Search NASA⌕ Search

SEARCH · Search NASA

Results for “conditional probability distribution”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Measuring Gap Fraction, Element Clumping Index and LAI in Sierra Forest Stands Using a Full-Waveform Ground-Based Lidar

The Echidna Validation Instrument (EVI), a ground-based, near-infrared (1064 nm) scanning lidar, provides gap fraction measurements, element clumping index measurements, effective leaf area index (LAIe) and leaf area index (LAI) measurements that are statistically similar to those from hemispherical photos. In this research, a new method integrating the range dimension is presented for retrieving element clumping index using a unique series of images of gap probability (Pgap) with range from EVI. From these images, we identified connected gap components and found the approximate physical, rather than angular, size of connected gap component. We conducted trials at 30 plots within six conifer stands of varying height and stocking densities in the Sierra National Forest, CA, in August 2008. The element clumping index measurements retrieved from EVI Pgap image series for the hinge angle region are highly consistent (R2=0.866) with those of hemispherical photos. Furthermore, the information contained in connected gap component size profiles does account for the difference between our method and gap-size distribution theory based method, suggesting a new perspective to measure element clumping index with EVI Pgap image series and also a potential advantage of three dimensional Lidar data for element clumping index retrieval. Therefore further exploration is required for better characterization of clumped condition from EVI Pgap image series.

Zhao, Feng↗
DOE OSTI2025

Voltage Probability Density Function Shaping Control Strategy Considering Grid Operational Uncertainties

It is well-known that power systems operation always affected by various uncertainties which make the bus voltage a random process that can be characterized by its probability density function (PDF) at any time instant. In this context, this paper presents a novel PDF-based voltage control framework for power systems. By modeling voltage as a stochastic process, we formulate a stochastic differential equationthat captures grid uncertainties. The associated Fokker-Planck-Kolmogorov equation is derived to describe the evolution of the voltage PDF, which enables the formulation of a PDF-shaping control strategy. To simplify the PDF control formulation, a B-spline neural network is introduced for real-time estimation and regulation of the voltage distribution. The proposed PDF control law updates voltage references for energy storage systems and synchronous generators using real-time PDF measurements and feedback signals. The proposed method is validated on a modified Kundur’s two-area system. Simulation results demonstrate that the controller can significantly improve the voltage stability under stochastic conditions, highlighting its effectiveness in modern inverter-rich grids.

Gui, Yonghao [ORNL] (ORCID:0000000250435534)↗
NASA NTRS1998

Biogeochemical cycling of manganese in Oneida Lake, New York: whole lake studies of manganese

Oneida Lake, New York is a eutrophic freshwater lake known for its abundant manganese nodules and a dynamic manganese cycle. Temporal and spatial distribution of soluble and particulate manganese in the water column of the lake were analyzed over a 3-year period and correlated with other variables such as oxygen, pH, and temperature. Only data from 1988 are shown. Manganese is removed from the water column in the spring via conversion to particulate form and deposited in the bottom sediments. This removal is due to biological factors, as the lake Eh/pH conditions alone can not account for the oxidation of the soluble manganese Mn(II). During the summer months the manganese from microbial reduction moves from the sediments to the water column. In periods of stratification the soluble Mn(II) builds up to concentrations of 20 micromoles or more in the bottom waters. When mixing occurs, the soluble Mn(II) is rapidly removed via oxidation. This cycle occurs more than once during the summer, with each manganese atom probably being used several times for the oxidation of organic carbon. At the end of the fall, whole lake concentrations of manganese stabilize, and remain at about 1 micromole until the following summer, when the cycle begins again. Inputs and outflows from the lake indicate that the active Mn cycle is primarily internal, with a small accumulation each year into ferromanganese nodules located in the oxic zones of the lake.

