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At least 1,693 records · Page 94

A Corrected Formulation of the Multilayer Model (MLM) for Inferring Gaseous Dry Deposition to Vegetated Surfaces

The Multilayer Model (MLM) has been used for many years to infer dry deposition fluxes from measured trace species concentrations and standard meteorological measurements for national networks in the U.S., including the U.S. Environmental Protection Agency's Clean Air Status and Trends Network (CASTNet). MLM utilizes a resistance analogy to calculate deposition velocities appropriate for whole vegetative canopies, while employing a multilayer integration to account for vertically varying meteorology, canopy morphology and radiative transfer within the canopy. However, the MLM formulation, as it was originally presented and as it has been subsequently employed, contains a non-physical representation related to the leaf-level quasi-laminar boundary layer resistance that affects the calculation of the total canopy resistance. In this note, the non-physical representation of the canopy resistance as originally formulated in MLM is discussed and a revised, physically consistent, formulation is suggested as a replacement. The revised canopy resistance formulation reduces estimates of HNO3 deposition velocities by as much as 38% during mid-day as compared to values generated by the original formulation. Inferred deposition velocities for SO2 and O3 are not significantly altered by the change in formulation (less than 3%). Inferred deposition loadings of oxidized and total nitrogen from CASTNet data may be reduced by 10-20% and 5-10%, respectively, for the Eastern U. S. when employing the revised formulation of MLM as compared to the original formulation.

vegetated surfaces↗

AeroCom Phase III Multi-Model Evaluation of the Aerosol Life Cycle and Optical Properties Using Ground and Space-Based Remote Sensing as Well as Surface In Situ Observations

Within the framework of the AeroCom (Aerosol Comparisons between Observations and Models) initiative, the state-of-the-art modelling of aerosol optical properties is assessed from 14 global models participating in the phase III control experiment (AP3). The models are similar to CMIP6/AerChemMIP Earth System Models (ESMs) and provide a robust multi-model ensemble. Inter-model spread of aerosol species lifetimes and emissions appears to be similar to that of mass extinction coefficients (MECs), suggesting that aerosol optical depth (AOD) uncertainties are associated with a broad spectrum of parameterised aerosol processes. Total AOD is approximately the same as in AeroCom phase I (AP1) simulations. However, we find a 50 % decrease in the optical depth (OD) of black carbon (BC), attributable to a combination of decreased emissions and lifetimes. Relative contributions from sea salt (SS) and dust (DU) have shifted from being approximately equal in AP1 to SS contributing about 2∕3 of the natural AOD in AP3. This shift is linked with a decrease in DU mass burden, a lower DU MEC, and a slight decrease in DU lifetime, suggesting coarser DU particle sizes in AP3 compared to AP1. Relative to observations, the AP3 ensemble median and most of the participating models underestimate all aerosol optical properties investigated, that is, total AOD as well as fine and coarse AOD (AODf, AODc), Ångström exponent (AE), dry surface scattering (SCdry), and absorption (ACdry) coefficients. Compared to AERONET, the models underestimate total AOD by ca. 21 % ± 20 % (as inferred from the ensemble median and interquartile range). Against satellite data, the ensemble AOD biases range from −37 % (MODIS-Terra) to −16 % (MERGED-FMI, a multi-satellite AOD product), which we explain by differences between individual satellites and AERONET measurements themselves. Correlation coefficients (R) between model and observation AOD records are generally high (R>0.75), suggesting that the models are capable of capturing spatio-temporal variations in AOD. We find a much larger underestimate in coarse AODc (∼ −45 % ± 25 %) than in fine AODf (∼ −15 % ± 25 %) with slightly increased inter-model spread compared to total AOD. These results indicate problems in the modelling of DU and SS. The AODc bias is likely due to missing DU over continental land masses (particularly over the United States, SE Asia, and S. America), while marine AERONET sites and the AATSR SU satellite data suggest more moderate oceanic biases in AODc. Column AEs are underestimated by about 10 % ± 16 %. For situations in which measurements show AE > 2, models underestimate AERONET AE by ca. 35 %. In contrast, all models (but one) exhibit large overestimates in AE when coarse aerosol dominates (bias ca. +140 % if observed AE < 0.5). Simulated AE does not span the observed AE variability. These results indicate that models overestimate particle size (or underestimate the fine-mode fraction) for fine-dominated aerosol and underestimate size (or overestimate the fine-mode fraction) for coarse-dominated aerosol. This must have implications for lifetime, water uptake, scattering enhancement, and the aerosol radiative effect, which we can not quantify at this moment. Comparison against Global Atmosphere Watch (GAW) in situ data results in mean bias and inter-model variations of −35 % ± 25 % and −20 % ± 18 % for SCdry and ACdry, respectively. The larger underestimate of SCdry than ACdry suggests the models will simulate an aerosol single scattering albedo that is too low. The larger underestimate of SCdry than ambient air AOD is consistent with recent findings that models overestimate scattering enhancement due to hygroscopic growth. The broadly consistent negative bias in AOD and surface scattering suggests an underestimate of aerosol radiative effects in current global aerosol models. Considerable inter-model diversity in the simulated optical properties is often found in regions that are, unfortunately, not or only sparsely covered by ground-based observations. This includes, for instance, the Sahara, Amazonia, central Australia, and the South Pacific. This highlights the need for a better site coverage in the observations, which would enable us to better assess the models, but also the performance of satellite products in these regions. Using fine-mode AOD as a proxy for present-day aerosol forcing estimates, our results suggest that models underestimate aerosol forcing by ca. −15 %, however, with a considerably large interquartile range, suggesting a spread between −35 % and +10 %.

