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NASA NTRS2016

Community-Wide Validation of Geospace Model Local K-Index Predictions to Support Model Transition to Operations

We present the latest result of a community-wide space weather model validation effort coordinated among the Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), model developers, and the broader science community. Validation of geospace models is a critical activity for both building confidence in the science results produced by the models and in assessing the suitability of the models for transition to operations. Indeed, a primary motivation of this work is supporting NOAA/SWPCs effort to select a model or models to be transitioned into operations. Our validation efforts focus on the ability of the models to reproduce a regional index of geomagnetic disturbance, the local K-index. Our analysis includes six events representing a range of geomagnetic activity conditions and six geomagnetic observatories representing midlatitude and high-latitude locations. Contingency tables, skill scores, and distribution metrics are used for the quantitative analysis of model performance. We consider model performance on an event-by-event basis, aggregated over events, at specific station locations, and separated into high-latitude and midlatitude domains. A summary of results is presented in this report, and an online tool for detailed analysis is available at the CCMC.

Glocer, A.↗

NASA NTRSDate not supplied

Application of the Global Precipitation Forecast dataset for Global Landslide Forecasting System

Landslides are extremely damaging, pervasive and cause fatalities and economic impacts. Extreme rainfall events, coupled with inopportune surface conditions, are the primary triggers of landslides around the world. Forecasting landslide events represent an area of open research. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model has been created that provides near real-time dynamic landslide characterization using Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG). However, it does not provide information on prediction of landslides into future. This study considers how global precipitation forecast data compares to satellite rainfall at different spatiotemporal scales and outlines the potential for its use in landslide hazard prediction/forecasting system. NASA Goddard Earth Observing System (GEOS)-Forecast model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The GEOS-Forecast model is initialized at 00 UTC and is evaluated with IMERG Early, using Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) precipitation product as a reference over contiguous United States (CONUS). Categorical and continuous statistics along with probability density functions and cumulative distribution functions are considered to assess the performance of the precipitation products, with a focus on landslide hotspots. Seasonality appears to influence the performance of both the GEOS-Forecast and IMERG Early product. Moreover, the global comparison between the GEOS-Forecast and IMERG Early is carried out in terms of percentile difference, correlation, and bias maps. For extreme rainfall events in regions such as Mekong, Colombia, and Tajikistan, GEOS-Forecast appears to resolve high rainfall relative to IMERG Early more frequently. Validation over landslide points reveal that for 24hr rainfall accumulations > 100mm, GEOS-Forecast and IMERG coincide. GEOS-Forecast and IMERG Early precipitation matches more closely for tropical cyclones than other types of storms. Overall, the performance varies with respect to location, rainfall intensities, and type of precipitation events.

Sana Khan↗

NASA NTRS1984

Microwave and X-ray observations of delayed brightenings at sites remote from the primary flare locations

Five examples of solar flares observed with the 17-GHz interferometer at Nobeyama in which a secondary microwave burst occurred at a distance of 100,000 km to 1,000,000 km from the primary flare site are presented. The secondary microwave burst in all five cases had a similar time profile to the primary burst with a delay of 2 to 25 s. The velocity of a triggering agent inferred from this delay and spatial separation is 10,000 km to 100,000 km/s. The intensity of the secondary burst was a factor of 3 to 25 smaller than that of the primary burst in all events except for one case in which it was a factor of 2 larger. The polarization degree of the secondary burst at 17 GHz was 35%, significantly higher than the average value for typical impulsive bursts. Two of the events were accompanied by meterwave type III/V bursts located high in the corona between the primary and secondary sites. For two of the other events, X-ray images of he secondary source were obtained with the hard-X-ray imaging spectrometer on the Solar Maximum Mission. These observations strongly suggest that the distant microwave bursts were produced by electrons with energies of 10 keV to 100 keV which were channeled along a huge loop from the main flare site to the remote location.