NASA Discipline Exobiology↗
NASA NTRS2008

Image-Based Empirical Modeling of the Plasmasphere

A new suite of empirical models of plasmaspheric plasma based on remote, global images from the IMAGE EUV instrument is proposed for development. The purpose of these empirical models is to establish the statistical properties of the plasmasphere as a function of conditions. This suite of models will mark the first time the plasmaspheric plume is included in an empirical model. Development of these empirical plasmaspheric models will support synoptic studies (such as for wave propagation and growth, energetic particle loss through collisions and dust transport as influenced by charging) and serves as a benchmark against which physical models can be tested. The ability to know that a specific global density distribution occurs in response to specific magnetospheric and solar wind factors is a huge advantage over all previous in-situ based empirical models. The consequence of creating these new plasmaspheric models will be to provide much higher fidelity and much richer quantitative descriptions of the statistical properties of plasmaspheric plasma in the inner magnetosphere, whether that plasma is in the main body of the plasmasphere, nearby during recovery or in the plasmaspheric plume. Model products to be presented include statistical probabilities for being in the plasmasphere, near thermal He+ density boundaries and the complexity of its spatial structure.

Adrian, Mark L.↗
NASA NTRS2013

Probabilistic Modeling of the Renal Stone Formation Module

The Integrated Medical Model (IMM) is a probabilistic tool, used in mission planning decision making and medical systems risk assessments. The IMM project maintains a database of over 80 medical conditions that could occur during a spaceflight, documenting an incidence rate and end case scenarios for each. In some cases, where observational data are insufficient to adequately define the inflight medical risk, the IMM utilizes external probabilistic modules to model and estimate the event likelihoods. One such medical event of interest is an unpassed renal stone. Due to a high salt diet and high concentrations of calcium in the blood (due to bone depletion caused by unloading in the microgravity environment) astronauts are at a considerable elevated risk for developing renal calculi (nephrolithiasis) while in space. Lack of observed incidences of nephrolithiasis has led HRP to initiate the development of the Renal Stone Formation Module (RSFM) to create a probabilistic simulator capable of estimating the likelihood of symptomatic renal stone presentation in astronauts on exploration missions. The model consists of two major parts. The first is the probabilistic component, which utilizes probability distributions to assess the range of urine electrolyte parameters and a multivariate regression to transform estimated crystal density and size distributions to the likelihood of the presentation of nephrolithiasis symptoms. The second is a deterministic physical and chemical model of renal stone growth in the kidney developed by Kassemi et al. The probabilistic component of the renal stone model couples the input probability distributions describing the urine chemistry, astronaut physiology, and system parameters with the physical and chemical outputs and inputs to the deterministic stone growth model. These two parts of the model are necessary to capture the uncertainty in the likelihood estimate. The model will be driven by Monte Carlo simulations, continuously randomly sampling the probability distributions of the electrolyte concentrations and system parameters that are inputs into the deterministic model. The total urine chemistry concentrations are used to determine the urine chemistry activity using the Joint Expert Speciation System (JESS), a biochemistry model. Information used from JESS is then fed into the deterministic growth model. Outputs from JESS and the deterministic model are passed back to the probabilistic model where a multivariate regression is used to assess the likelihood of a stone forming and the likelihood of a stone requiring clinical intervention. The parameters used to determine to quantify these risks include: relative supersaturation (RS) of calcium oxalate, citrate/calcium ratio, crystal number density, total urine volume, pH, magnesium excretion, maximum stone width, and ureteral location. Methods and Validation: The RSFM is designed to perform a Monte Carlo simulation to generate probability distributions of clinically significant renal stones, as well as provide an associated uncertainty in the estimate. Initially, early versions will be used to test integration of the components and assess component validation and verification (V&V), with later versions used to address questions regarding design reference mission scenarios. Once integrated with the deterministic component, the credibility assessment of the integrated model will follow NASA STD 7009 requirements.

