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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Improving North American Wildfire Prediction by Integrating a Machine-Learning Fire Model in a Land Surface Model

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Simulated wildfire burned area over the CONUS during 2001-2020

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM). A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

Liu, Ye↗

NASA NTRS2022

Investigation of the Potential Saturation of Information from Global Navigation Satellite System Radio Occultation Observations with an Observing System Simulation Experiment

The potential impact of large numbers of Global Navigation Satellite System radio occultation (GNSS-RO) observations on numerical weather prediction is investigated using a global observing system simulation experiment (OSSE). The hybrid four-dimensional ensemble variational Gridpoint Statistical Interpolation (GSI) data assimilation system and Global Earth Observing System (GEOS) model are used to ingest up to 100,000 GNSS-RO soundings per day in addition to the current suite of conventional and radiance data. Analysis quality, forecast skill, and forecast sensitivity to observation impact are examined with differing quantities of additional GNSS-RO profiles. It is found that saturation of information from additional RO soundings has not been reached with 100,000 soundings per day. There are some indications of suboptimal performance of the GSI in handling GNSS-RO observations particularly in the middle and lower tropospheric extratropics.

GNSS-RO↗

DOE OSTI2024

Ignition Delay Times and Chemical Kinetic Model Validation for Hydrogen and Ammonia Blending With Natural Gas at Gas Turbine Relevant Conditions

Ignition delay times from undiluted mixtures of natural gas (NG)/H 2 /Air and NG/NH 3 /Air were measured using a high-pressure shock tube at the University of Central Florida. The combustion temperatures were experimentally tested between 1000 and 1500 K near a constant pressure of 25 bar. As mentioned, mixtures were kept undiluted to replicate the same chemistry pathways seen in gas turbine combustion chambers. Recorded combustion pressures exceeded 200 bar due to the large energy release, hence why these were performed at the high-pressure shock tube facility. The data are compared to the predictions of the NUIGMech 1.1 mechanism for chemical kinetic model validation and refinement. An exceptional agreement was shown for stoichiometric conditions in all cases but strayed at lean and rich equivalence ratios, especially in the lower temperature regime of H 2 addition and all temperature ranges of the baseline NG mixture. Hydrogen addition also decreased ignition delay times by nearly 90%, while NH 3 fuel addition made no noticeable difference in ignition time. NG/NH 3 exhibited similar chemistry to pure NG under the same conditions, which is shown in a sensitivity analysis. Here, the reaction CH 3 + O 2 = CH 3 O + O is identified and suggested as a possible modification target to improve model performance. Increasing the robustness of chemical kinetic models via experimental validation will directly aid in designing next-generation combustion chambers for use in gas turbines, which in turn will greatly lower global emissions and reduce greenhouse effects.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1999

Global Aerosol Climatology Project: An Update

This paper outlines the methodology of interpreting channe1 1 and 2 AVHRR (Advanced Very High Resolution Radiometer) radiance data over the oceans and describes a detailed analysis of the sensitivity of monthly averages of retrieved aerosol parameters to the assumptions made in different retrieval algorithms. The analysis is based on using real AVHRR data and exploiting accurate numerical techniques for computing single and multiple scattering and spectral absorption of light in the vertically inhomogeneous atmospheric-ocean system. We show that two-channel algorithms can be expected tp provide significantly more biased retrievals of the aerosol optical thickness than one-channel algorithms and that imperfect cloud screening and calibration uncertainties are by far the largest sources of errors in the retrieved aerosol parameters. Both underestimating and overestimating aerosol absorption as well as the potentially strong variability of the real part of the aerosol refractive index may lead to regional and/or seasonal biases in optical thickness retrievals. The Angstrom exponent appears to be the most invariant aerosol size characteristic and should be retrieved along with optical thickness as the second aerosol parameter.

Mishchenko, Michael I.↗

DOE OSTI2025

Neural Active Manifolds: Nonlinear Dimensionality Reduction for Uncertainty Quantification

We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a one-dimensional neural active manifold (NeurAM) capturing the model output variability, through the aid of a simultaneously learnt surrogate model with inputs on this manifold. Our method only relies on model evaluations and does not require the knowledge of gradients. The proposed dimensionality reduction framework can then be applied to assist outer loop many-query tasks in scientific computing, like sensitivity analysis and multifidelity uncertainty propagation. In particular, we prove, both theoretically under idealized conditions, and numerically in challenging test cases, how NeurAM can be used to obtain multifidelity sampling estimators with reduced variance by sampling the models on the discovered low-dimensional and shared manifold among models. Several numerical examples illustrate the main features of the proposed dimensionality reduction strategy and highlight its advantages with respect to existing approaches in the literature.

