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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.

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Actor-based Runtime Verification with MESA

This work presents a runtime verification approach implemented in the tool MESA (MEssage-based System Analysis) which allows for using concurrent monitors to check for properties specified in data parameterized temporal logic and state machines. The tool is implemented as an internal Scala DSL. We employ the actor programming model to implement MESA where monitors are captured by concurrent actors that communicate via messaging. The paper presents a case study in which MESA is used to effectively monitor a large number of flights from live US airspace data streams. We also perform an empirical study by conducting experiments using monitoring systems with different numbers of concurrent monitors and different layers of indexing on the data contained in events. The paper describes the experiments, evaluates the results, and discusses challenges faced during the study. The evaluation shows the value of combining concurrency with indexing to handle data rich events.

runtime verification↗
NASA NTRS2020

Actor-based Runtime Verification with MESA

This work presents a runtime verification approach implemented in the tool MESA (MEssage-based System Analysis) which allows for using concurrent monitors to check for properties specified in data parameterized temporal logic and state machines. The tool is implemented as an internal Scala DSL. We employ the actor programming model to implement MESA where monitors are captured by concurrent actors that communicate via messaging. The paper presents a case study in which MESA is used to effectively monitor a large number of flights from live US airspace data streams. We also perform an empirical study by conducting experiments using monitoring systems with different num- bers of concurrent monitors and different layers of indexing on the data contained in events. The paper describes the experiments, evaluates the results, and discusses challenges faced during the study. The evaluation shows the value of combining concurrency with indexing to handle data rich events.

Mehlitz, Peter↗
DOE OSTI2024

X-Band Radar and Surface-Based Observations of Cold-Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River watershed of western Colorado, are used to examine a multistorm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ∼30-mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold-season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations. Significance Statement The amount and density of snowpack from western Colorado winter storms have significant impacts on water resources in the Upper Colorado River basin. Snowpack characteristics are affected by small-scale differences in how snow forms in the atmosphere. These differences are hard to study in the complex terrain of the Rockies, but data from the SPLASH and SAIL field campaigns allows us to investigate how snow crystal formation and mountain-driven wind patterns affect snow near the surface. Our study finds that snow crystal growth varies over small space and time scales and is likely controlled by the terrain beneath a given location and resultant local wind patterns. These results imply that predicting snowpack in the Rockies requires properly representing local wind patterns and crystal growth processes in models.

Heflin, Stella↗
NASA NTRS2007

Empirical Storm-Time Correction to the International Reference Ionosphere Model E-Region Electron and Ion Density Parameterizations Using Observations from TIMED/SABER

The response of the ionospheric E-region to solar-geomagnetic storms can be characterized using observations of infrared 4.3 micrometers emission. In particular, we utilize nighttime TIMED/SABER measurements of broadband 4.3 micrometers limb emission and derive a new data product, the NO+(v) volume emission rate, which is our primary observation-based quantity for developing an empirical storm-time correction the IRI E-region electron density. In this paper we describe our E-region proxy and outline our strategy for developing the empirical storm model. In our initial studies, we analyzed a six day storm period during the Halloween 2003 event. The results of this analysis are promising and suggest that the ap-index is a viable candidate to use as a magnetic driver for our model.

Mertens, Christoper J.↗
DOE OSTI2026

Poisson Log-Normal Process for Count Data Prediction

Modeling count data is important in physics and other scientific disciplines, where measurements often involve discrete, non-negative quantities such as photon or neutrino detection events. Traditional parametric approaches can be trained to generate integer-count predictions but may struggle with capturing complex, non-linear dependencies often observed in the data. Gaussian process (GP) regression provides a robust non-parametric alternative to modeling continuous data; however, it cannot generate integer outputs. We propose the Poisson Log-Normal (PoLoN) process, a framework that employs GP to model Poisson log-rates. As in GP regression, our approach relies on the correlations between data points captured via GP kernel structure rather than explicit functional parameterizations. We demonstrate that the PoLoN predictive distribution is Poisson-LogNormal and provide an algorithm for optimizing kernel hyperparameters. Furthermore, we adapt the PoLoN approach to the problem of detecting weak localized signals superimposed on a smoothly varying background - a task of considerable interest in many areas of science and engineering. Our framework allows us to predict the strength, location and width of the detected signals. We evaluate PoLoN's performance using both synthetic and real-world datasets, including the open dataset from CERN which was used to detect the Higgs boson at the Large Hadron Collider. Our results indicate that the PoLoN process can be used as a non-parametric alternative for analyzing, predicting, and extracting signals from integer-valued data.