Nakajima, H.↗

NASA NTRS1985

Microwave and X-ray observations of delayed brightenings at sites remote from the primary flare locations

Five examples of solar flares observed with the 17-GHz interferometer at Nobeyama in which a secondary microwave burst occurred at a distance of 100,000 km to 1,000,000 km from the primary flare site are presented. The secondary microwave burst in all five cases had a similar time profile to the primary burst with a delay of 2 to 25 s. The velocity of a triggering agent inferred from this delay and spatial separation is 10,000 km to 100,000 km/s. The intensity of the secondary burst was a factor of 3 to 25 smaller than that of the primary burst in all events except for one case in which it was a factor of 2 larger. The polarization degree of the secondary burst at 17 GHz was 35 percent, significantly higher than the average value for typical impulsive bursts. Two of the events were accompanied by meterwave type III/V bursts located high in the corona between the primary and secondary sites. For two of the other events, X-ray images of the secondary source were obtained with the hard-X-ray imaging spectrometer on the Solar Maximum Mission. These observations strongly suggest that the distant microwave bursts were produced by electrons with energies of 10 keV to 100 keV which were channeled along a huge loop from the main flare site to the remote location.

Nakajima, H.↗

NASA NTRS2011

STEREO Observations of Solar Energetic Particles

We report on observations of Solar Energetic Particle (SEP) events as observed by instruments on the STEREO Ahead and Behind spacecraft and on the ACE spacecraft. We will show observations of an electron event observed by the STEREO Ahead spacecraft on June 12, 2010 located at W74 essentially simultaneously with electrons seen at STEREO Behind at E70. Some similar events observed by Helios were ascribed to fast electron propagation in longitude close to the sun. We will look for independent verification of this possibility. We will also show observations of what appears to be a single proton event with very similar time-history profiles at both of the STEREO spacecraft at a similar wide separation. This is unexpected. We will attempt to understand all of these events in terms of corresponding CME and radio burst observations.

vonRosenvinge, Tycho↗

NASA NTRS2007

The Impact of Microphysics on Intensity and Structure of Hurricanes and Mesoscale Convective Systems

During the past decade, both research and operational numerical weather prediction models, e.g. Weather Research and Forecast (WRF) model, have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with a 1-2 km or less horizontal resolutions. WRF is a next-generation mesoscale forecast model and assimilation system that has incorporated modern software framework, advanced dynamics, numeric and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WRF model can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options such as Purdue Lin et al. (1983), WSM 6-class and Thompson microphysics schemes. We have recently implemented three sophisticated cloud microphysics schemes into WRF. The cloud microphysics schemes have been extensively tested and applied for different mesoscale systems in different geographical locations. The performances of these schemes have been compared to those from other WRF microphysics options. We are performing sensitivity tests in using WRF to examine the impact of six different cloud microphysical schemes on precipitation processes associated hurricanes and mesoscale convective systems developed at different geographic locations [Oklahoma (IHOP), Louisiana (Hurricane Katrina), Canada (C3VP - snow events), Washington (fire storm), India (Monsoon), Taiwan (TiMREX - terrain)]. We will determine the microphysical schemes for good simulated convective systems in these geographic locations. We are also performing the inline tracer calculation to comprehend the physical processes (i.e., boundary layer and each quadrant in the boundary layer) related to the development and structure of hurricanes and mesoscale convective systems.

Tao, Wei-Kuo↗

DOE OSTI2026

Multi‐Sensor Trajectory Reconstruction of the 24 April 2025 Alaska Fireball and Implications for Planetary Defense