kidney stones↗
DOE OSTI2025

Thermal stress in degraded forests in the Brazilian Amazon Arc of Deforestation

Understanding thermal stress in tropical forests has taken on new urgency in light of accelerating climate change and expansion of deforestation and forest degradation. Degraded tropical forests in particular may be approaching critical temperature thresholds even more rapidly than intact forests, with implications for tree survival and ecosystem recovery. We investigate thermal stress in degraded tropical forests within the Brazilian Amazon Arc of Deforestation. Using land surface temperature data from the ECOsystem Spaceborne Thermal Radiometer Experiment on the international Space Station (ECOSTRESS), we compared canopy temperatures of intact, selectively logged, and burned forests in Feliz Natal, Mato Grosso, Brazil. Upper canopy temperatures in previously burned forests were 4.1% higher (mean = 36.5 °C) and 50.9% more variable compared to intact and logged forests, which showed remarkably similar temperature distributions (means of 34.9 °C and 35.1 °C, respectively). Modeled leaf temperature distributions based on canopy temperature measurements from one of the warmest days in a 2 year dry season record indicated that 87% of leaves in the warmest burned forest patches exceeded the temperature threshold where respiration surpasses photosynthesis, compared to approximately 72%–74% in intact and logged forests, respectively, in the same time period. After controlling for environmental factors, burned forests were predicted to be 2.6 °C warmer on average [1.39 °C–3.96 °C, 95% credible interval] than intact forests across a 5–40 m canopy height range. Burned forests showed modest thermal recovery over time, with temperatures decreasing by approximately 1.2 °C over a 30 year recovery period. In contrast, logged forests showed minimal thermal differences from intact forests (−0.353 on average, [−1.125–0.274, 95% credible interval]) and negligible change (0.15 °C) over the same timeframe. While the absolute probabilities of exceeding damaging thermal thresholds remain low across all forest types under current climate conditions, the probability of leaves exceeding temperatures that cause permanent leaf damage was ten times higher in burned forests, with implications for the future of burned forest regeneration in water-limited regions of the Amazon basin. In particular, the combination of higher mean temperatures, greater temperature variability, and more frequent exposure to damaging thermal thresholds implies that burned tropical forests will experience substantially higher mortality rates and slower biomass recovery compared to intact and selectively logged forests, especially in water-limited regions where trees cannot rely on evaporative cooling to moderate canopy temperatures.

Brazilian Amazon Arc of Deforestation↗
DOE OSTI2025

​​Hydrogen Transportation and Distributed Energy Systems Seismic Risk Assessment for Cascadia Subduction Zone Airport Facilities​

Portland International Airport in Oregon is exploring operating a fleet of 28 fuel cell electric buses to support airport operations and possibly provide backup power during outages. In this report, we evaluate the risk of hydrogen deployment at an airport considering the potential for seismic activity. We present a seismic risk assessment for pressurized piping for a generic hydrogen refueling station at the airport. Only pressurized piping was considered in the risk analysis because of its vulnerability and availability of fragility information unlike the other components. Seismic capacity of other components should be included as more information becomes available. To characterize the seismic hazard, we considered the initiating event frequency of a Cascadia Subduction Zone earthquake event based on data from the literature. Seismic stressors that translate to the site from an earthquake event are expressed as peak ground acceleration. Site disturbance is characterized as a function of soil conditions, represented by different shear wave velocity values, which affect how seismic waves propagate and impact structures. Pipe fragility curves as a function of seismic capacity were used to correlate ground acceleration and failure probability. These fragility characteristics were combined with site-specific soil conditions to calculate the probability of pipe rupture during a seismic event. It should be noted that multiple simultaneous failures due to an earthquake event as a common cause are not considered. Some epistemic uncertainties such as self-exciting shocks, aftershocks, and the time variant nature of the ground motion are not considered either.

08 HYDROGEN↗
DOE OSTI2024

Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data

This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory. Two distinct deep learning models, conditional Multi-Layer Perceptron (MLP) and unconditional MLP, were developed to forecast power outage probabilities, leveraging a rich array of input features gathered from publicly available sources including weather data, weather station locations, power infrastructure maps, socio-economic and demographic statistics, and power outage records. Given a one-hour-ahead weather forecast, the models predict the power outage probability for each census tract, taking into account both the weather prediction and the location's characteristics. The deep learning models employed different loss functions to optimize prediction performance. Our experimental results underscore the significance of socio-economic factors in enhancing the accuracy of power outage predictions at the census tract level.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1989

Application of statistical distribution theory to launch-on-time for space construction logistic support

