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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Adjustment of footprint correction for airborne flux mapping over the FIFE site

A meaningful interpretation of airborne flux estimates over nonuniform terrain must consider local advection from nonhomogeneous source distributions (footprint correction). An empirical procedure for footprint correction of airborne flux data obtained in the First ISLSCP Field Experiment (FIFE) 1989 is presented, based on simple analytical solutions of the diffusion equation which have been adjusted to approximate more realistic solutions. Optimized estimates of surface flux 'maps' derived from airborne observations are then constructed for maximum correlation between flux estimates and independently observed surface characteristics. This paper also includes an analysis of resolution for the given procedure, in terms of amplitude recovery of hypothetical square-wave distributions of surface flux density, and discusses its implications for the interpretation of the optimized estimates of FIFE surface flux maps. Results show that corrected spatial sink distributions are obtained by the current empirical procedure at the cost of considerable reduction in amplitude recovery of small-scale variations in surface flux density. The high spatial correlation observed between corrected flux maps and surface characteristics such as greenness or surface temperature excess is ascribed to the approximately correct positioning of the main flux gradients across the site, which can be expected to have been reproduced with amplitude recovery equal to or greater than 60 percent.

Schuepp, P. H.↗
NASA NTRS1994

Angular momentum exchange among the solid Earth, atmosphere, and oceans: A case study of the 1982-1983 El Nino event

The 1982-1983 El Nino/Southern Oscillation (ENSO) event was accompanied by the largest interannual variation in the Earth's rotation rate on record. In this study we demonstrate that atmospheric forcing was the dominant cause for this rotational anomaly, with atmospheric angular momentum (AAM) integrated from 1000 to 1 mbar (troposphere plus stratosphere) accounting for up to 92% of the interannual variance in the length of day (LOD). Winds between 100 and 1 mbar contributed nearly 20% of the variance explained, indicating that the stratosphere can play a significant role in the Earth's angular momentum budget on interannual time scales. Examination of LOD, AAM, and Southern Oscillation Index (SOI) data for a 15-year span surrounding the 1982-1983 event suggests that the strong rotational response resulted from constructive interference between the low-frequency (approximately 4-6 year) and quasi-biennial (approximately 2-3 year) components of the ENSO phenomenon, as well as the stratospheric Quasi-Biennial Oscillation (QBO). Sources of the remaining LOD discrepancy (approximately 55 and 64 microseconds rms residual for the European Centre for Medium-Range Forecasting (EC) and U.S. National Meteorological Center (NMC) analyses) are explored; noise and systematic errors in the AAM data are estimated to contribute 18 and 33 microseconds, respectively, leaving a residual (rms) of 40 (52) microseconds unaccounted for by the EC (NMC) analysis. Oceanic angular momentum contributions (both moment of inertia changes associated with baroclinic waves and motion terms) are shown to be candidates in closing the interannual axial angular momentum budget.

Dickey, J. O.↗
NASA NTRS2007

Comparison of Predicted and Measured Attenuation of Turbine Noise from a Static Engine Test

Aircraft noise has become an increasing concern for commercial airlines. Worldwide demand for quieter aircraft is increasing, making the prediction of engine noise suppression one of the most important fields of research. The Low-Pressure Turbine (LPT) can be an important noise source during the approach condition for commercial aircraft. The National Aeronautics and Space Administration (NASA), Pratt & Whitney (P&W), and Goodrich Aerostructures (Goodrich) conducted a joint program to validate a method for predicting turbine noise attenuation. The method includes noise-source estimation, acoustic treatment impedance prediction, and in-duct noise propagation analysis. Two noise propagation prediction codes, Eversman Finite Element Method (FEM) code [1] and the CDUCT-LaRC [2] code, were used in this study to compare the predicted and the measured turbine noise attenuation from a static engine test. In this paper, the test setup, test configurations and test results are detailed in Section II. A description of the input parameters, including estimated noise modal content (in terms of acoustic potential), and acoustic treatment impedance values are provided in Section III. The prediction-to-test correlation study results are illustrated and discussed in Section IV and V for the FEM and the CDUCT-LaRC codes, respectively, and a summary of the results is presented in Section VI.