Autoencoders↗

NASA NTRS2006

Error Analysis for High Resolution Topography with Bi-Static Single-Pass SAR Interferometry

We present a flow down error analysis from the radar system to topographic height errors for bi-static single pass SAR interferometry for a satellite tandem pair. Because of orbital dynamics the baseline length and baseline orientation evolve spatially and temporally, the height accuracy of the system is modeled as a function of the spacecraft position and ground location. Vector sensitivity equations of height and the planar error components due to metrology, media effects, and radar system errors are derived and evaluated globally for a baseline mission. Included in the model are terrain effects that contribute to layover and shadow and slope effects on height errors. The analysis also accounts for nonoverlapping spectra and the non-overlapping bandwidth due to differences between the two platforms' viewing geometries. The model is applied to a 514 km altitude 97.4 degree inclination tandem satellite mission with a 300 m baseline separation and X-band SAR. Results from our model indicate that global DTED level 3 can be achieved.

synthetic aperture radar (SAR)↗

DOE OSTI2022

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

Assimilation of Soil Moisture Observations Over Land Improves Analysis and Prediction of Tropical Cyclone Idai

Soil moisture conditions can impact the circulation and structure of a tropical cyclone (TC) when part or all of the circulation is over land. Dry land surface conditions may lead to faster dissipation of a TC over land, whereas very wet conditions may lead to a prolonged maintenance of its intensity. While this relationship is relatively well understood in theory, applications of these findings in the context of numerical weather prediction (NWP) have been limited. Here we present a case study that explores the potential of improving TC predictions through an improved soil moisture initialization in an NWP framework. Specifically, we examine the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). SMAP provides accurate L-band (1.4 GHz) brightness temperatures (Tb) observations that are sensitive to soil moisture globally and at high revisit times of 2-3 days. It has previously been shown that the assimilation of SMAP Tbs significantly improves modeled land surface states. Here we evaluate: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis (without SMAP Tb assimilation) and (ii) forecasts initialized from an analysis that additionally assimilates SMAP Tb observations. We find that in the analysis with SMAP assimilation, the TC has a better-defined, more aligned vertical structure over land relative to the control run; moreover, the analyzed TC size, as measured by the wind speed radius, better matches the observed TC size. We further find significant reductions in the forecast intensity error and the forecast along-track error, measured against observations. The largest error reductions occur at lead times of 36 to 72 hours, suggesting that the land with its longer memory gains in importance as a source of predictability at this timescale. An investigation of the underlying mechanisms leading to the skill improvements from SMAP data assimilation revealed that the assimilation of SMAP leads to wetter soil moisture conditions and an increased latent heat flux in the SMAP analysis, which results in a TC with higher column-integrated total moisture content and total energy compared to the control analysis.

Jana Kolassa↗

DOE OSTI2021

Global Microphysical Sensitivity of Superparameterized Precipitation Extremes

Abstract A recent study found statistically significant differences in extreme precipitation distributions over the contiguous United States (CONUS) when changing the microphysics scheme in a superparameterized global climate model. Here, we repeat the analysis globally and similarly find that differences are widespread when varying the number of predicted moments in the microphysics parameterization, but not when comparing variants of the double‐moment scheme. However, contrary to the previous study in which differences largely disappeared over CONUS when 5‐day simulations were conducted, we found that the signal in these shorter integrations remains within the tropics, implying a direct local effect of microphysics on precipitation extremes in these regions. The effect on precipitation is traced back to changes in vertical velocity profiles changes that are then amplified in the climatological simulations compared to the 5‐day ones. Finally, the superparameterized extremes, regardless of the microphysics scheme, are shown to be larger than those from the Global Precipitation Climatology Project One‐Degree Daily data set and generally smaller than those from the Tropical Rainfall Measuring Mission 3B42 data set.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

