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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Going With the Flow: Using a Particle Dispersion Model to Reveal Links Between Ocean Ecosystems and Marine Aerosols

Marine biogenic particle contributions to atmospheric aerosol concentrations are not well understood though they are important for determining cloud optical and cloud nucleating properties. Here we examine the relationship between marine aerosol measurements with satellite and model fields of ocean biology and meteorological variables during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). NAAMES consisted of four field campaigns between November 2015 and April 2018 that aligned with the four major phases of the annual phytoplankton bloom cycle. The FLEXPART Lagrangian particle dispersion model is used to connect these variables spatiotemporally to ship-based aerosol. We find that correlations between some aerosol measurements with satellite measured and modelled variables increase with increasing trajectory length. This indicates biological and meteorological processes over the air mass history are influential to measured particle properties. For example, results show organic aerosol mass is driven by biogenic volatile organic compound (VOC) emissions from photosynthesis by phytoplankton stocks during advection into the region. In contrast, primary marine aerosol (PMA) concentrations showed better correlations to variables weighted with shorter air mass residence times, which reflects their localized origin as primary emissions. Evidence also suggest variability in PMA concentrations likely play an important role in VOC emission removal. The removal of VOCs lessen their impact on particle number and mass concentrations. The sensitivity of marine aerosols to the main ecosystem-atmosphere drivers and its dependence on air mass history are to be further discussed.

Kevin J Sanchez↗
NASA NTRS1973

Joint Conference on Sensing of Environmental Pollutants, 2nd, Washington, D.C., December 10-12, 1973, Proceedings

Subjects considered are related to the remote passive sensing of atmosphere pollutants, the extension of laboratory measurement techniques for field use, instrument quality and measurement standardization, the remote active sensing of atmospheric pollutants, stationary source sensing, and air quality standards and measurement accuracy. Aspects of radiological, electromagnetic, and acoustic pollution monitoring are discussed together with new methods in particulate analysis, the measurement of meteorological variables that impact on atmospheric pollutants, and the impact of meteorological parameters on pollution analysis. The in-situ sensing of acoustic chemical and biological pollutants is reported along with global scale pollution monitoring and the remote sensing of water pollutants. Individual items are announced in this issue.

Source record↗
NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗
NASA NTRS1997

The Aggregate Description of Semi-Arid Vegetation with Precipitation-Generated Soil Moisture Heterogeneity

Meteorological measurements in the Walnut Gulch catchment in Arizona were used to synthesize a distributed, hourly-average time series of data across a 26.9 by 12.5 km area with a grid resolution of 480 m for a continuous 18-month period which included two seasons of monsoonal rainfall. Coupled surface-atmosphere model runs established the acceptability (for modelling purposes) of assuming uniformity in all meteorological variables other than rainfall. Rainfall was interpolated onto the grid from an array of 82 recording rain gauges. These meteorological data were used as forcing variables for an equivalent array of stand-alone Biosphere-Atmosphere Transfer Scheme (BATS) models to describe the evolution of soil moisture and surface energy fluxes in response to the prevalent, heterogeneous pattern of convective precipitation. The calculated area-average behaviour was compared with that given by a single aggregate BATS simulation forced with area-average meteorological data. Heterogeneous rainfall gives rise to significant but partly compensating differences in the transpiration and the intercepted rainfall components of total evaporation during rain storms. However, the calculated area-average surface energy fluxes given by the two simulations in rain-free conditions with strong heterogeneity in soil moisture were always close to identical, a result which is independent of whether default or site-specific vegetation and soil parameters were used. Because the spatial variability in soil moisture throughout the catchment has the same order of magnitude as the amount of rain failing in a typical convective storm (commonly 10% of the vegetation's root zone saturation) in a semi-arid environment, non-linearitv in the relationship between transpiration and the soil moisture available to the vegetation has limited influence on area-average surface fluxes.

White, Cary B.↗
NASA NTRS1976

Meteorological adjustment of yearly mean values for air pollutant concentration comparison

Using multiple linear regression analysis, models which estimate mean concentrations of Total Suspended Particulate (TSP), sulfur dioxide, and nitrogen dioxide as a function of several meteorologic variables, two rough economic indicators, and a simple trend in time are studied. Meteorologic data were obtained and do not include inversion heights. The goodness of fit of the estimated models is partially reflected by the squared coefficient of multiple correlation which indicates that, at the various sampling stations, the models accounted for about 23 to 47 percent of the total variance of the observed TSP concentrations. If the resulting model equations are used in place of simple overall means of the observed concentrations, there is about a 20 percent improvement in either: (1) predicting mean concentrations for specified meteorological conditions; or (2) adjusting successive yearly averages to allow for comparisons devoid of meteorological effects. An application to source identification is presented using regression coefficients of wind velocity predictor variables.

