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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Temperature-moisture biases in ECMWF analyses based on clear sky longwave simulations constrained by SSMI and MSU measurements and comparisons to ERBE estimates

Clear sky longwave radiation fluxes for the summer of 1988 and winter of 1989 have been simulated with a radiative transfer model that includes detailed treatment of atmospheric gas absorption. The input data to the model are humidity and temperature profiles from European Center for Medium-Range Weather Forecasts (ECMWF) analyses and surface temperature measurements from the International Satellite Cloud Climatology Project. To reduce the inherent uncertainties in humidity profiles, the ECMWF moisture fields have been adjusted, based on the climatological relationship between the moisture profile and total precipitable water (PW), so that the ECMWF PW is equal to that derived from Special Sensor Microwave Imager (SSMI) data. SSMI and ECMWF PW patterns are generally similar, but significant differences in magnitude are found in the low latitudes, particularly over the subtropical oceans in the Southern Hemisphere. At the same time the tropospheric brightness temperature (T(sub B)) at 53.74 GHz has been computed using a microwave transfer model with ECMWF temperature and humidity fields as inputs. The comparison of computed T(sub B) with the Microwave Sounding Unit (MSU) channel 2 T(sub B) revealed differences larger than 1.5 K over most of the oceans, suggesting that the ECMWF model atmosphere has a cold bias. Maximum biases greater than 3 K are found in low latitudes. After removing the bias from the ECMWF temperature field, a new set of temperature profiles yielding brightness temperature equal to MSU channel 2 T(sub B) has been obtained. Simulation results clearly demonstrate that inclusion of satellite estimates of PW and T(sub B) enhances the accuracy of the clear sky LW flux simulation, substantially reducing the differences from Earth Radiation Budget Experiment (ERBE) estimates. Global averages indicate that the model-derived top-of-atmosphere (TOA) clear sky fluxes from the adjusted moisture and temperature profiles are in very good agreement with satellite-measured ERBE values, differing by only 1.4 W/sq m. By contrast, the calculation using only ECMWF analyses without any adjustments gives a significant underestimation of TOA clear sky fluxes up to 10.7 W/sq m compared to the satellite measurements, suggesting that the constraint method can significantly improve the accuracy in the radiation budget simulation. The results also indicate that the ECMWF-based simulation errors are, at least for the two seasons studied, mainly attributed to the error in the temperature field rather than to errors in the moisture field.

Sohn, Byung-Ju↗
NASA NTRS1996

PROP3D: A Program for 3D Euler Unsteady Aerodynamic and Aeroelastic (Flutter and Forced Response) Analysis of Propellers. Version 1.0

This guide describes the input data required, for steady or unsteady aerodynamic and aeroelastic analysis of propellers and the output files generated, in using PROP3D. The aerodynamic forces are obtained by solving three dimensional unsteady, compressible Euler equations. A normal mode structural analysis is used to obtain the aeroelastic equations, which are solved using either time domain or frequency domain solution method. Sample input and output files are included in this guide for steady aerodynamic analysis of single and counter-rotation propellers, and aeroelastic analysis of single-rotation propeller.

Srivastava, R.↗
NASA NTRS2008

The TRMM Multi-Satellite Precipitation Analysis (TMPA)

The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) is intended to provide a "best" estimate of quasi-global precipitation from the wide variety of modern satellite-borne precipitation-related sensors. Estimates are provided at relatively fine scales (0.25degx0.25deg, 3-hourly) in both real and post-real time to accommodate a wide range of researchers. However, the errors inherent in the finest scale estimates are large. The most successful use of the TMPA data is when the analysis takes advantage of the fine-scale data to create time/space averages appropriate to the user s application. We review the conceptual basis for the TMPA, summarize the processing sequence, and focus on two new activities. First, a recent upgrade to the real-time version incorporates several additional satellite data sources and employs monthly climatological adjustments to approximate the bias characteristics of the research quality post-real-time product. Second, an upgrade of the research quality post-real-time TMPA from Version 6 to Version 7 (in beta test at press time) is designed to provide a variety of improvements that increase the list of input data sets and correct several issues. Future enhancements for the TMPA will include improved error estimation, extension to higher latitudes, and a shift to a Lagrangian time interpolation scheme.