On 24 April 2025 at 18:30:57 UTC, a bright daytime fireball over Southcentral Alaska was detected by 37 seismic stations, 16 single infrasound sensors, and four infrasound arrays, yielding 30 ballistic and multiple fragmentation arrivals. Here, the unprecedented density of seismoacoustic coverage enabled detailed reconstruction of the event using acoustic signals, with fragmentation source locations further guiding the identification of Doppler weather radar signatures of a meteorite fall. Incorporation of a radar-derived terminal point yielded a final trajectory solution, which agreed closely with an independent optical trajectory solution from video analysis. The reconstructed entry parameters from seismoacoustic analysis indicate a velocity of 25.3 km/s, an entry angle of 19°, and an energy release of ∼38 t TNT equivalent. Assuming a chondritic composition, the pre-entry object diameter was ∼0.7 m. Using orbital parameters from the optical solution, we estimate meteoroid composition as most likely an L-type ordinary chondrite. The event occurred in the sub-Arctic, where space-based optical systems face challenges in detection, demonstrating the critical role of dense ground-based seismoacoustic networks in characterizing high-latitude atmospheric entries. This uniquely well-recorded event demonstrates the capability of dense seismoacoustic networks to constrain bolide trajectories, energetics, and fragmentation, with radar and optical data providing critical confirmation and complementary perspectives. These results bridge the methodological gap between planetary-defense monitoring of natural impactors and space-traffic analyses of artificial reentries, illustrating how multi-sensor integration can deliver calibration-grade trajectories even for unpredicted events.

Fireball↗

DOE OSTI2023

1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems

This paper presents a 1-D convolutional and graph convolutional networks for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined 1-D convolutional and graph convolutional networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The accuracy of 99.5%, 98.4%, 99.2%, and 95.5% are achieved in fault event detection, fault type classification, fault phase identification, and fault location respectively. The detailed confusion matrices of fault type and fault phase classification are provided for validation.

deep neural network↗

DOE OSTI2020

Lowering the Energy Threshold of the CUORE Experiment: Benefits in the Surface Alpha Events Reconstruction

CUORE is a tonne-scale cryogenic experiment located at the Laboratori Nazionali del Gran Sasso that exploits bolometric technique to search for neutrinoless double beta decay of 130 Te. Thanks to its very low background and large mass, CUORE is also a powerful tool to study a broad class of phenomena, such as solar axions and WIMP scattering. The ability to conduct such sensitive searches crucially depends on the energy threshold, which has to be kept as low as possible. Here, we show how the trigger algorithm affects the sensitivity to low-energy phenomena and the interpretation of the energy spectrum. In particular, we focus on the impact that the trigger algorithm has on the identification of the coincidence events among different crystals and, consequently, on the reconstruction of the background.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2021

Impact of Recent Climate Extremes on Mosquito-borne Disease Transmission in Kenya

Climate change and variability influence temperature and rainfall, which impact vector abundance and the dynamics of vector-borne disease transmission. Climate change is projected to increase the frequency and intensity of extreme climate events. Mosquito-borne diseases, such as dengue fever, are primarily transmitted by Aedes aegypti mosquitoes. Freshwater availability and temperature affect dengue vector populations via a variety of biological processes and thus influence the ability of mosquitoes to effectively transmit disease. However, the effect of droughts, floods, heat waves, and cold waves is not well understood. Using vector, climate, and dengue disease data collected between 2013 and 2019 in Kenya, this retrospective cohort study aims to elucidate the impact of extreme rainfall and temperature on mosquito abundance and the risk of arboviral infections. To define extreme periods of rainfall and land surface temperature (LST), we calculated monthly anomalies as deviations from long-term means (1983–2019 for rainfall, 2000–2019 for LST) across four study locations in Kenya. We classified extreme climate events as the upper and lower 10% of these calculated LST or rainfall deviations. Monthly Ae. aegypti abundance was recorded in Kenya using four trapping methods. Blood samples were also collected from children with febrile illness presenting to four field sites and tested for dengue virus using an IgG enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR). We found that mosquito eggs and adults were significantly more abundant one month following an abnormally wet month. The relationship between mosquito abundance and dengue risk follows a non-linear association. Our findings suggest that early warnings and targeted interventions during periods of abnormal rainfall and temperature, especially flooding, can potentially contribute to reductions in risk of viral transmission

Aedes aegypti mosquitoes↗

NASA NTRS2019

Aircraft Engine Icing Event Avoidance and Mitigation Through Real-Time Simulation and Controls

A process for mitigating or proactively avoiding an aircraft engine icing event may include detecting ice crystals in the atmosphere using one or more sensors on board an aircraft in real time. The process may also include modulating one or more engine operating conditions to proactively change an ice accretion location, to avoid the occurrence of an icing event. The process may further include implementing one or more modulated engine operating conditions in engine controls software, hardware, or both.