Sidik, S. M.↗
DOE OSTI2022

Evaluation of interactive and prescribed agricultural ammonia emissions for simulating atmospheric composition in CAM-chem

Abstract. Ammonia (NH3) plays a central role in the chemistry of inorganic secondary aerosols in the atmosphere. The largest emission sector for NH3 is agriculture, where NH3 is volatilized from livestock wastes and fertilized soils. Although the NH3 volatilization from soils is driven by the soil temperature and moisture, many atmospheric chemistry models prescribe the emission using yearly emission inventories and climatological seasonal variations. Here we evaluate an alternative approach where the NH3 emissions from agriculture are simulated interactively using the process model FANv2 (Flow of Agricultural Nitrogen, version 2) coupled to the Community Atmospheric Model with Chemistry (CAM-chem). We run a set of 6-year global simulations using the NH3 emission from FANv2 and three global emission inventories (EDGAR, CEDS and HTAP) and evaluate the model performance using a global set of multi-component (atmospheric NH3 and NH4+, and NH4+ wet deposition) in situ observations. Over East Asia, Europe and North America, the simulations with different emissions perform similarly when compared with the observed geographical patterns. The seasonal distributions of NH3 emissions differ between the inventories, and the comparison to observations suggests that both FANv2 and the inventories would benefit from more realistic timing of fertilizer applications. The largest differences between the simulations occur over data-scarce regions. In Africa, the emissions simulated by FANv2 are 200 %–300 % higher than in the inventories, and the available in situ observations from western and central Africa, as well as NH3 retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) instrument, are consistent with the higher NH3 emissions as simulated by FANv2. Overall, in simulating ammonia and ammonium concentrations over regions with detailed regional emission inventories, the inventories based on these details (HTAP, CEDS) capture the atmospheric concentrations and their seasonal variability the best. However these inventories cannot capture the impact of meteorological variability on the emissions, nor can these inventories couple the emissions to the biogeochemical cycles and their changes with climate drivers. Finally, we show with sensitivity experiments that the simulated time-averaged nitrate concentration in air is sensitive to the temporal resolution of the NH3 emissions. Over the CASTNET monitoring network covering the US, resolving the NH3 emissions hourly instead monthly reduced the positive model bias from approximately 80 % to 60 % of the observed yearly mean nitrate concentration. This suggests that some of the commonly reported overestimation of aerosol nitrate over the US may be related to unresolved temporal variability in the NH3 emissions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Identifying Meteorological Influences on Marine Low Cloud Mesoscale Morphology Using Satellite Classifications

Marine low cloud mesoscale morphology in the southeastern Pacific Ocean is analyzed using a large dataset of machine-learning generated classifications spanning three years. Meteorological variables and cloud properties are composited 10by mesoscale cloud type, showing distinct meteorological regimes of marine low cloud organization from the tropics to the midlatitudes. The presentation of mesoscale cellular convection, with respect to geographic distribution, boundary layer structure, and large-scale environmental conditions, agrees with prior knowledge. Two tropical and subtropical cumuliform boundary layer regimes, suppressed cumulus and clustered cumulus, are studied in detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the representation of marine shallow mesoscale convective 15 self-aggregation by large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud types are found to be well-separated by variables associated with low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Clustered regimes are associated with surface convergence and suppressed regimes are associated with surface divergence.

Johannes Mohrmann↗
NASA NTRS1999

Quality Control of Meteorological Observations

For the first time, a problem of the meteorological observation quality control (QC) was formulated by L.S. Gandin at the Main Geophysical Observatory in the 70's. Later in 1988 L.S. Gandin began adapting his ideas in complex quality control (CQC) to the operational environment at the National Centers for Environmental Prediction. The CQC was first applied by L.S.Gandin and his colleagues to detection and correction of errors in rawinsonde heights and temperatures using a complex of hydrostatic residuals.Later, a full complex of residuals, vertical and horizontal optimal interpolations and baseline checks were added for the checking and correction of a wide range of meteorological variables. some other of Gandin's ideas were applied and substantially developed at other meteorological centers. A new statistical QC was recently implemented in the Goddard Data Assimilation System. The central component of any quality control is a buddy check which is a test of individual suspect observations against available nearby non-suspect observations. A novel feature of this test is that the error variances which are used for QC decision are re-estimated on-line. As a result, the allowed tolerances for suspect observations can depend on local atmospheric conditions. The system is then better able to accept extreme values observed in deep cyclones, jet streams and so on. The basic statements of this adaptive buddy check are described. Some results of the on-line QC including moisture QC are presented.

