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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 OSTI2022

Wave resource spatial and temporal variability dependence on WEC size

As the wave energy sector grows and looks to the Blue Economy for commercialization opportunities, there is a distinct and pressing need to clearly understand and quantify the coupled impacts of wave energy converter (WEC) size and wave resource characteristics on the annual energy production, spatial variability and temporal variability. Utilizing generic frequency domain representations of the Oscilla Power Triton WEC and spectral wave conditions at PacWave (Oregon), Los Angeles (California) and WETS (Hawaii), a series of interesting results emerge. Firstly, the ‘optimal’ WEC size, from an energy standpoint, is fundamentally dependent on the frequency distribution of the incoming wave variance density spectrum. Secondly, and from a seasonality perspective, the seasonal WEC energy generation doesn’t necessarily follow the seasonal distribution of gross wave power. Finally, from an hourly power variability perspective, a reduction in WEC size generally decreases variability. However, for each of the locations investigated, there appears to be a WEC size threshold; a threshold where further reducing WEC size results in increased power variability.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Wave Resource Spatial and Temporal Variability Dependence on WEC Size

As the wave energy sector grows and looks to the Blue Economy for commercialization opportunities, there is a distinct and pressing need to clearly understand and quantify the coupled impacts of wave energy converter (WEC) size and wave resource characteristics on the annual energy production, spatial variability and temporal variability. Utilizing generic frequency domain representations of the Oscilla Power Triton WEC and spectral wave conditions at PacWave (Oregon), Los Angeles (California) and WETS (Hawaii), a series of interesting results emerge. Firstly, the 'optimal' WEC size, from an energy standpoint, is fundamentally dependent on the frequency distribution of the incoming wave variance density spectrum. Secondly, and from a seasonality perspective, the seasonal WEC energy generation doesn't necessarily follow the seasonal distribution of gross wave power. Finally, from an hourly power variability perspective, a reduction in WEC size generally decreases variability. However, for each of the locations investigated, there appears to be a WEC size threshold; a threshold where further reducing WEC size results in increased power variability.

blue economy↗
DOE OSTI2020

Temporal Variability of Emissions Revealed by Continuous, Long-Term Monitoring of an Underground Natural Gas Storage Facility

Temporal variability contributes to uncertainty in inventories of methane emissions from the natural gas supply chain. Extrapolation of instantaneous, “snapshot-in-time” measurements, for example, can miss temporal intermittency and confound bottom-up/top-down comparisons. Importantly, no continuous long-term datasets record emission variability from underground natural gas storage facilities despite substantial contributions to sector-wide emissions. We present 11 months of continuous observations on a section of a storage site using dual-frequency comb spectroscopy (DCS observing system) and aircraft measurements. Here, we find high emission variability and a skewed distribution in which the 10% highest 3 h emission periods observed by the continuous DCS observing system comprise 41% of the total observed 3-hourly emissions. Monthly emission rates differ by >12×, and 3-hourly rates vary by 17× in 24 h. We find links to the operating phase of the facility—emission rates, including as a percentage of the total gas flow rate, are significantly higher during periods of injection compared to those of withdrawal. We find that if a high frequency of aircraft flights can occur, then the ground- and aircraft-based approaches show excellent agreement in emission distributions. A better understanding of emission variability at underground natural gas storage sites will improve inventories and models of methane emissions and clarify pathways toward mitigation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Regional and Temporal Variability of Atmospheric River Seasonality: Influences of Detection Algorithms and Moisture Transport Dynamics

