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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data and code for Daily and Multi-Day Extreme Rainfall Analysis Under Future Climates Using Stochastic Storm Transposition and NEX-GDDP-CMIP6 Over CONUS

This data package provides inputs, codes, and outputs for a comprehensive analysis of projected changes in extreme precipitation across 10 regions of the continental United States, using 34 downscaled Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections, Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) dataset. These models are part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a coordinated climate modeling framework widely used to assess climate change impacts. The analysis applies a stochastic storm transposition method to quantify changes in extreme rainfall under two Shared Socioeconomic Pathway (SSP) climate scenarios—SSP2-4.5 (moderate emissions) and SSP5-8.5 (high emissions)—compared to historical conditions (1995–2014 vs. 2081–2100). The dataset includes rainfall depth estimates for extreme events with return periods from 2 to 500 years across multiple storm durations (1, 3, and 5 days) for each of the 10 U.S. regions. Weighted ensemble statistics are derived from individual ESM performance against historical precipitation patterns, enabling robust uncertainty quantification through both sign-based and permutation-test-based model agreement assessments. Key analyses address: (1) relative changes in extreme precipitation for each climate scenario, (2) differences between SSP scenarios (SSP5-8.5 vs. SSP2-4.5), (3) contrasts between rare and frequent events, and (4) variations between multi-day and daily storm durations. The workflow produces ensemble statistics—median, 5th, 25th, 75th, and 95th percentiles—along with model agreement metrics that identify regions and event types with robust climate change signals. The dataset includes: processed rainfall depth outputs (netCDF format) from the RainyDay Python package, ESM weights from historical performance evaluation using DayMet observations, ensemble statistics across all storm dimensions, and figures summarizing key findings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Future projections of storm surge in Hurricane Katrina and sensitivity to meteorological forcing resolution

In this study, we investigated whether and how the storm surge induced by Hurricane Katrina could change if it occurs in a future warmer climate, and the sensitivity of the changes to atmospheric forcing resolution. Climate model simulations of Hurricane Katrina at 27 km, 4.5 km, and 3 km resolutions were used to drive storm surge simulations in historical and future climates using the ADvanced CIRCulation (ADCIRC) model. We found that peak surge height increased significantly in the future with all forcing resolutions. However, the future projection is 22% greater in the 3 km forcing, typical of regional climate models, compared to the 27 km forcing, typical of state-of-the-art global climate models. Additionally, the spatial extent of the future change is highly sensitive to forcing resolution, extending most broadly under the 27 km forcing. Furthermore, we found that storm surge duration decreases in the future with all forcing resolutions due to increasing TC translation speed and decreasing ocean lifetime. However, the future change in the surge duration is sensitive to the forcing resolution, decreasing by 31% in the 27 km forcing and 6% in the 3 km forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Review of the August 1972 and March 1989 (Allen) Space Weather Events: Can We Learn Anything New From Them?

Abstract Updated summaries of the August 1972 and March 1989 space weather events have been constructed. The features of these two events are compared to the Carrington 1859 event and a few other major space weather events. It is concluded that solar active regions release energy in a variety of forms (X‐rays, EUV photons, visible light, coronal mass ejection (CME) plasmas and fields) and they in turn can produce other energetic effects (solar energetic particles (SEPs), magnetic storms) in a variety of ways. It is clear that there is no strong one‐to‐one relationship between these various energy sinks. The energy is often distributed differently from one space weather event to the next. Concerning SEPs accelerated at interplanetary CME (ICME) shocks, it is concluded that the Fermi mechanism associated with quasi‐parallel shocks is relatively weak and that the gradient drift mechanism (electric fields) at quasi‐perpendicular shocks will produce harder spectra and higher fluxes. If the 4 August 1972 intrinsic magnetic cloud condition (southward interplanetary magnetic field instead of northward) and the interplanetary Sun to 1 au conditions were different, a 4 August 1972 magnetic storm and magnetospheric dawn‐to‐dusk electric fields substantially larger than the Carrington event would have occurred. Under these special interplanetary conditions, a Miyake et al. (2012), https://doi.org/10.1038/nature11123 ‐like extreme SEP event may have been formed. The long duration complex 1989 storm was probably greater than the Carrington storm in the sense that the total ring current particle energy was larger.