Veres, Joseph P.↗

DOE OSTI2023

Construction of deep-learning based WWBs parameterization for ENSO prediction

Westerly wind bursts (WWBs) significantly impact the occurrence and development of the El Niño-Southern Oscillation (ENSO). Current dynamical models, however, face significant challenges in representing WWBs. In this study, deep learning techniques were used to develop a new parameterization scheme for WWBs and further compared against two widely used schemes. Further, the results show that the scheme developed in this study has greater capability than previous schemes in reproducing WWBs characteristics, particularly in terms of occurrence probability, location, and duration. This improvement was mainly reflected in El Niño years, especially in strong events when the deep-learning-based scheme much realistically captures the location and strength of WWBs. It is expected that the new parameterization scheme will further improve ENSO prediction in dynamical models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Contracting and Expanding Binary Black Holes in 3D Low-mass AGN Disks: The Importance of Separation

LIGO/Virgo has detected several binary black hole (BBH) merger events that may have originated in the accretion disks of active galactic nuclei (AGNs). These events require individual black hole masses that fall within the pair instability supernova mass gap, and therefore these black holes may have been grown from hierarchical mergers. AGN disks are a prime environment for hierarchical mergers and thus a potential location for the progenitors of BBH gravitational wave events. Understanding how a BBH embedded in an AGN disk interacts with the surrounding environment is thus crucial for determining whether this interaction can lead to its merger. However, there are few high-fidelity simulations of this process, and almost all are two-dimensional. We present the results from 3D, high-resolution, local shearing-box simulations of an embedded BBH interacting with an AGN disk. In these first simulations of their kind, we focus on determining the mass accretion rate and the orbital evolution rate at different BBH separations. We find that circular, equal-mass BBHs with separations greater than 10% of their Hill radius contract while accreting at a super-Eddington rate. At smaller separations, however, our 3D simulations find that BBHs expand their orbits. This result suggests that it may be difficult for an AGN disk to push a BBH to merger, but we discuss several mechanisms, including MHD turbulence and radiative and mechanical feedback, which could alleviate this difficulty.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2000

Implementation of Joint Multi-Segment Training

The highest level of training for ISS flight is Joint Multi-Segment Training (JMST) simulations. These simulations allow two or more partners to conduct multi-segment training for their respective Mission Control Centers (MCC), include actual crew members, and usually include training facilities in each of the participating International Partner (IP) locations. It is the dress rehearsal for those events that exercise the interface between different IP modules and/or the decision making process between the different MCCs involved. This presentation will describe the challenge of successfully implementing JMST. It will start with a brief overview of who is involved, where they are located, and when JMSTs are required. Finally, it will illustrate many of the complications involved in just running a JMST between MCC-M and MCC-H. The viewer will leave with a much better appreciation for the complexities involved in successfully conducting a JMST of this nature, as well as an idea of how the picture will change as the other partners and payloads become involved.

Reagan, Marc↗

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 NTRS2011

Vehicle Shield Optimization and Risk Assessment for Future Human Space Missions

As the focus of future human space missions shifts to destinations beyond low Earth orbit such as Near Earth Objects (NEO), the moon, or Mars, risks associated with extended stay in hostile radiation environment need to be well understood and assessed. Since future spacecrafts designs and shapes are evolving continuous assessments of shielding and radiation risks are needed. In this study, we use a predictive software capability that calculates risks to humans inside a spacecraft prototype that builds on previous designs. The software uses CAD software Pro/Engineer and Fishbowl tool kit to quantify radiation shielding provided by the spacecraft geometry by calculating the areal density seen at a certain point, dose point, inside the spacecraft. Shielding results are used by NASA-developed software, BRYNTRN, to quantify organ doses received in a human body located in the vehicle in case of solar particle event (SPE) during such prolonged space missions. Organ doses are used to quantify risks on astronauts health and life using NASA Space Cancer Model. The software can also locate shielding weak points-hotspots-on the spacecraft s outer surface. This capability is used to reinforce weak areas in the design. Results of shielding optimization and risk calculation on an exploration vehicle design for missions of 6 months and 30 months are provided in this study. Vehicle capsule is made of aluminum shell that includes main cabin and airlock. The capsule contains 5 sets of racks that surround working and living areas. Water shelter is provided in the main cabin of the vehicle to enhance shielding in case of SPE.

