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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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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A dendritic strontium river isoscape for fisheries applications in the Sacramento River basin, California, USA

Objective Understanding the origins and movements of fish is fundamental to effective conservation and fisheries management. Strontium isotope ratios ( 87 Sr/ 86 Sr) in otoliths provide a powerful tracer of natal origin and migratory pathways. However, existing 87 Sr/ 86 Sr isoscapes for the Sacramento River basin, an ecosystem that supports ecologically and economically important salmon populations, rely on discrete classification approaches that overlook unsampled habitats and do not incorporate spatial uncertainty. Our objective was to develop a continuous, network-explicit 87 Sr/ 86 Sr isoscape with quantified uncertainty to fill in data gaps and enable probabilistic assignments of fish origin and movement. Methods We used river water 87 Sr/ 86 Sr data from 106 sites (1997–2021) to develop spatial stream network models that use dendritic connectivity and watershed characteristics (lithology, bedrock age, and land cover) to predict river water 87 Sr/ 86 Sr throughout the basin. Models were fitted using maximum and restricted likelihood and were evaluated via Akaike’s information criterion and leave-one-out cross validation. We produced both historical (pre-dam) and present-day (below-dam) isoscapes, delineated uncertainty-informed isotopic ranges using k -means clustering, and applied a proof-of-concept Bayesian assignment to estimate natal origins and early rearing habitats for two endangered winter-run Chinook Salmon Oncorhynchus tshawytscha. Results Cross validation indicated strong performance of the 87 Sr/ 86 Sr model (leave-one-out cross validation: R 2 = 0.91; root mean square error = 0.0005). Uncertainty-informed clustering identified 19 isotopic “suites” (reaches with indistinguishable 87 Sr/ 86 Sr values) in present-day anadromous habitats and 25 suites in the historical network. Example natal and early rearing assignments included predictions that challenged expectations for juvenile salmon migration based on predicted river 87 Sr/ 86 Sr compositions. Conclusions This study developed a continuous, network-explicit 87 Sr/ 86 Sr isoscape that integrates existing river data to predict 87 Sr/ 86 Sr in unsampled reaches and the likely achievable range and resolution of otolith-based origin and life history inference. The resulting river isoscape provides a valuable tool to predict salmon movements and identify habitats supporting their survival and growth that otherwise might remain undetected. Coupling these predictions with complementary approaches that ground-truth juvenile presence (e.g., targeted fish surveys) represents an important step toward science-informed restoration and management of critical habitats throughout the Sacramento River basin.

Environmental sciences↗
DOE OSTI2022

BioTransformer 3.0 – A Web Server for Accurately Predicting Metabolic Transformation Products

BioTransformer 3.0 is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively viewable/sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes a few seconds. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0, uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human and/or environmental biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40-50% more accurate, much less prone to combinatorial “explosions” and far more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Identification of a new isomeric state in 76 Zn following the β decay of 76 Cu

Background: The evolution of nuclear shell structure far from stability can be explored by identifying and measuring the properties of isomers. Neutron-rich nuclei between the Z = 28 and Z = 50 closed shells have been the subject of recent studies which have identified a number of 0.1 - 10 µs isomers and measured detailed spectroscopic properties. Purpose: The purpose of this analysis was to identify and measure the properties of short-lived isomeric states populated following β decay in Z ≈ 30, N ≈ 50 nuclei near the doubly magic nucleus 78 Ni. Methods: Here, radioactive ions produced by beam fragmentation at the National Superconducting Cyclotron Laboratory were implanted into a CeBr 3 scintillator coupled to a pixelated photomultiplier tube. Ancillary arrays of HPGe clover and LaBr 3 detectors were positioned around the implantation detector to measure β-delayed γ rays. Results: The previously observed 2634-keV level in 76 Zn, populated following the β decay of 76 Cu, was identified as isomeric with a half-life of 25.4(4) ns. A combination of timing and γ-ray spectroscopy was used to confirm this assignment. Shell-model calculations were performed and indicate that this state may be a high-spin negativeparity state formed by the occupation of the ν0g 9/2 orbital. Conclusions: A new isomeric state in 76 Zn has been identified and its half-life was measured. Ambiguity about the structure of this state could be resolved with further experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Wavelet multiresolution analysis of particle-laden turbulence