Tsurutani, Bruce T.↗
DOE OSTI2026

Recent Tropical Cyclone Outer Size Increases in the Western North Atlantic

The outer size of a tropical cyclone (TC) plays a pivotal role in its impact through a modulation of wind duration, rainfall extent and storm surge. While global and basin scale trends in TC size have been examined previously, whether there have been regional changes over time remains unclear. Here, using a suite of observations and reanalyses, we detect an increasing TC outer size over the western North Atlantic during 1979–2022. Over the western subtropical North Atlantic where the largest changes are found, the area-averaged increase in the radius of 17 ms -1 winds is about 7.5% over this period. Analysis of the large-scale environment reveals that the air-sea thermodynamic state has become more favorable for storm intensification, and thereby expansion, over that region. Further, numerical model simulations link changes in the environment with those in storm size, and suggest a combination of natural variability and external forcing in driving them.

Balaguru, Karthik [Pacific Northwest National Labo↗
DOE OSTI2024

Increasing extreme hourly precipitation risk for New York City after Hurricane Ida

The remnants of Hurricane Ida caused major damage and death in the United States on September 1st, 2021, and 11 people drowned in flooded basement apartments within New York City (NYC). It was catastrophic because the maximum hourly precipitation intensity, recorded as 3.47 inches (88.1 mm) per hour at Central Park, was unprecedentedly high for the NYC region. The stormwater infrastructure in NYC is built for 1.75 inches (44.5 mm) per hour, and so understanding the dynamic risk associated with Ida can inform city planning efforts for climate change’s impact on short duration extreme precipitation events. We contextualize this storm’s record-breaking hourly intensity within the historical record as well as project its risk in the near- to medium-term future using nonstationary stochastic models. These models are conditioned on average temperature (T avg ) and cooling degree day (CDD) projections from three climate models as a covariate, each with a SSP 126 and SSP 370 scenario. The likelihood of such a storm was slowly increasing even before Ida happened, but the projected aggregate reoccurrence risk of an event of Ida’s magnitude over time from the non-stationary models ranges from 4 to 52 times higher than the risk given by the stationary model. Using CDD as a covariate resulted in risks that were more than twice the magnitude than when using T avg . Presenting both covariates provides a broader envelope of uncertainty, which highlights the importance and nuances in the choice of a regionally appropriate covariate for non-stationary risk analysis.

Mossel, Carolien↗
DOE OSTI2026

Phoenix Dust Storm (PHX-DUST) Scale: A Cooperatively Developed Dust Storm Scale for Phoenix, Arizona

Using extensive localized meteorological and air quality data for central Arizona from 2010 to 2023, we create a postevent dust storm scale for use by the primary stakeholders in central Arizona for the Phoenix metropolitan area called the Phoenix Dust Storm (PHX-DUST) scale. To ensure usability across a wide spectrum of users, a core concept was to “keep it simple.” The PHX-DUST scale is based on (i) maximum dust concentration for particulate matter (PM10) across the network in micrograms per cubic meter which determines category, e.g., “category 5 (the highest observed category)” and category 4; (ii) the number of network monitors achieving dust concentrations above a threshold of 500 μg m−3 (categorized as “widespread” or “isolated,” as measures of spatial extent); (iii) a duration parameter (which determines “long duration” or “short duration”); and (iv) a measure of wind speed over the affected area (maximum wind gust recorded over the network, which may not be the maximum wind of the storm). Using this index for the 189 dust storms impacting the Phoenix metropolitan area for the period 2010–23, the most severe dust storm occurred on 5 July 2011 and is classified as a “Category 5, Widespread, Long-Duration High-Gust” event. This initial attempt at defining the PHX-DUST scale holds valuable applications for research, education, and applied analyses. It demonstrates that a consortium of interested groups can effectively work to create products benefiting the weather community and general public. Here, the PHX-DUST scale can also serve as a foundational framework for application in other regions where dust storms pose a threat to the well-being of populations, infrastructure, and ecosystems.