Nounu, Hatem N.↗

NASA NTRS2015

Ice Particle Analysis of the Honeywell AL502 Engine Booster

A flow and ice particle trajectory analysis was performed for the booster of the Honeywell ALF502 engine. The analysis focused on two closely related conditions one of which produced an icing event and another which did not during testing of the ALF502 engine in the Propulsion Systems Lab (PSL) at NASA Glenn Research Center. The flow analysis was generated using the NASA Glenn GlennHT flow solver and the particle analysis was generated using the NASA Glenn LEWICE3D v3.63 ice accretion software. The inflow conditions for the two conditions were similar with the main differences being that the condition that produced the icing event was 6.8 K colder than the non-icing event case and the inflow ice water content (IWC) for the non-icing event case was 50% less than for the icing event case. The particle analysis, which considered sublimation, evaporation and phase change, was generated for a 5 micron ice particle with a sticky impact model and for a 24 micron median volume diameter (MVD), 7 bin ice particle distribution with a supercooled large droplet (SLD) splash model used to simulate ice particle breakup. The particle analysis did not consider the effect of the runback and re-impingement of water resulting from the heated spinner and anti-icing system. The results from the analysis showed that the amount of impingement for the components were similar for the same particle size and impact model for the icing and non-icing event conditions. This was attributed to the similar aerodynamic conditions in the booster for the two cases. The particle temperature and melt fraction were higher at the same location and particle size for the non-icing event than for the icing event case due to the higher incoming inflow temperature for the non-event case. The 5 micron ice particle case produced higher impact temperatures and higher melt fractions on the components downstream of the fan than the 24 micron MVD case because the average particle size generated by the particle breakup was larger than 5 microns which yielded less warming and melting. The analysis also showed that the melt fraction and wet bulb temperature icing criterion developed during tests in the Research Altitude Test Facility (RATFac) at the National Research Council (NRC) of Canada were useful in predicting icing events in the ALF502 engine. The development of an ice particle impact model which includes the effects of particle breakup, phase change, and surface state is necessary to further improve the prediction of ice particle transport with phase change through turbomachinery.

Ice Accretion Codes↗

DOE OSTI2024

Transmission Data-Driven User-Defined Model for Inverter-based and Conventional Power Plants

Recent events in Odessa [1], [2] have shed light on the complexities of integrating large Inverter-Based Resource (IBR) plants with the transmission system, prompting NERC to stress continuous performance monitoring by transmission operators. Challenges such as plant control updates, IBR model revisions, Phase-locked loop loss of synchronism, and protection events have been identified, underscoring the need for enhanced monitoring protocols by regulatory bodies. The recent FERC 901 order underscores the importance of accurate data exchange regarding IBRs for reliability studies. However, limited access to IBR plant-related data hampers effective decision-making for transmission operators (TOP). This paper proposes a method for constructing data-driven User-Defined dynamic Models (UDM) for power plants for validating multiple-event data using field measurements from interconnection bus locations. The problem is formulated as a power plant model identification problem and a multi-task learning approach under partial input observability assumptions is proposed in this work. This approach aims to predict aggregated responses of conventional and IBR power plants during various dynamic physical events which is useful for planning studies under diverse disturbance conditions. Ultimately, this methodology emphasizes the importance of plant visibility to operators in addressing power system challenges, facilitating improved planning and operational studies.

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