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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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14 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Projecting Changes in Societally Impactful Northeastern U.S. Snowstorms

The northeastern United States is vulnerable to many impacts from snowfall-producing winter cyclones that are amplified by the proximity of population centers to storm tracks. Historically, climatic snowfall assessments have centered around seasonal means even though local impacts typically occur at scales of hours to days. To detect snowstorms at the event level, an objective algorithm is defined based on the Regional Snowfall Index. The metric collocates storm snowfall with population to produce statistics of snowstorms with societal impacts. When applied to the Community Earth System Model Large Ensemble, broad declines in snowstorm frequency are projected by the later 21 st century. These decreases are primarily due to a warmer atmosphere less conducive to snowfall as the predominant precipitation type. However, we note reductions are less significant for major events, since more hostile thermodynamic environments are partially offset by increased precipitation associated with cyclones that dynamically drive high-impact snowstorms.

58 GEOSCIENCES↗
DOE OSTI2026

Lake-Effect Snowstorm Events and Associated Snowfall Totals Integrated from NOAA Storm Reports, ERA5, and HRRR for the Laurentian Great Lakes (1997–2024)

Lake-effect snowstorms are localized, impactful winter weather phenomena that can generate substantial snowfall totals and pose significant challenges for forecasting, transportation, and regional infrastructure. To support the analysis and modeling of these events, this dataset compiles observational reports of lake-effect snowstorms alongside corresponding snowfall estimates derived from gridded atmospheric datasets. The observational component of the data originates from the National Weather Service (NWS) winter storm report, subset to lake-effect snow event type, covering 1997–2024. For each lake-effect snow event, this data provides the impacted county, event start and end datetimes at an hourly resolution, as well as relevant storm narratives. The complementary reanalysis-derived data is sourced from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and High-Resolution Rapid Refresh (HRRR) gridded data. For both gridded datasets, the maximum total snowfall (in units mm) was extracted, constrained by the county and datetimes specified by the observational report. ERA5 data covers the entire observational period (1997–2024), whereas HRRR data is only available from November 2016 – December 2024. Three CSV files are provided here: (1) the observational lake-effect snow event report, (2) ERA5 maximum snowfall detections for each event, and (3) HRRR maximum snowfall detections for each event. Relevant data from the observational files, such as impacted state and county, event datetimes, and event IDs, were included for convenience. Users can inspect and visualize the data using tools such as Microsoft Excel and Python pandas/matplotlib packages. This dataset may support a variety of applications, including climatological analyses of lake-effect snowfall, evaluation of snowfall representation in atmospheric datasets and numerical weather prediction models, and the development of machine learning approaches for detecting or predicting lake-effect snowfall events.

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > SOLID↗
DOE OSTI2021

Remote Sensing of Electric Fields Observed Within Winter Precipitation During the 2020 Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Field Campaign

Aircraft electric fields from NASA's Lightning Instrument Package (LIP) were coupled with other airborne and ground-based, and in situ measurements to understand electrification in winter clouds that did not produce lightning. The measurements were made during seven research flights by a NASA ER-2 during the 2020 Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Observed total electric field magnitudes were as high as 80 V m -1 and variability in the electric field was observed along the flight path of the ER-2, indicating horizontal and/or vertical inhomogeneity in the cloud's electrical structure. X-band airborne radar data indicated 20-dBZ echo tops above 5 km in regions where electrification exceeded 10 V m -1 . In these regions, 85-GHz brightness temperatures (TB) from an airborne radiometer were lower than 265 K, with the lowest T B (~210 K) associated with ice scattering collocated with the strongest electric fields. In situ microphysical measurements from the NASA P-3 aircraft on February 7 indicated that regions near strong electric field contained supercooled water, rimed ice hydrometeors, ice water p- content as high as 1 g m -3 , liquid water content as high as 0.15 g m -3 , and supersaturation as high as 3.5%. Furthermore, these observations support the role of mixed phase microphysics in the generation of electric fields in clouds. In three case studies, ground based S-band polarimetric radar observed depolarization streaks in differential reflectivity near areas where the strongest electrification was observed. This observation reinforces the utility of depolarization streaks to identify areas of electrification prior to lightning occurrence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Field Campaign Report