Atmosphere↗
DOE OSTI2024

Anticipating how rain-on-snow events will change through the 21st century: lessons from the 1997 new year’s flood event

The California-Nevada 1997 New Year’s flood was an atmospheric river (AR)-driven rain-on-snow (RoS) event and remains the costliest in their history. The joint occurrence of saturated soils, rainfall, and snowmelt generated inundation throughout northern California-Nevada. Although AR RoS events are projected to occur more frequently with climate change, the warming sensitivity of their flood drivers across scales remains understudied. We leverage the regionally refined mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) to recreate the 1997 New Year’s flood with horizontal grid spacings of 3.5 km across California, with forecast lead times of up to 4 days, and across six warming levels ranging from pre-industrial conditions to +3.5° C. We describe the sensitivity of the flood drivers to warming including AR duration and intensity, precipitation phase, intensity and efficiency, snowpack mass and energy changes, and runoff efficiency. Our findings indicate current levels of climate change negligibly influence the flood drivers. At warming levels ≥ 1.7° C, AR hazard potential increases, snowpack nonlinearly decreases, antecedent soil moisture decreases (except where the snowline retreats), and runoff decreases (except in the southern Sierra Nevada where antecedent snowpack persists). Storm total precipitation increases, but at rates below warming-induced increases in saturation-specific humidity. Warming intensifies short-duration, high-intensity rainfall, particularly where snowfall-to-rainfall transitions occur. This study highlights the nonlinear tradeoffs in 21st-century RoS flood hazards with warming and provides water management and infrastructure investment adaptation considerations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Long-Duration High-Resolution Large-Eddy Simulations of Hurricane Laura (2020) for Infrastructure Design

This dataset provides long-duration large-eddy simulations (LES) of Hurricane Laura (2020), capturing the turbulent boundary-layer wind field during the storm's passage at horizontal grid spacings of 55.55 m and 10 m. Hurricane Laura made landfall as a Category 4 storm in southwestern Louisiana at 06:00 UTC on 27 August 2020, producing widespread damage to coastal infrastructure. The simulations characterize the spatial and temporal variability of the turbulent wind at heights spanning the lowest 300 m of the boundary layer, throughout the approach and passage of the storm's eyewall over a fixed location. To resolve the spatial heterogeneity of wind and turbulence conditions across the storm, seven independent refined LES runs are performed at radial locations spanning the storm relative to its direction of motion. The refinement positions are defined on a normalized radial coordinate r ̂=r/R, where R≈21 km is Laura's radius of maximum wind, and lie between r ̂=-1.5 and r ̂=+1.5 in ∆r ̂=0.5 increments along a line perpendicular to the storm's track at the midpoint of the simulation. Negative r ̂ corresponds to locations south-southwest of the storm center and positive r ̂ to locations north-northeast. The dataset is segmented into seven radial refinement groups.

17 WIND ENERGY↗
DOE OSTI2024

Current Practices in Distribution Utility Resilience Planning for Winter Storms

This report is part of a series of hazard-focused case studies examining common practices in electric utility resilience planning. We use standard terminology defining resilience as the ability to anticipate, withstand, absorb, and recover from hazards that cause long duration outages. We distinguish between reliability and resilience using Institute of Electrical and Electronics Engineers (IEEE) 1366-2022, which defines major events as an event that exceeds reasonable design and/or operational limits of the electric power system. Resilience planning is focused on major event days and reliability planning is focused on nonmajor event days. Utility resilience plans are assessed according to common resilience components identified in existing resilience frameworks. The focus of this report is on winter storms in which the primary hazards are heavy snowfall, freezing rain, ice, extreme cold, severe wind, and flooding. These hazards can also contribute to generation shortages, resulting in bulk power system impacts that have consequences for the distribution system, such as load shedding. Stand-alone reports focusing on wildfires and nonwinter storms have been published in parallel with this report. This report can be used as a starting point for understanding potential investment prioritization processes and investment options. This report is intended to improve utility resilience planning by supporting constructive dialogue among utilities, regulators, and other stakeholders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Current Practices in Distribution Utility Resilience Planning for Hurricanes and Non-Winter Storms