Chien, Eugene W.↗
NASA NTRS2013

Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental US (CONUS)

In the middle to high latitude and alpine regions, the seasonal snow pack can dominate the surface energy and water budgets due to its high albedo, low thermal conductivity, high emissivity, considerable spatial and temporal variability, and ability to store and then later release a winters cumulative snowfall (Cohen, 1994; Hall, 1998). With this in mind, the snow drought across the U.S. has raised questions about impacts on water supply, ski resorts and agriculture. Knowledge of various snow pack properties is crucial for short-term weather forecasts, climate change prediction, and hydrologic forecasting for producing reliable daily to seasonal forecasts. One potential source of this information is the multi-institution North American Land Data Assimilation System (NLDAS) project (Mitchell et al., 2004). Real-time NLDAS products are used for drought monitoring to support the National Integrated Drought Information System (NIDIS) and as initial conditions for a future NCEP drought forecast system. Additionally, efforts are currently underway to assimilate remotely-sensed estimates of land-surface states such as snowpack information into NLDAS. It is believed that this assimilation will not only produce improved snowpack states that better represent snow evolving conditions, but will directly improve the monitoring of drought.

MODIS↗
NASA NTRS2019

Solar Activity Modeling: From Subgranular Dynamical Scales to the Solar Cycles

Dynamical effects of solar magnetoconvection span a wide range spatial and temporal scales that extends from the interior to the corona and from fast turbulent motions to the global-Sun magnetic activity. To study the solar activity on short temporal scales (from minutes to hours), we use 3D radiative MHD simulations that allow us to investigate complex turbulent interactions that drive various phenomena, such as plasma eruptions, spontaneous formation of magnetic structures, funnel-like structures and magnetic loops in the corona, and others. In particular, we focus on multi-scale processes of energy exchange across the different layers, which contribute to the corona heating and eruptive dynamics, as well as interlinks between different layers of the solar interior and atmosphere. For modeling the global-scale activity we use the data assimilation approach that has demonstrated great potential for building reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to the Parker-Kleeorin-Ruzmakin dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of observational limitation on the prediction accuracy. We present the EnKF predictions of the upcoming Solar Cycle 25 based on both the sunspot number series and observed magnetic fields, and discuss the uncertainties and potential of the data assimilation approach for modeling and forecasting the solar activity.

Kitiashvili, I. N.↗
NASA NTRS2019

Solar Activity Modeling: From Subgranular Dynamical Scales to the Solar Cycles

The dynamical effects of solar magnetoconvection span a wide range spatial and temporal scales that extend from the interior to the corona and from fast turbulent motions to global magnetic activity. To study the solar activity on short temporal scales (from minutes to hours), we use 3D radiative MHD simulations that allow us to investigate complex turbulent interactions that drive various phenomena, such as plasma eruptions, spontaneous formation of magnetic structures, funnel-like structures and magnetic loops in the corona, and others. In particular, we focus on multi-scale processes of energy exchange across layers of the solar interior and atmosphere, which contribute to coronal heating and eruptive dynamics. For modeling global-scale activity, we use a data assimilation approach that has demonstrated great potential for building reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to the Parker-Kleeorin-Ruzmakin dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of observational limitations on prediction accuracy, and we present the EnKF predictions of the upcoming Solar Cycle (25) based on both the sunspot number series and observed magnetic fields and discuss the uncertainties and potential of the data assimilation approach for modeling and forecasting solar activity.

