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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 415 records · Page 23

Association Between Cardiovascular and Intraocular Pressure Changes in a 14-day 6 deg Head Down Tilt (HDT) Bed Rest Study: Possible Implications in Retinal Anatomy

Mean IOP significantly increased while at 6deg HDT and returned towards pre-bed rest values upon leaving bed rest. While mean IOP increased during bed rest, it remained within the normal limits for subject safety. A diuretic shift and cardiovascular deconditioning occurs during in-bed rest, as expected. There was no demonstrable correlation between the largest change in IOP (pre/post) and cardiovascular measure changes (pre/post). Additional mixed effects linear regression modeling may reveal some subclinical physiological changes that might assist in describing the VIIP syndrome pathophysiology.

Cromwell, R. L.↗

Snow Cover and Precipitation Impacts on Dry Season Streamflow in the Lower Mekong Basin

Climate change impacts on dry season streamflow in the Mekong River are relatively understudied, despite the fact that water availability during this time is critically important for agricultural and ecological systems. Analyses of two gauging stations (Vientiane and Kratie) in the Lower Mekong Basin (LMB) show significant positive correlations between dry season (March through May, MAM) discharge and upper basin snow cover and local precipitation. Using snow cover, precipitation, and upstream discharge as predictors, we develop skillful regression models for MAM streamflow at Vientiane and Kratie, and force these models with output from a suite of general circulation model (GCM) experiments for the twentieth and twenty-first centuries. The GCM simulations predict divergent trends in snow cover (decreasing) and precipitation (increasing) over the twenty-first century, driving overall negligible long-term trends in dry season streamflow. Our study demonstrates how future changes in dry season streamflow in the LMB will depend on changes in snow cover and precipitation, factors that will need to be considered when assessing the full basin response to other climatic and non-climatic drivers.

Cook, Benjamin I.↗

Examining the Link Between Food Prices and Food Insecurity: a Multi-Level Analysis of Maize Price and Birthweight in Kenya

In developing countries where many poor people rely on rainfed, locally produced food for the majority of their caloric intake, shifts in climate and weather patterns can dramatically reduce agricultural productivity. The reduction in agricultural productivity reduces overall food availability and ultimately impacts food accessibility, putting millions of people at risk for malnutrition. In this project we focus on Kenya where roughly a third of households are food insecure. We examine the relationship of the price of maize and low birth weight to help quantify the impact of local food prices on one outcome of household food insecurity. Using spatially referenced data from recent Kenyan Demographic and Health Survey datasets, price data, livelihood information, and a remotely sensed-based measure of local growing season productivity, we develop a dataset linking pregnancies occurring from 2001 to 2008 to the spatially and temporally relevant maize price data. We construct several regression models to examine the impact of local maize prices and remotely sensed based estimates of crop production on infant birth weight - specifically low birth weight. The results of the models highlight the importance of including community crop production to evaluate maize price impacts on low birth weight outcomes. Also, because of the positive correlation between pre-pregnancy maize prices and birth weight, the results suggest that some households may benefit from high prices or that high prices may impact the number of conceptions. More generally, our work demonstrates that multilevel models that account for community-level variation are important for disentangling these complex relationships and can contribute to the discussion of how to design more effective food policies.

Low birth weight↗

Dynamics of Aboveground Phytomass of the Circumpolar Arctic Tundra During the Past Three Decades

Numerous studies have evaluated the dynamics of Arctic tundra vegetation throughout the past few decades, using remotely sensed proxies of vegetation, such as the normalized difference vegetation index (NDVI). While extremely useful, these coarse-scale satellite-derived measurements give us minimal information with regard to how these changes are being expressed on the ground, in terms of tundra structure and function. In this analysis, we used a strong regression model between NDVI and aboveground tundra phytomass, developed from extensive field-harvested measurements of vegetation biomass, to estimate the biomass dynamics of the circumpolar Arctic tundra over the period of continuous satellite records (1982-2010). We found that the southernmost tundra subzones (C-E) dominate the increases in biomass, ranging from 20 to 26%, although there was a high degree of heterogeneity across regions, floristic provinces, and vegetation types. The estimated increase in carbon of the aboveground live vegetation of 0.40 Pg C over the past three decades is substantial, although quite small relative to anthropogenic C emissions. However, a 19.8% average increase in aboveground biomass has major implications for nearly all aspects of tundra ecosystems including hydrology, active layer depths, permafrost regimes, wildlife and human use of Arctic landscapes. While spatially extensive on-the-ground measurements of tundra biomass were conducted in the development of this analysis, validation is still impossible without more repeated, long-term monitoring of Arctic tundra biomass in the field.