This report is part of a series of hazard-focused case studies examining common practices in electric utility resilience planning. We use standard terminology defining resilience as the ability to anticipate, withstand, absorb, and recover from hazards that cause long duration outages. We distinguish between reliability and resilience using IEEE 1366-2022, which defines "major events" as "an event that exceeds reasonable design and/or operational limits of the electric power system." Resilience planning is focused on "major event days" and reliability planning is focused on non-major event days. Utility resilience plans are assessed according to common recommended resilience components that we have identified in existing resilience frameworks. The focus of this report is on hurricanes and severe storms in which the primary hazards are precipitation or high winds. We exclude winter storms with primary hazards of ice and extreme cold, as they are a unique set of hazards with different resilience considerations. Standalone reports focusing on wildfires and winter storms have been published in parallel with this report. This report can be used as a starting point for understanding potential investment prioritization processes and investment options. This report is intended to improve utility resilience planning by supporting constructive dialogue among utilities, regulators, and other stakeholders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Multispectral UAV imagery of experimental freshwater wetlands under 5 ppt saltwater intrusion, Louisiana, 2023 and 2024

Multispectral imagery was collected using an unmanned aerial vehicle (UAV) to evaluate how freshwater vegetation responds to short-term simulated saltwater intrusion events. The purpose of this data collection was to understand how plant health changes in response to acute salinity exposure, which is increasingly relevant in coastal wetland ecosystems facing sea level rise and storm surge events, such as in coastal Louisiana. Three experimental saltwater intrusions were conducted at a salinity of approximately 5 parts per thousand (ppt) for durations of 6-days, 10-days, and 17-days. UAV flights occurred both before and after each treatment. The resulting imagery was processed using Pix4DMapper software to georeference the images and generate orthomosaics. The multispectral sensor used in this study captures reflectance in five bands: blue, green, red, red-edge, and near-infrared. The uploaded data consist of georeferenced .tif orthomosaics for each spectral band, which are compatible with GIS software for vegetation analysis. This imagery can be utilized in investigations into vegetation stress, remote sensing of freshwater wetland ecosystems, and modeling of plant response to environmental changes.

EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS↗
DOE OSTI2025

Applying Corrective Machine Learning in the E3SM Atmosphere Model in C++ (EAMxx)

The Simplified Cloud-Resolving E3SM Atmosphere Model (SCREAM) is the newest addition to the family of Earth System Models capable of explicitly resolving convective systems. SCREAM is a kilometer-scale configuration of the advanced E3SM Atmosphere Model (EAMxx), designed for heterogeneous systems. While the enhanced accuracy of kilometer-scale modeling offers significant benefits, it comes with a substantial computational cost, limiting feasible simulation durations to only a few years, even on the fastest supercomputers. Machine learning presents an opportunity for scientists to achieve the high accuracy of storm-resolving models at a significantly reduced cost. Building on the previous success of applying corrective machine learning (ML) to the FV3 model, this study explores the effects of implementing corrective ML in EAMxx-SCREAM. We also address the computational challenges of integrating the corrective ML, which is written in Python, with the C++/Kokkos EAMxx driver, as well as the potential pitfalls of generalizing an approach that was effective with one atmosphere model to another.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

State Spotlight on Resilience: The Michigan Public Service Commission and Data Informed Accountability

