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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Seasonal nitrous oxide flux from an intensively managed pasture in a humid subtropical ecosystem

Nitrous oxide (N2O) flux from vented chambers was measured over intensively and minimally managed bermuda grass hay meadows in a humid, subtropical ecosystem for several years during scheduled sampling protocol following harvest, fertilization and rainfall events while measuring diel N2O emissions once during each of 5 seasonal day growth cycles which divided each calendar year. Soil pools of nitrite NO2(-), nitrate NO3(-), and ammonia (NH3) were measured in soil samples taken at 2 and 10 cm depths during each emission collection to determine transformations of the nitrogen pools coupled with N2O emissions. The highest diel N2O emission occur midday in the Spring cycle, measuring 9.0 g N/ha/d only for several weeks, while emissions dropped to less than 1.0 g N/ha/day during hot, dry, and colder months. Intensively managed meadows (4 fertilizations and harvests per year plus pest management) induced higher seasonal N2O emissions than minimal treatment (1 fertilization and harvest) averaging 2.75 and 5.97 g N/ha/day. Nitrous oxide emission data as responses to soil parameters and environmental parameters were also measured where air temperature, soil moisture, and fertilization were the most powerful factors.

Brams, Eugene A.↗
NASA NTRS1982

The seasonal and global behavior of water vapor in the Mars atmosphere - Complete global results of the Viking atmospheric water detector experiment

A key question regarding the evolution of Mars is related to the behavior of its volatiles. The present investigation is concerned with the global and seasonal abundances of water vapor in the Mars atmosphere as mapped by the Viking Mars Atmospheric Water Detector (MAWD) instrument for almost 1-1/2 Martian years from June 1976 to April 1979. Attention is given to the implications of the observed variations for determining the relative importance of those processes which may be controlling the vapor cycle on a seasonal basis. The processes considered include buffering of the atmosphere water by a surface or subsurface reservior of ground ice, physically adsorbed water, or chemically bound water. Other processes are related to the supply of water from the residual or seasonal north polar ice cap, the redistribution of the vapor resulting from atmospheric circulation, and control of the vapor holding capacity of the atmosphere by the local atmospheric temperatures.

Jakosky, B. M.↗
NASA NTRS2000

Determination of Martian Northern Polar Insolation Levels Using a Geodetic Elevation Model

Solar insolation levels at the Martian polar caps bear significantly on the seasonal and climatic cycling of volatiles on that planet. In the northern hemisphere, the Martian surface slopes downhill from the equator to the pole such that the north polar cap is situated in a 5-km-deep hemispheric-scale depression. This large-scale topographic setting plays an important role in the insolation of the northern polar cap. Elevations measured by the Mars Orbiter Laser Altimeter (MOLA) provide comprehensive, high-accuracy topographical information required to precisely determine polar insolation. In this study, we employ a geodetic elevation model to quantify the north polar insolation and consider implications for seasonal and climatic changes. Additional information is contained in original extended abstract.

Arrell, J. R.↗
DOE OSTI2026

Can ERA5 Be Used to Study Mesoscale Convective System Climatological Characteristics?

Mesoscale convective systems (MCSs) produce more than half of tropical rainfall and are central to the global hydrologic cycle. As the climate warms, environments favorable for MCSs may become more common; however, limited observational records hamper understanding of how MCSs respond to variations and changes in their environments. Here, we evaluate how well MCSs are represented in ERA5, a widely used global high‐resolution reanalysis product. Using PyFLEXTRKR, which jointly tracks top‐of‐atmosphere infrared brightness temperature and surface precipitation, we identified MCSs in ERA5 and compared them with those identified in satellite observations using the same detection algorithm. This comparison analysis spans 2007–2020 using hourly data at 0.25° horizontal resolution focusing over the tropics. ERA5 reproduces observed brightness‐temperature statistics and captures the geographic distribution and seasonal and diurnal cycles of MCS cold cloud shields. However, ERA5 precipitation exhibits an intensity bias—too much light rain and too little heavy rain—which shifts the rain‐rate distribution and reduces the frequency of MCSs relative to observations. Within MCSs, ERA5 precipitation exhibits the same pattern of bias, yielding a systematic underestimation of MCS precipitation intensity. Consistent with these biases, ERA5 underestimates the contribution of MCS to tropical rainfall by 25%–34% in key regions. Overall, ERA5 is suitable for studying MCS cold cloud‐shield climatology and evolution, but precipitation‐based MCS characteristics (including event‐level precipitation features and the geospatial distribution of MCS precipitation) should be interpreted with caution. These findings clarify which aspects of MCS behavior are robustly represented in ERA5 for climatological applications.