Huffman, George J.↗
NASA NTRS2006

Equations for Scoring Rules When Data Are Missing

A document presents equations for scoring rules in a diagnostic and/or prognostic artificial-intelligence software system of the rule-based inference-engine type. The equations define a set of metrics that characterize the evaluation of a rule when data required for the antecedence clause(s) of the rule are missing. The metrics include a primary measure denoted the rule completeness metric (RCM) plus a number of subsidiary measures that contribute to the RCM. The RCM is derived from an analysis of a rule with respect to its truth and a measure of the completeness of its input data. The derivation is such that the truth value of an antecedent is independent of the measure of its completeness. The RCM can be used to compare the degree of completeness of two or more rules with respect to a given set of data. Hence, the RCM can be used as a guide to choosing among rules during the rule-selection phase of operation of the artificial-intelligence system..

James, Mark↗
NASA NTRS1979

Block adaptive rate controlled image data compression

A block adaptive rate controlled (BARC) image data compression algorithm is described. It is noted that in the algorithm's principal rate controlled mode, image lines can be coded at selected rates by combining practical universal noiseless coding techniques with block adaptive adjustments in linear quantization. Compression of any source data at chosen rates of 3.0 bits/sample and above can be expected to yield visual image quality with imperceptible degradation. Exact reconstruction will be obtained if the one-dimensional difference entropy is below the selected compression rate. It is noted that the compressor can also be operated as a floating rate noiseless coder by simply not altering the input data quantization. Here, the universal noiseless coder ensures that the code rate is always close to the entropy. Application of BARC image data compression to the Galileo orbiter mission of Jupiter is considered.

Rice, R. F.↗
NASA NTRS2008

Using an Informative Missing Data Model to Predict the Ability to Assess Recovery of Balance Control after Spaceflight

Astronauts show degraded balance control immediately after spaceflight. To assess this change, astronauts' ability to maintain a fixed stance under several challenging stimuli on a movable platform is quantified by "equilibrium" scores (EQs) on a scale of 0 to 100, where 100 represents perfect control (sway angle of 0) and 0 represents data loss where no sway angle is observed because the subject has to be restrained from falling. By comparing post- to pre-flight EQs for actual astronauts vs. controls, we built a classifier for deciding when an astronaut has recovered. Future diagnostic performance depends both on the sampling distribution of the classifier as well as the distribution of its input data. Taking this into consideration, we constructed a predictive ROC by simulation after modeling P(EQ = 0) in terms of a latent EQ-like beta-distributed random variable with random effects.

Feiveson, Alan H.↗
NASA NTRS1976

User data dissemination concepts for earth resources

Domestic data dissemination networks for earth-resources data in the 1985-1995 time frame were evaluated. The following topics were addressed: (1) earth-resources data sources and expected data volumes, (2) future user demand in terms of data volume and timeliness, (3) space-to-space and earth point-to-point transmission link requirements and implementation, (4) preprocessing requirements and implementation, (5) network costs, and (6) technological development to support this implementation. This study was parametric in that the data input (supply) was varied by a factor of about fifteen while the user request (demand) was varied by a factor of about nineteen. Correspondingly, the time from observation to delivery to the user was varied. This parametric evaluation was performed by a computer simulation that was based on network alternatives and resulted in preliminary transmission and preprocessing requirements. The earth-resource data sources considered were: shuttle sorties, synchronous satellites (e.g., SEOS), aircraft, and satellites in polar orbits.