Resilience activities are an emerging area within regulatory utility policy. As resilience frameworks develop, many Public Utility Commissions (PUCs) are looking to initiate resilience activities and have identified enhancing grid reliability as a practical starting point. Reliability describes the grid’s ability to deliver electricity steadily and without interruptions under normal or “blue sky” conditions. Reliability is measured using industry standard metrics such as outage frequency (SAIFI) and outage duration (SAIDI). Resilience is broader, reflecting the grid’s ability to both withstand and recover from disruptions, particularly those caused by extreme events such as severe storms. A reliable grid minimizes routine disruptions; a resilient grid ensures the system can recover quickly when disruptions occur, preventing them from escalating into widespread or prolonged crises.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Relationships Between Mesoscale Convective System Properties and Midlevel Dynamic Perturbations

Abstract Past studies implicate dynamic anomalies operating on subsynoptic scales as a possible initiation source of summertime (July–August) mesoscale convective systems (MCSs) in the central United States during northwesterly flow regimes. To improve our understanding of warm season MCSs occurring over a variety of flow regimes, we track midlevel (600 hPa) vorticity perturbations (“MPs”) as 2D objects comprising wavelengths of 500–2,500 km over the central US from May–August of 2004–2021. We perform statistical analysis of relationships between metrics of MP objects (e.g., duration, size, intensity, and origin) and high‐resolution MCS precipitation characteristics (e.g., duration, total rainfall, rain coverage area, and motion) that occur while collocated with or in the absence of MPs to discern predictive capability of background dynamic features on storm precipitation potential. Although the majority of MPs collocated with MCS initiation occur during July–August, a significant number (40%) occur between May and June. Northwesterly flow MPs comprise a relative minority of our events, suggesting that MPs can affect MCSs across a variety of warm season flow regimes. MPs affecting MCSs initiated primarily over the high plains near the central Rockies. Only approximately 20% of tracked MCS initiation events were collocated with MPs, but these storms produced ∼25% greater lifetime rainfall and coverage area, and ∼29% more stratiform rain than non‐MP‐induced MCSs. In general, larger and more vigorous MPs resulted in more hydrologically impactful MCSs. The most directly attributable benefit to MCS initiation was from MP‐enhanced background vertical motion and thermodynamic instability (e.g., increased CAPE).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Applying corrective machine learning in the E3SM atmosphere model in C++ (EAMxx)

The Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) is the newest addition to the family of earth system models capable of explicitly resolving convective systems. SCREAM is a kilometer-scale configuration of the advanced E3SM Atmosphere Model (EAMxx), designed for heterogeneous computing architectures. While the enhanced accuracy of kilometer-scale modeling offers significant benefits, it comes with a substantial computational cost, limiting feasible simulation durations to only a few years to a few decades, even on the fastest supercomputers. Machine learning presents an opportunity for scientists to achieve the high accuracy of storm-resolving models at a significantly reduced cost. Building on the previous success of applying corrective machine learning (ML) to the FV3GFS earth system model, this study explores the effects of implementing corrective-ML in EAMxx-SCREAM. We also address the computational challenges of integrating our implementation of corrective-ML, which is written in Python, with the C++/Kokkos EAMxx driver, as well as potential reasons why this approach has not proved as effective for EAMxx-SCREAM as for FV3GFS.

Environmental sciences↗
DOE OSTI2025

A Climatology and Life‐Cycle Characteristics of Atmospheric Fronts and Their Associated Precipitation

Abstract Atmospheric fronts are one of the main sources of mid‐latitude variability. We employ a novel method for identifying and tracking fronts and frontal precipitation. Thermal and dynamical variables are used to identify fronts as areal objects in space, which are tracked in time using the open‐source TempestExtremes software package. Precipitation objects are co‐located to identify frontal precipitation. The method is subjected to validation and sensitivity tests using manually curated data from the National Weather Service. Climatologies of fronts and frontal precipitation are computed from reanalysis and observations; fronts are present upwards of 14% of the time in the storm tracks, and represent the majority (up to 90%) of total and extreme precipitation. Novel aspects of the method are showcased through the lifetime characteristics of fronts across North America. Three sets of warm and cold fronts were discovered, and their duration, distance‐traveled, and translation velocity are examined. Plain Language Summary Mid‐latitude low‐pressure systems and weather fronts are important for our day‐to‐day experience of weather events, particularly in the mid‐latitudes. This work makes use of standardized atmospheric data and creates a method of automatically tracking these important atmospheric features and their precipitation to quantify their relative role in global precipitation. Weather fronts are persistent in the mid‐latitudes and are associated with the majority of precipitation–particularly the most intense precipitation. Trajectories of fronts over North America are categorized to create a set of archetypal fronts that occur in that region. The differences between these types of fronts are characterized. Key Points An automated, efficient, and skillful frontal detection algorithm is developed and validated Fronts contribute a larger fraction of extreme precipitation than all precipitation in mid‐latitude storm tracks Fronts across North America have substantial variation in characteristics depending on their origin location

