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All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

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↗
NASA NTRS2017

Temperature Increase Reduces Global Yields of Major Crops in Four Independent Estimates

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multi-method analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.

Zhao, Chuang↗
NASA NTRS1989

Use of anomolous thermal imaging effects for multi-mode systems control during crystal growth

Real time image processing techniques, combined with multitasking computational capabilities are used to establish thermal imaging as a multimode sensor for systems control during crystal growth. Whereas certain regions of the high temperature scene are presently unusable for quantitative determination of temperature, the anomalous information thus obtained is found to serve as a potentially low noise source of other important systems control output. Using this approach, the light emission/reflection characteristics of the crystal, meniscus and melt system are used to infer the crystal diameter and a linear regression algorithm is employed to determine the local diameter trend. This data is utilized as input for closed loop control of crystal shape. No performance penalty in thermal imaging speed is paid for this added functionality. Approach to secondary (diameter) sensor design and systems control structure is discussed. Preliminary experimental results are presented.

Wargo, Michael J.↗
NASA NTRS2016

Quantifying PM2.5-Meteorology Sensitivities in a Global Climate Model

Climate change can influence fine particulate matter concentrations (PM2.5) through changes in air pollution meteorology. Knowledge of the extent to which climate change can exacerbate or alleviate air pollution in the future is needed for robust climate and air pollution policy decision-making. To examine the influence of climate on PM2.5, we use the Geophysical Fluid Dynamics Laboratory Coupled Model version 3 (GFDL CM3), a fully-coupled chemistry-climate model, combined with future emissions and concentrations provided by the four Representative Concentration Pathways (RCPs). For each of the RCPs, we conduct future simulations in which emissions of aerosols and their precursors are held at 2005 levels while other climate forcing agents evolve in time, such that only climate (and thus meteorology) can influence PM2.5 surface concentrations. We find a small increase in global, annual mean PM2.5 of about 0.21 micro-g/cu m3 (5%) for RCP8.5, a scenario with maximum warming. Changes in global mean PM2.5 are at a maximum in the fall and are mainly controlled by sulfate followed by organic aerosol with minimal influence of black carbon. RCP2.6 is the only scenario that projects a decrease in global PM2.5 with future climate changes, albeit only by -0.06 micro-g/cu m (1.5%) by the end of the 21st century. Regional and local changes in PM2.5 are larger, reaching upwards of 2 micro-g/cu m for polluted (eastern China) and dusty (western Africa) locations on an annually averaged basis in RCP8.5. Using multiple linear regression, we find that future PM2.5 concentrations are most sensitive to local temperature, followed by surface wind and precipitation. PM2.5 concentrations are robustly positively associated with temperature, while negatively related with precipitation and wind speed. Present-day (2006-2015) modeled sensitivities of PM2.5 to meteorological variables are evaluated against observations and found to agree reasonably well with observed sensitivities (within 10e50% over the eastern United States for several variables), although the modeled PM2.5 is less sensitive to precipitation than in the observations due to weaker convective scavenging. We conclude that the hypothesized "climate penalty" of future increases in PM2.5 is relatively minor on a global scale compared to the influence of emissions on PM2.5 concentrations.

PM2.5↗
NASA NTRS2024

Urban Air Quality Management at Low-Cost Using Micro Air Sensors: A Case Study From Accra, Ghana

Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to non-existent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of the high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was co-located at the Physics Department, University of Ghana with a reference grade instrument to evaluate its performance for estimating PM2.5 pollution accurately at fine scales and the value of this data in identification of local sources and their behavior over time. For this study 15 weeks of data at hourly resolution with approximately 2500 data pairs are generated and analyzed (June 01, 2023, to September 15, 2023). For this time period a coefficient of determination (r 2 ) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μgm -3 between the pre local calibration micro air sensor (i.e. out of box) and the reference-grade instrument. Following currently accepted best practice methods (see e.g., PAS4023) a domain specific (i.e. local) calibration factor was generated using a multi-linear regression model and when this factor is applied to the micro air sensor data, a reduction i.e., improvement in MAE to 1.43 μgm -3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM2.5/PM10 ratio scatter and count plots to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of box, domain calibrated and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyper localized sources associated with urban areas, such as those found in major African cities.

Source apportionment↗
NASA NTRS1961

Earth Oblateness and Relative Sun Motion Considerations in the Determination of an Ideal Orbit for the Nimbus Meteorological Satellite

It is desired that the Nimbus meteorological satellite always cross the equator around local noon and, half-an-orbit later, cross the equator in the other direction around local midnight. The application of the phenomenon of nodal regression toward this end is discussed, and an analysis of the parameters angles of inclination, periods, and heights of such "ideal" circular orbits is presented. Also, the relative motion of the apparent versus the fictitious mean sun is briefly discussed.