Davies, R.↗
NASA NTRS1985

Response time correlations for platinum resistance thermometers in flowing fluids

The thermal response of two types of Platinum Resistance Thermometers (PRT's), which are being considered for use in the National Transonic Wind Tunnel Facility, were studied. Response time correlations for each PRT, in flowing water, oil and air, were established separately. A universal correlation, tau WOA = 2.0 + 1264, 9/h, for a Hy-Cal Sensor (with a reference resistance of 100 ohm) within an error of 20% was established while the universal correlation for the Rosemount Sensor (with a reference resistance of 1000 ohm), tau OA = 0.122 + 1105.6/h, was found with a maximum percentage error of 30%. The correlation for the Rosemount Sensor was based on air and oil data only which is certainly not sufficient to make a correlation applicable to every condition. Therefore, the correlation needs more data to be gathered in different fluids. Also, it is necessary to state that the calculation of the parameter, h, was based on the available heat transfer correlations, whose accuracies are already reported in literature uncertain within 20-30%. Therefore, the universal response constant correlations established here for the Hy-Cal and Rosemount sensors are consistent with the uncertainty in the input data and are recommended for future use in flowing liquids and gases.

Pandey, D. K.↗
NASA NTRS2005

Sensor Data Distribution With Robustness and Reliability: Toward Distributed Components Model

In planetary surface exploration mission, sensor data distribution is required in many aspects, for example, in navigation, scheduling, planning, monitoring, diagnostics, and automation of the field tasks. The challenge is to distribute such data in the robust and reliable way so that we can minimize the errors caused by miscalculations, and misjudgments that based on the error data input in the mission. The ad-hoc wireless network on planetary surface is not constantly connected because of the nature of the rough terrain and lack of permanent establishments on the surface. There are some disconnected moments that the computation nodes will re-associate with different repeaters or access points until connections are reestablished. Such a nature requires our sensor data distribution software robust and reliable with ability to tolerant disconnected moments. This paper presents a distributed components model as a framework to accomplish such tasks. The software is written in Java and utilized the available Java Message Services schema and the Boss implementation. The results of field experimentations show that the model is very effective in completing the tasks.

Alena, Richard L.↗
NASA NTRS1983

Experiments using least square lattice filters for the identification of structural dynamics

An approach for identifying the dynamics of large space structures is applied to a free-free beam. In this approach the system's order is determined on-line, along with mode shapes, using recursive lattice filters which provide a least square estimate of the measurement data. The mode shapes determined are orthonormal in the space of the measurements and, hence, are not the natural modes of the structure. To determine the natural modes of the structure, a method based on the fast Fourier transform is used on the outputs of the lattice filter. These natural modes are used to obtain the modal amplitude time series which provides the input data for an output error parameter identification scheme that identifies the ARMA parameters of the difference equation model of the modes. the approach is applied to both simulated and experimental data.

Sundararajan, N.↗
NASA NTRS2018

Multidecadal Changes in the UTLS Ozone from the MERRA-2 Reanalysis and the GMI Chemistry Model

Long-term changes of ozone in the UTLS (Upper Troposphere / Lower Stratosphere) reflect the response to decreases in the stratospheric concentrations of ozone-depleting substances as well as changes in the stratospheric circulation induced by climate change. To date, studies of UTLS ozone changes and variability have relied mainly on satellite and in-situ observations as well as chemistry-climate model simulations. By comparison, the potential of reanalysis ozone data remains relatively untapped. This is despite evidence from recent studies, including detailed analyses conducted under SPARC (Scalable Processor Architecture) Reanalysis Intercomparison Project (S-RIP), that demonstrate that stratospheric ozone fields from modern atmospheric reanalyses exhibit good agreement with independent data while delineating issues related to inhomogeneities in the assimilated observations. In this presentation, we will explore the possibility of inferring long-term geographically and vertically resolved behavior of the lower stratospheric (LS) ozone from NASA's MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications -2) reanalysis after accounting for the few known discontinuities and gaps in its assimilated input data. This work builds upon previous studies that have documented excellent agreement between MERRA-2 ozone and ozonesonde observations in the LS. Of particular importance is a relatively good vertical resolution of MERRA-2 allowing precise separation of tropospheric and stratospheric ozone contents. We also compare the MERRA-2 LS ozone results with the recently completed 37-year simulation produced using Goddard Earth Observing System in "replay" mode coupled with the GMI (Global Modeling Initiative) chemistry mechanism. Replay mode dynamically constrains the model with the MERRA-2 reanalysis winds, temperature, and pressure. We will emphasize the areas of agreement of the reanalysis and replay and interpret differences between them in the context of our increasing understanding of model transport driven by assimilated winds.