Abstract Understanding the regional and temporal variability of atmospheric river (AR) seasonality is crucial for preparedness and mitigation of extreme events. While ARs were thought to peak in winter, recent research shows they exhibit region‐specific seasonality and are heavily influenced by the chosen detection algorithm. This study examines the link between the year‐to‐year consistency of peak‐AR activity to the presence of a dominant seasonal pattern, considering both location and algorithm choice. Regions are categorized by their temporal characteristics: consistent patterns (e.g., East Asia), patterns with occasional outliers (e.g., British Columbia coast), and regions lacking a clear dominant peak season (e.g., South Atlantic, parts of Australia). Hence, not all regions display a consistent seasonal cycle of AR activity. This study quantifies the extent to which a region experiences a dominant peak season of AR activity (or lacks one) and offers insights to enhance decision‐making in water management, natural hazard preparedness, and forecasting. Furthermore, given our finding that detection algorithms influence the peak season of AR activity, we also examine two diagnostic variables representative of moisture transport to corroborate our results. Integrated vapor transport, which captures meridional and zonal moisture transport, and Moist Wave Activity, representing moisture intrusions from lower to higher latitudes, are examined. Our analysis indicates that inconsistencies in the seasonal cycle of AR activity are not solely due to discrepancies in detection algorithms but also arise from changes in moisture transport. Plain Language Summary Atmospheric rivers (ARs) are critical weather phenomena that can cause extreme events like heavy rainfall and flooding. Understanding when and where ARs are most likely to occur throughout the year is essential for preparing and responding to these events. Traditionally, ARs were thought to peak in winter, but recent studies show this varies by region. Our study helps address the challenge decision‐makers face in anticipating and preparing for AR events by providing insights into the consistency of peak seasonal patterns across different areas. Some regions, like East Asia, and the British Columbia coast, show a consistent peak season, while others, like the South Atlantic and parts of Australia, have significant year‐to‐year variations, making it hard to identify a dominant season. To better understand these changes over time, the study also examines how moisture moves in the atmosphere, using Integrated Vapor Transport (which looks at moisture movement in various directions) and Moist Wave Activity (which tracks moisture shifts from lower to higher latitudes). The findings suggest that inconsistencies in AR patterns are due not only to detection methods but also due to changes in moisture transport. Key Points The peak season of atmospheric river activity can change depending on the year in some areas Interannual variations in the peak season can make identifying a dominant season challenging for some regions Frequent shifts in peak season across years reflect inconsistencies tied to detection algorithms and to underlying dynamics

Kamnani, Diya↗
DOE OSTI2022

Temporal variabilities of soil carbon dioxide fluxes from cornfield impacted by temperature and precipitation changes through high-frequent measurement and DAYCENT modelling

Soil carbon dioxide (CO 2 ) emissions from the field of corn (Zea mays L.) play an important role in global warming. This study investigated temporal variability of soil CO 2 fluxes (R s ) with soil temperature (T s ) and moisture (θ) and built DAYCENT models for predicting future impacts of climate changes on R s using the measured high-frequency data. R s trend was tested by Mann–Kendall and Sen Estimator. Predicted R s s under different climate scenarios were compared using Parallel-line Analysis. Herein, the findings indicated that daily R s exponentially increased with T s constrained by θ. During the θ of 27–31%, there was a strong exponential relationship between R s and T s , but the relationship was weaker for the θ of 38–41% and 22–26%. Soil environmental index (SEI, T s × θ) significantly impacted R s with linear regression R s 0.5 = 0.4599 + 0.002059 × SEI in 2008, 2009 and 2011. At the diurnal scale, there were different trends in R s s and relationships among R s and T s and θ in different years. Predicted yearly R s s, root R s s and corn yield in 2014–2049 increased with an increase in temperature scenarios, but the R s s significantly increased as temperature rose by 1°C or higher. Predicted yearly R s s, root R s s and yield reduced with precipitation scenario increase, and the root R s s and yield significantly diminished as precipitation increased by 15 and 30%. Predicted yearly R s from cornfields had a significantly increasing trend. Future research is needed to explore methods for mitigating cornfield R s and evaluating sensitivities of different cropland R s s to temperature changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Soil respiration spatial and temporal variability in China between 1961 and 2014