The ability to launch-on-time and to send payloads into space has progressed dramatically since the days of the earliest missile and space programs. Causes for delay during launch, i.e., unplanned 'holds', are attributable to several sources: weather, range activities, vehicle conditions, human performance, etc. Recent developments in space program, particularly the need for highly reliable logistic support of space construction and the subsequent planned operation of space stations, large unmanned space structures, lunar and Mars bases, and the necessity of providing 'guaranteed' commercial launches have placed increased emphasis on understanding and mastering every aspect of launch vehicle operations. The Center of Space Construction has acquired historical launch vehicle data and is applying these data to the analysis of space launch vehicle logistic support of space construction. This analysis will include development of a better understanding of launch-on-time capability and simulation of required support systems for vehicle assembly and launch which are necessary to support national space program construction schedules. In this paper, the author presents actual launch data on unscheduled 'hold' distributions of various launch vehicles. The data have been supplied by industrial associate companies of the Center for Space Construction. The paper seeks to determine suitable probability models which describe these historical data and that can be used for several purposes such as: inputs to broader simulations of launch vehicle logistic space construction support processes and the determination of which launch operations sources cause the majority of the unscheduled 'holds', and hence to suggest changes which might improve launch-on-time. In particular, the paper investigates the ability of a compound distribution probability model to fit actual data, versus alternative models, and recommends the most productive avenues for future statistical work.

Morgenthaler, George W.↗
NASA NTRSDate not supplied

NASA's Moon to Mars Autonomous Habitat Status

NASA is developing a strategy for sending humans to the Mars vicinity, known broadly as the Moon to Mars (M2M) Campaign. A critical part of this campaign is the development of in-space and surface habitation systems capable of substantially extending human presence beyond Low Earth Orbit (LEO). Mars missions feature an in-space transit habitat capable of supporting crews of four on ~850-1200-day missions, including transit to and from Mars and time in Mars orbit. Surface and transit habitats are complex elements which must keep crewmembers healthy and productive in deep-space environments with limited resources, long rescue times in contingency situations, and communication delays; all within constrained mass, volume, and power budgets. These habitats provide crew both living and workspace as well as most of the resources needed to support crew life. For deep space habitats, automation needs to be employed due to latency and for significant amounts of time when the habitats are uncrewed. Automation of systems is possible in space applications, but there are limitations. Outside of the Earth’s (or any) magnetosphere, radiation environments are harsh to both the physical hardware and the software components. Radiation (charged particles and ionizing electromagnetic waves) degrades and damages the hardware and causes single event upsets (SEUs) in software. If the hardware is damaged, data can be lost, or control actions not made. For software, SEUs cause algorithms to result in different solutions, or incorrect commands to be sent out. This means that algorithms and hardware used for deep space systems are different than what is used on Earth. Radiation-tolerant hardware is generations behind the current state-of-the-art hardware. Recent NASA missions, such as James Webb Space Telescope, continue to rely on older technologies such as the RAD750 processor, and the most advanced processors are still single core and less than 1.5 GHz. There have been attempts to use higher performance processors, but these often take multiple mitigation steps to handle the radiation environments, which limits the processing power and/or throughput. Current techniques for radiation mitigation have been redundancies, voting, physical separation of hardware, encasing materials, under-clocking hardware, and more. Some radiation mitigation techniques do provide benefits such as having a redundant system to improve the probability that a system will be available when needed. Autonomous software systems will have fewer interactions with humans on deep space missions and therefore need to be able to handle more off-nominal conditions. Microgravity also complicates the autonomous aspects of the mission because autonomous systems are usually built from known deterministic states, but microgravity causes physical objects to shift and move changing the location an autonomous system placed the object. Not only does the software need to be reliable and deterministic, losing resources due to a software error is not only costly but detrimental to reputation. The combination of having lower performance hardware and having to be able to verify and deterministically run software and an ever-changing environment makes deep space autonomous systems more complicated. Multiple gaps have been identified including verification of autonomous software algorithms (including artificial intelligence and machine learning), higher performance processors (graphics and general purpose), high speed networks (onboard and transmissions), memory, power distribution, data security, and variations from these. These gaps need to be closed for more advanced systems to be deployed and reduce the size, weight, and power impacts on the habitats.