Kitiashvili, I. N.↗
NASA NTRS1979

Spectroscopy of the extreme ultraviolet dayglow at 6.5A resolution - Atomic and ionic emissions between 530 and 1240A

EUV spectra (530-1500A) of the day airglow in up, down and horizontal aspect orientations have been obtained with 6.5A resolution and a limiting sensitivity of 5R from a rocket experiment. Below 834A the spectrum is rich in previously unobserved OII transitions connecting with 4S(0), 2D(0), and 2P(0) states. Recent broad-band photometric observations of geocoronal HeI 584A emission in terms of the newly observed OII emissions are shown. The OI 989A and OI 1304A emissions exhibit similar dependence on altitude and viewing geometry with the OI 989A brightness 1/15 that of OI 1340. Emission at 1026A is identified as geocoronal HI Lyman beta rather than OI multiplet emission and observed intensities agree well with model estimates. An unexpectedly high NI 1200/NI 1134A brightness ratio is evidence of a significant contribution from photodissociative excitation of N2 to the NI 1200A source function.

Gentieu, E. P.↗
NASA NTRS2013

A Single-column Model Ensemble Approach Applied to the TWP-ICE Experiment

Single-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.

numerical weather forecasting↗
NASA NTRSDate not supplied

Building a Real-Time Flood Prediction Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the local government of Howard County, Maryland, to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a statistical model capable of hindcasting the two severe flash flood events that devastated Ellicott City and transitioned to a ‘Long Short-Term Memory’ based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using Nash-Sutcliffe Efficiency. The final product, the Sequentially Trained Real-time EstimAted Model (STREAM) predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

NASA DEVELOP↗
NASA NTRS2002

Toward a Comprehensive Carbon Budget for North America: Potential Applications of Adjoint Methods with Diverse Datasets

A detailed mechanistic understanding of the sources and sinks of CO2 will be required to reliably predict future COS levels and climate. A commonly used technique for deriving information about CO2 exchange with surface reservoirs is to solve an "inverse problem," where CO2 observations are used with an atmospheric transport model to find the optimal distribution of sources and sinks. Synthesis inversion methods are powerful tools for addressing this question, but the results are disturbingly sensitive to the details of the calculation. Studies done using different atmospheric transport models and combinations of surface station data have produced substantially different distributions of surface fluxes. Adjoint methods are now being developed that will more effectively incorporate diverse datasets in estimates of surface fluxes of CO2. In an adjoint framework, it will be possible to combine CO2 concentration data from long-term surface monitoring stations with data from intensive field campaigns and with proposed future satellite observations. A major advantage of the adjoint approach is that meteorological and surface data, as well as data for other atmospheric constituents and pollutants can be efficiently included in addition to observations of CO2 mixing ratios. This presentation will provide an overview of potentially useful datasets for carbon cycle research in general with an emphasis on planning for the North American Carbon Project. Areas of overlap with ongoing and proposed work on air quality/air pollution issues will be highlighted.

Andrews, A.↗
NASA NTRS1979

A model for the noise radiated by turbulent boundary layers and its interaction with laminar layers in supersonic flow

Motivated by the noise-boundary layer transition problem in supersonic flow, the interaction between incoming aerodynamic noise and the laminar boundary layer was studied. The noise field was modeled as a Mach wave radiation field consisting of discrete waves emanating from coherent turbulent entities moving downstream within the supersonic turbulent boundary layer. The individual disturbances are likened to miniature sonic booms and the laminar boundary layer is strafed by the waves as the sources move downstream. The mean, autocorrelation, and power spectral density of the field are expressed in terms of the wave shapes and their average arrival rates. The emphasis in the interaction analysis is on the behavior of the shocklets in the noise field. The shocklets are shown to be focused by the laminar boundary layer in its outer region. Borrowing wave-propagation terminology, this region is termed the caustic region. Using scaling laws from sonic boom work, focus factors at the caustic are estimated to vary from 2 to 6 for incoming shocklet strengths of 1 to .01 percent of the free stream pressure level. The situation regarding experimental evidence of the caustic region is reviewed. It is not clear whether the focusing effectively promotes early transition. Additional observations concerning the interaction problem are presented.