Saha, Anushka [Rutgers U., Piscataway]↗
DOE OSTI2018

Storm-driven particulate organic matter flux connects a tidal tributary floodplain wetland, mainstem river, and estuary

The transport of terrestrial plant matter into coastal waters is important to regional and global biogeochemical cycles, and methods for assessing and predicting fluxes in such dynamic environments are needed. We investigated the hypothesis that upon reconnection of a floodplain wetland to its mainstem river, organic matter produced in the wetland would reach other parts of the ecosystem. If so, we can infer that the organic matter would ultimately become a source for the food web in the mainstem river and estuary. To accomplish this, we adapted numerical hydrodynamic and transport modeling methods to estimate the mass of particulate organic matter (POM) derived from the annually senescent aboveground parts of herbaceous marsh plants (H–POM). The Finite–Volume Community Ocean Model (FVCOM), parameterized with flow, tide, and aboveground biomass data, simulated H–POM mobilization from fluid shear stress during tidal exchange, flooding, and variable river flow; entrainment into the water column; transport via channel and overland flow; and entrapment when wetted surfaces dry. We examined export from a recently reconnected, restoring tidal emergent marsh on the Grays River, a tributary to the Columbia River estuary. Modeling indicated that hydrologically reconnecting 65 ha at the site resulted in export of about 96 × 10 3 kg of H–POM, primarily during pulsed storm flooding events in autumn and early winter. This exported mass amounted to about 19% of the summer peak aboveground biomass measured at the site. Of that 19%, about 48% (47 × 10 3 kg) was deposited downstream in the Grays River and floodplain wetlands, and the remaining 52% (50 × 10 3 kg) passed the confluence of the Grays River and the mainstem estuary located about 7 km from the study site. The colonization of the restoring study site largely by nonnative Phalaris arundinacea (reed canarygrass) may have resulted in 18–28% lower H–POM mobilization than typical marsh plant communities on this floodplain, based on estimates from regional studies of marshes dominated by less recalcitrant species. Here, we concluded that restored floodplain wetlands can contribute significant amounts of organic matter to the estuarine ecosystem and thereby contribute to the restoration of historical trophic structure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Updates to the Regional Seismic Travel Time (RSTT) Model: 2. Path-dependent Travel-time Uncertainty

Abstract The regional seismic travel time (RSTT) model and software were developed to improve travel-time prediction accuracy by accounting for three-dimensional crust and upper mantle structure. Travel-time uncertainty estimates are used in the process of associating seismic phases to events and to accurately calculate location uncertainty bounds (i.e. event location error ellipses). We improve on the current distance-dependent uncertainty parameterization for RSTT using a random effects model to estimate slowness (inverse velocity) uncertainty as a mean squared error for each model parameter. The random effects model separates the error between observed slowness and model predicted slowness into bias and random components. The path-specific travel-time uncertainty is calculated by integrating these mean squared errors along a seismic-phase ray path. We demonstrate that event location error ellipses computed for a 90% coverage ellipse metric (used by the Comprehensive Nuclear-Test-Ban Treaty Organization International Data Centre (IDC)), and using the path-specific travel-time uncertainty approach, are more representative (median 82.5% ellipse percentage) of true location error than error ellipses computed using distance-dependent travel-time uncertainties (median 70.1%). We also demonstrate measurable improvement in location uncertainties using the RSTT method compared to the current station correction approach used at the IDC (median 74.3% coverage ellipse).