The scientific focus here is interagency collaboration to support a National Aeronautics and Space Administration (NASA)-sponsored 2023 field campaign: Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS). IMPACTS objectives are to provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; study how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and improve snowfall remote-sensing interpretation and modeling to significantly advance predictive capabilities. The relevance to The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility's mission was to support the Interagency Council for Advancing Meteorological Services (ICAMS)’ objectives to enhance communication and coordination, and promote sharing, within and across agencies. NASA therefore requested to borrow ARM’s two-dimensional stereo probe (2DS) for possible use during the 2023 IMPACTS project. Additionally, this use of the 2DS could improve understanding of ARM 2DS measurements collected on previous field campaigns. The plan was to have the 2DS available for possible backup deployment on the NASA Lockheed Orion P-3 aircraft during the 2023 IMPACTS field project. NASA has a 2DS probe and the ARM 2DS is a hot swappable probe in case the project's 2DS fails. The 2DS is a critical probe and we had issues during a 2022 field project that prevented us from obtaining 2DS data on several flights. Hence, we wanted a hot swappable 2DS on stand-by. We have a data acquisition system, along with a backup data acquisition system for the 2DS, so we only needed the 2DS probe. David Delene was responsible for the use of the 2DS on the NASA P-3 aircraft. He has experience deploying the 2DS on several aircraft, including previously on the NASA P-3 in 2022. The team ensured that the aircraft wiring and deployment of the 2DS were completed correctly for the ARM 2DS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Efficient propagation of systematic uncertainties from calibration to analysis with the SnowStorm method in IceCube

Efficient treatment of systematic uncertainties that depend on a large number of nuisance parameters is a persistent difficulty in particle physics and astrophysics experiments. Where low-level effects are not amenable to simple parameterization or re-weighting, analyses often rely on discrete simulation sets to quantify the effects of nuisance parameters on key analysis observables. Such methods may become computationally untenable for analyses requiring high statistics Monte Carlo with a large number of nuisance degrees of freedom, especially in cases where these degrees of freedom parameterize the shape of a continuous distribution. In this paper we present a method for treating systematic uncertainties in a computationally efficient and comprehensive manner using a single simulation set with multiple and continuously varied nuisance parameters. Finally, this method is demonstrated for the case of the depth-dependent effective dust distribution within the IceCube Neutrino Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Multi-level impacts of climate change and supply disruption events on a potato supply chain: An agent-based modeling approach

Context: The world is experiencing frequent extreme weather events like droughts, snowstorms, and shifting of seasons due to climate change. Increased frequency and severity of these extreme weather events threaten food security because agriculture depends on climate conditions. Impacts of climate change on the agricultural system not only occur at the grower’s level, but also cascade to other levels along the supply chain. Objective: In this study, we aim to quantify a wide range of economic impacts of different extreme climate events on different stages of a food supply chain. Methods: We chose the potato supply chain in Idaho as a case study. We developed a multi-echelon supply chain simulation model using an agent-based modeling (ABM) approach with five types of agents—farmers, shippers, processors, retailers, and logistics companies. In addition to the business-as-usual (BAU) scenario, we designed two climate-related disruption events—drought and snowstorm. We quantified the heterogeneous impacts at different stages of the supply chain for both fresh and processed potatoes using key performance indicators (KPIs) including revenues, prices, lead times, traded quantities, and food waste quantity. Results and Conclusion: The impacts of the disruption events are different on different agents in the supply chain for different product categories. The price hike of fresh potatoes is far higher than processed potatoes during disruption events. This price hike makes consumers switch to processed potatoes, which require more fresh potatoes as an input that further reinforces the price hike. However, because processed potatoes have an elastic demand, once their prices go up due to higher input cost, their demand drops. Non-contracted farmers gain additional revenues from the disruption events, whereas contracted farmers incur a loss due to lock-in price and lower than usual harvest. Significance: The methodology developed in this study could be applied to other food and agricultural supply chains for understanding the vulnerabilities at agent levels due to climate change disruption events. Furthermore, the findings would help develop mitigation strategies or policies to improve the well-being of the overall supply chain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Multi-Level Impacts of Extreme Climate-Related Events on Food Supply Chain: Idaho Potato's Case Study