Collins, William↗
DOE OSTI2025

Savannah River Site Climatology for Research Activities Conducted Onsite and in P-Area (1994-2023)

This report presents a climatology of meteorological variables at the Savannah River Site (SRS) using measurements from the P-Area and Central Climatology meteorological towers to support research activities at the Savannah River Site. Key values included in this report are wind, surface pressure, temperature, relative humidity, soil moisture, cloud fraction, cloud base, lightning, atmospheric stability, and boundary layer parameters. The Central Climatology tower is useful in that it is instrumented at four levels, allowing for an examination of height dependence on some variables.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Savannah River Site Climatology for Research Activities Conducted Onsite and in P-Area (1994-2023)

Executive Summary This report presents a climatology of meteorological variables at the Savannah River Site (SRS) using measurements from the P-Area and Central Climatology meteorological towers to support research activities at the Savannah River Site. Key values included in this report are wind, surface pressure, temperature, relative humidity, soil moisture, cloud fraction, cloud base, lightning, atmospheric stability, and boundary layer parameters. The Central Climatology tower is useful in that it is instrumented at four levels, allowing for an examination of height dependence on some variables.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using automated machine learning for the upscaling of gross primary productivity

Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r 2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

How will United States commercial building energy use be impacted by IPCC climate scenarios?

Climate change and anthropogenically-forced shift of weather in the future will impact energy use and resilience of both the built environment and the electric grid. The aim of this analysis is to understand how future climate scenarios will impact electricity and natural gas use of commercial buildings in the United States. Here, this study analyzes this impact for 2030, 2045, and 2100 using Representative Concentration Pathways (RCP) scenarios defined in Intergovernmental Panel on Climate Change (IPCC) Assessment Report 5. The large, gridded simulation of meteorological variables for RCPs 2.6, 4.5, 6.0, and 8.5 are selected and downscaled to make available hourly Future Meteorological Year (FMY) weather files for use and improvement in subsequent studies. High performance computing resources use these FMYs to simulate commercial prototype buildings in every American Society of Heating, Refrigeration, and Air Conditioning Engineers (ASHRAE) climate zone of the United States (US), and results are scaled to nation-wide energy use using conditioned floor area multipliers. The analysis is conducted without speculating the physical and performance traits of future buildings or the grid characteristics. This analysis quantifies the impact of climate change on source electrical and natural gas usage for commercial buildings in the United States over the next 80 years. If US commercial floorspace remained constant, total energy use by 2100 is predicted between an 1.75% decrease under the greatest emission scenario (8.5) and a 1.76% increase under the lowest emission scenario (2.6). When adjusted for anticipated urban growth by 2100, the predicted range is 65% increase (8.5) and 71% increase (2.6). Under a global temperature rise climate scenario, the warmest US climate zones will see a large increases in electricity use derived from space cooling while the coldest US climate zones will see significant decreases in natural gas use caused by the decrease in heating necessary. While climate change may ultimately require adaptations of the built environment to withstand its effects and because the United States is a country that requires more heating than cooling, from a building energy perspective, climate change (average temperature rise) is a net energy saver for the United States.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2013

Attribution of Trends and Variability in Surface Ozone over the United States

Concentrations of tropospheric ozone, a greenhouse gas and air pollutant, are impacted by changes in precursor emissions as well meteorology and influx from the stratosphere. Observations show a decreasing trend in summertime surface ozone at rural stations in the eastern United States, while some western stations show increasing trends, particularly in springtime. We use the Global Modeling Initiative (GMI) global chemical transport model to investigate the roles of precursor emission changes, meteorological variability, and stratosphere-troposphere exchange (STE) in explaining observed trends in surface ozone from rural sites in the United States from 1991-2010. The model's interannual variability shows significant correlations with observations from many of the surface sites. We also compare the simulated ozone to ozonesonde data for several locations with sufficiently long records. We compare a simulation with time-dependent precursor emissions, including emission reductions over the United States and Europe and increases over Asia, to a simulation with fixed emissions to quantify the impact of changing emissions on the surface trends. The simulation with varying emissions reproduces much of the east-west difference in summertime ozone over the U.S., although it generally underestimates the negative trend in the East. In contrast, the fixed-emission simulation shows increasing ozone at both eastern and western sites. We will discuss possible causes of this behavior, including long-range transport and STE.

ozone↗
NASA NTRS1947

The Calculation of the Heat Required for Wing Thermal Ice Prevention in Specified Icing Conditions

Flight tests were made in natural icing conditions with two 8-ft-chord heated airfoils of different sections. Measurements of meteorological variables conducive to ice formation were made simultaneously with the procurement of airfoil thermal data. The extent of knowledge on the meteorology of icing, the impingement of water drops on airfoil surfaces, and the processes of heat transfer and evaporation from a wetted airfoil surface have been increased to a point where the design of heated wings on a fundamental, wet-air basis now can be undertaken with reasonable certainty.