58 GEOSCIENCES↗
DOE OSTI2024

Relating flow resistance to equivalent roughness

Describing flow resistance using the physical properties of an underlying surface is a recalcitrant problem in overland flow models. If discharge measurements are available, an equivalent roughness (e.g., Manning’s n) can be calibrated to represent the effects of surface properties within the domain with a single numerical value. Alternatively, the flow resistance can be estimated from discharge and velocity measured at a point, typically a runoff plot outlet. However, such experimental estimates are often inconsistent with the equivalent roughness determined from calibration to discharge, even if both derive from the same dataset. For example, if Manning’s equation is used to parameterize flow resistance, the Manning’s n obtained by calibrating a model to discharge differs from the value of n calculated from measured flow and velocity at the hillslope outlet. Here, this discrepancy is resolved by deriving a correction factor relating experimentally-determined flow resistance to the equivalent roughness. The derived correction factor is tested for four commonly-used resistance formulations using 129 rainfall simulator experiments. The correction factor is necessary to reproduce measured velocities, and yields minor improvements in discharge prediction. Plain Language Summary: Accurate runoff prediction is needed for land and water management in dryland regions, where sporadic and limited rainfall necessitate efficient water use and drought mitigation strategies. The skill of runoff models is known to be hindered by out ability to estimate flow resistance, which is the quantity that describes how energy is lost from flowing water to the underlying surface. Typically, models represent flow resistance with an equivalent roughness, e.g., Manning’s n, that is adjusted until the model can reproduce available discharge observations at watershed scale. However, the flow resistance measured in plot-scale experiments (1–10 m) often exceeds equivalent roughness coefficients by a factor of 10. This means that the direct use of plot-scale experimental data to parameterize runoff models could cause errors in discharge and runoff velocity predictions. Here, we resolve these differences by deriving an analytic correction factor that relates flow resistance to the equivalent roughness required for models to reproduce experimental velocity and discharge data. This correction factor is tested using rainfall simulator data from 129 experiments performed in the US Southwest covering a wide range of precipitation intensities, soil textures and vegetation types. Use of the correction factor substantially improves model prediction of flow velocity, which is needed for reproducing the timing of flood events and the estimation of erosion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Observations and simulations of a wind farm modifying a thunderstorm outflow boundary

Abstract. On 18 June 2019, National Weather Service (NWS) radar reflectivity data indicated the presence of thunderstorm-generated outflow propagating east-southeastward near Lubbock, Texas. A section of the outflow boundary encountered a wind farm and then experienced a notable reduction in ground-relative velocity, suggesting that interactions with the wind farm impacted the outflow boundary progression. We use the Weather Research and Forecasting model and its wind farm parameterization to address the extent to which wind farms can modify the near-surface environment of thunderstorm outflow boundaries. We conduct two simulations of the June 2019 outflow event: one containing the wind farm and one without. We specifically investigate the outflow speed of the section of the boundary that encounters the wind farm and the associated impacts on near-surface wind speed, moisture, temperature, and changes to precipitation features as the storm and associated outflow pass over the wind farm domain. The NWS radar and nearby West Texas Mesonet surface stations provide observations for validation of the simulations. The presence of the wind farm in the simulation clearly slows the progress of the outflow boundary by over 20 km h -1 , similar to what was observed. Simulated perturbations of surface wind speed, temperature, and moisture associated with outflow passage were delayed by up to 6 min when the wind farm was present in the simulation compared to the simulation without the wind farm. However, impacts on precipitation were localized and transient, with no change to total accumulation across the domain.

