread was the wide longitudinal range of about 110 o covered by distinct SEP injections, which is also supported by our SEP transport modeling.

sun↗

NASA NTRS2024

Evaluating Precipitation Events Using GPM IMERG 30-Minute Near Real-Time Precipitation Estimates

Accurately detecting and estimating precipitation at near–real time (NRT) is of utmost importance for the early detection and monitoring of hydrometeorological hazards. The precipitation product, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), provides NRT 0.1° and 30-min precipitation estimates across the globe with only a 4-h latency. This study was an evaluation of the GPM IMERG version 6 level-3 early run 30-min precipitation product for precipitation events from 2014 through 2020. The purpose of this research was to identify when, where, and why GPM IMERG misidentified and failed to detect precipitation events in California, Nevada, Arizona, and Utah in the United States. Precipitation events were identified based on 15-min precipitation from gauges and 30-min precipitation from the IMERG multisatellite constellation. False-positive and false-negative precipitation events were identified and analyzed to determine their characteristics. Precipitation events identified by gauges had longer duration and had higher cumulative precipitation than those identified by GPM IMERG. GPM IMERG had many false event detections during the summer months, suggesting possible virga event detection, which is when precipitation falls from a cloud but evaporates before it reaches the ground. The frequency and timing of the merged passive microwave (PMW) product and forward propagation were responsible for IMERG overestimating cumulative precipitation during some precipitation events and underestimating others. This work can inform experts that are using the GPM IMERG NRT product to be mindful of situations where GPM IMERG–estimated precipitation events may not fully resolve the hydrometeorological conditions driving these hazards.

Jessica R P Sutton↗

DOE OSTI2023

Extreme Weather Events and the Impact on PV Time Series Data

The impact of extreme weather events on PV performance was studied by comparing the National Oceanic and Atmospheric Administration database on severe weather with the PV Fleet database on continuous PV performance. We identified 170 systems that were immediately impacted by weather events. These severe weather events lead to a median loss of only 1% of annual production. However, flooding and high wind events were found to have an extremely long tail extending to 60 % loss showing that these discrete events can pose a substantial risk to PV systems. Besides the short-term impact of lost production due to outages, we also found a statistically significant increased performance loss rate (PLR) for high wind events comparing PLR before and after these weather events. In addition, hail events caused a higher PLR for 2 out of 3 systems. More data are required to better quantify the impact, but these first results illustrate the substantial risk these events pose short-and long-term.

degradation↗

DOE OSTI2023

Extreme Weather Events and PV Systems Impact

The impact of extreme weather events on PV performance was studied by comparing the National Oceanic and Atmospheric Administration database on severe weather with the National Renewable Energy Laboratory's (NREL) PV Fleet database on continuous PV performance. We identified 170 systems that were immediately impacted by weather events. These severe weather events lead to a median loss of only 1 % of annual production. However, flooding and high wind events were found to have an extremely long tail extending to 60 % loss, showing that these discrete events can pose a substantial risk to PV systems. Besides the short-term impact of lost production due to outages, we also found a statistically significant increased performance loss rate (PLR) for high wind events above 90 kilometer/hour, by comparing PLR before and after these events. Similarly, hail events caused higher PLR for hail sizes of and above 25 mm indicating that more stringent hail testing for PV modules is required. In addition, very high snow loads may also cause increased PLRs, but more data are required to better quantify the impact. These findings illustrate the substantial risk extreme weather events pose short-and long-term to fielded PV installations.

extreme weather↗

DOE OSTI2025

Achilles and GENIE Model Comparisons Between Quasi-Elastic Proton Exclusive Events in DUNE PRISM ND-LAr

In this study, we analyze simulated CCQE proton exclusive events in DUNE s ND-LAr using Achilles and GENIE Monte Carlo event generators. We generate events using muon neutrino exclusive flux files of DUNE s on-axis and 20m off-axis PRISM positions and compare various kinematics of proton exclusive events between the event generators. Events are further analyzed in categories according to the number of final-state protons. This study was motivated by QE s dominance in statistics for off-axis events over other channels in which off-axis events are very useful for characterizing DUNE s flux. Furthermore, our motivation for comparing results between generators is driven by off-axis QE neutrino spectra peaking below 1 GeV. In this region, many low-energy proton events are produced in which models have been shown to have disagreements.

43 PARTICLE ACCELERATORS↗

NASA NTRS2017

Single Event Effect Testing of the Analog Devices ADV212

The Analog Devices ADV212 was initially tested for single event effects (SEE) at the Texas AM University Cyclotron Facility (TAMU) in July of 2013. Testing revealed a sensitivity to device hang-ups classified as single event functional interrupts (SEFI), soft data errors classified as single event upsets (SEU), and, of particular concern, single event latch-ups (SEL). All error types occurred so frequently as to make accurate measurements of the exposure time, and thus total particle fluence, challenging. To mitigate some of the risk posed by single event latch-ups, circuitry was added to the electrical design to detect a high current event and automatically recycle power and reboot the device. An additional heavy-ion test was scheduled to validate the operation of the recovery circuitry and the continuing functionality of the ADV212 after a substantial number of latch-up events. As a secondary goal, more precise data would be gathered by an improved test method, described in this test report.

single event latch-ups (SEL)↗

DOE OSTI2021

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

This paper proposes a novel data-driven power system event detection and classification method based on 5TB of actual PMU measurements collected from the US western interconnect. Firstly, a set of comprehensive power quality rules are proposed to pre-filter the raw data and extract the regions of interest (ROI). Six distinct event categories are defined and corresponding patterns are chosen as references. Meanwhile, detailed characteristics of patterns are summarized to enhance our understanding of the actual events. Then, the time-independent feature vectors are generated by extracting the statistical, temporal, and spectral features from the raw time-series data. Furthermore, an ensemble model is proposed to cluster the events by combining multiple K-means clustering models using a voting strategy. Besides, both system-level and PMU-level clustering models are developed. The accuracy and robustness of the event detection method are further improved through interactive evaluation of the two-level clustering results. This paper summarizes the actual characteristics of each event category and provides a reliable basis for accurate label generation. The experiments demonstrate the effectiveness of the proposed event detection and classification method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Understanding collective human movement dynamics during large-scale events using big geosocial data analytics

Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. Finally, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Data-driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosting Trees

Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. Furthermore, the proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2016

A Review of Recent Advances in Research on Extreme Heat Events

Reviewing recent literature, we report that changes in extreme heat event characteristics such as magnitude, frequency, and duration are highly sensitive to changes in mean global-scale warming. Numerous studies have detected significant changes in the observed occurrence of extreme heat events, irrespective of how such events are defined. Further, a number of these studies have attributed present-day changes in the risk of individual heat events and the documented global-scale increase in such events to anthropogenic-driven warming. Advances in process-based studies of heat events have focused on the proximate land-atmosphere interactions through soil moisture anomalies, and changes in occurrence of the underlying atmospheric circulation associated with heat events in the mid-latitudes. While evidence for a number of hypotheses remains limited, climate change nevertheless points to tail risks of possible changes in heat extremes that could exceed estimates generated from model outputs of mean temperature. We also explore risks associated with compound extreme events and nonlinear impacts associated with extreme heat.

Heat events↗