Schopper, M. R.↗
NASA NTRS1979

A model for the noise radiated by turbulent boundary layers and its interaction with laminar layers in supersonic flow

Motivated by the noise-boundary layer transition problem in supersonic flow, the interaction between incoming aerodynamic noise and the laminar boundary layer has been studied. The noise field has been modeled as a Mach wave radiation field consisting of discrete waves emanating from coherent turbulent entities moving downstream within the supersonic turbulent boundary layer. The individual disturbances are likened to miniature sonic booms and the laminar boundary layer is strafed by the waves as the sources move downstream. The mean, autocorrelation, and power spectral density of the field are expressed in terms of the wave shapes and their average arrival rates. The emphasis in the interaction analysis is on the behavior of the shocklets in the noise field. The shocklets are shown to be focused by the laminar boundary layer in its outer region. Borrowing wave-propagation terminology, this region is termed the caustic region. Using scaling laws from sonic boom work, focus factors at the caustic are estimated to vary from 2 to 6 for incoming shocklet strengths of 1 to .01 percent of the free stream pressure level.

Schopper, M. R.↗
NASA NTRS1990

A parameterization for the shortwave transmissivity of stratiform water clouds based on empirical data and radiative transfer theory

Surface measurements of solar flux and total integrated liquid-water content, radiosonde data, and infrared satellite images are analyzed in conjunction with radiative transfer calculations to derive an empirical parameterization for the shortwave transmissivity of continental stratiform water clouds. The data were collected near Denver, Colorado, over a period of six years. A multiple reflection radiative transfer model is employed to estimate stratus cloud transmissivity in terms of the measurable liquid-water path (LWP). A nonlinear fit of estimated transmissivities to the corresponding observations of LWP yields close agreement with a previous, more complicated parameterization. The derived expression for cloud transmissivity is used to predict mean daily surface fluxes for 61 days during which periods of stratiform clouds were observed over the Denver area. A comparison between predicted and measured fluxes shows agreement to within + or - 4 percent, with best agreement for clouds of moderate optical thickness. Potential sources of error are identified with sensitivity studies.

Derr, V. E.↗
NASA NTRS1985

Remote sensing of atmospheric and surface parameters from HIRS2/MSU on TIROS-N

At the Goddard Laboratory for Atmospheric Sciences (GLAS) a physically based satellite temperature sounding retrieval system was developed, involving the simultaneous analysis of sounding data, for determining atmospheric and surface conditions that are consistent with the observed radiances. In addition to determining accurate atmospheric temperature profiles even in the presence of clouds, the system provides global estimates of day and night sea or land surface temperatures, snow and ice cover, cloud amounts, cloudtop heights and temperatures, and albedo. The characteristics of the atmospheric and surface parameters derived from the GLAS Physical Retrieval System are reviewed, results for the FGGE year are presented and, whenever possible, compared with other measurements of the same fields. Some of the model-derived diagnostic fields available from the GLAS Analysis/Forecast System are also discussed. These fields may lead to more accurate determination of the source terms of the atmospheric heat and moisture budgets.

Susskind, J.↗
NASA NTRS1998

Impact of Radiation Hardness and Operating Temperatures of Silicon Carbide Electronics on Space Power System Mass

The effect of silicon carbide (SiC) electronics operating temperatures on Power Management and Distribution (PMAD), or Power Conditioning (PC), subsystem radiator size and mass requirements was evaluated for three power output levels (100 kW(e) , 1 MW(e), and 10 MW(e)) for near term technology ( i.e. 1500 K turbine inlet temperature) Closed Cycle Gas Turbine (CCGT) power systems with a High Temperature Gas Reactor (HTGR) heat source. The study was conducted for assumed PC radiator temperatures ranging from 370 to 845 K and for three scenarios of electrical energy to heat conversion levels which needed to be rejected to space by means of the PC radiator. In addition, during part of the study the radiation hardness of the PC electronics was varied at a fixed separation distance to estimate its effect on the mass of the instrument rated reactor shadow shield. With both the PC radiator and the conical shadow shield representing major components of the overall power system the influence of the above on total power system mass was also determined. As expected, results show that the greatest actual mass savings achieved by the use of SiC electronics occur with high capacity power systems. Moreover, raising the PC radiator temperature above 600 K yields only small additional system mass savings. The effect of increased radiation hardness on total system mass is to reduce system mass by virtue of lowering the shield mass.