17 WIND ENERGY↗
NASA NTRS2015

Constraints on Cumulus Parameterization from Simulations of Observed MJO Events

Two recent activities offer an opportunity to test general circulation model (GCM) convection and its interaction with large-scale dynamics for observed Madden-Julian oscillation (MJO) events. This study evaluates the sensitivity of the Goddard Institute for Space Studies (GISS) GCM to entrainment, rain evaporation, downdrafts, and cold pools. Single Column Model versions that restrict weakly entraining convection produce the most realistic dependence of convection depth on column water vapor (CWV) during the Atmospheric Radiation Measurement MJO Investigation Experiment at Gan Island. Differences among models are primarily at intermediate CWV where the transition from shallow to deeper convection occurs. GCM 20-day hindcasts during the Year of Tropical Convection that best capture the shallow–deep transition also produce strong MJOs, with significant predictability compared to Tropical Rainfall Measuring Mission data. The dry anomaly east of the disturbance on hindcast day 1 is a good predictor of MJO onset and evolution. Initial CWV there is near the shallow–deep transition point, implicating premature onset of deep convection as a predictor of a poor MJO simulation. Convection weakly moistens the dry region in good MJO simulations in the first week; weakening of large-scale subsidence over this time may also affect MJO onset. Longwave radiation anomalies are weakest in the worst model version, consistent with previous analyses of cloud/moisture greenhouse enhancement as the primary MJO energy source. The authors’ results suggest that both cloud-/moisture-radiative interactions and convection–moisture sensitivity are required to produce a successful MJO simulation.

Madden-Julian Oscillation↗
DOE OSTI2022

Effects of Vegetation on Fluxes of Nitric Oxide, Nitrogen Dioxide, and Nitrous Oxide in a Mixed Deciduous Forest Clearing

Atmosphere-terrestrial exchange of nitrogen oxides, such as nitric oxide (NO), nitrogen dioxide (NO2) and nitrous oxide (N2O), plays an important role in controlling atmospheric composition and the radiative forcing of Earth’s atmosphere. In this study, the impact of vegetation ground cover on NOx (NOx = NO + NO2) and N2O fluxes in a deciduous forest clearing was studied using dynamic flow chambers located on three experimental plots with differing vegetation ground cover. Microbial emissions of NO and N2O were observed throughout the measurement period, while NO2 was exclusively deposited. Vegetation had no observable impact on NO2 deposition. Under dry conditions, total measured nitrogen emissions were numerically similar across all plots, although the N2O:NO ratio was higher for plots containing plants. Vegetation elevates soil water content through enhanced water retention, a condition that favors denitrification and more N2O production, relative to NO. Two rain events induced pulses of NO and N2O that were higher for bare soil compared to the vegetated plots. Lower NO and N2O emissions in plant-containing chambers stemmed from enhanced root uptake of N following rewetting, which diminished the amount of N substrate available for microbial NO and N2O production. The sensitivity of soil N emissions to vegetation ground cover highlights the need for more studies that investigate this effect over a wide range of ecosystems and to include vegetation effects in future parameterizations of soil NOx and N2O used in atmospheric models. This dataset includes processed data collected from 4 dynamic flux chambers and 17 sensors. Data includes ambient gas measurements made 1 m from the surface and soil flux measurements of NO, NO2, N2O, ozone (O3) and carbon dioxide (CO2). Also included is a suite of meteorological (air temperature, humidity, pressure) and soil parameters (water filled pore space, soil temperature). Refer to the readme for more information about each of the data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Dark Matter in 3D

We discuss the relevance of directional detection experiments in the post-discovery era and propose a method to extract the local dark matter phase space distribution from directional data. The first feature of this method is a parameterization of the dark matter distribution function in terms of integrals of motion, which can be analytically extended to infer properties of the global distribution if certain equilibrium conditions hold. The second feature of our method is a decomposition of the distribution function in moments of a model independent basis, with minimal reliance on the ansatz for its functional form. We illustrate our method using the Via Lactea II N-body simulation as well as an analytical model for the dark matter halo. We conclude that O(1000) events are necessary to measure deviations from the Standard Halo Model and constrain or measure the presence of anisotropies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Dark matter in 3D

