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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Investigating the opioid epidemic across the United States: Associations between county-level characteristics and overdose mortality

The opioid crisis remains a critical public health challenge in the United States. Despite national efforts that reduced opioid prescribing by nearly 44% between 2011 and 2021, opioid overdose deaths more than tripled during the same period. This alarming trend reflects a major shift in the crisis, with illegal opioids now driving the majority of overdose deaths instead of prescription opioids. Although supply-side factors fueling this transition have been widely studied, the structural and community-level conditions that shape overdose mortality are less well understood. To help address this gap, this study has three primary objectives: (1) overcome structural gaps in national data to construct a complete nationwide county-level dataset from 2010 to 2022; (2) using data analysis, identify and investigate spatiotemporal anomalies in overdose mortality; and (3) using two machine-learning models, quantify the importance of thirteen social vulnerability variables in predicting overdose mortality. Our results identify unemployment and limited vehicle access as key county-level predictors of overdose mortality. Higher levels of these vulnerabilities are associated with elevated mortality, whereas lower levels are associated with reduced mortality. These findings highlight factors that may be relevant for public health planning and policy prioritization within the context of the opioid crisis.

Anomaly analysis↗
DOE OSTI2025

The Power of DESI for Photometric Redshift Calibration: A Case Study with KiDS-1000

Accurate redshift estimates are a critical requirement for weak lensing surveys and one of the main uncertainties in constraints on dark energy and large-scale cosmic structure. In this paper, we study the potential to calibrate photometric redshift (photo-z) distributions for gravitational lensing using the Dark Energy Spectroscopic Instrument (DESI). Since beginning its science operations in 2021, DESI has collected more than 50 million redshifts, adding about one million monthly. In addition to its large-scale structure samples, DESI has also acquired over 256k high-quality spectroscopic redshifts (spec-zs) in the COSMOS and XMM and VVDS fields. This is already a factor of 3 larger than previous spec-z calibration compilations in these two regions. Here, we explore calibrating photo-zs for the subset of KiDS-1000 galaxies that fall into joint self-organizing map (SOM) cells overlapping the DESI COSMOS footprint using the DESI COSMOS observations. Estimating the redshift distribution in KiDS-1000 with the new DESI data, we find broad consistency with previously published results while also detecting differences in the mean redshift in some tomographic bins with an average shifts of Delta Mean(z) = -0.028 in the mean and Delta Median(z) = +0.011 in the median across tomographic bins. However, we also find that incompleteness per SOM cell, i.e., groups of galaxies with similar colors and magnitudes, can modify n(z) distributions. Finally, we comment on the fact that larger photometric catalogs, aligned with the DESI COSMOS and DESI XMM and VVDS footprints, would be needed to fully exploit the DESI dataset and would extend the coverage to nearly eight times the area of existing 9-band photometry.

Blanco, Diana [UC, Santa Cruz; UC, Santa Cruz, Ins↗
DOE OSTI2025

Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty

Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO2 emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO2 processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from 15% to 0% is observed to lead to an 83.7% reduction in CO2 processing capacity and a 77.1% decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions.

Olson, Daniel↗
NASA NTRS1994

Interannual variability in stratiform cloudiness and sea surface temperature

Marine stratiform cloudiness (MSC)(stratus, stratocumulus, and fog) is widespread over subtropical oceans west of the continents and over midlatitude oceans during summer, the season when MSC has maximum influence on surface downward radiation and is most influenced by boundary-layer processes. Long-term datasets of cloudiness and sea surface teperature (SST) from surface observations from 1952 to 1981 are used to examine interannual variations in MSC and SST. Linear correlations of anomalies in seasonal MSC amount with seasonal SST anomalies are negative and significant in midlatitude and eastern subtropical oceans, especially during summer. Significant negative correlations between SST and nimbostratus and nonprecipitating midlevel cloudiness are also observed at midlatitudes during summer, suggesting that summer storm tracks shift from year to year following year-to-year meridional shifts in the SST gradient. Over the 30-yr period, there are significant upward trends in MSC amount over the northern midlatitude oceans and a significant downward trend off the coast of California. The highest correlations and trends occur where gradients in MSC and SST are strongest. During summer, correlations between SST and MSC anomalies peak at zero lag in midlatitudes where warm advection prevails, but SST lags MSC in subtropical regions where cold advection predominates. This difference is attributed to a tendency for anomalies in latent heat flux to compensate anomalies in surface downward radiation in warm advection regions but not in cold advection regions.