mesoscale convection↗
DOE OSTI2023

PLAT domain protein 1 (PLAT1/PLAFP) binds to the Arabidopsis thaliana plasma membrane and inserts a lipid

Robust agricultural yields depend on the plant's ability to fix carbon amid variable environmental conditions. Over seasonal and diurnal cycles, the plant must constantly adjust its metabolism according to available resources or external stressors. The metabolic changes that a plant undergoes in response to stress are well understood, but the long-distance signaling mechanisms that facilitate communication throughout the plant are less studied. The phloem is considered the predominant conduit for the bidirectional transport of these signals in the form of metabolites, nucleic acids, proteins, and lipids. Lipid trafficking through the phloem in particular attracted our attention due to its reliance on soluble lipid-binding proteins (LBP) that generate and solubilize otherwise membrane-associated lipids. The Phloem Lipid-Associated Family Protein (PLAFP) from Arabidopsis thaliana is generated in response to abiotic stress as is its lipid-ligand phosphatidic acid (PA). PLAFP is proposed to transport PA through the phloem in response to drought stress. To understand the interactions between PLAFP and PA, nearly 100 independent systems comprised of the protein and one PA, or a plasma membrane containing varying amounts of PA, were simulated using atomistic classical molecular dynamics methods. In these simulations, PLAFP is found to bind to plant plasma membrane models independent of the PA concentration. When bound to the membrane, PLAFP adopts a binding pose where W41 and R82 penetrate the membrane surface and anchor PLAFP. This triggers a separation of the two loop regions containing W41 and R82. Subsequent simulations indicate that PA insert into the β -sandwich of PLAFP, driven by interactions with multiple amino acids besides the W41 and R82 identified during the insertion process. Finally, fine-tuning the protein-membrane and protein-PA interface by mutating a selection of these amino acids may facilitate engineering plant signaling processes by modulating the binding response.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The GFDL Variable-Resolution Global Chemistry-Climate Model for Research at the Nexus of US Climate and Air Quality Extremes

We present a variable-resolution global chemistry-climate model (AM4VR) developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) for research at the nexus of US climate and air quality extremes. AM4VR has a horizontal resolution of 13 km over the US, allowing it to resolve urban-to-rural chemical regimes, mesoscale convective systems, and land-surface heterogeneity. With the resolution gradually reducing to 100 km over the Indian Ocean, we achieve multi-decadal simulations driven by observed sea surface temperatures at 50% of the computational cost for a 25-km uniform-resolution grid. In contrast with GFDL's AM4.1 contributing to the sixth Coupled Model Intercomparison Project at 100 km resolution, AM4VR features much improved US climate mean patterns and variability. In particular, AM4VR shows improved representation of: precipitation seasonal-to-diurnal cycles and extremes, notably reducing the central US dry-and-warm bias; western US snowpack and summer drought, with implications for wildfires; and the North American monsoon, affecting dust storms. AM4VR exhibits excellent representation of winter precipitation, summer drought, and air pollution meteorology in California with complex terrain, enabling skillful prediction of both extreme summer ozone pollution and winter haze events in the Central Valley. AM4VR also provides vast improvements in the process-level representations of biogenic volatile organic compound emissions, interactive dust emissions from land, and removal of air pollutants by terrestrial ecosystems. We highlight the value of increased model resolution in representing climate–air quality interactions through land-biosphere feedbacks. AM4VR offers a novel opportunity to study global dimensions to US air quality, especially the role of Earth system feedbacks in a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Climatology of Severe Local Storm Environments and Synoptic-Scale Features over North America in ERA5 Reanalysis and CAM6 Simulation