space-based remote sensing↗

Automated Membership Inference Attacks: Discovering MIA Signal Computations using LLM Agents

Membership inference attacks (MIAs), which enable adversaries to determine whether specific data points were part of a model's training dataset, have emerged as an important framework to understand, assess, and quantify the potential information leakage associated with machine learning systems. Designing effective MIAs is a challenging task that usually requires extensive manual exploration of model behaviors to identify potential vulnerabilities. In this paper, we introduce AutoMIA -- a novel framework that leverages large language model (LLM) agents to automate the design and implementation of new MIA signal computations. By utilizing LLM agents, we can systematically explore a vast space of potential attack strategies, enabling the discovery of novel strategies. Our experiments demonstrate AutoMIA can successfully discover new MIAs that are specifically tailored to user-configured target model and dataset, resulting in improvements of up to 0.18 in absolute AUC over existing MIAs. This work provides the first demonstration that LLM agents can serve as an effective and scalable paradigm for designing and implementing MIAs with SOTA performance, opening up new avenues for future exploration.

Tran, Toan Viet [Emory University]↗

The Atmospheric Response to High Nonthermal Electron Beam Fluxes in Solar Flares. I. Modeling the Brightest NUV Footpoints in the X1 Solar Flare of 2014 March 29

The 2014 March 29 X1 solar flare (SOL20140329T17:48) produced bright continuum emission in the far- and near-ultraviolet (NUV) and highly asymmetric chromospheric emission lines, providing long-sought constraints on the heating mechanisms of the lower atmosphere in solar flares. We analyze the continuum and emission line data from the Interface Region Imaging Spectrograph (IRIS) of the brightest flaring magnetic footpoints in this flare. We compare the NUV spectra of the brightest pixels to new radiative-hydrodynamic predictions calculated with the RADYN code using constraints on a nonthermal electron beam inferred from the collisional thick-target modeling of hard X-ray data from Reuven Ramaty High Energy Solar Spectroscopic Imager. We show that the atmospheric response to a high beam flux density satisfactorily achieves the observed continuum brightness in the NUV. The NUV continuum emission in this flare is consistent with hydrogen (Balmer) recombination radiation that originates from low optical depth in a dense chromospheric condensation and from the stationary beam-heated layers just below the condensation. A model producing two flaring regions (a condensation and stationary layers) in the lower atmosphere is also consistent with the asymmetric Fe II chromospheric emission line profiles observed in the impulsive phase.