Scott B. Tashakkor↗
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 NTRS1991

Isotopic compositions of carbonates and organic carbon from upper Proterozoic successions in Namibia: stratigraphic variation and the effects of diagenesis and metamorphism

The carbon isotope geochemistry of carbonates and organic carbon in the late Proterozoic Damara Supergroup of Namibia, including the Nama, Witvlei, and Gariep groups on the Kalahari Craton and the Mulden and Otavi groups on the Congo Craton, has been investigated as an extension of previous studies of secular variations in the isotopic composition of late Proterozoic seawater. Subsamples of microspar and dolomicrospar were determined, through petrographic and cathodoluminescence examination, to represent the "least-altered" portions of the rock. Carbon-isotopic abundances in these phases are nearly equal to those in total carbonate, suggesting that 13C abundances of late Proterozoic fine-grained carbonates have not been significantly altered by meteoric diagenesis, although 18O abundances often differ significantly. Reduced and variable carbon-isotopic differences between carbonates and organic carbon in these sediments indicate that isotopic compositions of organic carbon have been altered significantly by thermal and deformational processes, likely associated with the Pan-African Orogeny. Distinctive stratigraphic patterns of secular variation, similar to those noted in other, widely separated late Proterozoic basins, are found in carbon-isotopic compositions of carbonates from the Nama and Otavi groups. For example, in Nama Group carbonates delta 13C values rise dramatically from -4 to +5% within a short stratigraphic interval. This excursion suggests correlation with similar excursions noted in Ediacaran-aged successions of Siberia, India, and China. Enrichment of 13C (delta 13C> +5%) in Otavi Group carbonates reflects those in Upper Riphean successions of the Akademikerbreen Group, Svalbard, its correlatives in East Greenland, and the Shaler Group, northwest Canada. The widespread distribution of successions with comparable isotopic signatures supports hypotheses that variations in delta 13C reflect global changes in the isotopic composition of late Proterozoic seawater. Within the Damara basin, carbon-isotopic compositions of carbonates provide a potentially useful tool for the correlation of units between the Kalahari and Congo cratons. Carbonates depleted in 13C were deposited during and immediately following three separate glacial episodes in Namibia. The correspondence between ice ages and negative delta 13C excursions may reflect the effects of lowered sea levels; enhanced circulation of deep, cold, O2-rich seawater; and/or the upwelling of 13C-depleted deep water. Iron-formation is additionally associated with one of the glacial horizons, the Chuos tillite. Carbon-13 enriched isotopic abundances in immediately pre-glacial carbonates suggest that oceanographic conditions favored high rates of organic burial. It is likely that marine waters were stratified, with deep waters anoxic. A prolonged period of ocean stratification would permit the build-up of ferrous iron, probably from hydrothermal sources. At the onset of glaciation, upwelling would have brought 13C-depleted and iron-rich deep water onto shallow shelves where contact with cold, oxygenated surface waters led to the precipitation of ferric iron.

NASA Program Exobiology↗
NASA NTRS1994

The global resurfacing of Venus

The impact cratering record on Venus is unique among the terrestrial planets. Fully 84% of the craters are in pristine condition, and only 12% are fractured. Remarkably, only 2.5% of the craters and crater-related features are embayed by lava, although intense volcanism and tectonism have affected the entire planet. Furthermore, the spatial and hypsometric distribution of the craters is consistent with a completely random one, including stochastic variations. Monte Carlo simulations of equilibrium resurfacing models result in a minimum of 17 times more embayed craters than observed, or unobserved nonrandom crater distributions for resurfacing areas between 0.03% and 100% of the planet's surface. These models also are not consistent with the number and nonrandom distribution of volcanoes, and the nonrandom distribution of embayed and heavily fractured craters. The constraints imposed by the cratering record strongly indicate that Venus experienced a global resurfacing event about 300 m.y. ago followed by a dramatic reduction of volcanism and tectonism. This global resurfacing event ended abruptly (less than 10 m.y.). The present crater population has accumulated since then and remains largely intact. Thermal history models suggest that similar global resurfacing events probably occured episodically in the past. We show that neither the present level and style of geologic activity nor anything less than global resurfacing could have produced the observed cratering record. The effects of recent geologic activity are much less than those of the earlier global resurfacing event, when the record of all the early heavy bombardment and much of the later light bombardment was erased from the surface by massive volcanism and tectonic activity. Episodic regional resurfacing events that had global effects also occurred on Earth (e.g., the mid-Cretaceous superplume) and probably on Mars. On Mars they may have triggered the catastrophic releases of water that formed the outflow channels.