Severe local storm (SLS) activity is known to occur within specific thermodynamic and kinematic environments. These environments are commonly associated with key synoptic-scale features—including southerly Great Plains low-level jets, drylines, elevated mixed layers, and extratropical cyclones—that link the large-scale climate to SLS environments. This work analyzes spatiotemporal distributions of both extreme values of SLS environmental parameters and synoptic-scale features in the ERA5 reanalysis and in the Community Atmosphere Model, version 6 (CAM6), historical simulation during 1980–2014 over North America. Compared to radiosondes, ERA5 successfully reproduces SLS environments, with strong spatiotemporal correlations and low biases, especially over the Great Plains. Both ERA5 and CAM6 reproduce the climatology of SLS environments over the central United States as well as its strong seasonal and diurnal cycles. ERA5 and CAM6 also reproduce the climatological occurrence of the synoptic-scale features, with the distribution pattern similar to that of SLS environments. Compared to ERA5, CAM6 exhibits a high bias in convective available potential energy over the eastern United States primarily due to a high bias in surface moisture and, to a lesser extent, storm-relative helicity due to enhanced low-level winds. Additionally, composite analysis indicates consistent synoptic anomaly patterns favorable for significant SLS environments over much of the eastern half of the United States in both ERA5 and CAM6, though the pattern differs for the southeastern United States. Overall, our results indicate that both ERA5 and CAM6 are capable of reproducing SLS environments as well as the synoptic-scale features and transient events that generate them.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Growth of Mesoscale Convective Systems in Observations and a Seasonal Convection-Permitting Simulation over Argentina

Abstract A 6.5-month, convection-permitting simulation is conducted over Argentina covering the Remote Sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations and Clouds, Aerosols, and Complex Terrain Interactions (RELAMPAGO-CACTI) field campaign and is compared with observations to evaluate mesoscale convective system (MCS) growth prediction. Observed and simulated MCSs are consistently identified, tracked, and separated into growth, mature, and decay stages using top-of-the-atmosphere infrared brightness temperature and surface rainfall. Simulated MCS number, lifetime, seasonal and diurnal cycles, and various cloud-shield characteristics including growth rate are similar to those observed. However, the simulation produces smaller rainfall areas, greater proportions of heavy rainfall, and faster system propagations. Rainfall area is significantly underestimated for long-lived MCSs but not for shorter-lived MCSs, and rain rates are always overestimated. These differences result from a combination of model and satellite retrieval biases, in which simulated MCS rain rates are shifted from light to heavy, while satellite-retrieved rainfall is too frequent relative to rain gauge estimates. However, the simulation reproduces satellite-retrieved MCS cloud-shield evolution well, supporting its usage to examine environmental controls on MCS growth. MCS initiation locations are associated with removal of convective inhibition more than maximized low-level moisture convergence or instability. Rapid growth is associated with a stronger upper-level jet (ULJ) and a deeper northwestern Argentinean low that causes a stronger northerly low-level jet (LLJ), increasing heat and moisture fluxes, low-level vertical wind shear, baroclinicity, and instability. Sustained growth corresponds to similar LLJ, baroclinicity, and instability conditions but is less sensitive to the ULJ, large-scale vertical motion, or low-level shear. Growth sustenance controls MCS maximum extent more than growth rate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Regime Characterization of Offshore Wind Resource Using Unsupervised Learning