Kowalski, Adam F.↗

A test of alternative Caribbean plate relative motion models

The new NUVEL-1 data set for global relative plate motions is used here to discriminate between the two prevailing models for Caribbean plate motion. One model, by Jordan (1975), assumes that North America-Caribbean motion is reflected by the spreading rate inferred from magnetic anomalies at the Cayman Spreading Center and the azimuths of nearby transforms. The other model, by Sykes et al. (1982), uses rates and azimuths inferred from the geometry of the Lesser Antilles Wadati-Benioff zone. Overall, it is found that the data fit the Jordan geometry better, that the data used in global plate motion models are more suitable than rates and azimuths inferred from the geometry of the Wadati-Benioff zone for determining relative motions, and that incorporation of all relevant plate boundaries is essential.

Stein, Seth↗

Basin-Scale Structural Features Database

The Basin-Scale Structural Features database provides spatial datasets of faults, fractures, folds, and earthquakes compiled from public, authoritative sources (e.g., U.S. Geological Survey and State Geological Surveys) and aggregated into derivative forms to support subsurface assessments. Recognizing that characterizing basin-scale structural features requires interpreting data that are often ambiguous or lack key information, the source data were evaluated using a knowledge-data framework and geospatial fuzzy logic method (Justman et al., 2020) to represent both measured (observed) and predicted (inferred or potential) structural features as derivative datasets. This workflow employs conceptual models for known structural features and predicted structural features, incorporating geospatial data to estimate potential, even with limited data. The aim is to aid and support an understanding of basin-scale features and identify potential gaps in data and knowledge. As of 4/30/2025, the database includes resources for nine sedimentary basins: Appalachian, Denver, U.S. Gulf Coast, Illinois, Michigan, Permian, Sacramento, San Joquin and Williston. The database is organized by basin and then data category: 1) Faults, fractures, folds, 2) Earthquakes, 3) Topographic, 4) Structural contours and isopachs, 5) Geophysical, and 6) Structural feature density assessment maps.

basin scale↗

NuSTAR Detection of X-Rays Concurrent with Gamma-Rays in the Nova V5855 Sgr

We report the first detection of hard (>10 keV) X-ray emission simultaneous with gamma-rays in a nova eruption. Observations of the nova V5855 Sgr carried out with the NuSTAR satellite on Day 12 of the eruption revealed faint, highly absorbed thermal X-rays. The extreme equivalent hydrogen column density toward the X-ray emitting region (∼3 × 10(exp 24 per sq.cm) indicates that the shock producing the X-rays was deeply embedded within the nova ejecta. The slope of the X-ray spectrum favors a thermal origin for the bulk of the emission, and the constraints of the temperature in the shocked region suggest a shock velocity compatible with the ejecta velocities inferred from optical spectroscopy. While we do not claim the detection of nonthermal X-rays, the data do not allow us to rule out an additional, fainter component dominating at energies above 20 keV, for which we obtained upper limits. The inferred luminosity of the thermal X-rays is too low to be consistent with the gamma-ray luminosities if both are powered by the same shock under standard assumptions regarding the efficiency of nonthermal particle acceleration and the temperature distribution of the shocked gas.

Nelson, Thomas↗

Towards self-consistent integrated modeling of the tokamak pedestal, scrape-off layer, and divertor using SOLPS-ITER and EPED

A predictive, self-consistent integrated model of the tokamak edge plasma and neutral system has been developed and validated against experimental data from DIII-D, spanning from the divertor target and scrape-off layer to the top of the pedestal. The model uses the SOLPS-ITER fluid boundary plasma and neutral code to constrain the EPED prediction of the pedestal height and width by taking the separatrix temperature and density calculated from SOLPS as inputs to EPED. An empirical proxy function based on data from a subset of these discharges is then used to infer the ratio of density at the pedestal top to that at the separatrix based on the electron temperature at the divertor target. This allows the density at the top of the pedestal to be provided to EPED as well, so that knowledge of this quantity is no longer required a priori, while SOLPS radial transport coefficients are needed instead. The gas puff rate provided to SOLPS is then a primary model input for this forward model coupling (along with radial transport coefficients, which are kept fixed through each scan in this study). Comparisons with measurements from DIII-D density scans through a range of target conditions find significant pedestal pressure degradation as the density is increased towards detachment in three different divertor configurations, consistent with the experimental data in these ballooning-limited pedestal regimes.