Strom, Robert G.↗
NASA NTRS1988

Photochemical modeling of the Antarctic stratosphere: Observational constraints from the airborne Antarctic ozone experiment and implications for ozone behavior

The rapid decrease in O3 column densities observed during Antarctic spring has been attributed to several chemical mechanisms involving nitrogen, bromine, or chlorine species, to dynamical mechanisms, or to a combination of the above. Chlorine-related theories, in particular, predict greatly elevated concentrations of ClO and OClO and suppressed abundances of NO2 below 22 km. The heterogeneous reactions and phase transitions proposed by these theories could also impact the concentrations of HCl, ClNO3 and HNO3 in this region. Observations of the above species have been carried out from the ground by the National Ozone Expedition (NOZE-I, 1986, and NOZE-II, 1987), and from aircrafts by the Airborne Antarctic Ozone Experiment (AAOE) during the austral spring of 1987. Observations of aerosol concentrations, size distribution and backscattering ratio from AAOE, and of aerosol extinction coefficients from the SAM-II satellite can also be used to deduce the altitude and temporal behavior of surfaces which catalyze heterogeneous mechanisms. All these observations provide important constraints on the photochemical processes suggested for the spring Antarctic stratosphere. Results are presented for the concentrations and time development of key trace gases in the Antarctic stratosphere, utilizing the AER photochemical model. This model includes complete gas-phase photochemistry, as well as heterogeneous reactions. Heterogeneous chemistry is parameterized in terms of surface concentrations of aerosols, collision frequencies between gas molecules and aerosol surfaces, concentrations of HCl/H2O in the frozen particles, and probability of reaction per collision (gamma). Values of gamma are taken from the latest laboratory measurements. The heterogeneous chemistry and phase transitions are assumed to occur between 12 and 22 km. The behavior of trace species at higher altitudes is calculated by the AER 2-D model without heterogeneous chemistry. Calculations are performed for solar illumination conditions typical of 60, 70, and 80 S, from July 15 to October 31.

Rodriguez, Jose M.↗
DOE OSTI2026

The impact of aerosol mixing state on immersion freezing: insights from classical nucleation theory and particle-resolved simulations

Immersion freezing, initiated by ice-nucleating particles (INPs) in supercooled aqueous droplets, plays an important role in the formation of ice crystals within clouds. The efficiency of immersion freezing depends strongly on INP composition and, crucially, on the mixing state – how chemical species are distributed across the particle population. Here, we quantify the impact of aerosol mixing state on immersion freezing using a combined theoretical and particle-resolved modeling approach. We derive analytical expressions for the frozen fraction of internally and externally mixed INP populations based on classical nucleation theory, showing that the frozen fraction is sensitive to whether ice-active species are present in all particles or only in a subset of the population. We introduce a multi-species immersion freezing scheme into the particle-resolved model PartMC, using the water activity-based immersion freezing model (ABIFM) to compute freezing probabilities for mixed-composition particles. To improve computational efficiency, we implement a Binned Tau-Leaping algorithm and demonstrate an order-of-magnitude speedup with minimal accuracy loss. Simulations reproduce the analytical trends in limiting cases and extend the analysis to more general aerosol populations, where mixing state continues to exert a substantial control on frozen fraction. Sensitivity analyses across particle size, species type, and cooling condition reveal that the mixing state effect is most pronounced when small amounts of highly efficient INPs are mixed with less efficient materials. These findings underscore the need to represent aerosol mixing state explicitly in models of heterogeneous ice nucleation to reduce uncertainty in cloud-phase partitioning.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

A Self-Contained Mapping Closure Approximation for Scalar Mixing

Scalar turbulence exhibits interplays of coherent structures and random fluctuations over a broad range of spatial and temporal scales. This feature necessitates a probabilistic description of the scalar dynamics, which can be achieved comprehensively by using probability density functions (PDFs). Therefore, the challenge is to obtain the scalar PDFs (Lundgren 1967; Dopazo 1979). Generally, the evolution of a scalar is governed by three dynamical processes: advection, diffusion and reaction. In a PDF approach (Pope 1985), the advection and reaction can be treated exactly but the effect of molecular diffusion has to be modeled. It has been shown (Pope 1985) that the effect of molecular diffusion can be expressed as conditional dissipation rates or conditional diffusions. The currently used models for the conditional dissipation rates and conditional diffusions (Pope 1991) have resisted deduction from the fundamental equations and are unable to yield satisfactory results for the basic test cases of decaying scalars in isotropic turbulence, although they have achieved some success in a variety of individual cases. The recently developed mapping closure approach (Pope 1991; Chen, Chen & Kraichnan 1989; Kraichnan 1990; Klimenko & Pope 2003) provides a deductive method for conditional dissipation rates and conditional di usions, and the models obtained can successfully describe the shape relaxation of the scalar PDF from an initial double delta distribution to a Gaussian one. However, the mapping closure approach is not able to provide the rate at which the scalar evolves. The evolution rate has to be modeled. Therefore, the mapping closure approach is not closed. In this letter, we will address this problem.

