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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Spatiotemporal Downscaling Model for Solar Irradiance Forecast Using Nearest-Neighbor Random Forest and Gaussian Process

Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing distributed energy resources (DERs) penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates Nearest-Neighbor Random Forest (NNRF) and Nearest-Neighbor Gaussian Process (NNGP) models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs.

Asiedu, Shadrack (ORCID:0009000646004826)↗
DOE OSTI2014

From land use to land cover: Restoring the afforestation signal in a coupled integrated assessment - earth system model and the implications for CMIP5 RCP simulations

Climate projections depend on scenarios of fossil fuel emissions and land use change, and the IPCC AR5 parallel process assumes consistent climate scenarios across Integrated Assessment and Earth System Models (IAMs and ESMs). To facilitate consistency, CMIP5 used a novel land use harmonization to provide ESMs with seamless, 1500-2100 land use trajectories generated by historical data and four IAMs. However, we have identified and partially addressed a major gap in the CMIP5 land coupling design. The CMIP5 Community ESM (CESM) global afforestation is only 22% of RCP4.5 afforestation from 2005 to 2100. Likewise, only 17% of the Global Change Assessment Model’s (GCAM’s) 2040 RCP4.5 afforestation signal, and none of the pasture loss, were transmitted to CESM within a newly integrated model. This is a critical problem because afforestation is necessary for achieving the RCP4.5 climate stabilization. We attempted to rectify this problem by modifying only the ESM component of the integrated model, enabling CESM to simulate 66% of GCAM’s afforestation in 2040, and 94% of GCAM’s pasture loss as grassland and shrubland losses. This additional afforestation increases vegetation carbon gain by 19 PgC and decreases atmospheric CO2 gain by 8 ppmv from 2005 to 2040, implying different climate scenarios between CMIP5 GCAM and CESM. Similar inconsistencies likely exist in other CMIP5 model results, primarily because land cover information is not shared between models, with possible contributions from afforestation exceeding model-specific, potentially viable forest area. Further work to harmonize land cover among models will be required to adequately rectify this problem.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Microwave scattering models and basic experiments

Progress is summarized which has been made in four areas of study: (1) scattering model development for sparsely populated media, such as a forested area; (2) scattering model development for dense media, such as a sea ice medium or a snow covered terrain; (3) model development for randomly rough surfaces; and (4) design and conduct of basic scattering and attenuation experiments suitable for the verification of theoretical models.

Fung, Adrian K.↗
NASA NTRS2010

An Illumination Correction ALgorithm on Landsat-TM Data

In this paper, a new illumination correction model, the rotation model, is introduced. The model is based on the empirical correlation between reflectance and the illumination condition (IL). The model eliminates the dependency of reflectance on IL through rotating the data in IL-reflectance space. This model is compared with widely used cosine model and C model over a sample forest region. We found that the newly developed rotation model consistently performs best on both atmospheric uncorrected and corrected Landsat images. Index Terms Landsat, illumination correction, change detection, LEDAPS

Tan, Bin↗
DOE OSTI2019

ASCENDS: Advanced Data SCiENce Toolkit for Non-Data Scientists

Data science capabilities are becoming important more and more. Even though there exist many data science and machine learning tools, they are not intuitive for many traditional scientists who are not familiar with programming. ASCENDS provides easy and intuitive interfaces (command-line interface, graphical user interface) to users so they can use data science capabilities without having to understand programming language. ASCENDS performs data visualization, machine learning model training, storing and prediction (regression problem), automatic hyperparameter tuning, and supports various machine learning models including random forest, kernel ridge, nearest neighbor, neural network, etc. Users can use standard CSV (comma separated values) format data set and investigate the feature columns that are highly correlated to a target column; further, they can learn ML models, then store and use them for predictions. The tool is supposed to be generically applicable so that it can be used for many different applications.

Sangkeun, MattLee↗
DOE OSTI2025

Hot Droughts and Forest Tree Dynamics in the Amazon - Statistical Models, Scripts, Data, and Outputs