core-edge integration↗

The state of the atmosphere as inferred from the FGGE satellite observing systems during SOP-1

The statistical properties, and coverage, of satellite temperature sounding data are described. Tropical regions are observed every two days, extratropics from one to four times a day. Oceans are covered two to three times a day. Asynoptic coverage is comparable to the U.S. rawinsonde network twice daily coverage. Lack of ground truth for data sparse areas makes accuracy difficult to assess. The rms differences of layer mean temperatures obtained from collocating rawinsonde observations with satellite temperature profiles in space and time differ from rms differences of layer mean satellite temperature soundings. The FGGE satellite systems can infer the three dimensional motion field and improve the representation of the large scale state of the atmosphere.

Halem, M.↗

Simulation-based inference for parameter estimation of complex watershed simulators

High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

54 ENVIRONMENTAL SCIENCES↗

Probabilistic inference of the structure and orbit of Milky Way satellites with semi-analytic modelling

Semi-analytic modelling furnishes an efficient avenue for characterizing dark matter haloes associated with satellites of Milky Way-like systems, as it easily accounts for uncertainties arising from halo-to-halo variance, the orbital disruption of satellites, baryonic feedback, and the stellar-to-halo mass (SMHM) relation. We use the SatGen semi-analytic satellite generator, which incorporates both empirical models of the galaxy–halo connection as well as analytic prescriptions for the orbital evolution of these satellites after accretion onto a host to create large samples of Milky Way-like systems and their satellites. By selecting satellites in the sample that match observed properties of a particular dwarf galaxy, we can infer arbitrary properties of the satellite galaxy within the cold dark matter paradigm. For the Milky Way’s classical dwarfs, we provide inferred values (with associated uncertainties) for the maximum circular velocity v max and the radius r max at which it occurs, varying over two choices of baryonic feedback model and two prescriptions for the SMHM relation. While simple empirical scaling relations can recover the median inferred value for v max and r max , this approach provides realistic correlated uncertainties and aids interpretability. We also demonstrate how the internal properties of a satellite’s dark matter profile correlate with its orbit, and we show that it is difficult to reproduce observations of the Fornax dwarf without strong baryonic feedback. Furthermore, the technique developed in this work is flexible in its application of observational data and can leverage arbitrary information about the satellite galaxies to make inferences about their dark matter haloes and population statistics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Empirical relationships among atmospheric variables from rawinsonde and field data as surrogates for AVIRIS measurements: Estimation of regional land surface evapotranspiration

Empirical relationships between variables are ways of securing estimates of quantities difficult to measure by remote sensing methods. The use of empirical functions was explored between: (1) atmospheric column moisture abundance W (gm H2O/cm(sup 2) and surface absolute water vapor density rho(q-bar) (gm H2O/cm(sup 3), with rho density of moist air (gm/cm(sup 3), q-bar specific humidity (gm H2O/gm moist air), and (2) column abundance and surface moisture flux E (gm H2O/(cm(sup 2)sec)) to infer regional evapotranspiration from Airborne Visible/Infrared Imaging Spectrometers (AVIRIS) water vapor mapping data. AVIRIS provides, via analysis of atmospheric water absorption features, estimates of column moisture abundance at very high mapping rate (at approximately 100 km(sup 2)/40 sec) over large areas at 20 m ground resolution.

Conel, James E.↗

A statistical model for interpreting computerized dynamic posturography data

Computerized dynamic posturography (CDP) is widely used for assessment of altered balance control. CDP trials are quantified using the equilibrium score (ES), which ranges from zero to 100, as a decreasing function of peak sway angle. The problem of how best to model and analyze ESs from a controlled study is considered. The ES often exhibits a skewed distribution in repeated trials, which can lead to incorrect inference when applying standard regression or analysis of variance models. Furthermore, CDP trials are terminated when a patient loses balance. In these situations, the ES is not observable, but is assigned the lowest possible score--zero. As a result, the response variable has a mixed discrete-continuous distribution, further compromising inference obtained by standard statistical methods. Here, we develop alternative methodology for analyzing ESs under a stochastic model extending the ES to a continuous latent random variable that always exists, but is unobserved in the event of a fall. Loss of balance occurs conditionally, with probability depending on the realized latent ES. After fitting the model by a form of quasi-maximum-likelihood, one may perform statistical inference to assess the effects of explanatory variables. An example is provided, using data from the NIH/NIA Baltimore Longitudinal Study on Aging.