Arctic tundra↗

Decline of Monarch Butterflies Overwintering in Mexico- Is the Migratory Phenomenon at Risk?

1.During the 2009-2010 overwintering season and following a 15-year downward trend, the total area in Mexico occupied by the eastern North American population of overwintering monarch butterflies reached an all-time low. Despite an increase, it remained low in 2010-2011. 2. Although the data set is small, the decline in abundance is statistically significant using both linear and exponential regression models. 3. Three factors appear to have contributed to reduce monarch abundance: degradation of the forest in the overwintering areas; the loss of breeding habitat in the United States due to the expansion ofGM herbicide-resistant crops, with consequent loss of milkweed host plants, as well as continued land development; and severe weather. 4. This decline calls into question the long-term survival of the monarchs' migratory phenomenon

Habitat↗

Integrating Solar Induced Fluorescence and the Photochemical Reflectance Index for Estimating Gross Primary Production in a Cornfield

The utilization of remotely sensed observations for light use efficiency (LUE) and tower-based gross primary production (GPP) estimates was studied in a USDA cornfield. Nadir hyperspectral reflectance measurements were acquired at canopy level during a collaborative field campaign conducted in four growing seasons. The Photochemical Reflectance Index (PRI) and solar induced chlorophyll fluorescence (SIF), were derived. SIF retrievals were accomplished in the two telluric atmospheric oxygen absorption features centered at 688 nm (O2-B) and 760 nm (O2-A). The PRI and SIF were examined in conjunction with GPP and LUE determined by flux tower-based measurements. All of these fluxes, environmental variables, and the PRI and SIF exhibited diurnal as well as day-to-day dynamics across the four growing seasons. Consistent with previous studies, the PRI was shown to be related to LUE (r2 = 0.54 with a logarithm fit), but the relationship varied each year. By combining the PRI and SIF in a linear regression model, stronger performances for GPP estimation were obtained. The strongest relationship (r2 = 0.80, RMSE = 0.186 mg CO2/m2/s) was achieved when using the PRI and SIF retrievals at 688 nm. Cross-validation approaches were utilized to demonstrate the robustness and consistency of the performance. This study highlights a GPP retrieval method based entirely on hyperspectral remote sensing observations.

cornfield↗

Surface Ozone Variability and Trends over the South African Highveld from 1990 to 2007

Surface ozone is a secondary air pollutant formed from reactions between nitrogen oxides (NOx = NO + NO2) and volatile organic compounds in the presence of sunlight. In this work we examine effects of the climate pattern known as the El Niño-Southern Oscillation (ENSO) and NOx variability on surface ozone from 1990 to 2007 over the South African Highveld, a heavily populated region in South Africa with numerous industrial facilities. Over summer and autumn (December-May) on the Highveld, El Niño, as signified by positive sea surface temperature (SST) anomalies over the central Pacific Ocean, is typically associated with drier and warmer than normal conditions favoring ozone formation. Conversely, La Niña, or negative SST anomalies over the central Pacific Ocean, is typically associated with cloudier and above normal rainfall conditions, hindering ozone production. We use a generalized regression model to identify any linear dependence that the Highveld ozone, measured at five air quality monitoring stations, may have on ENSO and NOx. Our results indicate that four out of the five stations exhibit a statistically significant sensitivity to ENSO at some point over the December-May period where El Niño amplifies ozone formation and La Niña reduces ozone formation. Three out of the five stations reveal statistically significant sensitivity to NOx variability, primarily in winter and spring. Accounting for ENSO and NOx effects throughout the study period of 18 years, two stations exhibit statistically significant negative ozone trends in spring, one station displays a statistically significant positive trend in August, and two stations show no statistically significant change in surface ozone.

NOx↗

Induction of Chromosomal Aberrations at Fluences of Less Than One HZE Particle per Cell Nucleus

The assumption of a linear dose response used to describe the biological effects of high LET radiation is fundamental in radiation protection methodologies. We investigated the dose response for chromosomal aberrations for exposures corresponding to less than one particle traversal per cell nucleus by high energy and charge (HZE) nuclei. Human fibroblast and lymphocyte cells where irradiated with several low doses of <0.1 Gy, and several higher doses of up to 1 Gy with O (77 keV/ (long-s)m), Si (99 keV/ (long-s)m), Fe (175 keV/ (long-s)m), Fe (195 keV/ (long-s)m) or Fe (240 keV/ (long-s)m) particles. Chromosomal aberrations at first mitosis were scored using fluorescence in situ hybridization (FISH) with chromosome specific paints for chromosomes 1, 2 and 4 and DAPI staining of background chromosomes. Non-linear regression models were used to evaluate possible linear and non-linear dose response models based on these data. Dose responses for simple exchanges for human fibroblast irradiated under confluent culture conditions were best fit by non-linear models motivated by a non-targeted effect (NTE). Best fits for the dose response data for human lymphocytes irradiated in blood tubes were a NTE model for O and a linear response model fit best for Si and Fe particles. Additional evidence for NTE were found in low dose experiments measuring gamma-H2AX foci, a marker of double strand breaks (DSB), and split-dose experiments with human fibroblasts. Our results suggest that simple exchanges in normal human fibroblasts have an important NTE contribution at low particle fluence. The current and prior experimental studies provide important evidence against the linear dose response assumption used in radiation protection for HZE particles and other high LET radiation at the relevant range of low doses.