This package contains data, outputs, equations, and R scripts for analyses for manuscript entitled "Hot droughts in the Amazon: A window to a future hypertropical climate" by J. Chambers et al., in particular it contains statistical models and analyses for the INPA BIONTE tree mortality study. The Models folder contains details for all statistical models in PDF files. The Scripts folder contains the R scripts for Bayesian Hierarchical Models (two text files) and SEMs (one text file) are separate and reasonably annotated. All data associated with these scripts are in the data folder. The Data folder contains two of the three CSV files used for the analyses and are called by the R scripts. Two of them are part of published datasets (`BIONTE_mortality-rates.csv` from Lima et al. 2024, DOI:10.15486/ngt/1898910 and `SPEI.csv` from Pastorello et al. 2023 DOI:10.15486/ngt/1958257) and also provided in this package for convenience (please see the corresponding datasets for usage and citation terms). The third dataset (`BIONTE_gapfilled_wd.csv`) contains sensitive information and can be obtained by contacting the manuscript lead author. The Outputs folder contains the two output files that provide extra information about the analyses. The file `figuresFeb2025d.pdf` contains all the figures from the manuscript - captions are in the manuscript. The file `ChambersMS.pdf` contains primary results from Bayesian statistical models, regression analyses, and validation steps applied to the tree mortality data from the INPA experiments. The document includes visual summaries, model diagnostics, and leave-one-out (LOO) validation results. A breakdown of file contents can be found in the README file that is part of this package.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Land Covering Classifications of Boreas Modeling Grid Using AIRSAR Images

Mapping forest types in the boreal ecosystem in an integrated part of any modeling excercise of biogeophysical processes characterizing the interaction of forest with the atmosphere. In this paper, we report the results of the land cover classification of the SAR data acquired during the BOREAS (BOReal Ecosystem Atmospheric Study) intensive field campaigns over the modeling sub-grid of the southern study area in Saskatchewan , Canada. A Bayesian-maximum-a-posteriori classifier has been applied on the NASA/JPL AIRSAR images covering the region during the peak of the growing season in July, 1994.

Atmospheric Study↗
DOE OSTI2015

Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity

Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate-change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process-oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large-scale remote-sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. In conclusion, improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate-change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Gila Water Resources III - Modeling the Impacts of Post-fire Restoration Methods on Vegetation Recovery in the Gila National Forest

In recent years, wildfires in New Mexico’s Gila National Forest have become increasingly common and more severe. Wildfires can have powerful impacts on hydrology and soil stability, including erosion, flooding, and debris-flows that threaten lives and infrastructure downstream. Vegetation restoration treatments like seeding and mulching can mitigate these effects and facilitate ecosystem recovery. Understanding the effectiveness of various restoration methods is vital to planning a cost-effective and successful post-fire recovery strategy. The immediate response to a fire on US Forest Service land is coordinated by a Burned Area Emergency Response (BAER) team, a group responsible for mitigating immediate post-fire risks to human life, property, and critical natural and cultural resources. This study created a proof-of-concept methodology for a decision-support tool designed to help BAER teams identify the restoration treatments most likely to succeed in a given burned area. Leveraging random forest regression, Google Earth Engine, and Landsat 7 and 8 Earth observations, this study modeled vegetation recovery after the 2013 Silver Fire for seeded areas, seeded/mulched areas, and untreated areas. Treatment type and initial burn severity were the largest drivers of vegetation recovery across the landscape. Seeded/mulched areas showed higher recovery levels than untreated areas three months post-fire, but by four years post-fire, treated and untreated areas displayed similar recovery levels. To produce a robust predictive tool for the Gila National Forest, the model should be trained on many more fires and incorporate post-fire weather conditions into the process. Such a model will help partners ensure efficient resource use and plan effective post-fire restoration strategies.

DEVELOP Project Summary↗
DOE OSTI2023

Seasonal drivers of dissolved oxygen across a tidal creek–marsh interface revealed by machine learning

Abstract Dissolved oxygen (DO) is a key biogeochemical control in coastal systems, and its concentration and drivers vary markedly through time and space. This makes it difficult to accurately represent coastal DO and associated biogeochemical processes in models, limiting our ability to predict how these systems will respond to global change. We obtained high‐frequency (5‐min) in situ measurements of DO collected at three locations across the interface of a tidal creek and coastal marsh in the Pacific Northwest, USA. Random Forest machine learning models quantified the importance of three categories of environmental drivers (Aquatic, Climatic, and Terrestrial) of DO variability across the creek–marsh interface. We selected two 4‐month datasets representing Summer and Winter seasonal periods to test two hypotheses on the dominant drivers of DO at the coastal interface. We found that the Terrestrial driver—characterized by long periods of anaerobic conditions and episodic pulses in DO after floods—was most important during the Winter, whereas the Aquatic driver—characterized by variability over tidal, diel, and lunar cycles—was most important during the Summer. We explored how future climate change scenarios could alter the drivers of DO variability using a cumulative sums driver–response framework. Our results suggest that under climate change, Aquatic and Climatic drivers may increase in importance during the Summer, potentially linked to changing metabolic regimes and sea level, with Terrestrial driver importance potentially increasing during the Winter. Our approach highlights useful methods for understanding the spatiotemporal complexity of oxygen across coastal interfaces and quantifying the relative importance of distinct environmental drivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Data from: "Warming and provenance limit tree recruitment across and beyond the elevation range of subalpine forest"