Norris, Joel R.↗
NASA NTRS2018

EOSDIS CMR: Shifting Data Discovery & Use into a Higher Gear

Earth observation data comes in many forms, formats, and from a multitude of sources; to make the best of a very large and diverse data catalog (data from a dozen different national Distributed Active Archive Centers (DAACs) as well as international sources), NASA has created a one-stop-shop for earth data consumers to view, find, and get the data they need regardless of its original source or format, which is powered by a Common Metadata Repository (CMR). CMR is the underpinning that allows for the visualization, search, discovery, manipulation, and acquisition of a variety of datasets, and as such it is constantly evolving to do more and serve our communities better; CMR has embraced community ownership by making itself an open-source API, being compatible with Catalog Services for the Web (CSW) and OpenSearch APIs, and by encouraging the user community to make and share improvements.

CMR↗
NASA NTRSDate not supplied

Developing A Continuous Ozone Record Through the SAGE and Aura Missions With NASA Reanalysis Products

During the last quarter of the 20th century, the Stratospheric Aerosol and Gas Experiment (SAGE) missions were crucial in monitoring the loss and subsequent recovery of the stratospheric ozone layer. Due to the employed solar occultation and self-calibration method, the SAGE monitors have produced stable data throughout the lifetime of each instrument. However, over ten years passed between the end of the SAGE II and SAGE III/M3M missions in 2005 and the launch of SAGE III/ISS instrument in 2017, leaving a gap in the data that must be bridged in order to assess trends in the ozone record. The Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis product, with output available starting in 1980, is an attractive candidate for trend analysis due to the statistically optimized combination of multiple observing systems and the regular temporal and spatial coverage. However, changes in the assimilated observation systems can introduce discontinuities within the MERRA-2 ozone record, such as in 2004 when the MERRA-2 system shifted from assimilating ozone retrievals collected by SBUV instruments to those collected by instruments onboard the Aura satellite. In this study, we explore using the SAGE II record as a transfer function to develop a stable reanalysis data product, suitable for trend analysis, from the start of the SAGE II record in 1984 through the present. We follow the procedure outlined by Wargan et al. (2018) to address discontinuities in the MERRA-2 ozone dataset at the 2004 transition and during the Aura record. SAGE II ozone profiles are used to correct discontinuities in upper stratospheric ozone associated with changes in the MERRA-2 meteorological observing system in 1998 and 1995. We will then assess the relative performance of the data from different SAGE sensors using the resulting bias-corrected MERRA-2 ozone fields.

Pamela Wales↗
DOE OSTI2024

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex process.Our approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant" relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant" representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public1.

Tabassum, Anika [ORNL] (ORCID:0000000254600955)↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
NASA NTRSDate not supplied