Soil respiration (Rs) plays an important role in terrestrial-atmospheric carbon exchange but remains one of the least studied components of the carbon cycle. How environmental changes influence Rs, and in turn, how Rs influences terrestrial carbon storage in China is unclear. Here, we estimated spatial patterns and temporal trends in Rs from 1961 to 2014 to determine the influence of the recent warming hiatus on the Rs temporal variability in China. We evaluated the relationship between Rs and a set of environmental factors and developed biome-specific Rs models that were parameterized based on 2,862 Rs records and 18 continuous variables (e.g., air temperature, precipitation and leaf area index). Based on the biome-specific Rs models and environmental information, we estimated the annual Rs as well as the change trend of Rs in China during the study period. The mean annual Rs from 1961 to 2014 was 3.50 (confidence interval +/- 0.87) Pg center dot C center dot year(-1). Rs in China increased at a rate of 0.008 Pg center dot C center dot year(-2) between 1982 and 1998 (warming period), but was static between 1999 and 2014 (warming hiatus). This reduction in Rs increase rate may have an important role in enhancing terrestrial carbon sequestration. These findings confirm that temporal heterogeneity in Rs could be influenced by regional environmental changes, and further help in improving the understanding of soil carbon dynamics in China.

Jian, Jinshi↗
DOE OSTI2023

Importance of incorporating spatial and temporal variability of biomass yield and quality in bioenergy supply chain

Abstract Biofuels made from biomass and waste residues will largely contribute to United States’ 2050 decarbonization goal in the aviation sector. While cellulosic biofuels have the potential fuel performance equivalent to petroleum-based jet fuel, the biofuel industry needs to overcome the supply chain barrier caused by temporal and spatial variability of biomass yield and quality. This study highlights the importance of incorporating spatial and temporal variability during biomass supply chain planning via optimization modeling that incorporates 10 years of drought index data, a primary factor contributing to yield and quality variability. The results imply that the cost of delivering biomass to biorefinery may be significantly underestimated if the multi-year temporal and spatial variation in biomass yield and quality is not captured. For long term sustainable biorefinery operations, the industry should optimize supply chain strategy by studying the variability of yield and quality of biomass in their supply sheds.

09 BIOMASS FUELS↗
DOE OSTI2020

Understanding Hailstone Temporal Variability and Contributing Factors over the U.S. Southern Great Plains

Abstract Hailstones are a natural hazard that pose a significant threat to property and are responsible for significant economic losses each year in the United States. Detailed understanding of their characteristics is essential to mitigate their impact. Identifying the dynamic and physical factors contributing to hail formation and hailstone sizes is of great importance to weather and climate prediction and policymakers. In this study, we have analyzed the temporal and spatial variabilities of severe hail occurrences over the U.S. southern Great Plains (SGP) states from 2004 to 2016 using two hail datasets: hail reports from the Storm Prediction Center and the newly developed radar-retrieved maximum expected size of hail (MESH). It is found that severe and significant severe hail occurrences have a considerable year-to-year temporal variability in the SGP region. The interannual variabilities have a strong correspondence with sea surface temperature anomalies over the northern Gulf of Mexico and there is no outlier. The year 2016 is identified as an outlier for the correlations with both El Niño–Southern Oscillation (ENSO) and aerosol loading. The correlations with ENSO and aerosol loading are not statistically robust to inclusion of the outlier 2016. Statistical analysis without the outlier 2016 shows that 1) aerosols that may be mainly from northern Mexico have the largest correlation with hail interannual variability among the three factors and 2) meteorological covariation does not significantly contribute to the high correlation. These analyses warrant further investigations of aerosol impacts on hail occurrence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Spatial and temporal variability of tritium in precipitation within South Africa and it's bearing on hydrological studies