We discuss the relevance of directional detection experiments in the post-discovery era and propose a method to extract the local dark matter phase space distribution from directional data. The first feature of this method is a parameterization of the dark matter distribution function in terms of integrals of motion, which can be analytically extended to infer properties of the global distribution if certain equilibrium conditions hold. The second feature of our method is a decomposition of the distribution function in moments of a model independent basis, with minimal reliance on the ansatz for its functional form. We illustrate our method using the Via Lactea II N-body simulation as well as an analytical model for the dark matter halo. Furthermore, we conclude that O(1000) events are necessary to measure deviations from the Standard Halo Model and constrain or measure the presence of anisotropies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1979

A sensitivity analysis of volcanic aerosol dispersion in the stratosphere

A computer sensitivity analysis was performed to determine the uncertainties involved in the calculation of volcanic aerosol dispersion in the stratosphere using a 2 dimensional model. The Fuego volcanic event of 1974 was used. Aerosol dispersion processes that were included are: transport, sedimentation, gas phase sulfur chemistry, and aerosol growth. Calculated uncertainties are established from variations in the stratospheric aerosol layer decay times at 37 latitude for each dispersion process. Model profiles are also compared with lidar measurements. Results of the computer study are quite sensitive (factor of 2) to the assumed volcanic aerosol source function and the large variations in the parameterized transport between 15 and 20 km at subtropical latitudes. Sedimentation effects are uncertain by up to a factor of 1.5 because of the lack of aerosol size distribution data. The aerosol chemistry and growth, assuming that the stated mechanisms are correct, are essentially complete in several months after the eruption and cannot explain the differences between measured and modeled results.

Butler, C. F.↗
DOE OSTI2015

Numerical simulation and analysis of the April 2013 Chicago floods

The weather event associated to record Chicago floods on April 2013 is investigated by using the Weather Research and Forecasting (WRF) model. Observations at Argonne National Laboratory and multi-sensor (weather radar and rain gauge) precipitation data from the National Weather Service were employed to evaluate the model’s performance. The WRF model captured the synoptic-scale atmospheric features well, but the simulated 24-h accumulated precipitation and short-period temporal evolution of precipitation over the heavy-rain region were less successful. To investigate the potential reasons for the model bias, four supplementary sensitivity experiments using various microphysics schemes and cumulus parameterizations were designed. Of the five tested parameterizations, the WRF Single-Moment 6-class (WSM6) graupel scheme and Kain-Fritsch (KF) cumulus parameterization outperformed the others, such as Grell-Dévényi (GD) cumulus parameterization, which underestimated the precipitation by 30–50% on a regional-average scale. Morrison microphysics and KF outperformed the others for the spatial patterns of 24-h accumulated precipitation. The spatial correlation between observation and Morrison-KF was 0.45, higher than those for other simulations. All of the simulations underestimated the precipitation over northeastern Illinois (especially at Argonne) during 0400–0800 UTC 18 April because of weak ascending motion or small moisture. In conclusion, all of the simulations except WSM6-GD also underestimated the precipitation during 1200–1600 UTC 18 April because of weak southerly flow.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Synoptic/planetary-scale interactions and blocking over the North Atlantic Ocean

Work was completed on the height tendency diagnoses of two extratropical cyclones that occurred upstream from the blocking event studied previously. One developed explosively over water 60 to 36 hours before the block first appeared, while the second developed explosively over the southeastern United States during the time of block formation. In both cases, both vorticity and temperature advection were consistently important forcing mechanisms. This is in contrast to the block itself, in which vorticity advection was easily the dominant forcing mechanism. Latent heat release was also significant, accounting for about 50 percent of the total height falls in the cyclone below 850 mb. Estimates of latent heat release were greatly enhanced by coupling parameterized estimates with values derived from GOES IR data using an algorithm developed by Marshall's F. R. Robertson. Among the difficulties encountered in this work was the identification of an appropriate lower boundary condition for the solution of the height tendency equation. The zero value currently used tends to yield underestimates of the lower troposphere height tendencies. To address this problem a new diagnostic technique was developed in cooperation with Dr. Peter Zwack of the University of Quebec at Montreal. Based on an equation Dr. Zwack had previously developed (the Zwack-Okossi development equation), researchers now have a relationship that is completely consistent with the height tendency equation and provides estimates of lower boundary geostrophic vorticity or height tendencies. Finally, comparison of the SAT (satellite data) and NOSAT (no satellite data) analyses is progressing well. The present focus is on both the new diagnostic technique and the SAT/NOSAT comparisons. The former is being tested on the southeastern United States cyclone case previously mentioned and compared with the height tendency diagnoses already completed. The latter are being examined for the blocking case described in the publications cited in this summary. In addition to obtaining statistics that will allow general comparison of the two analyses, it will be possible to determine whether conclusions about the dynamics of the block development are influenced by the analysis set used.