CHP-PRA Proof-of-Concept [Simulation] Sensitivity Assessment

An effort is underway to establish a Crew Health and Performance system (CHP) tradespace tool using a Probabilistic Risk Assessment (PRA) modeling and simulation system. The goal of the CHP-PRA effort is to provide a means of quantifying the integrated influence of CHP functions and capabilities on risk outcome metrics associated with health, performance, and long-term health. These metrics can then be used to establish potential risk-based trades on CHP system designed functionality and capabilities. Previously, our team demonstrated a proof-of-concept PRA approach that estimated the integrated influence of exercise countermeasures on 8 human system risks (Figure 1a) with outcomes associated with health and medical risk metrics. We reported that the change in the integrated relative health risk was small (Figure 1b) and that the small change in overall risk resulted from compounding and competing contribution levels of the individual risks. This interesting observation illustrates the emergent complexity of even straightforward representations of the human health and performance risk space and the ability of PRA models to capture this balance of global risk concerns. A key question that is not addressed in the initial analysis is “even though the global risk is relatively nominal, do any of the local risks become unacceptable?” In essence, we seek to determine what relative change in the human system risks are contributing to the relatively muted sensitivity of the proof-of-concept model combined risk assessments. Evaluations at the component risk level should elucidate if any individual risk reaches a high level that is subsequentially balanced by reductions in other areas. To further understand the relative changes in the component risks in the proof-of-concept model, and to elucidate how future refinements can be targeted, a means of establishing the contributions of the robustness of the proof-of-concept approach will be demonstrated.

astronaut health↗

NASA NTRS1991

Reconnection voltage as a function of IMF clock angle

Magnetic reconnection between the IMF and the geomagnetic field is thought to play a major role in the transfer of solar wind momentum and energy to the magnetosphere. Both analytic modeling and analysis of geophysical data have shown that this coupling process should be a sensitive function of the clock angle of the IMF. Results are presented from a three-dimensional, MHD, global numerical simulation code for the reconnection voltage between the closed geomagnetic field and the IMF as a function of the IMF clock angle. These results are consistent with a sin(theta/2) functional behavior.

Fedder, J. A.↗

NASA NTRS1992

Multidisciplinary optimization of a controlled space structure using 150 design variables

A general optimization-based method for the design of large space platforms through integration of the disciplines of structural dynamics and control is presented. The method uses the global sensitivity equations approach and is especially appropriate for preliminary design problems in which the structural and control analyses are tightly coupled. The method is capable of coordinating general purpose structural analysis, multivariable control, and optimization codes, and thus, can be adapted to a variety of controls-structures integrated design projects. The method is used to minimize the total weight of a space platform while maintaining a specified vibration decay rate after slewing maneuvers.

James, Benjamin B.↗

NASA NTRSDate not supplied

Global NWP Impacts of Infrared Sounders from Geostationary Orbit

The Geostationary eXtended Observations (GeoXO) program, expected to launch in the 2030s, includes a proposed infrared (IR) sounder that would provide persistent atmospheric profile observations over much of the western hemisphere. In preparation, the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office (GMAO) observing system simulation experiment (OSSE) framework was used to assess the impact of such an instrument on global numerical weather prediction (NWP) as part of a global “ring” of such instruments. Building on previous preliminary studies, an evaluation of the impact of geostationary IR sounders will be presented with foci on the analysis, forecasts, and the forecast sensitivity observation impact (FSOI) metric, including an examination of the consequences for tropical cyclone representation. Overall, assimilation of geostationary IR provide a beneficial impact for NWP applications.

Erica L. Mcgrath-spangler↗

DOE OSTI2025

Files and scripts to support manuscript Needham et al. Canopy Gradients of Respiration

This dataset includes the parameter files, relevant output files, and scripts to perform analysis with Jupyter notebooks that support the manuscript Needham et al 2025 “Canopy Gradients of Respiration Drive Plant Carbon Budgets and Leaf Area Index.” We add functionality to the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) to allow flexible vertical gradients of leaf maintenance respiration (Rdark) and maximum carboxylation rate (Vcmax) through the canopy. We test the sensitivity of FATES to canopy gradients in Rdark, both in global simulations to assess broad scale impacts on leaf area index (LAI) and vegetation carbon, and in single site simulations where we assess impacts on plant functional type (PFT) competitive dynamics. Parameter files are netcdf files that can be converted to human readable .cdl files using NCO tools. Analysis scripts are Jupyter notebook files. These can be opened and run using the open source Jupyter notebook software. Model outputs are netcdf files.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Strangeness in the proton from $W+$ charm production and SIDIS data