Tritium, the radioactive isotope of hydrogen, has been used to understand groundwater recharge processes for decades. The current variation of tritium in the atmosphere is largely attributed to stratospheric production and fall out rates as well as global circulation phenomena controlling the hydrological cycle. Global controls on the variability in atmospheric tritium activity are poorly suited to explain local variation and tritium activities in precipitation are often assumed to be uniform over both local and regional catchments and watersheds. This assumption can result in both over and under estimation of modern recharge within an aquifer when using tritium as the recharge proxy. In order to minimize the inherent prediction residuals associated with tritium based recharge investigations, the variability of tritium in precipitation was modelled from 127 spatial precipitation samples taken over a two year period, combined with a 76 precipitation sample group-set taken over a one year period in a single location. Precipitation events were traced backward in time, from the point of collection, using HYSPLIT modelling to ascertain the origins of moisture content as well as the altitudes of moisture origin reached along the particle track. Tritium activities, collected over a one year period in Paarl, range from 0.45 to 4.16 TU and have a mean of 1.59 TU. Spatial storm events in the Western Cape in 2017 and 2018 had a range from 0 to 2.2 and 0.37 to 3.27 TU, respectively, with mean activities of 1.18 (n = 34) and 1.25 TU (n = 32). Both storm events had similar tritium variability (σ = 0.5 n = 35 and 0.48 n = 32). Regional precipitation events had the largest range of tritium activities (0.55–12.2 TU). Although not all tritium activities can be explained by interrogating the water mass origin, this study suggests that approximately 90% of events can be completely or partially attributed to the origin of the water mass. The variability of tritium, both spatially and temporally, was higher than expected, confirming that when uniform tritium inputs are used, the groundwater system would provide inaccurate modern recharge estimates. Higher spatial resolution of tritium variation in precipitation for a particular region will improve our ability to relate tritium activities in groundwater to local precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Temporal Variability in Reservoir Surface Area Is an Important Source of Uncertainty in GHG Emission Estimates

Ebullitive methane (CH 4 ) emissions in lentic ecosystems tend to concentrate at river-lake interfaces and within shallow littoral zones. However, inconsistent definitions of the littoral zone and static representations of the lake or reservoir surface area contribute to major uncertainties in greenhouse gas (GHG) emissions estimates, particularly in reservoirs with large water-level fluctuations. This study examines temporal variation in littoral and total surface areas of US reservoirs and demonstrates how different methods and data sources lead to discrepencies in reservoir GHG emissions at large scales and over time. We also explore variability in remotely sensed water occurrence according to maximum surface area, reservoir purposes, and hydrologic regions. Notably, the largest relative variability in surface area is exhibited by small reservoirs with a maximum surface area <1 km 2 and non-hydroelectric reservoirs. Additionally, we use a case study of measured CH 4 emissions from the southeastern United States (Douglas Reservoir) to illustrate the effects of varying surface area on reservoir-wide GHG estimates. Upscaled CH 4 emissions in Douglas Reservoir differed by nearly two-fold depending on the source of total surface area data and whether estimates accounted for seasonal fluctuations in surface area. During seasonal drawdown in Douglas Reservoir, relative littoral area varies non-linearly; periods of lower pool elevation (and thus larger relative littoral area) likely contribute disproportionately high CH 4 emission rates compared to the commonly sampled summer season when water levels are at full-pool elevation. Improved GHG monitoring and upscaling techniques require accounting for temporal variability in reservoir surface extent and littoral area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Spatial and Temporal Variability of Vertical Velocity under Shallow Cumulus

Vertical velocity distribution below cloud is one of the key determinants of cloud life cycle, but observations of this variable are extremely sparse in space. Doppler lidar retrievals and large-eddy simulations at the U.S. Department of Energy’s Atmospheric Radiation Measurement User Facility Southern Great Plains site are used to determine whether vertical velocity statistics from temporally dense profiles at a single location can be substituted for spatial vertical velocity statistics. We show that even a small number (five) of widely distributed [ O (1°) latitude/longitude spacing] lidars is sufficient sampling to reconstruct domainwide spatial vertical velocity variance, but not higher moments of the vertical velocity distribution. Spatial and temporal vertical velocity variances in the Doppler lidar observations are nearly interchangeable as long as the spatial variance is temporally averaged and the temporal variance is averaged across lidars. This is true even though the dominant spatial scales of vertical velocity variability are ≲ 3 km, more than an order of magnitude smaller than the spacing between the lidars. Further, in the limit where the temporal variance does not vary across a spatial domain (e.g., if the meteorological and surface forcing of the atmospheric turbulence is homogeneous across the domain) and the domain-mean vertical velocity is zero, the commonly available retrieval of temporal vertical velocity variance at one site is equivalent to the spatial variance over the domain. We use an updraft parcel model to show that substituting temporal for spatial vertical velocity statistics will have a relatively minor effect on cloud droplet number concentrations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multiscale Temporal Variability of the Global Air‐Sea CO 2 Flux Anomaly