This data package contains data used to support conclusions drawn in “Warming and provenance limit tree recruitment across and beyond the elevation range of subalpine forest”, by Kueppers et al. 2017. Data were collected in field sites within the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, on the eastern slope of the Colorado Rocky Mountains, USA. Files containing geospatial data are also included, to provide additional locational context.There are four document formats associated with this archive: three comma-separated values (.csv) files, three Microsoft Excel (.xlsx) files, one .pdf data user’s guide, four keyhole markup language (.kml) files, and a compressed folder containing seven ESRI shapefiles (.shp). The .csv files can be opened using any simple text-editor software, R, or Microsoft Excel. The .xlsx files can only be opened using Microsoft Excel. The .kml file can be opened by Google Earth and Google Maps, and the shapefiles can be opened with any GIS application compatible with the file type, such as ESRI’s ArcGIS, and QGIS.We provide two versions of the seedling data file: “PIEN_PIFLseedlings20150522_20150525rev12222020.csv/.xlsx” (hereafter PIEN_PIFLseedlings2015) and “PIEN_PIFLseedlings20160408rev12222020.csv/.xlsx” (hereafter PIEN_PIFLseedlings2016). PIEN_PIFLseedlings2015 contains the data we used in the paper. PIEN_PIFLseedlings2016 contains an updated version of these data that includes sampling from later years. The main differences between the two files lie in the columns titled “k[YEAR],” which describe the number of seedlings that were killed in a particular year. In PIEN_PIFLseedlings2016, there also is an additional year of data for k2015, and k2014 also has additional data input for the 2014 cohort. Additionally, in years 2010-2014, there are minor differences in the number of seedlings killed -- in as few as 0 plots (in 2011) to as many as 5 plots (in 2014) -- due to errors in data input that were rectified in later years.------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Upslope range shifts by subalpine tree species are a widely anticipated effect of climate change. Climate niche models predict subalpine forests to expand upslope, given more suitable growing conditions for adult trees. However, these models do not take into account climates required for successful seedling recruitment and establishment, an essential element for expansion. Further, localized upper treeline populations are hypothesized to contain favorable traits for colonizing the alpine. To test these expectations and to expand our knowledge of seedling recruitment under climate change, we designed a common garden, climate-warming experiment spread across an elevation gradient at Niwot Ridge in the Colorado Rocky Mountains. We focus on two widespread Western North American species, Engelmann spruce (Picea engelmannii Parry ex. Engelm) and limber pine (Pinus flexilis James), which occur at treeline. While the former is considered a late-seral species more tolerant of shade, limber pine is a shade-intolerant pioneer species able to establish on infertile sites.Every autumn, seeds of the two species were collected from high- (3370 m–3570 m) and low-provenance (2910–3240 m) sources close to the experimental sites and sown in our plots. A subset of plots were heated and another subset watered over the summer months to offset the effects of warming. Across five years, we found that seeds originating from low elevation recruited more strongly for both species, although this provenance difference diminished by the fourth year for Engelmann spruce, likely due to small sample sizes. Despite the recruitment of low-provenance seed, warming treatments decreased recruitment at all elevations. Combining this with the likeliness and availability of lower-quality, high provenance seed moving upslope at the treeline, tree migration into the alpine may be slowed. Overall, our findings suggest that the hardier limber pine is likely to become a more significant species in subalpine forest communities in the future, while the more sensitive Engelmann spruce may experience range contraction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Geographical Insights into Suicide Mortality Through Spatial Machine Learning

Suicide mortality is a leading cause of death in the United States, with an upward trend that emphasizes its significance as a public health issue. Previous research has employed global models like ordinary least squares (OLS) regression and local models such as geographically weighted regression (GWR). While local models are useful for analyzing spatial variations in suicide mortality, they share limitations with traditional global models, particularly about their inability to handle multi-collinearity and non-linear relationships. Machine learning approaches, like random forests (RF), can address some of these limitations but often fail to account for spatial variability. This gap highlights the need for spatial ML models specifically designed to tackle suicide mortality. This research seeks to fill this void by using a geographically weighted random forest model (GWRF) to examine the associations between county-level suicide mortality in the U.S. from 2010 to 2020 and various social and environmental determinants of health. A key aspect of our methodology is disciplined feature selection, which reduces the pool of explanatory variables by about 90%. This refinement enhances the explanatory power of both global (R2 improved from 0.59 to 0.67) and local (R2 improved from 0.64 to 0.67) RF models while reducing their run times. An analysis of the importance scores for these selected features reveals that the drivers of suicide mortality vary by context. Thus, to effectively address regional disparities and inform targeted public health interventions, a holistic approach that incorporates multiple county-level characteristics is essential.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗
NASA NTRS2020

Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model

As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget, and planetary boundary layer processes. This study presents the work on BLSN storm identification and analysis with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Spectral analysis shows that BLSN identification is feasible with MODIS daytime data. A random forest machine learning model is developed and observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization are used for training. Model performance results show that machine‐learning based classification can achieve over 90% overall accuracy when classifying MODIS pixels into cloud, clear, and BLSN categories. The machine learning model is applied to MODIS observations during the month of October 2009 for BLSN storm analysis. Results show that the size of BLSN storms has a large spectrum and can reach hundreds of thousands km2. The MODIS based BLSN storm frequency map extends the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations coverage limit from 82°S to the South Pole. A BLSN storm belt, which extends from the South Pole region to the coastal area between 130°E and 160°E along the Transantarctic Mountains, provides a potential pathway of snow transport. These results are important in improving the understanding of BLSN impact on Antarctic surface mass balance and boundary layer processes.

Antarctic↗
DOE OSTI2021

Coarse Woody Debris Decomposition Assessment Tool: Model validation and application

Coarse woody debris (CWD) is a significant component of the forest biomass pool; hence a model is warranted to predict CWD decomposition and its role in forest carbon (C) and nutrient cycling under varying management and climatic conditions. A process-based model, CWDDAT (Coarse Woody Debris Decomposition Assessment Tool) was calibrated and validated using data from the FACE (Free Air Carbon Dioxide Enrichment) Wood Decomposition Experiment utilizing pine ( Pinus taeda ), aspen ( Populous tremuloides ) and birch ( Betula papyrifera ) on nine Experimental Forests (EF) covering a range of climate, hydrology, and soil conditions across the continental USA. The model predictions were evaluated against measured FACE log mass loss over 6 years. Four widely applied metrics of model performance demonstrated that the CWDDAT model can accurately predict CWD decomposition. The R 2 (squared Pearson’s correlation coefficient) between the simulation and measurement was 0.80 for the model calibration and 0.82 for the model validation ( P <0.01). The predicted mean mass loss from all logs was 5.4% lower than the measured mass loss and 1.4% lower than the calculated loss. The model was also used to assess the decomposition of mixed pine-hardwood CWD produced by Hurricane Hugo in 1989 on the Santee Experimental Forest in South Carolina, USA. The simulation reflected rapid CWD decomposition of the forest in this subtropical setting. The predicted dissolved organic carbon (DOC) derived from the CWD decomposition and incorporated into the mineral soil averaged 1.01 g C m -2 y -1 over the 30 years. The main agents for CWD mass loss were fungi (72.0%) and termites (24.5%), the remainder was attributed to a mix of other wood decomposers. These findings demonstrate the applicability of CWDDAT for large-scale assessments of CWD dynamics, and fine-scale considerations regarding the fate of CWD carbon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Understanding and control of Zener pinning via phase field and ensemble learning

Zener pinning refers to the dispersion of fine particles which influences grain size distribution via movement of grain boundaries in a polycrystalline material. Grain size distribution in polycrystals has a significant impact on their properties including physical, chemical, mechanical, and optical to name a few. We explore the use of Phase-field modeling and machine-learning techniques to understand and improve the control of grain size distribution via Zener pinning in polycrystalline materials. We develop a machine learning model that determines the relative importance of various parameters to exercise microstructure control via Zener pinning. Our workflow combines high-throughput phase-field simulations and machine learning to address the computational bottlenecks associated with large-scale simulations as well as identify features necessary for microstructure control in polycrystals. A random forest (RF) regression model was developed to predict grain sizes based on five Phase-field model parameters, achieving an average prediction error of 0.72 nm for the training data and 1.44 nm for the test data. The importance of the input parameters is analyzed using the SHapley Additive exPlanations (SHAP) approach which reveals that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. These findings will allow us to select the best second-phase particles, optimize grain size distributions and thus design microstructures with the desired properties. The developed method is a highly versatile and generalizable approach that can be used to assess the combined effects of individual features in the presence of multiple variables.

36 MATERIALS SCIENCE↗