Juhasz, Albert J.↗
NASA NTRS1993

Long-term productivity in the cryptoendolithic microbial community of the Ross Desert, Antarctica

Annual gross productivity of the lichen-dominated cryptoendolithic community was calculated from a computer analysis of photosynthetic response based on laboratory measurements of CO2 exchange and three years (1985-1988) of field nanoclimate data. Photosynthetic optimum increased from -3 to 2 degrees C between irradiance levels of 100 and 1500 micromoles photons m-2 s-1, while the upper compensation point rose from 1 to 17 degrees C. The mean yearly total time available for metabolic activity (temperature above -10 degrees C and moisture present) was 771.3 h for horizontal rock, 421.5 h for northeast-oriented sloped rock, and 1042.2 h for a small depression in horizontal rock (the characteristic site of occasional lichen apothecia). The calculated mean gross productivity value for a horizontal rock was 1215 mg C m-2 y-1, and net photosynthetic gain was 606 mg C m-2 y-1. Net ecosystem productivity (annual accretion of cellular biomass) estimated from long-term events amounted to only about 3 mg C m-2 y-1. The difference between these two values may represent the long-term metabolic costs of the frequent dehydration-rehydration and freezing-thawing cycles or of overwintering, and may account for the leaching of organic substances to the rock. The yearly gross productivity of the cryptoendolithic microbial community of the entire Ross Desert area was estimated at approximately 120,000-180,000 kg C. Of this, 600-900 kg C is in microbial biomass, and much of the rest is soluble compounds that leach into the rocks and possibly percolate to the valleys, providing a source of organic matter for lakes, rivers, and soils.

NASA Program Exobiology↗
NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗
NASA NTRS2021

Air Pollution Trends Measured From Terra: CO and AOD Over Industrial, Fire-prone, and Background Regions

Following past studies to quantify decadal trends in global carbon monoxide (CO) using satellite observations, we update estimates and find a CO trend in column amounts of about −0.50 % per year between 2002 to 2018, which is a deceleration compared to analyses performed on shorter records that found −1 % per year. Aerosols are co-emitted with CO from both fires and anthropogenic sources but with a shorter lifetime than CO. A combined trend analysis of CO and aerosol optical depth (AOD) measurements from space helps to diagnose the drivers of regional differences in the CO trend. We use the long-term records of CO from the Measurements of Pollution in the Troposphere (MOPITT) and AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Other satellite instruments measuring CO in the thermal infrared, AIRS, TES, IASI, and CrIS, show consistent hemispheric CO variability and corroborate results from the trend analysis performed with MOPITT CO. Trends are examined by hemisphere and in regions for 2002 to 2018, with uncertainties quantified. The CO and AOD records are split into two sub-periods (2002 to 2010 and 2010 to 2018) in order to assess trend changes over the 16 years. We focus on four major population centers: Northeast China, North India, Europe, and Eastern USA, as well as fire-prone regions in both hemispheres. In general, CO declines faster in the first half of the record compared to the second half, while AOD trends show more variability across regions. We find evidence of the atmospheric impact of air quality management policies. The large decline in CO found over Northeast China is initially associated with an improvement in combustion efficiency, with subsequent additional air quality improvements from 2010 onwards. Industrial regions with minimal emission control measures such as North India become more globally relevant as the global CO trend weakens. We also examine the CO trends in monthly percentile values to understand seasonal implications and find that local changes in biomass burning are sufficiently strong to counteract the global downward trend in atmospheric CO, particularly in late summer.

carbon monoxide↗