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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

A Method for Assessing the Quality of Model-Based Estimates of Ground Temperature and Atmospheric Moisture Using Satellite Data

A method is developed for validating model-based estimates of atmospheric moisture and ground temperature using satellite data. The approach relates errors in estimates of clear-sky longwave fluxes at the top of the Earth-atmosphere system to errors in geophysical parameters. The fluxes include clear-sky outgoing longwave radiation (CLR) and radiative flux in the window region between 8 and 12 microns (RadWn). The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data, and multiple global four-dimensional data assimilation (4-DDA) products. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic clear-sky longwave fluxes from two different 4-DDA data sets. Simple linear regression is used to relate the clear-sky longwave flux discrepancies to discrepancies in ground temperature ((delta)T(sub g)) and broad-layer integrated atmospheric precipitable water ((delta)pw). The slopes of the regression lines define sensitivity parameters which can be exploited to help interpret mismatches between satellite observations and model-based estimates of clear-sky longwave fluxes. For illustration we analyze the discrepancies in the clear-sky longwave fluxes between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS2) and a recent operational version of the European Centre for Medium-Range Weather Forecasts data assimilation system. The analysis of the synthetic clear-sky flux data shows that simple linear regression employing (delta)T(sub g)) and broad layer (delta)pw provides a good approximation to the full radiative transfer calculations, typically explaining more thin 90% of the 6 hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters, Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for (delta)T(sub g) to approx. 20% for the lower tropospheric moisture between 500 hPa and surface. The regression relationships developed from the synthetic flux data, together with CLR and RadWn observed with the Clouds and Earth Radiant Energy System instrument, ire used to assess the quality of the GEOS2 T(sub g) and pw. Results showed that the GEOS2 T(sub g) is too cold over land, and pw in upper layers is too high over the tropical oceans and too low in the lower atmosphere.

Wu, Man Li C.↗
NASA NTRS2000

Estimates of Ground Temperature and Atmospheric Moisture from CERES Observations

A method is developed to retrieve surface ground temperature (Tg) and atmospheric moisture using clear sky fluxes (CSF) from CERES-TRMM observations. In general, the clear sky outgoing long-wave radiation (CLR) is sensitive to upper level moisture (q(sub h)) over wet regions and Tg over dry regions The clear sky window flux from 800 to 1200 /cm (RadWn) is sensitive to low level moisture (q(sub j)) and Tg. Combining these two measurements (CLR and RadWn), Tg and q(sub h) can be estimated over land, while q(sub h) and q(sub t) can be estimated over the oceans. The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic CSF data from two different global 4-dimensional data assimilation products. Simple linear regression is used to relate discrepancies in CSF to discrepancies in Tg, q(sub h) and q(sub t). The slopes of the regression lines define sensitivity parameters that can be exploited to help interpret mismatches between satellite observations and model-based estimates of CSF. For illustration, we analyze the discrepancies in the CSF between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) and a recent operational version of the European Center for Medium-Range Weather Prediction data assimilation system. In particular, our analysis of synthetic total and window region SCF differences (computed from two different assimilated data sets) shows that simple linear regression employing (Delta)Tg and broad layer (Delta)q(sub l) from 500 hPa to surface and (Delta)q(sub h) from 200 to 500 hPa provides a good approximation to the full radiative transfer calculations, typically explaining more than 90% of the 6-hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters. Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for (Delta)T to about 20% for (Delta)q(sub t). Our initial application of the methodology employed an early CERES-TRMM data set (CLR and Radwn) to assess the quality of the GEOS2 data. The results showed that over the tropical and subtropical oceans GEOS2 is, in general, too wet in the upper troposphere (mean bias of 0.99 mm) and too dry in the lower troposphere (mean bias of -4.7 mm). We note that these errors, as well as a cold bias in the Tg, have largely been corrected in the current version of GEOS-2 with the introduction of a land surface model, a moist turbulence scheme and the assimilation of SSTM/I total precipitable water.