Predictability of wind resource conditions is critical for offshore wind design and operations. While many studies of extreme wind conditions focus on specific events such as low-level jets or ramps, these rely on threshold definitions that limit generality. Here we present a data-driven framework that combines principal component analysis (PCA), self-organizing maps (SOM), and k-means clustering to classify wind resource conditions as typical and anomalous from climatological data. Anomalies are defined not by fixed thresholds but by flagging samples located far from SOM node centers inside the baseline SOM structure. This reframes extremes as rare ebents and hence, likely difficult to anticipate by numerical weather prediction models. We applied this approach to 23 years (2000–2022) of hourly profiles from the NOW-23 hindcast model at the Humboldt Wind Energy Area. Classification is conducted on a feature space consisting of 10 m wind speed and direction, bulk shear and veer across 30–270 m, and a low-level jet index. Dimensionality reduction is achieved through PC. A 2 × 3 OM lattice trained on the PCA vectors identified six baseline regimes spanning weak to strong flow states. High quantization-error profiles are identified and re-clustered into four anomalous regimes. The baseline regimes exhibited clear seasonal and diurnal cycles. Meanwhile, the anomalous regimes represented <10 % of all hours but showed distinct combinations of speed, shear, and veer, when compared to the baseline regimes. Anomalous regimes are typically short-lived (~few hours), yet their transitions can lead to hub-height wind changes of −18 to +9 m s -1 . For a representative 15 MW turbine, these shifts imply rapid swings in capacity factor from near-full output to negligible generation. Validation with lidar buoy data showed 51% agreement in SOM labels across ~6,000 overlapping hours, with most mismatches confined to adjacent speed classes. HRRR comparisons further revealed that anomalous regimes were disproportionately associated with forecast biases exceeding 5 m s -1 . Together, these results reframe extremes in offshore wind from absolute maxima or minima to weather states that are difficult to anticipate from models.

17 WIND ENERGY↗
NASA NTRS1974

On climatic changes related to the 22-year solar cycle

The 22-year or double sunspot cycle as a cause for longitudinal displacements of atmospheric semi-permanent centers of action is studied. A difference in frequency of occurence of Icelandic lows between the two halves of the double sunspot cycle during winter seasons is found.

Schuurmans, C. J. E.↗
NASA NTRS1974

The dynamics of the turbopause

The investigation reported shows the variability of eddy diffusion at the turbopause on diurnal, seasonal, and solar cycle time scales, and also on latitude. Realistic vertical eddy diffusion profiles for the lower thermosphere are presented. The results of the studies illustrate the importance of global winds in the dynamics of the lower thermosphere. Difficulties regarding the direct measurement of eddy diffusivity in the lower thermosphere are discussed.

Roper, R. G.↗
NASA NTRS1977

Characteristics of a solar-terrestrial observatory

The operation of the Solar-Terrestrial Observatory is outlined. It will monitor the diurnal, solar rotational, seasonal, and solar cycle variation to aid the understanding of the solar-terrestrial interaction mechanism. The continuous measurements at a space station include in-situ remote sensing, and active probing techniques.

Source record↗
NASA NTRS1978

Ionospheric range-rate effects in satellite-to-satellite tracking

Ionospheric range and range rate corrections in satellite-to-satellite tracking were investigated and the magnitude of errors that have to be considered for communications between satellites and related experiments was defined. The major causes of the sudden and sometimes large variations apparent in the ionospheric range and range rate corrections along the satellite arc are the geometric effects of the raypath and the curved ionosphere, and the localized perturbations in electron density along the satellite-to-satellite line of sight. Ionospheric tracking errors are greatly effected by the following parameters, whose influence was thoroughly investigated: satellite height, height of maximum electron density, localized ionospheric perturbations and gradients, and electron density variations with diurnal, day-to-day, seasonal, and solar cycle patterns. The results point to the need of using a sophisticated modeling approach incorporating daily solar data, and where possible actual ionospheric measurements as update information. Simulations were performed for satellites at various heights: Apollo, Geos, and Nimbus tracked by ATS-6; and in two different geometric configurations: coplanar and perpendicular orbits.

Bent, R. B.↗
NASA NTRS1977

Long-term storage effects-up-date

The long-term effects of storing batteries over a period of years prior to their use in a synchronous orbit application are discussed. A previous experiment is discussed where life testing was performed on cells after having stored the battery for a period of three years with the cell shorted and at a temperature on the order of 5-10 C. The test consisted of performing a sequence of 10 simulated eclipse seasons (equivalent in cycling of five years in synchronous orbit). Results of this test indicated very little degradation and change in the physical and chemical conditions of the cell. The program was extended for an additional year (battery was stored for four years) under the same conditions and testing. The results of the four year storage (one exception) indicate that no further change occurred that is significant from the three year test.

Scott, W. R.↗