Direct numerical simulations of incompressible homogeneous-isotropic turbulence laden with a dilute suspension of inertial point particles are performed in conjunction with a wavelet multiresolution analysis of the results. The use of spatially localized wavelet basis functions enables the simultaneous consideration of physical and scale spaces in the spectral characterization of the flow field of the carrier phase and the concentration field of the dispersed phase. The multiresolution analysis of the dispersed phase provides statistical information about the spatial variabilities of a scale-dependent coarse-grained number density field and the local energy spectra of its fluctuations, characterizing the sensitivities of those quantities to variations in scale and Stokes number. In particular, the spatial variabilities of the wavelet energy spectrum of the particle concentration fluctuations are observed to be maximum in regimes where the particles preferentially concentrate. The results highlight the scale-dependent inhomogeneities of the structures in the concentration field generated by preferential concentration, and the existence of characteristic scales of interaction between the dispersed and carrier phases. Additionally, an interphase multiresolution analysis is performed that indicates the occurrence of a spatial anticorrelation between the enstrophy and kinetic-energy spectra of the carrier phase and the particle concentration at small scales in regimes where preferential concentration is important. In conclusion, this anticorrelation vanishes as the scale is increased and is largely suppressed when the preferential-concentration effect is negligible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A Deep Learning Pipeline for Optimizing Large-scale Phase Field Simulations

Phase field (PF) simulations are computationally expensive but remain a key analysis tool to understand the complex mechanisms of additive manufacturing (AM) processes. Each PF simulation-aided analysis requires thousands of node hours on leadership-class supercomputers. One of the main goals of these analyses is the study of microstructure evolution during the build process which begins with the onset of nucleation. Nucleation occurs under certain thermomechanical conditions which are not known a priori and many PF simulations are required to identify ranges of input thermo-mechanical parameters that can result in the onset of nucleation. Since many of the simulations do not result in nucleation, an analysis campaign often ends up wasting tremendous amounts of precious computing resources executing nucleation-absent simulations. The goal of this work is to design and train deep learning models to inform a PF simulation about the likelihood of the occurrence of nucleation in a future simulation time-step based on the state summary over a finite number of past time-steps of a running simulation. If the prediction determines that the running simulation is unlikely to reach nucleation in the allotted time, then its execution is stopped immediately ultimately resulting in vast reduction in wasted computations when accrued over all the PF simulations typically performed in a single or multiple analysis campaign(s). The paper presents the performance of a machine learning pipeline that uses a convolutional neural network (CNN) model to learn an embedding which is then used with a self-attention network to build a multi-task deep learning model to predict the likelihood of nucleation. The model also predicts the input parameters used in a simulation. Performance is compared with a baseline pipeline that uses an off-the-shelf LeNet-5 model to learn the initial embedding. Despite their smaller size, performance results indicate significant improvement in accuracy of the proposed models compared to the larger baseline models.

Kannan, Ramakrishnan {ramki}↗
DOE OSTI2019

Towards Native Execution of Deep Learning on a Leadership-Class HPC System

Large parallel machines generally offer the best parallel performance with "native execution" that is achieved using codes developed with the optimized compilers, communication libraries, and runtimes offered on the machines. In this paper, we report and analyze performance results from native execution of deep learning on a leadership-class high-performance computing (HPC) system. Using our new code called DeepEx, we present a study of the parallel speed up and convergence rates of learning achieved with native parallel execution. In the trade-off between computational parallelism and synchronized convergence, we first focus on maximizing parallelism while still obtaining convergence. Scaling results are reported from execution on up to 15,000 GPUs using two scientific data sets from atom microscopy and protein folding applications, and also using the popular ImageNet data set. In terms of the traditional measure of parallel speed up, excellent scaling is observed up to 12,000 GPUs. Additionally, accounting for convergence rates of deep learning accuracy or error, a deep learning-specific metric called "learning speed up" is also tracked. The performance results indicate the need to evaluate parallel deep learning execution in terms of learning speed up, and point to additional directions for improved exploitation of high-end HPC systems.

Yoginath, Srikanth↗
DOE OSTI2025

Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments

Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.

Saranathan, Gayathri [Hewlett-Packard]↗
DOE OSTI2018

Modeling Discrete Forest Anisotropic Reflectance Over a Sloped Surface With an Extended GOMS and SAIL Model