Abstract The global air‐sea CO 2 flux (F) impacts and is impacted by a plethora of climate‐related processes operating at multiple time scales. In bulk mass transfer formulations, F is driven by physico‐ and bio‐chemical factors such as the air‐sea partial pressure difference (∆pCO 2 ), gas transfer velocity, sea surface temperature, and salinity–all varying at multiple time scales. To de‐convolve the impact of these factors on variability in F at different time scales, time‐resolved estimates of F were computed using a global data set assembled between 1988 and 2015. The F anomalies were defined as temporal deviations from the 28‐year time‐averaged value. Spectral analysis revealed four dominant timescales of variability in F–subseasonal, seasonal, interannual, and decadal with relative amplitude differences varying across regions. A second‐order Taylor series expansion was then conducted along these four timescales to separate drivers across differing regions. The analysis showed that on subseasonal timescales, wind speed variability explains some 66% of the global F anomaly and is the dominant driver. On seasonal, interannual, and decadal timescales, the ∆pCO 2 effect controlled by the ∆pCO 2 anomaly, explained much of the F anomaly. On decadal timescales, the F anomaly was almost entirely governed by the ∆pCO 2 effect with large contributions from high latitudes. The main drivers across timescales also dominate the regional F anomaly, particularly in the mid‐high latitude regions. Finally, the driver of the ∆pCO 2 effect was closely connected with the relative strength of atmospheric pCO 2 and the nonthermal component of oceanic pCO 2 anomaly associated with dissolved inorganic carbon and alkalinity.

Environmental Sciences & Ecology↗
DOE OSTI2026

Characterizing Spatial and Temporal Variability of California’s Coastal Upwelling Using a Satellite-Derived CUTI Index

Ocean upwelling off California is a wind-driven coastal process with complex interactions between spatial wind patterns and sea surface temperatures. Large-scale spatial and temporal upwelling characterizations are traditionally based on numerical models, while quantifying upwelling with direct observations is not straightforward. To complement model-based upwelling characterizations, here we use satellite estimates of winds, sea surface temperatures, and ocean currents along the California coast to calculate the coastal upwelling transport index (CUTI), originally calculated from a numerical ocean model. The CUTI framework accounts for both Ekman and geostrophic offshore-directed ocean transport, the latter of which is often neglected in satellite-based upwelling assessments. The approach yields the CUTI over a 30-yr period at a daily temporal and 0.25° spatial resolution to analyze upwelling patterns, with focus on potential Morro Bay and Humboldt wind energy areas. Morro Bay (CUTI median of 0.63 m 2 s −1 ) exhibits stronger, more consistent upwelling throughout the year, whereas Humboldt (CUTI median of 0.45 m 2 s −1 ) exhibits a pronounced seasonal cycle with strongest upwelling in the summer and occasional downwelling in winter. The cluster analysis of 1993–2022 satellite maps of CUTI identifies four characteristic spatial upwelling patterns along the coast, corresponding to distinct spatial distributions of sea surface temperature anomalies, Ekman transport, and geostrophic ocean water transport. This dataset provides an observation-based foundation for characterizing natural coastal upwelling off the California coast, showing good agreement with the original model CUTI while being based on satellite data and offering increased spatial resolution.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Understanding the Microphysical Control and Spatial‐Temporal Variability of Warm Rain Probability Using CloudSat and MODIS Observations

Abstract By combining measurements from MODIS and the CloudSat radar, we develop a parameterization scheme to quantify the combined microphysical controls by liquid water path (LWP) and cloud droplet number concentration (CDNC) of the probability of precipitation (PoP) in marine low cloud over tropical oceans. We demonstrate that the spatial‐temporal variation of grid‐mean in‐cloud can be largely explained by the variation of the joint probability density function of LWP and CDNC in the phase space specified by the bivariate PoP (LWP and CDNC) function. Through a series of sensitivity tests guided by this understanding, we find that in the Southeastern Pacific and Atlantic the stratocumulus to cumulus transition of the is mainly due to the variation of CDNC while the annual cycle is mainly due to the variation of LWP. The results of this study provide a viable way to diagnose the root cause of warm rain problems in global climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The Spatial and Temporal Variability of the Clear Convective Boundary Layer at the ARM SGP Supersite