Wu, Man Li C.↗
NASA NTRS2000

Estimates of Ground Temperature and Atmospheric Moisture from CERES Observations

A method is developed to retrieve surface ground temperature (T(sub g)) and atmospheric moisture using clear sky fluxes (CSF) from CERES-TRMM observations. In general, the clear sky outgoing longwave radiation (CLR) is sensitive to upper level moisture (q(sub l)) over wet regions and (T(sub g)) over dry regions The clear sky window flux from 800 to 1200/cm (RadWn) is sensitive to low level moisture (q(sub t)) and T(sub g). Combining these two measurements (CLR and RadWn), Tg and q(sub h) can be estimated over land, while q(sub h) and q(sub l) can be estimated over the oceans. The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic CSF data from two different global 4-dimensional data assimilation products. Simple linear regression is used to relate discrepancies in CSF to discrepancies in T(sub g), q(sub h) and q(sub l). The slopes of the regression lines define sensitivity parameters that can be exploited to help interpret mismatches between satellite observations and model-based estimates of CSF. For illustration, we analyze the discrepancies in the CSF between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) and a recent operational version of the European Center for Medium-Range Weather Prediction data assimilation system. In particular, our analysis of synthetic total and window region SCF differences (computed from two different assimilated data sets) shows that simple linear regression employing Delta(T(sub g)) and broad layer Delta(q(sub l) from .500 hPa to surface and Delta(q(sub h)) from 200 to .300 hPa provides a good approximation to the full radiative transfer calculations. typically explaining more than 90% of the 6-hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters. Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for Delta(T(sub g)) to about 20% for Delta(q(sub l)). Our initial application of the methodology employed an early CERES-TRMM data set (CLR and Radwn) to assess the quality of the GEOS2 data. The results showed that over the tropical and subtropical oceans GEOS2 is, in general, too wet in the upper troposphere (mean bias of 0.99 mm) and too dry in the lower troposphere (mean bias of -4.7 min). We note that these errors, as well as a cold bias in the T(sub g). have largely been corrected in the current version of GEOS-2 with the introduction of a land surface model, a moist turbulence scheme and the assimilation of SSM/I total precipitable water.

Wu, Man Li C.↗
NASA NTRS1987

The empirical accuracy of uncertain inference models

Uncertainty is a pervasive feature of the domains in which expert systems are designed to function. Research design to test uncertain inference methods for accuracy and robustness, in accordance with standard engineering practice is reviewed. Several studies were conducted to assess how well various methods perform on problems constructed so that correct answers are known, and to find out what underlying features of a problem cause strong or weak performance. For each method studied, situations were identified in which performance deteriorates dramatically. Over a broad range of problems, some well known methods do only about as well as a simple linear regression model, and often much worse than a simple independence probability model. The results indicate that some commercially available expert system shells should be used with caution, because the uncertain inference models that they implement can yield rather inaccurate results.

Vaughan, David S.↗
NASA NTRS1980

The influence of snow depth and surface air temperature on satellite-derived microwave brightness temperature

Areas of the steppes of central Russia, the high plains of Montana and North Dakota, and the high plains of Canada were studied in an effort to determine the relationship between passive microwave satellite brightness temperature, surface air temperature, and snow depth. Significant regression relationships were developed in each of these homogeneous areas. Results show that sq R values obtained for air temperature versus snow depth and the ratio of microwave brightness temperature and air temperature versus snow depth were not as the sq R values obtained by simply plotting microwave brightness temperature versus snow depth. Multiple regression analysis provided only marginal improvement over the results obtained by using simple linear regression.

Foster, J. L.↗
NASA NTRS1981

Application of thermal model for pan evaporation to the hydrology of a defined medium, the sponge

A technique is presented which estimates pan evaporation from the commonly observed values of daily maximum and minimum air temperatures. These two variables are transformed to saturation vapor pressure equivalents which are used in a simple linear regression model. The model provides reasonably accurate estimates of pan evaporation rates over a large geographic area. The derived evaporation algorithm is combined with precipitation to obtain a simple moisture variable. A hypothetical medium with a capacity of 8 inches of water is initialized at 4 inches. The medium behaves like a sponge: it absorbs all incident precipitation, with runoff or drainage occurring only after it is saturated. Water is lost from this simple system through evaporation just as from a Class A pan, but at a rate proportional to its degree of saturation. The contents of the sponge is a moisture index calculated from only the maximum and minium temperatures and precipitation.