Topographic effects on canopy reflectance play a pivotal role in the retrieval of surface biophysical variables over rugged terrain. In this paper, we proposed a new canopy anisotropic reflectance model for discrete forests, Geometric Optical and Mutual Shadowing and Scattering-from-Arbitrarily-Inclined-Leaves model coupled with Topography (GOSAILT), which considers the effects of slope, aspect, geotropic nature of tree growth, multiple scattering, and diffuse skylight. GOSAILT-simulated areal proportions of four scene components (i.e., sunlit crown, shaded crown, sunlit background, and shaded background) were evaluated using the Geometric Optical model for Sloping Terrains (GOST) model. The canopy reflectances simulated by GOSAILT were validated against two reflectance data sets: Discrete anisotropic radiative transfer (DART) simulations and wide-angle infrared dual-model line/area array scanner (WIDAS) observations. Compared with a horizontal surface, the forest canopy reflectance over a steep slope (60°) is significantly distorted with absolute (relative) bias values of 0.048 (79.60%) and 0.056 (12.02%) for the red and near-infrared (NIR) bands, respectively. The GOSAILT-simulated component areal proportions show close agreements with GOST. Moreover, GOSAILT simulations have high overall accuracy (red band: coefficient of determination (R²) = 0.96; root-mean-square error (RMSE) = 0.003; and mean absolute percentage error (MAPE) = 3.91%; and NIR band: R² = 0.78, RMSE = 0.019; MAPE = 3.94%) when compared with the DART simulations. In conclusion, these extensive validations indicate good performances of GOSAILT in canopy reflectance simulations over sloped surfaces.

58 GEOSCIENCES↗
DOE OSTI2024

Multi-Edge Graph Convolutional Networks for Power Systems

The exponential electrification of transportation has contributed to highly intermittent load variations in the distribution grid. This uncertainty has raised challenges for distribution system operation and control. Accurate nodal voltage estimation is highly essential for the safe and reliable operation of the grid. Graph convolutional networks have been used in machine-learning-based models for power grid applications like voltage estimation for their ability to capture the network topology of the grid. This paper presents a novel multi-edge graph convolutional layer that considers resistance and reactance as edge attributes. This layer is created by modifying the message-passing function within the graph convolutional network. The novel layer is then used to create a multi-edge graph convolutional network-based surrogate model for estimating voltage in the distribution network with highly uncertain electric vehicle loads. Results indicate improved performance of the multi-edge graph convolutional network model when compared to a standard graph convolutional network model.

Ravi, Abhijith↗
DOE OSTI2023

Continental Scale Hydrostratigraphy: Basin‐Scale Testing of Alternative Data‐Driven Approaches

Abstract Integrated hydrological modeling is an effective method for understanding interactions between parts of the hydrologic cycle, quantifying water resources, and furthering knowledge of hydrologic processes. However, these models are dependent on robust and accurate datasets that physically represent spatial characteristics as model inputs. This study evaluates multiple data‐driven approaches for estimating hydraulic conductivity and subsurface properties at the continental‐scale, constructed from existing subsurface dataset components. Each subsurface configuration represents upper (unconfined) hydrogeology, lower (confined) hydrogeology, and the presence of a vertical flow barrier. Configurations are tested in two large‐scale U.S. watersheds using an integrated model. Model results are compared to observed streamflow and steady state water table depth (WTD). We provide model results for a range of configurations and show that both WTD and surface water partitioning are important indicators of performance. We also show that geology data source, total subsurface depth, anisotropy, and inclusion of a vertical flow barrier are the most important considerations for subsurface configurations. While a range of configurations proved viable, we provide a recommended Selected National Configuration 1 km resolution subsurface dataset for use in distributed large‐and continental‐scale hydrologic modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Convection in Scaled Turbine Internal Cooling Passages With Additive Manufacturing Roughness

Abstract Additive manufacturing processes, such as direct metal laser sintering (DMLS), enable the creation of novel turbine cooling internal passages and systems. However, the DMLS method produces a significant and unique surface roughness. Previous work in scaled passages analyzed pressure losses and friction factors associated with the rough surfaces, as well as investigated the velocity profiles and turbulent flow characteristics within the passage. In this study, the heat transfer characteristics of scaled additively manufactured surfaces were measured using infrared (IR) thermography. Roughness panels were CNC machined from plates of aluminum 6061 to create near isothermal roughness elements when heated. Fluid resistance differences between the aluminum roughness panels and roughness panels constructed from ABS plastic using the same roughness patterns from McClain et al. (2020, “Flow in a Simulated Turbine Blade Cooling Channel With Spatially Varying Roughness Caused by Additive Manufacturing Orientation,” ASME Turbo Expo 2020, Turbomachinery Technical Conference and Exposition, Virtual Conference, Sept. 21–25, GT2020-16069) were investigated. Finally, the overall thermal performance enhancements and friction losses were assessed through the calculation of surface averaged “global thermal performance” ratios. The global thermal performance characterizations indicate results in-line with those found for traditional commercial roughness and slightly below traditional internal passage convection enhancement methods such as swirl chambers, dimples, and ribs. The passages investigated in this study do not include compressibility effects or the long-wavelength artifacts and channel geometric deviations observed by Wildgoose et al. (2020, “Impact of Additive Manufacturing on Internal Cooling Channels with Varying Diameters and Build Directions,” ASME Turbo Expo 2020, Turbomachinery Technical Conference and Exposition, Virtual Conference, Sept. 21–25, GT2020-15049). However, the results of this study indicate that, based on the roughness augmentation alone, artificial convective cooling enhancers such as turbulators or dimples may still be required for additively manufactured turbine component cooling.