Ozone↗
NASA NTRSDate not supplied

Medium-Range River Flood Forecasts Using a Long Short-Term Memory Network

River flooding and the impacts are a concern for decision makers throughout the United States. Accurate medium-range forecasts (~3-7 days) are critical for providing advanced outlooks to emergency management officials. Unfortunately, accurately forecasting rainfall-runoff and the subsequent rise and fall within rivers remain a challenge in hydrological modeling. While complex physical modeling systems are the standard for representing the hydrological processes, they are computationally demanding and can require extensive calibration. Further, uncertainties remain in the model parameters and input data. The use of machine learning can reduce some of the computational demand while maintaining high accuracy. Therefore, this project makes use of a Long Short-Term Memory (LSTM) network which explicitly accounts for the time-dependent nature of rainfall-runoff modeling. The developed LSTM was trained to predict river gauge height, or stage height, based on time-lagged input features which include: gauge height to initialize the model, the NASA Short-term Prediction Research and Transition Center’s instance of the Land Information System (SPoRT-LIS) relative soil moisture to describe the rainfall infiltration rate, and 6-hr Multi-Radar Multi-Sensor quantitative precipitation estimate (MRMS QPE). The developed LSTM based system is then used to produce 7-day forecasts with a 6-hr temporal resolution using three different quantitative precipitation forecasts (QPF) from the NWS’s Weather Prediction Center (WPC), the NCEP Global Forecast System (GFS) model and the National Blend of Models (NBM). This trained modeling system has been implemented as an experimental product at over 100 different rivers in collaboration with at multiple National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC) across the eastern half of the United States. The developed LSTM model achieved average Nash-Sutcliffe efficiency (NSE) 0.89 higher than the equivalent medium-range National Water Model ensemble member forecast over a 7-day forecast. In addition to the initial development and evaluation, this project has continued to expand. While the initial model was developed for precipitation dominated basins, expansion of the project has taken it to basins effected by snow melt. This presentation will provide an overview of the project with focus on recent developments on incorporating snow melt processes into the model.

Andrew T. White↗
NASA NTRS2010

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Wagstaff, Kiri L.↗
NASA NTRS2011

Incorporating JULES into NASA's Land Information System (LIS) and Investigations of Land-Atmosphere Coupling

NASA's Land Information System (LIS; lis.gsfc.nasa.gov) is a flexible land surface modeling and data assimilation framework developed over the past decade with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. LIS features a high performance and flexible design, and operates on an ensemble of land surface models for extension over user-specified regional or global domains. The extensible interfaces of LIS allow the incorporation of new domains, land surface models (LSMs), land surface parameters, meteorological inputs, data assimilation and optimization algorithms. In addition, LIS has also been demonstrated for parameter estimation and uncertainty estimation, and has been coupled to the Weather Research and Forecasting (WRF) mesoscale model. A visiting fellowship is currently underway to implement JULES into LIS and to undertake some fundamental science on the feedbacks between the land surface and the atmosphere. An overview of the LIS system, features, and sample results will be presented in an effort to engage the community in the potential advantages of LIS-JULES for a range of applications. Ongoing efforts to develop a framework for diagnosing land-atmosphere coupling will also be presented using the suite of LSM and PBL schemes available in LIS and WRF along with observations from the U. S .. Southern Great Plains. This methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Santanello, Joseph↗
NASA NTRS1991

The use of Doppler radar to help forecast the development of thunderstorms

The role of Doppler radars in forecasting the location and timing of the initiation of thunderstorms both over Kennedy Space Center and central Florida is analyzed. The data used in this analysis was collected with two Doppler radars with high temporal and spatial resolution throughout the troposphere. Previous studies of thunderstorm initiation in the vicinity of Kennedy Space Center, including synoptic and site-specific observations are outlined. As a result of the analysis of nine cases of thunderstorm initiation, it is concluded that an expert system could be developed that would take input data from the existing field mill network, instrumented tower network, lightning detection system, from sounding, and from Doppler-radar-based algorithms. Such a system would be able to determine a probability of thunderstorm development in the next 10, 20, and 30 minutes for the area of interest.