Trenchard, M. H.↗
NASA NTRS1984

The application of dimensional analysis to the problem of solar wind-magnetosphere energy coupling

The constraints imposed by dimensional analysis are used to find how the solar wind-magnetosphere energy transfer rate depends upon interplanetary parameters. The analyses assume that only magnetohydrodynamic processes are important in controlling the rate of energy transfer. The study utilizes ISEE-3 solar wind observations, the AE index, and UT from three 10-day intervals during the International Magnetospheric Study. Simple linear regression and histogram techniques are used to find the value of the magnetohydrodynamic coupling exponent, alpha, which is consistent with observations of magnetospheric response. Once alpha is estimated, the form of the solar wind energy transfer rate is obtained by substitution into an equation of the interplanetary variables whose exponents depend upon alpha.

Bargatze, L. F.↗
NASA NTRS1992

Analysis of the lettuce data from the variable pressure growth chamber at NASA Johnson Space Center: A three-stage nested design model

A model of three-stage nested experimental design was applied to analyze the lettuce data obtained from the variable pressure growth chamber test bed at NASA-Johnson Space Center. From the results of an application of the analysis of variance and covariance on the data set, it was noted that all of the (uncontrollable) factors, Side, Zone, Height and (controllable) PAR (photosynthetically active radiation), had nonhomogeneous effects on the dry weight of the edible biomass of lettuce per pot. Incidentally, the variations accountable to the (uncontrollable) factorial heterogeneities are merely 9 percent and 17 percent of the total variation for both the first and second crop test, respectively. After adjusting for the PAR as a covariate in the no-intercept model, the accountable variations to all the four factors are 94 percent and 92 percent for the first and the second crop test, respectively. With the use of a no-intercept simple linear regression model, the accountable variations to the factor PAR are 92 percent and 90 percent for the first and the second crop test, respectively. Evidently, the (controllable) factor PAR is the dominating one.

Lee, Tze-San↗
NASA NTRS1996

Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration

Consider a normally distributed response variable, related to an explanatory variable through the simple linear regression model. Data obtained on the response variable, corresponding to known values of the explanatory variable (i.e., calibration data), are to be used for testing hypotheses concerning unknown values of the explanatory variable. We consider the problem of testing an unlimited sequence of one sided hypotheses concerning the explanatory variable, using the corresponding sequence of values of the response variable and the same set of calibration data. This is the situation of multiple use of the calibration data. The tests derived in this context are characterized by two types of uncertainties: one uncertainty associated with the sequence of values of the response variable, and a second uncertainty associated with the calibration data. We derive tests based on a condition that incorporates both of these uncertainties. The solution has practical applications in the decision limit problem. We illustrate our results using an example dealing with the estimation of blood alcohol concentration based on breath estimates of the alcohol concentration. In the example, the problem is to test if the unknown blood alcohol concentration of an individual exceeds a threshold that is safe for driving.

Krishnamoorthy, K.↗
NASA NTRS2003

Instrumentation and methodology for quantifying GFP fluorescence in intact plant organs

The General Fluorescence Plant Meter (GFP-Meter) is a portable spectrofluorometer that utilizes a fiber-optic cable and a leaf clip to gather spectrofluorescence data. In contrast to traditional analytical systems, this instrument allows for the rapid detection and fluorescence measurement of proteins under field conditions with no damage to plant tissue. Here we discuss the methodology of gathering and standardizing spectrofluorescence data from tobacco and canola plants expressing GFP. Furthermore, we demonstrate the accuracy and effectiveness of the GFP-Meter. We first compared GFP fluorescence measurements taken by the GFP-Meter to those taken by a standard laboratory-based spectrofluorometer, the FluoroMax-2. Spectrofluorescence measurements were taken from the same location on intact leaves. When these measurements were tested by simple linear regression analysis, we found that there was a positive functional relationship between instruments. Finally, to exhibit that the GFP-Meter recorded accurate measurements over a span of time, we completed a time-course analysis of GFP fluorescence measurements. We found that only initial measurements were accurate; however, subsequent measurements could be used for qualitative purposes.

Evaluation Studies↗
NASA NTRS2005

Homozygous diploid deletion strains of Saccharomyces cerevisiae that determine lag phase and dehydration tolerance

This study identifies genes that determine length of lag phase, using the model eukaryotic organism, Saccharomyces cerevisiae. We report growth of a yeast deletion series following variations in the lag phase induced by variable storage times after drying-down yeast on filters. Using a homozygous diploid deletion pool, lag times ranging from 0 h to 90 h were associated with increased drop-out of mitochondrial genes and increased survival of nuclear genes. Simple linear regression (R2 analysis) shows that there are over 500 genes for which > 70% of the variation can be explained by lag alone. In the genes with a positive correlation, such that the gene abundance increases with lag and hence the deletion strain is suitable for survival during prolonged storage, there is a strong predominance of nucleonic genes. In the genes with a negative correlation, such that the gene abundance decreases with lag and hence the strain may be critical for getting yeast out of the lag phase, there is a strong predominance of glycoproteins and transmembrane proteins. This study identifies yeast deletion strains with survival advantage on prolonged storage and amplifies our understanding of the genes critical for getting out of the lag phase.