Background: The world is experiencing frequent extreme weather events like droughts, floods, snowstorms, and shifting of seasons due to climate change. Increasing frequencies and severity of these extreme weather events threaten food security because agriculture is extremely vulnerable to climate change. Higher temperature reduces overall yields of the desirable crops but proliferates weeds and harmful pests. Current data shows that every year U.S. farmers spend more than 11 billion USD to fight weeds and this number is expected to rise in the coming years due to climate change. Due to droughts, the water required for irrigation is becoming scarcer. Since many areas are becoming climatically unusable for crop production, the amount of farmland area is shrinking rapidly. In addition, changes in precipitation patterns are causing crop failures and negatively impacting expected yields. In this study, we investigated how different extreme climate events impact different stages of a food supply chain. Approach: As a case study, we chose Idaho’s potato supply chain (SC). Idaho is the top potato producing state in the U.S. and contributes around 30% of the nation’s potato production. Using the agent-based modeling (ABM) approach, we developed a multi-echelon potato supply chain computer simulation model with six types of agents – seed producers, farmers, shippers, processors, retailers, and logistics companies. In addition to the baseline scenario, we developed two additional climate-related disruption events – drought and snowstorm. We investigated how each of these disruption events impacts different stakeholders across the supply chain in terms of revenue and some other key performance metrics such as lead time, food waste, number of delayed orders, and order fulfillment rate. Results: Extreme climate events not only impact the growers but there is also cascading ripple effects downstream of the supply chain. The methodology developed in this study could be applied to other food and agricultural SC to understand the vulnerabilities of the different links. The findings would potentially help to adopt new policies for the well-being of the overall SC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Challenges and Capabilities in Estimating Snow Mass Intercepted in Conifer Canopies With Tree Sway Monitoring

Snowpack accumulation in forested watersheds depends on the amount of snow intercepted in the canopy and its partitioning into sublimation, unloading, and melt. A lack of canopy snow measurements limits our ability to evaluate models that simulate canopy processes and predict snowpack. We tested whether monitoring changes in wind-induced tree sway is a viable technique for detecting snow interception and quantifying canopy snow water equivalent (SWE). Over a 6 year period in Colorado, we monitored hourly sway of two conifers, each instrumented with an accelerometer sampling at 12 Hz. We developed an approach to distinguish changes in sway frequency due to thermal effects on tree rigidity versus intercepted snow mass. Over 60% of days with canopy snow had a sway signal that could not be distinguished from thermal effects. However, larger changes in tree sway could not generally be attributed to thermal effects, and canopy snow was present 93%–95% of the time, as confirmed with classified PhenoCam imagery. Using sway tests, we converted changes in sway to canopy SWE, which were correlated with total snowstorm amounts from a nearby SNOTEL site (Spearman r = 0.72 to 0.80, p < 0.001). Greater canopy SWE was associated with storm temperatures between -7°C and 0°C and wind speeds less than 4 m s -1 . Lower canopy SWE prevailed in storms with lower temperatures and higher wind speeds. Monitoring tree sway is a viable approach for quantifying canopy SWE, but challenges remain in converting changes in sway to mass and distinguishing thermal and snow mass effects on tree sway.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Visualizing Comparisons of Bill of Materials

Protecting critical infrastructure from cyber attacks, natural disasters, and other disruptions is a priority of the U.S. Government. Critical infrastructure includes providing electricity to homes and businesses, supplying natural gas for heating, and producing renewable energy sources. A loss of these services, as seen in the Solarwinds supply chain attack in 2020 , Texas snowstorm of 2021, the Colonial Pipeline cyber incident of 2021, and the Washington power substation attacks in 2022 result in high costs to consumers, disruption of everyday life, and even death. To protect the infrastructure, we first have to know what equipment we are protecting. The complexity of distributed manufacturing and development coupled with the increasing prevalence of cyber and supply chain attacks necessitates a greater understanding of the hardware and software components that comprise equipment in critical infrastructure. When a vulnerability in a single software library can have disastrous consequences, it is vital to understand critical equipment and systems at a granular level. This need has led to increased energy around the development and incorporation of bill-of-materials (BOM) into existing asset management practices to aid in mitigating, and responding to future attacks \cite{noauthor_software_nodate}. While much of the current research is devoted to creating BOMs, it is equally important to develop methodologies for leveraging BOMs to answer questions, such as: How has my software changed? Are two pieces of equipment equivalent? Does this piece of equipment that just arrived match my historical information? In this work, we demonstrate how BOMs can be represented by graph structures. We then describe how these structures can be fed into a graph comparison algorithm to produce a novel interactive visualization that allows us to not only identify differences in BOMs, but show exactly where they are in the product.