Smith, Phillip J.↗
NASA NTRS2012

Virtual Machine Language 2.1

VML (Virtual Machine Language) is an advanced computing environment that allows spacecraft to operate using mechanisms ranging from simple, time-oriented sequencing to advanced, multicomponent reactive systems. VML has developed in four evolutionary stages. VML 0 is a core execution capability providing multi-threaded command execution, integer data types, and rudimentary branching. VML 1 added named parameterized procedures, extensive polymorphism, data typing, branching, looping issuance of commands using run-time parameters, and named global variables. VML 2 added for loops, data verification, telemetry reaction, and an open flight adaptation architecture. VML 2.1 contains major advances in control flow capabilities for executable state machines. On the resource requirements front, VML 2.1 features a reduced memory footprint in order to fit more capability into modestly sized flight processors, and endian-neutral data access for compatibility with Intel little-endian processors. Sequence packaging has been improved with object-oriented programming constructs and the use of implicit (rather than explicit) time tags on statements. Sequence event detection has been significantly enhanced with multi-variable waiting, which allows a sequence to detect and react to conditions defined by complex expressions with multiple global variables. This multi-variable waiting serves as the basis for implementing parallel rule checking, which in turn, makes possible executable state machines. The new state machine feature in VML 2.1 allows the creation of sophisticated autonomous reactive systems without the need to develop expensive flight software. Users specify named states and transitions, along with the truth conditions required, before taking transitions. Transitions with the same signal name allow separate state machines to coordinate actions: the conditions distributed across all state machines necessary to arm a particular signal are evaluated, and once found true, that signal is raised. The selected signal then causes all identically named transitions in all present state machines to be taken simultaneously. VML 2.1 has relevance to all potential space missions, both manned and unmanned. It was under consideration for use on Orion.

Riedel, Joseph E.↗
DOE OSTI2017

Large Contribution of Coarse Mode to Aerosol Microphysical and Optical Properties: Evidence from Ground-Based Observations of a Transpacific Dust Outbreak at a High-Elevation North American Site

This work is motivated by previous studies of transatlantic transport of Saharan dust and the observed quasi-static nature of coarse mode aerosol with a volume median diameter (VMD) of approximately 3.5 μm. The authors examine coarse mode contributions from transpacific transport of dust to North American aerosol properties using a dataset collected at the high-elevation Storm Peak Laboratory (SPL) and the nearby Atmospheric Radiation Measurement (ARM) Mobile Facility. Collected ground-based data are complemented by quasi-global model simulations and satellite and ground-based observations. The authors identify a major dust event associated mostly with a transpacific plume (about 65% of near-surface aerosol mass) in which the coarse mode with moderate (~3 μm) VMD is distinct and contributes substantially to total aerosol volume (up to 70%) and scattering (up to 40%). The results demonstrate that the identified plume at the SPL site has a considerable fraction of supermicron particles (VMD ~3 μm) and, thus, suggest that these particles have a fairly invariant behavior despite transpacific transport. If confirmed in additional studies, this invariant behavior may simplify considerably parameterizations for size-dependent processes associated with dust transport and removal.

54 ENVIRONMENTAL SCIENCES↗