extratropical cyclone↗
DOE OSTI2025

Reviews and syntheses: Variable inundation across Earth's terrestrial ecosystems

Abstract. The structure, function, and dynamics of Earth's terrestrial ecosystems are profoundly influenced by how often (frequency) and how long (duration) they are inundated with water. A diverse array of natural and human-engineered systems experience temporally variable inundation whereby they fluctuate between inundated and non-inundated states. Variable inundation spans extreme events to predictable sub-daily cycles. Variably inundated ecosystems (VIEs) include hillslopes, non-perennial streams, wetlands, floodplains, temporary ponds, tidal systems, storm-impacted coastal zones, and human-engineered systems. VIEs are diverse in terms of inundation regimes, water chemistry and flow velocity, soil and sediment properties, vegetation, and many other properties. The spatial and temporal scales of variable inundation are vast, ranging from sub-meter to whole landscapes and from sub-hourly to multi-decadal. The broad range of system types and scales makes it challenging to predict the hydrology, biogeochemistry, ecology, and physical evolution of VIEs. Despite all experiencing the loss and gain of an overlying water column, VIEs are rarely considered together in conceptual, theoretical, modeling, or measurement frameworks and approaches. Studying VIEs together has the potential to generate mechanistic understanding that is transferable across a much broader range of environmental conditions, relative to knowledge generated by studying any one VIE type. We postulate that enhanced transferability will be important for predicting changes in VIE function in response to global change. Here we aim to catalyze cross-VIE science that studies drivers and impacts of variable inundation across Earth's VIEs. To this end, we complement expert mini-reviews of eight major VIE systems with overviews of VIE-relevant methods and challenges associated with scale. We conclude with perspectives on how cross-VIE science can derive transferable understanding via unifying conceptual models in which the impacts of variable inundation are studied across multi-dimensional environmental space.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High Performance, High Fidelity: A GPU‐Accelerated Doubly‐Periodic Configuration of the Simple Cloud‐Resolving E3SM Atmosphere Model Version 1 (DP‐SCREAMv1)

The development of the Simplified Cloud Resolving Energy Exascale Earth System Atmosphere Model (SCREAMv1) enables global storm-resolving simulations on modern GPU-based supercomputers. However, the high computational cost of SCREAMv1 limits its routine use for process-level studies, creating a need for efficient proxy configurations. This study addresses this gap by introducing DP-SCREAMv1, a doubly periodic cloud-resolving model designed to be fully consistent with SCREAMv1 while enabling high-resolution, long-duration simulations at significantly reduced computational expense by simulating a limited doubly periodic domain rather than the entire globe. Built on a C++/Kokkos architecture, DP-SCREAMv1 achieves exceptional performance scalability on GPU systems and includes a rich library of cases for validation and scientific exploration. In this work, we demonstrate short wall-clock times at SCREAMv1's default resolution and show that DP-SCREAMv1 supports routine execution of large-domain, high-resolution experiments that were previously challenging in practice. Furthermore, we show that DP-SCREAMv1 enables routine execution of “Giga-LES” style simulations and facilitates large-domain, high-resolution simulations that were recently considered burdensome to perform. These results document an efficient, fully consistent process-level configuration for SCREAMv1 (DP-SCREAMv1) and illustrate its use for long-duration and large-domain experiments at cloud-resolving to eddy-permitting resolution.

Environmental sciences↗