Biological Data for Deep Space Mission Support

Increased biomedical risks and challenges associated with deep space missions (cis-Lunar, Mars transit, Mars surface) require new knowledge discovery and development of novel ecosystem and biomedical support capabilities. This paradigm shift supporting distant and long-duration missions requires biological data to be findable, accessible, interoperable, reusable (FAIR), and maximally open-access (i.e., there is a data governance continuum from closed to mediated to embargoed to open). The NASA “Open Science Data Repositories” (OSDR) aims to meet scientific, technical, and operational spaceflight needs, and offers the ability to upload, download, search, share, analyze, and visualize data across physiological, behavioral, ‘omics, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive (ALSDA), and NASA Biological Institutional Scientific Collection (NBISC). In the past year, ALSDA has undergone a transformation in its data collection, curation, and architecture methods. Standardizing non-genomic (phenotypic) datasets was, and will continue to be, a challenge because of their diverse nature (e.g., molecular, cellular, tissue, whole organism, behavior; micro-computed tomography, intraocular pressure, fluorescence microscopy, western blot, ultrasonography; tabular, images, video). This year ALSDA, alongside GeneLab, introduced the Biological Data Management Environment (BDME) with the purpose to accept submission of data from space relevant experiments including spaceflight, radiation, simulated gravity, gravitropism, isolation and confinement, hostile closed environments and/or distance from Earth. In addition to bringing together omics, phenotypic, physiological, bioimaging, and behavioral data into one repository. By integrating with GeneLab a multi-project submission portal aims to reduce the burden on PIs submitting data and enabling the discovery of both omics and phenotypic data. The purpose of ALSDA is to collect, curate, and make all non-human space-relevant biological data maximally findable, accessible, interoperable, and reusable (FAIR). These scope of ALSDA data collected and submitted by PIs include study design metadata, subject metadata, assay metadata (parameters), raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). In 2021, a community of researchers rallied to form the ALSDA Analysis Working Group (AWG) and provided scientific consensus on dataset sample and assay metadata. The community and excitement around the ALSDA/OSDR system has already led to several data reuse studies, demonstrating value using machine learning (ML), knowledge graphs, and meta-analysis approaches.

space biology↗
DOE OSTI2021

Radial structure and formation of the Milky Way disc

The formation of the Galactic disc is an enthusiastically debated issue. Numerous studies and models seek to identify the dominant physical process(es) that shaped its observed properties; for example, satellite accretion, starburst, quenching, gas infall, and stellar radial migration. Taking advantage of the improved coverage of the inner Milky Way provided by the SDSS DR16 APOGEE catalogue and of the ages published in the APOGEE-AstroNN Value Added Catalogue (VAC), we examined the radial evolution of the chemical and age properties of the Galactic stellar disc with the aim of better constraining its formation. Using a sample of 199 307 giant stars with precise APOGEE abundances and APOGEE-AstroNN ages, selected in a ±2 kpc layer around the galactic plane, we assessed the dependency as a function of guiding radius of (i) the median metallicity, (ii) the ridge lines of the [Fe/H] – [Mg/Fe] and age–[Mg/Fe] distributions, and (iii) the age distribution function (ADF). The giant star sample allows us to probe the radial behaviour of the Galactic disc from R g = 0 to 14–16 kpc. The thick disc [Fe/H] – [Mg/Fe] ridge lines follow closely grouped parallel paths, supporting the idea that the thick disc did form from a well-mixed medium. However, the ridge lines present a small drift in [Mg/Fe], which decreases with increasing guiding radius. At sub-solar metallicity, the intermediate and outer thin disc [Fe/H] – [Mg/Fe] ridge lines follow parallel sequences shifted to lower metallicity as the guiding radius increases. We interpret this pattern as the signature of a dilution of the interstellar medium from R g ~ 6 kpc to the outskirts of the disc, which occurred before the onset of the thin disc formation. The APOGEE-AstroNN VAC provides stellar ages for statistically significant samples of thin disc stars from the Galactic centre up to R g ~ 14 kpc. An important result provided by this dataset is that the thin disc presents evidence of an inside-out formation up to R g ~ 10 – 12 kpc. Moreover, about ~7 Gyr ago, the [Mg/Fe] ratio in the outer thin disc (R g > 10 kpc) was higher by about ~0.03–0.05 dex than in the more internal regions of the thin disc. This could be the fossil record of a pollution of the outer disc gas reservoir by the thick disc during its starburst phase.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2020

Hiatus of wetland methane emissions associated with recent La Niña episodes in the Asian monsoon region