We perform a global QCD analysis of unpolarized parton distribution functions (PDFs) in the proton, including new 𝑊+⁢ charm production data from 𝑝⁢𝑝 collisions at the LHC and semi-inclusive pion and kaon production data in lepton-nucleon deep-inelastic scattering, both of which have been suggested for constraining the strange quark PDF. Compared with a baseline global fit that does not include these datasets, the new analysis reduces the uncertainty on the strange quark distribution over the range 0.01 < 𝑥 < 0.3, and provides a consistent description of processes sensitive to strangeness in the proton. Including the new datasets, the ratio of strange to nonstrange sea quark distributions is $R_s = (s + \bar{s})/(\bar{u} +\bar{d})$ $=$ {$0.7⁢2^{+0.52}_{−0.34}, 0.4⁢6^{+0.30}_{−0.20}, 0.3⁢2^{+0.23}_{−0.15}$} for 𝑥 ={$0.01, 0.04, 0.1$} at 𝑄 2 $=$ 4 GeV 2 . The data place more stringent constraints on the strange asymmetry $(s - \bar{s})$, which is found to be consistent with zero in this range.

Anderson, Trey [College of William and Mary, Willi↗

DOE OSTI2026

Kinetics Modeling and Reactor Design Study of Glucose-to-Terpenes Cell-Free Conversion

Cell-free systems offer many advantages over traditional biological conversion by eliminating biological growth constraints. It also offers easy manipulation and finetuning of the reaction conditions for each individual enzyme. The conversion of cellulosic glucose to Limonene, a terpene, is a promising pathway for producing fuels and chemicals. Recent advances in developing cell-free systems focuses on bench scale optimization of terpene yield and to demonstrate its feasibility towards commercialization [1,2]. There is significant knowledge gap regarding reaction kinetics of these cell-free systems to further study how it will perform at larger scale. We present here, our studies on reaction kinetics and reactor design implications of cell-free glucose to Limonene conversion to facilitate the further development and commercialization of this process. We developed a novel kinetic model based on the metabolic-network structure of the cell-free system with multi-substrate reversible Michaelis-Menten rate law. To estimate kinetic parameters for this system of rate equations, we employed Bayesian optimization to perform global search with the assistance of gaussian processes to balance exploration and exploitation. The model parameters estimated showed good results compared with experimental data. The estimated parameters were used to perform sensitivity analysis. We found that Hexokinase is one of the most critical enzymes that affect the conversion of the glucose. We also observed that abundance of co-factors is also critical to the conversion of glucose to limonene. We investigated packed bed reactors with enzymes immobilized on the surface of particles to convert glucose stream into Limonene for larger scale production. The reactor design such as particle size, enzyme loading, and flow rate are found to be critical for improving yields. [1] Dudley, Q.M., Nash, C.J. and Jewett, M.C., 2019. Synthetic Biology, 4(1), p.ysz003. [2] Korman, T.P., Opgenorth, P.H. and Bowie, J.U., 2017. Nature communications, 8(1), p.15526.

09 BIOMASS FUELS↗

DOE OSTI2024

Significance of radiative corrections on measurements of the EMC effect

Deep inelastic scattering (DIS) from nuclear targets probes the parton distribution functions (PDFs) in nuclei. Comparisons of the PDFs from heavy nuclei and the deuteron show deviations that demonstrate a non-trivial nuclear dependence to these distributions, referred to as the EMC effect. A global analysis of the worlds data on the EMC effect reveals tensions between different extractions. Precise measurements at Jefferson Lab, studying the dependence on both the quark momentum fraction, x, and nuclear mass, show systematic discrepancies among experiments, making the extraction of the A dependence of the EMC effect sensitive to the selection of datasets. Further, by comparing various methods and assumptions used to calculate radiative corrections, we have identified differences that, while not large, significantly impact the EMC ratios and show that using a consistent radiative correction procedure resolves this discrepancy, leading to a more coherent global picture, and allowing for a more robust extraction of the EMC effect for infinite nuclear matter.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