Jones, Rebecca D.↗
DOE OSTI2022

Radar Retrieval Evaluation and Investigation of Dendritic Growth Layer Polarimetric Signatures in a Winter Storm

Abstract This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed K dp and Z DR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using Z DR and K dp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals. significance statement Developing snow is often detectable using weather radars. Meteorologists combine these radar measurements with mathematical equations to study how snow forms in order to determine how much snow will fall. This study evaluates current methods for estimating the total number and mass, sizes, and shapes of snowflakes from radar using images of individual snowflakes taken during two aircraft legs. Radar estimates of snowflake properties were most consistent with aircraft data inside regions with prominent radar signatures. However, radar estimates of snowflake shapes were not consistent with observed shapes estimated from the snowflake images. Although additional research is needed, these results bolster understanding of snow-growth physics and uncertainties between radar measurements and snow production that can improve future snowfall forecasting.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Evaluation of Physical Microphysical Property Retrieval Algorithms During the 2020 IMPACTS Field Campaign

The NASA Investigation of Microphysics and Precipitation for Atlantic Coast Threatening Snowstorms (IMPACTS) field campaign provides high-quality, high-altitude aircraft lidar (532 nm), radar (W-band) and in-cloud microphysical aircraft data taken during wintertime storm events impacting the United States. This study evaluates two mass-dimensional relationships (Brown and Francis (1995, BF95); Heymsfield (2014, H14) and two lidar-radar microphysical retrieval algorithms (Cloudsat and CALIPSO Ice Cloud Property Product (2C-ICE); VarPy (a variational method derived from the satellite lidar-radar data community)) to estimate aircraft-retrieved volume extinction coefficient (σ), ice water content (IWC), and effective radius (r e ) during the 2020 IMPACTS deployment. BF95 and H14 have a close 1:1 correlation (R 2 = 0.98) with in-situ observations of σ. However, only BF95 displays a linear, consistent, and almost temperature-independent low bias for IWC and r e , which likely arises from the environmental conditions used to determine each. Unlike the field-campaign-derived BF95 and H14 relationships, VarPy and 2C-ICE directly ingest the aircraft-based lidar and radar data to simulate σ, IWC, and r e . For all three microphysical parameters, VarPy and 2C-ICE retrieval errors became notably more pronounced around the dendritic growth zone (-15°C to -10°C) and near freezing (≥-5°C), which suggests that both algorithms experience difficulty addressing riming and aggregation processes and with larger particles (dendrites and plates) due in part to their simplified ice particle assumptions. However, the mean-melt diameter ice-particle assumption did yield more accurate IWC estimates, which led to slightly better overall results for VarPy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Final DOE-ASR Report for the Project “Advancing our Understanding and the Remote Sensing of Ice Clouds”