The variability of methane emissions from wetlands in the tropics and northern temperate regions can explain more than 70% of the interannual variation in global wetland methane emissions, which are largely driven by climate variability. We use climate reanalysis, remote sensing wetland area dataset and simulations from 11 land models contributing to Global Methane Budget to investigate the interannual variation and anomalies of wetland methane emissions in the Asian Monsoon region. Methane emissions in this region steadily increased over 2000–2012. However, abnormally low methane emissions were found in equatorial fully humid (Af), warm temperate winter dry (Cw), and warm temperate fully humid (Cf) Asian Monsoon climate sub-regions in 2008, 2009 and 2011, respectively. These spatially-shifting low emissions occurred simultaneously with observed wetland area shrinkage due to abnormally low precipitation. Interannual variability of wetland methane emissions in Asian Monsoon region are primarily driven by South Asian Monsoon system. However, the abnormally low emissions are related to strong La Niña events, and its accompanying effect of weakened East Asian Monsoon system and eastward Western Pacific subtropical high, which drives the shifting pattern of rainfall, and thus the spatial pattern of methane emission anomalies.

wetlands↗
NASA NTRS1991

Comet P/Halley's periodic brightness variations in 1910

Periodic variations in Comet Halley's production of gas and dust during its 1910 apparition were searched for using measurements from a homogeneous set of photographic plates obtained at the National Observatory of Argentina at Cordoba. After applying corrections to the observations to compensate for geometric and heliocentric distance effects, the data, spanning the two months immediately following perihelion, were split into two sections for analysis. Of the possible periods indicated in either half of the dataset, only those in the range of 7.3-7.8 days were present in both halves. The additional constraint of similarity in the shape of the resulting, phased light curves further constrained the period from about 7.35 to 7.60 days. Allowing for the inherent limitations of the 1910 data, comparison of these light curves to the equivalent 1986 light curve indicates that a match in features between the two apparitions is made possible by utilizng one of two phase shifts. While not conclusive, this ability to match features suggests long-term stability in many aspects of Halley's intrinsic activity.

Schleicher, David G.↗
NASA NTRS1991

Venus mapping

Semicontrolled image mosaics of Venus, based on Magellan data, are being compiled at 1:50,000,000, 1:10,000,000, 1:5,000,000, and 1:1,000,000 scales to support the Magellan Radar Investigator (RADIG) team. The mosaics are semicontrolled in the sense that data gaps were not filled and significant cosmetic inconsistencies exist. Contours are based on preliminary radar altimetry data that is subjected to revision and improvement. Final maps to support geologic mapping and other scientific investigations, to be compiled as the dataset becomes complete, will be sponsored by the Planetary Geology and Geophysics Program and/or the Venus Data Analysis Program. All maps, both semicontrolled and final, will be published as I-maps by the United States Geological Survey. All of the mapping is based on existing knowledge of the spacecraft orbit; photogrammetric triangulation, a traditional basis for geodetic control on planets where framing cameras were used, is not feasible with the radar images of Venus, although an eventual shift of coordinate system to a revised spin-axis location is anticipated. This is expected to be small enough that it will affect only large-scale maps.

Batson, R. M.↗
DOE OSTI2021

Mapping Post-Climate Change Biogeographical Regions with Deep Latent Variable Models

Forecasting future changes to biodiversity due to shifts in climate is challenging due to nonlinear interactions between species as recorded in their presence/absence data. This work proposes using variational autoencoders with environmental covariates to identify low-dimensional structure in species’ joint co-occurrence patterns and leveraging this simplified representation to provide multivariate predictions of their habitat extent under future climate scenarios. We pursue a latent space clustering approach to map biogeographical regions of frequently co-occurring species and apply this methodology to a dataset from northern Belgium, generating predictive maps illustrating how these regions may expand or contract with changing temperature under a future climate scenario.