NASA Discipline Cell Biology↗
NASA NTRS2011

Evaluation and Windspeed Dependence of MODIS Aerosol Retrievals Over Open Ocean

The Maritime Aerosol Network (MAN) data set provides high quality ground-truth to validate the MODIS aerosol product over open ocean. Prior validation of the ocean aerosol product has been limited to coastal and island sites. Comparing MODIS Collection 5 ocean aerosol retrieval products with collocated MAN measurements from ships shows that MODIS is meeting the pre-launch uncertainty estimates for aerosol optical depth (AOD) with 64% and 67% of retrievals at 550 nm, and 74% and 78% of retrievals at 870 nm, falling within expected uncertainty for Terra and Aqua, respectively. Angstrom Exponent comparisons show a high correlation between MODIS retrievals and shipboard measurements (R= 0.85 Terra, 0.83 Aqua), although the MODIS aerosol algorithm tends to underestimate particle size for large particles and overestimate size for small particles, as seen in earlier Collections. Prior analysis noted an offset between Terra and Aqua ocean AOD, without concluding which sensor was more accurate. The simple linear regression reported here, is consistent with other anecdotal evidence that Aqua agreement with AERONET is marginally better. However we cannot claim based on the current study that the better Aqua comparison is statistically significant. Systematic increase of error as a function of wind speed is noted in both Terra and Aqua retrievals. This wind speed dependency enters the retrieval when winds deviate from the 6 m/s value assumed in the rough ocean surface and white cap parameterizations. Wind speed dependency in the results can be mitigated by using auxiliary NCEP wind speed information in the retrieval process.

Kleidman, Richard G.↗
NASA NTRS2018

Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis

The tropical rainforests are the largest reserves of terrestrial carbon and, therefore, the future of these rainforests is a question that is of immense importance in the geoscience research community. With the recent severe Amazonian droughts in 2005 and 2010 and on-going drought in the Congo region for more than two decades, there is growing concern that these forests could succumb to precipitation reduction, causing extensive carbon release and feedback to the carbon cycle. However, there is no single ecosystem model that quantifies the relationship between vegetation health in these rainforests and climatic factors. Small scale studies have used statistical correlation measure and simple linear regression to model climate-vegetation interactions, but suffer from the lack of comprehensive data representation as well as simplistic assumptions about dependency of the target on the covariates. In this paper we use genetic programming (GP) based symbolic regression for discovering equations that govern the vegetation climate dynamics in the rainforests. Expecting micro-regions within the rainforests to have unique characteristics compared to the overall general characteristics, we use a modified regression-tree based hierarchical partitioning of the space to build individual models for each partition. The discovery of these equations reveal very interesting characteristics about the Amazon and the Congo rainforests. Our method GP-tree shows that the rainforests exhibit tremendous resiliency in the face of extreme climatic events by adapting to changing conditions.

genetic programming↗
NASA NTRS2021

Chapter 5: Extravehicular Activity Metabolic Rate Model: Metabolic Rate Estimated from Heart Rate

In-flight monitoring of crew metabolic rates during extravehicular activity (EVA) provides crucial information in mitigating injury. The purpose of this study was to investigate the relationship of crewmember heart rate (HR) and metabolic rate (MR) during EVA operations to develop a predictive linear model. HR and MR data was collected from 132 EVAs from Shuttle and International Space Station (ISS) missions. MR was collected every 2-min from portable life support system delta oxygen decay, while HR was collected every 20-sec via electrocardiogram. HR was down sampled to every 2-min to match MR during EVA for evaluation. Further, a new metric was observed from direct relations between metabolic rate with HR over EVA time (MR/HR) measured as a BTU/beat. A range of BTU/beat was collected as a conversion scale between MR and HR at different EVA workloads categorized by increased MR. Both HR and MR values were observed to decrease through the duration of EVA. Similarly, HR and MR slopes decreased at start of EVA compared to end of EVA. MR/HR values were used to predict MR from HR over the entire duration of EVA with root mean square error less than 200 BTU/Hr. Additionally, MR was predicted based on HR values during EVA via a calculated simple linear regression. A regression equation was found for each EVA drawing relations between HR and MR (F (2923.84) and P<0.0001) with an R(sup 2) value of 0.402. Individual crew regressions improved prediction and R(sup 2) to greater than 0.82. Two models are presented to determine metabolic rate from heart rate during EVA. Results draw correlations for heart rate and metabolic rate fluctuations during EVA for individualized crew predictions during future operations. The linear models correlate to Apollo prediction data during historic EVAs.