Engineering↗
DOE OSTI2023

Experimental and Numerical Investigation of Flow Boiling in Additive Manufactured Foam Structures With Vapor Pathways

Abstract The unique properties of metal foams make them potential candidates for a range of applications, including microsystem thermal management. Using additive manufacturing to create foam-type structures can improve upon prior thermal solutions by eliminating thermal interface materials and allowing for customization/local control of parameters. In the present investigation, flow boiling in additive-manufactured metal foams is investigated both experimentally and numerically. Two test samples, one with uniform structure and the other with pathways for vapor removal, are compared both experimentally and numerically. A conjugate computational fluid dynamics and heat transfer (CFD-HT) model utilizing a three-dimensional volume of fluid (VOF) model with accompanying evaporation/condensation model provided in-depth visualization of the boiling flow phenomena. The experiments generated the thermohydraulic performance over a range of heat fluxes, demonstrating that the sample incorporating dedicated vapor pathways performed better in both pressure and heat transfer performance metrics compared to the uniform foam. Additionally, negative system-level effects (i.e., hydraulic oscillations) were shown to be abated using the vapor removal structures. The numerical model yielded further insight into the factors contributing to the improved performance. Results indicated the pathways functioned as vapor removal channels, allowing the generated vapor to vent from the foam structure into the lanes. Further computational investigations demonstrated changes in flow regimes, where the addition of vapor channels caused the flow to change from churn to annular. Bubble behavior unique to the vapor pathway structure was studied, showing stagnant regions that eject vapor into the channel.

Engineering↗
DOE OSTI2025

Investigating the Combustion Performance of Dual Fuel Combustion with Diesel and Port Injected Hydrogen in a Large Bore Locomotive Engine

The heavy-duty transportation sector has primarily relied on conventional diesel combustion engines given their reliability and high thermal efficiency relative to spark ignition engines, but increased focus on reducing greenhouse gas emissions has led to investigation into alternative fuels. Gaseous hydrogen fuel has garnered a great deal of recent interest in the engine community given it has zero carbon, but hydrogen is not available at the scale and cost that petroleum fuels are currently available, and this is a barrier to adoption for industries that are looking to decarbonize their operations. Because of the fuel flexibility provided, dual fuel technology offers a pathway for some industries to adopt hydrogen as a fuel source while maintaining sufficient flexibility in times and locations where the new fuel is not yet available. This computational study investigates dual fuel combustion in a large bore locomotive engine architecture using direct injected diesel and port injected gaseous hydrogen fuel. With an optimal port fuel injection configuration from previous work, simulations of varying substitution ratio, compression ratio, manifold air temperature, diesel injection timing, and diesel injection pressure were performed to understand their effect on combustion performance. Results indicated that both increased substitution ratio and higher intake air temperature accelerates hydrogen flame propagation and can result in high peak cylinder pressures. Additionally, diesel injection timing and injection pressure were demonstrated as effective methods for controlling dual fuel combustion heat release rates.

ODonnell, Patrick Christopher↗
DOE OSTI2019

Screening Compounds for Fast Pyrolysis and Catalytic Biofuel Upgrading Using Artificial Neural Networks

Abstract There is significant interest among researchers in finding economically sustainable alternatives to fossil-derived drop-in fuels and fuel additives. Fast pyrolysis, a method for converting biomass into liquid hydrocarbons with the potential for use as fuels or fuel additives, is a promising technology that can be two to three times less expensive at scale when compared to alternative approaches such as gasification and fermentation. However, many bio-oils directly derived from fast pyrolysis have a high oxygen content and high acidity, indicating poor performance in diesel engines when used as fuels or fuel additives. Thus, a combination of selective fast pyrolysis and chemical catalysis could produce tuned bioblendstocks that perform optimally in diesel engines. The variance in performance for derived compounds introduces a feedback loop in researching acceptable fuels and fuel additives, as various combustion properties for these compounds must be determined after pyrolysis and catalytic upgrading occurs. The present work aims to reduce this feedback loop by utilizing artificial neural networks trained with quantitative structure-property relationship values to preemptively screen pure component compounds that will be produced from fast pyrolysis and catalytic upgrading. The quantitative structure-property relationship values selected as inputs for models are discussed, the cetane number and sooting propensity of compounds derived from the catalytic upgrading of phenol are predicted, and the viability of these compounds as fuels and fuel additives is analyzed. The model constructed to predict cetane number has a test set prediction root-mean-squared error of 9.874 cetane units, and the model constructed to predict yield sooting index has a test set prediction root-mean-squared error of 13.478 yield sooting index units (on the unified scale).