Krapu, Chris↗
DOE OSTI2026

Re-evaluating probable maximum precipitation estimates: sensitivity to transposition domains and storm rotation using modern datasets

This study examines the sensitivity of Probable Maximum Precipitation (PMP) estimates to key methodological decisions embedded in the legacy approach adopted in the U.S. National Weather Service Hydrometeorological Reports No. 51 and No. 52. Although widely used for infrastructure design and risk regulation, fundamental aspects of PMP estimation—such as storm sample size, transposition domain, maximization procedures, and storm rotation—remain poorly constrained and lack formal guidance. Using the Red Rock watershed in Iowa as a case study, and leveraging the 2002–2023 NOAA Analysis of Record for Calibration (AORC) precipitation dataset, we systematically evaluate how each methodological choice, individually and in combination, influences PMP estimates. Our findings demonstrate that PMP is not a fixed physical upper bound but rather a modeling construct shaped heavily by user-defined assumptions. Notably, PMP values derived from modern gridded rainfall datasets can be substantially higher than the legacy estimate used in the original spillway design for Red Rock Dam. Decisions regarding storm sample size, domain extent, climatological window, and particularly storm rotation all contributed to higher PMP estimates. Storm rotation alone—a loosely constrained element in the current PMP practice—can amplify PMP by more than 25%. These results reveal the lack of standardized bounds in current PMP workflows and the need for systematic sensitivity and uncertainty analysis. As PMP estimation shifts toward probabilistic approaches, incorporating physically meaningful storm attributes will be key to developing more transparent, defensible methods for dam safety and climate-resilient infrastructure.

Probable maximum precipitation↗
DOE OSTI2025

Computational investigation of the impact of metal–organic framework topology on hydrogen storage capacity

Metal–organic frameworks (MOFs) are promising, tunable materials for hydrogen storage. For application under cryogenic operating conditions, past work has run into a ceiling on performance due to a trade-off in the volumetric deliverable capacity (VDC) versus the gravimetric deliverable capacity (GDC). In this study, we computationally constructed and screened 105 230 MOF structures based on 529 nets to explore the effect of underlying topology on the hydrogen storage performance of the resulting materials. A machine learning model was developed based on simulated hydrogen uptake to facilitate screening of the entire dataset, and it successfully identified the top 10% of materials with a root-mean-square error of approximately 1 g L −1 as validated by subsequent grand canonical Monte Carlo simulations. We identified a promising structure based on the tsx topology that exhibits both VDC and GDC higher than the current benchmark material, MOF-5. Our data-driven analysis indicates that nets with higher net density yield MOFs with enhanced volumetric and gravimetric surface areas, thereby improving maximum VDC while shifting the capacity trade-off toward higher GDC.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

An Intercomparison of High Spectral Resolution Lidar and Satellite Ocean Color Backscatter Retrievals

We present an intercomparison of satellite ocean color and high spectral resolution lidar (HSRL) retrievals of upper ocean particulate backscattering (bbp) and diffuse attenuation coefficients (Kd). Ocean retrievals of bbp and Kd have been performed over a wide variety of optical and ecological domains using the NASA Langley Research Center HSRL-1 (532 nm) and the recently upgraded HSRL-2 (532 & 355 nm). These datasets provide a unique opportunity to perform a critical assessment of the HSRL technique and to explore the utility of HSRL measurements for evaluating the quality of ocean color remote sensing retrievals on regional scales. A matchup dataset of co-located and high-quality HSRL and ocean color remote sensing measurements was created using data from research flights conducted over the western North Atlantic Ocean during the NASA sponsored SABOR, NAAMES, and ACTIVATE campaigns. Overall, comparisons showed a strong agreement between HSRL and ocean color retrievals of bbp and Kd, providing confidence in our ability to retrieve upper ocean optical properties using oceanographic lidar. However, comparisons from individual flights can exhibit deviations that were not attributed to shifts in water column optical domains. In one of these cases, the simultaneous HSRL atmosphere-ocean retrievals from two consecutive days and within the same region were used to identify ocean color atmospheric correction errors resulting from the presence of absorbing aerosols. These results highlight the utility of airborne HSRL as a standalone ocean observing technology and as an independent and calibrated technique for assessing the quality of ocean color remote sensing retrievals.

Brian Collister↗
DOE OSTI2021

Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings

Abstract With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