Metabolic rate predictions↗
NASA NTRS2010

The Prediction Properties of Inverse and Reverse Regression for the Simple Linear Calibration Problem

The calibration of measurement systems is a fundamental but under-studied problem within industrial statistics. The origins of this problem go back to basic chemical analysis based on NIST standards. In today's world these issues extend to mechanical, electrical, and materials engineering. Often, these new scenarios do not provide "gold standards" such as the standard weights provided by NIST. This paper considers the classic "forward regression followed by inverse regression" approach. In this approach the initial experiment treats the "standards" as the regressor and the observed values as the response to calibrate the instrument. The analyst then must invert the resulting regression model in order to use the instrument to make actual measurements in practice. This paper compares this classical approach to "reverse regression," which treats the standards as the response and the observed measurements as the regressor in the calibration experiment. Such an approach is intuitively appealing because it avoids the need for the inverse regression. However, it also violates some of the basic regression assumptions.

Parker, Peter A.↗
NASA NTRS2021

Expanding the application of soil moisture monitoring systems through regression-based transformation

Relative to other geophysical variables, soil moisture (SM) estimates derived from land surface models (LSMs) and land data assimilation systems (LDAS) are difficult to transfer between platforms and applications. This difficulty stems from the highly model-dependent nature of LSM SM estimates and differences in the vertical support of discretized SM values. As a result, operational SM estimates generated by one LSM (or LDAS) cannot generally be directly applied to a hydrologic monitoring or forecast system designed around a second LSM. This lack of transferability is particularly problematic for LDAS applications, where the time, expertise, and computational resources required to generate an operational LDAS analysis cannot be practically duplicated for every LSM-specific application. Here, we develop a set of simple regression tools for translating SM estimates between LSMs and multiple LDAS analyses. Results demonstrate that simple multivariate linear regression - utilizing independent variables based on multi-layer and temporally lagged SM estimates - can significantly improve upon baseline transformation approaches using direct percentile matching. The proposed regression approaches are effective for both the LSM-to-LSM and LDAS-to-LDAS transformation of multi-layer SM percentiles. Application of this approach will expand the utility of existing, high-quality (but LSM-specific) operational sources of SM information like the NASA Soil Moisture Active Passive Level-4 Soil Moisture product.

Soil Moisture↗
NASA NTRS1979

Magnetometer bias determination and attitude determination for near-earth spacecraft

A simple linear-regression algorithm is used to determine simultaneously magnetometer biases, misalignments, and scale factor corrections, as well as the dependence of the measured magnetic field on magnetic control systems. This algorithm has been applied to data from the Seasat-1 and the Atmosphere Explorer Mission-1/Heat Capacity Mapping Mission (AEM-1/HCMM) spacecraft. Results show that complete inflight calibration as described here can improve significantly the accuracy of attitude solutions obtained from magnetometer measurements. This report discusses the difficulties involved in obtaining attitude information from three-axis magnetometers, briefly derives the calibration algorithm, and presents numerical results for the Seasat-1 and AEM-1/HCMM spacecraft.

Lerner, G. M.↗
NASA NTRS1976

Ground cover estimated from aerial photographs

Estimates of per cent ground cover made by ground observers were compared with independent estimates made on the basis of low-altitude (640-1219 m) aerial photographs of the same fields. Standard statistical simple correlation and linear regression analyses revealed a high correlation between the two estimation methods. In crops such as grain, sorghum, corn, and forage sorghum, in which the broadest part of the leaf canopy is near the top of the plant, there was a tendency to overestimate the per cent ground cover from aerial photographs.

Gerbermann, A. H.↗