Hada, Megumi↗

Reevaluation of Stratospheric Ozone Trends From SAGE II Data Using a Simultaneous Temporal and Spatial Analysis

This paper details a new method of regression for sparsely sampled data sets for use with time-series analysis, in particular the Stratospheric Aerosol and Gas Experiment (SAGE) II ozone data set. Non-uniform spatial, temporal, and diurnal sampling present in the data set result in biased values for the long-term trend if not accounted for. This new method is performed close to the native resolution of measurements and is a simultaneous temporal and spatial analysis that accounts for potential diurnal ozone variation. Results show biases, introduced by the way data is prepared for use with traditional methods, can be as high as 10%. Derived long-term changes show declines in ozone similar to other studies but very different trends in the presumed recovery period, with differences up to 2% per decade. The regression model allows for a variable turnaround time and reveals a hemispheric asymmetry in derived trends in the middle to upper stratosphere. Similar methodology is also applied to SAGE II aerosol optical depth data to create a new volcanic proxy that covers the SAGE II mission period. Ultimately this technique may be extensible towards the inclusion of multiple data sets without the need for homogenization.

Damadeo, R. P.↗

Estimating Uncertainty in Long Term Total Ozone Records from Multiple Sources

Total ozone measurements derived from the TOMS and SBUV backscattered solar UV instrument series cover the period from late 1978 to the present. As the SBUV series of instruments comes to an end, we look to the 10 years of data from the AURA Ozone Monitoring Instrument (OMI) and two years of data from the Ozone Mapping Profiler Suite (OMPS) on board the Suomi National Polar-orbiting Partnership satellite to continue the record. When combining these records to construct a single long-term data set for analysis we must estimate the uncertainty in the record resulting from potential biases and drifts in the individual measurement records. In this study we present a Monte Carlo analysis used to estimate uncertainties in the Merged Ozone Dataset (MOD), constructed from the Version 8.6 SBUV2 series of instruments. We extend this analysis to incorporate OMI and OMPS total ozone data into the record and investigate the impact of multiple overlapping measurements on the estimated error. We also present an updated column ozone trend analysis and compare the size of statistical error (error from variability not explained by our linear regression model) to that from instrument uncertainty.

TOMS and SBUV↗

Snowfall Rate Retrieval using NPP ATMS Passive Microwave Measurements

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2014). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The ATMS SFR product is validated against radar and gauge snowfall data and shows that the ATMS algorithm outperforms the AMSU/MHS SFR.

Meng, Huan↗

NPP ATMS Snowfall Rate Product

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2015). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. NCEP CMORPH analysis has shown that integration of ATMS SFR has improved the performance of CMORPH‐Snow. The ATMS SFR product is also being assessed at several NWS Weather Forecast Offices for its usefulness in weather forecast.

Forecasting↗

Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

(AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.

Meng, Huan↗

System and Method for Monitoring Distributed Asset Data

A computer-based monitoring system and monitoring method implemented in computer software for detecting, estimating, and reporting the condition states, their changes, and anomalies for many assets. The assets are of same type, are operated over a period of time, and outfitted with data collection systems. The proposed monitoring method accounts for variability of working conditions for each asset by using regression model that characterizes asset performance. The assets are of the same type but not identical. The proposed monitoring method accounts for asset-to-asset variability; it also accounts for drifts and trends in the asset condition and data. The proposed monitoring system can perform distributed processing of massive amounts of historical data without discarding any useful information where moving all the asset data into one central computing system might be infeasible. The overall processing is includes distributed preprocessing data records from each asset to produce compressed data.