NASA Discipline Neuroscience↗

Validation of TRMM Satellite Rainfall Products Over Oklahoma for a Three Year Period (1998-2000)

This study evaluates the level 2 and level 3 rainfall products from the Tropical Rainfall Measuring Mission gridded distribution of 118 high resolution tipping buckets from the Oklahoma Mesonet. The Tropical Rainfall Measuring Mission, a joint satellite mission between NASA and the National Space Development Agency (NASDA) of Japan, was designed to estimate global precipitation between 40 S and 40 N latitude. The TRMM satellite, consisting of two main precipitation sensors, a passive microwave (TMI) and precipitation radar (PR) sensors, was launched in 1997. Although the great advantage of TRMM has been its ability to sample precipitation globally over the tropical oceans in places where ground sensors do not exist, regional points over land still offer the best opportunity for validating these rain estimates. For this study, the gauge data was gridded at I x I degree resolution between 34 and 36 N and -95 and -100 W. The location of Oklahoma is somewhat unique, in that, it is located near the turning point for the satellite. This study investigates effects of temporal sampling on the satellite measurements and the resulting rainfall bias observed between space and ground based sensors. The first part of the study analyzes the monthly rainfall statistics of the 3AI2 (TMI), the 3A25 (PR) and the 31142 (combined sensors) with the rain gauges. It uses regression techniques and a computation of the probability density function (PDF) and cumulative probability density function (CDF) for each sensor to evaluate the effects of temporal sampling on the satellite products and the sufficiency of using two overpasses a day to estimate rainfall on a monthly scale. In further probing this issue, the study also looked at the direct coincidence of ground and satellite measurements by comparing instantaneous comparisons obtained from the level 2 orbital track data, e.g. 2A12, 2A25. Lastly, the effects of temporal sampling were further studied by sub-sampling the gauge data only at overpass times. This methodology provides a quantitative way of separately inferring the fraction of bias due to temporal sampling and the rainfall algorithm. In addition to showing the salient effects of temporal sampling, the study also revisits the historical problem of correlating point gauge estimates with area estimates, though, the problem is ameliorated by averaging and gridding the data to the area resolution scale of the satellite.

Fisher, Brad↗

Development of an Information Fusion System for Engine Diagnostics and Health Management

Aircraft gas-turbine engine data are available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P&W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples.

Volponi, Allan J.↗

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗

Wind-Driven Angular Dependence of Sea-Surface Reflectance Measured with an Airborne Doppler Lidar

The effects of wind-stress on the optical properties of the ocean surface have been studied for several decades. In particular, the classic study by Cox and Munk (1954) linking sea-surface wind field to wave slope statistics provides a phenomenology by which the sea-surface wind velocity can be estimated from direct measurement of the wave-modulated surface reflectance. A limited number of studies along these lines have been conducted using airborne or spaceborne lidar systems. In these instances, truthing was provided by in situ ship reports or satellite microwave remote sensing instruments (e.g., ERS scatterometer, SSM/I). During the second deployment of the MACAWS Doppler wind lidar in the summer of 1996 measurements of sea-surface reflectance as a function of azimuth- and nadir-viewing angles were acquired off the California coast. MACAWS data products include directly measured winds, as well as calibrated backscatter/reflectance profiles, thus enabling comparison of the winds inferred from sea-surface reflectance measurements with those deriving from the Doppler-processed direct line-of-sight (LOS) estimates. Additional validation data was extracted from the ERS and SSM/I satellite microwave sensor archives maintained by the JPL Physical Oceanography Distributed Active Archive Center (PO- DAAC).

Tratt, David M.↗