Bergrun, Norman R.↗
NASA NTRS1982

The Mars reference atmosphere

A nominal atmospheric model of Mars is presented, with detailed consideration being given to the various sources of variability in the Martian atmosphere. A basic model for the midlatitude summer is outlined for the northern and southern hemispheres. Attention is given to meteorological variability due to winds, diurnal and seasonal pressure variations, temperature changes, and the effects of dust opacity, particularly on temperature stratification. Viking lander IR thermal mapper data are examined in terms of diurnal and latitudinal temperature variations, and cloud and haze formation and locations are discussed. Mass spectrometry of the atmosphere is used to describe the molecular abundances, and water vapor measurements are reviewed. Finally, radio occultation, UV airglow, and mass spectrometry of the Martian upper atmosphere are investigated, along with the temperature structure of the upper atmosphere.

Kliore, A.↗
NASA NTRSDate not supplied

Ocean-Land Teleconnections and Chaotic Atmospheric Variability in Climate Models, with Implications for Real-World Behavior

This study introduces a unique approach to estimating a fundamental but unmeasurable property of Nature: ρo 2 , the fraction of the variance of a given meteorological variable that is determined by boundary forcing (specifically, SSTs) as opposed to chaotic atmospheric dynamics. Here, we focus on continental monthly-averaged near-surface air temperature, or T2M. Rather than determining the fractions of SST-explained T2M variance found for a specific atmospheric general circulation model (AGCM) and then assuming that these fractions also represent Nature, we perform an analysis that entails: (i) evaluating a specific AGCM’s ability to capture observed variability with an infinite number of ensemble members, (ii) quantifying the degree to which different modeling systems agree on SST-land teleconnections, and (iii) combining these results into our novel estimates of ρo 2 . The derived ρo 2 fields suggest, with caveats, that AGCMs tend to overestimate the fraction of explained T2M variance (the signal-to-total variance ratio) in the Tropics.

Randal D Koster↗
DOE OSTI2021

Quantifying contributions of natural variability and anthropogenic forcings on increased fire weather risk over the western United States

Previous studies have identified a recent increase in wildfire activity in the western United States (WUS). However, the extent to which this trend is due to weather pattern changes dominated by natural variability versus anthropogenic warming has been unclear. Using an ensemble constructed flow analogue approach, we have employed observations to estimate vapor pressure deficit (VPD), the leading meteorological variable that controls wildfires, associated with different atmospheric circulation patterns. Our results show that for the period 1979 to 2020, variation in the atmospheric circulation explains, on average, only 32% of the observed VPD trend of 0.48 ± 0.25 hPa/decade (95% CI) over the WUS during the warm season (May to September). The remaining 68% of the upward VPD trend is likely due to anthropogenic warming. The ensemble simulations of climate models participating in the sixth phase of the Coupled Model Intercomparison Project suggest that anthropogenic forcing explains an even larger fraction of the observed VPD trend (88%) for the same period and region. These models and observational estimates likely provide a lower and an upper bound on the true impact of anthropogenic warming on the VPD trend over the WUS. During August 2020, when the August Complex “Gigafire” occurred in the WUS, anthropogenic warming likely explains 50% of the unprecedented high VPD anomalies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States)

Abstract. High-resolution gridded datasets of meteorological variables are needed in order to resolve fine-scale hydrological gradients in complex mountainous terrain. Across the United States, the highest available spatial resolution of gridded datasets of daily meteorological records is approximately 800 m. This work presents gridded datasets of daily precipitation and mean temperature for the East–Taylor subbasin (in the western United States) covering a 12-year period (2008–2019) at a high spatial resolution (400 m). The datasets are generated using a downscaling framework that uses data-driven models to learn relationships between climate variables and topography. We observe that downscaled datasets of precipitation and mean temperature exhibit smoother spatial gradients (while preserving the spatial variability) when compared to their coarser counterparts. Additionally, we also observe that when downscaled datasets are upscaled to the original resolution (800 m), the mean residual error is almost zero, ensuring no bias when compared with the original data. Furthermore, the downscaled datasets are observed to be linearly related to elevation, which is consistent with the methodology underlying the original 800 m product. Finally, we validate the spatial patterns exhibited by downscaled datasets via an example use case that models lidar-derived estimates of snowpack. The presented dataset constitutes a valuable resource to resolve fine-scale hydrological gradients in the mountainous terrain of the East–Taylor subbasin, which is an important study area in the context of water security for the southwestern United States and Mexico. The dataset is publicly available at https://doi.org/10.15485/1822259 (Mital et al., 2021).

54 ENVIRONMENTAL SCIENCES↗