Gorinevsky, Dimitry↗

Experiment Design for Complex VTOL Aircraft with Distributed Propulsion and Tilt Wing

Selected experimental results from a wind tunnel study of a subscale VTOL concept with distributed propulsion and tilt lifting surfaces are presented. The vehicle complexity and automated test facility were ideal for use with a randomized designed experiment. Design of Experiments and Response Surface Methods were invoked to produce run efficient, statistically rigorous regression models with minimized prediction error. Static tests were conducted at the NASA Langley 12-Foot Low-Speed Tunnel to model all six aerodynamic coefficients over a large flight envelope. This work supports investigations at NASA Langley in developing advanced configurations, simulations, and advanced control systems.

Murphy, Patrick C.↗

Use of the NASA Giovanni Data System for Geospatial Public Health Research: Example of Weather-Influenza Connection

The NASA Giovanni data analysis system has been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, hydrology, and air quality research. The use of Giovanni for researching connections between public health issues and Earths environment and climate, potentially exacerbated by anthropogenic influence, has been increasingly demonstrated. In this communication, the pertinence of several different data parameters to public health will be described. This communication also provides a case study of the use of remote sensing data from Giovanni in assessing the associations between seasonal influenza and meteorological parameters. In this study, logistic regression was employed with precipitation, temperature and specific humidity as predictors. Specific humidity was found to be associated (p 0.05) with influenza activity in both temperate and tropical climate. In the two temperate locations studied, specific humidity was negatively correlated with influenza; conversely, in the three tropical locations, specific humidity was positively correlated with influenza. Influenza prediction using the regression models showed good agreement with the observed data (correlation coefficient of 0.50.83).

public health↗

Improved Regression Analysis of Temperature-Dependent Strain-Gage Balance Calibration Data

An improved approach is discussed that may be used to directly include first and second order temperature effects in the load prediction algorithm of a wind tunnel strain-gage balance. The improved approach was designed for the Iterative Method that fits strain-gage outputs as a function of calibration loads and uses a load iteration scheme during the wind tunnel test to predict loads from measured gage outputs. The improved approach assumes that the strain-gage balance is at a constant uniform temperature when it is calibrated and used. First, the method introduces a new independent variable for the regression analysis of the balance calibration data. The new variable is designed as the difference between the uniform temperature of the balance and a global reference temperature. This reference temperature should be the primary calibration temperature of the balance so that, if needed, a tare load iteration can be performed. Then, two temperature{dependent terms are included in the regression models of the gage outputs. They are the temperature difference itself and the square of the temperature difference. Simulated temperature{dependent data obtained from Triumph Aerospace's 2013 calibration of NASA's ARC-30K five component semi{span balance is used to illustrate the application of the improved approach.

wind tunnel testing↗

Tropospheric Ozone Increases over the Southern Africa Region: Bellwether for Rapid Growth in Southern Hemisphere Pollution?

Increases in free-tropospheric (FT) ozone based on ozonesonde records from the early 1990s through 2008 over two subtropical stations, Irene (near Pretoria, South Africa) and Réunion (21 deg. S, 55 deg. E; approx. 2800 km NE of Irene in the Indian Ocean), have been reported. Over Irene a large increase in the urban-influenced boundary layer (BL, 1.5-4 km) was also observed during the 18-year period, equivalent to 30%decade−1. Here we show that the Irene BL trend is at least partly due to a gradual change in the sonde launch times from early morning to the midday period. The FT ozone profiles over Irene in 1990-2007 are re-examined, filling in a 1995-1999 gap with ozone profiles taken during the Measurements of Ozone by Airbus In-service Aircraft (MOZAIC) project over nearby Johannesburg. A multivariate regression model that accounts for the annual ozone cycle, El Niño-Southern Oscillation (ENSO) and possible tropopause changes was applied to monthly averaged Irene data from 4 to 11 km and to 1992-2011 Réunion sonde data from 4 to 15 km. Statistically significant trends appear predominantly in the middle and upper troposphere (UT; 4-11 km over Irene, 4-15 km over Réunion) in winter (June-August), with increases 1 ppbv yr(exp. −1) over Irene and approx. 2 ppbv yr(exp. −1) over Réunion. These changes are equivalent to approx. 25 and 35-45%decade( exp. −1), respectively. Both stations also display smaller positive trends in summer, with a 45%decade(exp. −1) ozone increase near the tropopause over Réunion in December. To explain the ozone increases, we investigated a time series of dynamical markers, e.g., potential vorticity (PV) at 330-350 K. PV affects UT ozone over Irene in November-December but displays little relationship with ozone over Réunion. A more likely reason for wintertime FT ozone increases over Irene and Réunion appears to be long-range transport of growing pollution in the Southern Hemisphere. The ozone increases are consistent with trajectory origins of air parcels sampled by the sondes and with recent NOx emissions trends estimated for Africa, South America and Madagascar. For Réunion trajectories also point to sources from the eastern Indian Ocean and Asia.

Trends↗