He, Guo-Wei↗
DOE OSTI2025

Huge ensembles – Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators

Abstract. In Part 1, we created an ensemble based on spherical Fourier neural operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multiple checkpoints trained independently from scratch. Based on diagnostics that assess the ensemble's physical fidelity, our ensemble has comparable performance to operational weather forecasting systems. However, it requires orders-of-magnitude fewer computational resources. Here in Part 2, we generate a huge ensemble (HENS), with 7424 members initialized each day of summer 2023. We enumerate the technical requirements for running huge ensembles at this scale. HENS precisely samples the tails of the forecast distribution and presents a detailed sampling of internal variability. HENS has two primary applications: (1) as a large dataset with which to study the statistics and drivers of extreme weather and (2) as a weather forecasting system. For extreme climate statistics, HENS samples events 4σ away from the ensemble mean. At each grid cell, HENS increases the skill of the most accurate ensemble member and enhances coverage of possible future trajectories. As a weather forecasting model, HENS issues extreme weather forecasts with better uncertainty quantification. It also reduces the probability of outlier events, in which the verification value lies outside the ensemble forecast distribution.

Mahesh, Ankur↗
NASA NTRS2006

Analysis of ISS Plasma Interaction

To date, the International Space Station (ISS) has been one of the largest objects flown in lower earth orbit (LEO). The ISS utilizes high voltage solar arrays (160V) that are negatively grounded leading to pressurized elements that can float negatively with respect to the plasma. Because laboratory measurements indicate a dielectric breakdown potential difference of 80V, arcing could occur on the ISS structure. To overcome the possibility of arcing and clamp the potential of the structure, two Plasma Contactor Units (PCUs) were designed, built, and flown. Also a limited amount of measurements of the floating potential for the present ISS configuration were made by a Floating Potential Probe (FPP), indicating a minimum potential of 24 Volts at the measurement location. A predictive tool, the ISS Plasma Interaction Model (PIM) has been developed accounting for the solar array electron collection, solar array mast wire and effective conductive area on the structure. The model has been used for predictions of the present ISS configuration. The conductive area has been inferred based on available floating potential measurements. Analysis of FPP and PCU data indicated distribution of the conductive area along the Russian segment of the ISS structure. A significant input to PIM is the plasma environment. The International Reference Ionosphere (IRI 2001) was initially used to obtain plasma temperature and density values. However, IRI provides mean parameters, leading to difficulties in interpretation of on-orbit data, especially at eclipse exit where maximum charging can occur. This limits our predicative capability. Satellite and Incoherent Scatter Radar (ISR) data of plasma parameters have also been collected. Approximately 130,000 electron temperature (Te) and density (Ne) pairs for typical ISS eclipse exit conditions have been extracted from the reduced Langmuir probe data flown aboard the NASA DE-2 satellite. Additionally, another 18,000 Te and Ne pairs of ISR data from several radar locations around the globe were used to assure consistency of the satellite data. PIM predictions for ISS charging made with this data correlated very well with FPP data, indicating that the general physics of spacecraft charging with high voltage solar arrays have been captured. The predictions also provided the probabilities of occurrences for ISS charging. These probabilities give a numerical measure of the number of times when the ISS will approach or exceed the vehicle plasma hazard conditions for each configuration. In this paper we shall present the interaction mechanisms between the ISS and the surrounding plasma and give an overview of the PIM components. PIM predictions are compared with available data followed by a discussion of the variability of plasma parameters and the conductive area on the ISS. The ISS PIM will be further tested and verified as data from the Floating Potential Measurement Unit become available, and construction of the ISS continues.

Reddell, Brandon↗