The convective boundary layer (CBL), also known as the mixing layer, constitutes the critical lower segment of the atmosphere that significantly influences daily human activities. The growing demand for precise weather forecasts is driven by the requirements of agriculture, transportation, and routine societal functions. Here, to enhance understanding of the CBL, this study investigates the spatiotemporal variability in the CBL and its controlling factors using four-year Doppler lidar, surface flux, and profiling measurements at five ARM Southern Great Plains sites within a 100 km radius. This investigation utilizes data collected exclusively under clear-sky conditions or scattered low-cloud conditions. Results reveal significant spatial differences in CBL evolutions. Daily mixing layer heights (MLHs) vary up to 1 km (30% of the mean) in late afternoon. There is a clear east–west contrast: western sites (C1, E32, E37) exhibit higher summer MLH (1.9–2.1 km) and vertical velocity variances (1.0–1.2 m 2 s −2 ) than eastern sites (1.6–1.8 km), reversing in winter. Temporally, the MLH peaks at 70% of the sunrise–sunset interval, the lagging heat flux (HF) peaks at 50%; and the seasonal MLH maxima lag the HF by approximately one month, influenced by nighttime PBL (planetary boundary layer) properties. The HF and lower tropospheric stability are the main factors of influence for the CBL, but site-specific dependencies highlight the critical roles of local factors, underscoring the need for including them in CBL modeling.

ARM SGP site↗
DOE OSTI2020

Strong temporal variability in methane fluxes from natural gas well pad soils

We measured methane and carbon dioxide fluxes at natural gas well pad soils and undisturbed soils in the Rocky Mountain and Gulf Coast regions of the United States, including producing and gas storage wells. We collected both short-term (15 min) and multi-day (between 3 and 8), continuous measurements at 47 well pads and two undisturbed locations. Methane fluxes varied by more than an order of magnitude over periods as short as 30 min (e.g., 19–593 mg m -2 h -1 in one instance), and diurnal and seasonal variability was also significant (e.g., spring-to-fall change from 509 to 14174 mg m -2 h -1 ). We hypothesize that short-term flux variability was caused by pulsed flow of methane during its migration through the subsurface. Barometric pressure and well conditions likely impacted fluxes, but we found only weak evidence for this. Bacterial methanotrophy appeared to impact methane flux magnitude and variability. We injected methane into the subsurface at one well, and we found that, while fluxes of methane and carbon dioxide, and combustible soil gas concentrations, increased in response to the injection, the response was not uniform, and fluxes exhibited high hourly-scale variability, in spite of a constant injection rate. Methane fluxes tended to be higher at well pad soils compared to background soils (often much higher), and fluxes tended to be higher at well pad locations closer to the well head.

03 NATURAL GAS↗
DOE OSTI2023

Multi‐Scale Temporal Variability of Turbulent Mixing During a Monsoon Intra‐Seasonal Oscillation in the Bay of Bengal: An LES Study

A process study using large‐eddy simulations is carried out to explore the dominant 1‐D processes that affect mixed layer (ML) properties during an event of summer Monsoon Intra‐seasonal Oscillations (MISO) in the Bay of Bengal (BOB). These simulations use realistic air‐sea fluxes and initial conditions that were collected during the summer 2018 MISO‐BOB field experiment to explore the roles of thermal inversion layer (TIL) and Langmuir turbulence (LT) in modulating ML properties. The simulations span an active period with heavy rain and strong winds and a break period with strong solar heat flux and little rain. The mixed layer depth (MLD), sea surface temperature (SST) and sea surface salinity (SSS) are most affected by the presence of near‐inertial oscillations, solar heating and precipitation, all of which occur at different timescales. The subsurface warming induced by the TIL reduces the SST variability at the MISO timescale when compared with the simulation without TIL. Comparison of simulations with and without LT indicates that LT enhances subsurface warming during the active phase and reduces diurnal SST modulation during the break phase. Finally, simulations with 1‐D mixing models show a wide disparity in the evolution of MLD, SST, and SSS.

54 ENVIRONMENTAL SCIENCES↗