Eilts, Michael D.↗
NASA NTRS1985

Snowmelt-runoff Model Utilizing Remotely-sensed Data

Remotely sensed snow cover information is the critical data input for the Snowmelt-Runoff Model (SRM), which was developed to simulatke discharge from mountain basins where snowmelt is an important component of runoff. Of simple structure, the model requires only input of temperature, precipitation, and snow covered area. SRM was run successfully on two widely separated basins. The simulations on the Kings River basin are significant because of the large basin area (4000 sq km) and the adequate performance in the most extreme drought year of record (1976). The performance of SRM on the Okutadami River basin was important because it was accomplished with minimum snow cover data available. Tables show: optimum and minimum conditions for model application; basin sizes and elevations where SRM was applied; and SRM strengths and weaknesses. Graphs show results of discharge simulation.

Rango, A.↗
NASA NTRS2002

Improving the Accuracy of Satellite Sea Surface Temperature Measurements by Explicitly Accounting for the Bulk-Skin Temperature Difference

The focus of this research was to determine whether the accuracy of satellite measurements of sea surface temperature (SST) could be improved by explicitly accounting for the complex temperature gradients at the surface of the ocean associated with the cool skin and diurnal warm layers. To achieve this goal, work was performed in two different major areas. The first centered on the development and deployment of low-cost infrared radiometers to enable the direct validation of satellite measurements of skin temperature. The second involved a modeling and data analysis effort whereby modeled near-surface temperature profiles were integrated into the retrieval of bulk SST estimates from existing satellite data. Under the first work area, two different seagoing infrared radiometers were designed and fabricated and the first of these was deployed on research ships during two major experiments. Analyses of these data contributed significantly to the Ph.D. thesis of one graduate student and these results are currently being converted into a journal publication. The results of the second portion of work demonstrated that, with presently available models and heat flux estimates, accuracy improvements in SST retrievals associated with better physical treatment of the near-surface layer were partially balanced by uncertainties in the models and extra required input data. While no significant accuracy improvement was observed in this experiment, the results are very encouraging for future applications where improved models and coincident environmental data will be available. These results are included in a manuscript undergoing final review with the Journal of Atmospheric and Oceanic Technology.

Wick, Gary A.↗
NASA NTRS2006

Analysis of Leaf Area Index and Fraction of PAR Absorbed by Vegetation Products from the Terra MODIS Sensor: 2000-2005

The analysis of two years of Collection 3 and five years of Collection 4 Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets is presented in this article with the goal of understanding product quality with respect to version (Collection 3 versus 4), algorithm (main versus backup), snow (snow-free versus snow on the ground), and cloud (cloud-free versus cloudy) conditions. Retrievals from the main radiative transfer algorithm increased from 55% in Collection 3 to 67% in Collection 4 due to algorithm refinements and improved inputs. Anomalously high LAI/FPAR values observed in Collection 3 product in some vegetation types were corrected in Collection 4. The problem of reflectance saturation and too few main algorithm retrievals in broadleaf forests persisted in Collection 4. The spurious seasonality in needleleaf LAI/FPAR fields was traced to fewer reliable input data and retrievals during the boreal winter period. About 97% of the snow covered pixels were processed by the backup Normalized Difference Vegetation Index-based algorithm. Similarly, a majority of retrievals under cloudy conditions were obtained from the backup algorithm. For these reasons, the users are advised to consult the quality flags accompanying the LAI and FPAR product.

Yang, Wenze↗