09 BIOMASS FUELS↗
DOE OSTI2020

Dynamic Response of a Semiactive Suspension System with Hysteretic Nonlinear Energy Sink Based on Random Excitation by means of Computer Simulation

This paper aims to investigate the property and behavior of the hysteretic nonlinear energy sink (HNES) coupled to a half vehicle system which is a nine-degree-of-freedom, nonlinear, and semiactive suspension system in order to improve the ride comfort and increase the stability in shock mitigation by using the computer simulation method. The HNES model is a semiactive suspension device, which comprises the famous Bouc–Wen (B-W) model employed to describe the force produced by both the purely hysteretic spring and linear elastic spring of potentially negative stiffness connected in parallel, for the half vehicle system. Nine nonlinear motion equations of the half vehicle system are derived in terms of the seven displacements and the two dimensionless hysteretic variables, which are integrated numerically by employing the direct time integration method for studying both the variables of vertical displacements, velocities, accelerations, chassis pitch angle, and the ride comfort and driver safety, respectively, based on the bump and random road inputs of the pseudoexcitation method as excitation signal. Simulation results show that, compared with the HNES model and the magnetorheological (MR) model coupled to the half vehicle system, the ride comfort and stability have been evidently improved. A successful validation process has been performed, which indicated that both the ride comfort and driver safety properties of the HNES model coupled to half vehicle significantly improved.

Chen, Hui↗
DOE OSTI2024

Berkeley Lab Finite Element Framework (BELFEM) v0.1

The software program, referred to as BELFEM, is a specialized finite element code designed for the magnetodynamic modeling of high-temperature superconducting (HTS) tapes. It incorporates novel mixed finite element formulations, particularly the h-ϕ-formulation with thin-shell simplification, to efficiently simulate larger geometries. This methodology is extremely promising for predicting the electrodynamic performance of HTS tapes used in superconducting cables and magnets, offering the benefit of reduced computational cost. Compared to similar technologies like COMSOL Multiphysics and GetDP, BELFEM's performance benchmarking indicates superior efficiency in its thin-shell implementation. In the future, it will also support features like thermal coupling and inter-tape current sharing, enhancing its utility in research and development, particularly in nuclear fusion applications. The intent is to develop BELFEM as a robust and efficient tool for the HTS community, contributing to the analysis and design of superconducting cables and magnets.

Messe, Christian↗
DOE OSTI2023

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Estimating Fine-Resolution Shortwave Broadband Albedo of Croplands from Harmonized Landsat and Sentinel-2 Data

Altered surface albedo due to land-cover conversions and management is a significant driver of global climate change. Albedo can be directly measured at ground stations, and remote sensing data can be used to scale-up albedo values to regional and global levels. Some previous studies have retrieved fine-resolution (10–30 m) instantaneous albedo and coarse-resolution (500–1000 m) daily mean albedo from remote sensing data, but they all required the input of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo information at 500-m resolution, and none have assembled both instantaneous and daily albedo based exclusively on fine-resolution satellite data. Here, to address this issue, we compiled 387 instantaneous and 346 daily albedo records using field net radiometer measurements from the bioenergy croplands at the W. K. Kellogg Biological Station in southwest Michigan. We then connected these albedo records with a suite of variables derived from harmonized Landsat and Sentinel-2 data through two machine learning algorithms (random forest regression and extreme gradient boosting) to retrieve clear-sky instantaneous and daily shortwave broadband albedo. The performance statistics indicate reasonable accuracy of model results [root-mean-square error (RMSE)] around or below 0.03 except for snow-covered surfaces), suggesting that the retrieval of both instantaneous and daily albedo based exclusively on fine-resolution satellite data is promising. To facilitate the use of fine-resolution albedo products at the global level, future efforts need to include more albedo records of diverse surface cover types, as well as to accurately model daily albedo for cloudy days to address the “clear-sky bias.”

Harmonized Landsat and Sentinel-2↗