This project has evolved during its execution, and what follows are the key project findings. This project has arguably provided the first global view of how cirrus cloud (defined as having cloud base temperature T < 235 K) nucleation physics (evaluated through satellite retrievals of ice particle number concentration Ni, effective diameter De and ice water content IWC) evolves with the seasons for a given temperature, latitude zone and surface type (e.g. ocean vs. land), based on a new satellite remote sensing method developed for this project. The retrieval method is unique in that it is very sensitive to the small ice crystals that govern the number concentration Ni, allowing Ni to be retrieved. The method currently samples single-layer cirrus clouds having visible optical depth ranging from about 0.3 to 3.0, using co-located observations from the Infrared Imaging Radiometer (IIR) and from the CALIOP (Cloud and Aerosol Lidar with Orthogonal Polarization) lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) polar orbiting satellite, employing IIR channels at 10.6 μm and 12.05 μm. Retrievals of Ni are primarily used to estimate the cirrus cloud formation mechanism; that is, either homo- or heterogeneous ice nucleation (henceforth hom and het). This is possible since, in general, hom produces more than an order of magnitude more ice crystals than does het. Thus the retrievals provide insight on how these mechanisms change with the seasons for a given latitude zone or region, based on the years 2008 and 2013. Using a conservative criterion for hom cirrus, on average, the sampled cirrus clouds formed through hom occur about 43% of the time in the Arctic and 50% of the time in the Antarctic, and during winter at mid-latitudes in the Northern Hemisphere, hom cirrus occur 37% of the time. Elsewhere (and during other seasons in the Northern Hemisphere mid-latitudes), this hom cirrus fraction is lower, and it is lowest in the tropics. Thus, the microphysical properties of cirrus clouds in the Polar Regions are much different than they are in the tropics; something unknown prior to this study. Moreover, the frequency of cirrus cloud occurrence in the Polar Regions varies strongly with season, peaking during winter in the Arctic and during spring in the Antarctic. Considering these seasonal changes in microphysics and inferred cloud coverage, this leads us to speculate that the buildup of Arctic cirrus during winter may significantly contribute to tropospheric heating in that region, possibly affecting winter jet-stream dynamics and mid-latitude weather patterns through the thermal-wind balance relationship. This cirrus cloud research provides essential guidance for realistically representing cirrus clouds in climate models; guidance previously unavailable. For example, mid-latitude hom cirrus were widespread during winter over or nearby mountainous terrain, evidently due to mountain-induced waves that produce strong updrafts at cirrus cloud levels. The treatment of turbulent mountain stress and gravity waves will likely need to be improved in climate models in order to adequately represent cirrus clouds outside the tropics. Another goal of this project was to develop a ground-based 94-GHz radar retrieval for winter snowstorms, based on (1) an improved analytical framework describing the interaction of radiation from radar with snowfall and (2) the development of a steady-state snow growth model that predicts the height-evolution of the ice particle size distribution through ice particle growth by vapor diffusion, aggregation and riming (i.e. the growth of snow through collisions with supercooled cloud droplets). Although activities (1) and (2) were completed, there was insufficient time to test and finalize the radar retrieval scheme. However, activity (2) provided a new method for relating ice particle mass “m” and projected area “A” to the ice particle maximum dimension “D”. The ice cloud microphysical processes (which determine ice cloud radiative properties) in climate models are parameterized in terms of these m-D and A-D relationships. By improving these relationships, the ice cloud radiative properties in Community Atmosphere Model version 5, or CAM5 (an atmosphere global climate model, or GCM) were improved. Student funding from the University of Nevada, Reno, was combined with funds from this project to conduct some basic research on the mechanism of the North American monsoon, or NAM. Federal research on the NAM has dwindled since 2006, but atmospheric soundings taken during research vessel cruises in the Gulf of California (GC) during the North American Monsoon Experiment (NAME) were used to reveal a likely mechanism that explains the relationship between an intrusion of tropical warm water into the GC during late spring-early summer and the onset of relatively heavy NAM rainfall in northwest Mexico and the southwestern United States. These soundings, combined with reanalysis data, satellite sea surface temperatures and satellite measurements of outgoing longwave radiation were used to develop and provide evidence for a planetary-scale NAM mechanism. As far as we know, no other physical explanation has been offered for the spring-summer evolution of the NAM system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Infrasound to Inform Avalanche Hazard Forecasts

Avalanches are natural hazards that occur when an unstable mass of snow breaks away from a mountain slope. It is expected that climate change will lead to increased avalanche activity, which can cause interruptions to water and power infrastructure, transportation blockages, higher risk for loss of life, and changes to ecosystems. Avalanche forecasts are key to mitigating hazards, and observations of recent avalanches comprise one of the key observations for deciding danger level. It is well understood that infrasound can be used to detect and locate snow avalanches in transitional snowpacks, even during snowstorms, but similar studies are lacking for maritime snowpacks. Here we show results from an infrasound field experiment in Tutl’uh (Turnagain Arm), Alaska, USA between January 31 – April 30 field deployment campaign. We show that (1) methods developed for transitional snowpacks can be applied to maritime snowpacks in Alaska, (2) background noise may be higher in this region due to natural and anthropogenic influences, and (3) low-cost infrasound sensors can withstand the harsh Alaskan winter and successfully collect data. We also discuss the impact of this work and a path forward.

58 GEOSCIENCES↗