Bandeen, William R.↗
NASA NTRS2021

Applying Machine Learning to Predict Alaskan Ionospheric Irregularities

In this work several machine-learning (ML) techniques for predicting ionospheric irregularities in the northern auroral zone were tested. The techniques include Ridge Regression, Long Short-Term Memory Neural Network (LSTM), Classification Neural Network (CNN), Autoencoder Classification Neural Network (ACNN), and LSTM Autoencoder Classification Neural Network (LACNN). These techniques were tested with the rate of total electron content (TEC) index (ROTI) data collected during 2008 and 2009 from a geodetic station in Fairbanks, Alaska (64.98°N, 147.50°W), which is in the auroral zone. Using ROTI data with the ML techniques, experiments were conducted to reach two goals: (1) examine what space weather measurements present good correlation with ROTI so that they may be helpful in ML-based prediction of ionospheric irregularities in the polar region; (2) predict ROTI hours and days ahead by training the neural network models with historical ROTI data alone. The Ridge Regression experiments indicate that a combination of measurements of local geomagnetic horizontal components, geomagnetic SYM-H index, 3-hour Kp and ap indices, and F10.7 solar flux index appears to be more correlated to the single-site ROTI measurements than other parameters. The neural network (NN) experiments show that although the LACNN model allows for predictions of non-irregularity and irregularity conditions defined by ROTI levels up to 3 hours in advance, with an overall accuracy ≥ 92%, a number of irregularity events can still be missed. Hence, further development is needed to reduce the number of missed events. In this paper, the models, data processing, model performance, prediction results, and potential applications are presented.

Pi, Xiaoqing↗
NASA NTRS2005

Forecasting Lightning at Kennedy Space Center/Cape Canaveral Air Force Station, Florida

The Applied Meteorology Unit (AMU) developed a set of statistical forecast equations that provide a probability of lightning occurrence on Kennedy Space Center (KSC) I Cape Canaveral Air Force Station (CCAFS) for the day during the warm season (May September). The 45th Weather Squadron (45 WS) forecasters at CCAFS in Florida include a probability of lightning occurrence in their daily 24-hour and weekly planning forecasts, which are briefed at 1100 UTC (0700 EDT). This information is used for general scheduling of operations at CCAFS and KSC. Forecasters at the Spaceflight Meteorology Group also make thunderstorm forecasts for the KSC/CCAFS area during Shuttle flight operations. Much of the current lightning probability forecast at both groups is based on a subjective analysis of model and observational data. The objective tool currently available is the Neumann-Pfeffer Thunderstorm Index (NPTI, Neumann 1971), developed specifically for the KSCICCAFS area over 30 years ago. However, recent studies have shown that 1-day persistence provides a better forecast than the NPTI, indicating that the NPTI needed to be upgraded or replaced. Because they require a tool that provides a reliable estimate of the daily thunderstorm probability forecast, the 45 WS forecasters requested that the AMU develop a new lightning probability forecast tool using recent data and more sophisticated techniques now possible through more computing power than that available over 30 years ago. The equation development incorporated results from two research projects that investigated causes of lightning occurrence near KSCICCAFS and over the Florida peninsula. One proved that logistic regression outperformed the linear regression method used in NPTI, even when the same predictors were used. The other study found relationships between large scale flow regimes and spatial lightning distributions over Florida. Lightning, probabilities based on these flow regimes were used as candidate predictors in the equation development. Fifteen years (1 989-2003) of warm season data were used to develop the forecast equations. The data sources included a local network of cloud-to-ground lightning sensors called the Cloud-to-Ground Lightning Surveillance System (CGLSS), 1200 UTC Florida synoptic soundings, and the 1000 UTC CCAFS sounding. Data from CGLSS were used to determine lightning occurrence for each day. The 1200 UTC soundings were used to calculate the synoptic-scale flow regimes and the 1000 UTC soundings were used to calculate local stability parameters, which were used as candidate predictors of lightning occurrence. Five logistic regression forecast equations were created through careful selection and elimination of the candidate predictors. The resulting equations contain five to six predictors each. Results from four performance tests indicated that the equations showed an increase in skill over several standard forecasting methods, good reliability, an ability to distinguish between non-lightning and lightning days, and good accuracy measures and skill scores. Given the overall good performance the 45 WS requested that the equations be transitioned to operations and added to the current set of tools used to determine the daily lightning probability of occurrence.

Lambert, Winfred↗
NASA NTRS1986

Heat and mass transfer rates during flow of dissociated hydrogen gas over graphite surface

To improve upon the performance of chemical rockets, the nuclear reactor has been applied to a rocket propulsion system using hydrogen gas as working fluid and a graphite-composite forming a part of the structure. Under the boundary layer approximation, theoretical predictions of skin friction coefficient, surface heat transfer rate and surface regression rate have been made for laminar/turbulent dissociated hydrogen gas flowing over a flat graphite surface. The external stream is assumed to be frozen. The analysis is restricted to Mach numbers low enough to deal with the situation of only surface-reaction between hydrogen and graphite. Empirical correlations of displacement thickness, local skin friction coefficient, local Nusselt number and local non-dimensional heat transfer rate have been obtained. The magnitude of the surface regression rate is found low enough to ensure the use of graphite as a linear or a component of the system over an extended period without loss of performance.

Nema, V. K.↗
NASA NTRS1992

Evaluation of fuzzy inference systems using fuzzy least squares

Efforts to develop evaluation methods for fuzzy inference systems which are not based on crisp, quantitative data or processes (i.e., where the phenomenon the system is built to describe or control is inherently fuzzy) are just beginning. This paper suggests that the method of fuzzy least squares can be used to perform such evaluations. Regressing the desired outputs onto the inferred outputs can provide both global and local measures of success. The global measures have some value in an absolute sense, but they are particularly useful when competing solutions (e.g., different numbers of rules, different fuzzy input partitions) are being compared. The local measure described here can be used to identify specific areas of poor fit where special measures (e.g., the use of emphatic or suppressive rules) can be applied. Several examples are discussed which illustrate the applicability of the method as an evaluation tool.

Barone, Joseph M.↗
NASA NTRS2006

Studies in Software Cost Model Behavior: Do We Really Understand Cost Model Performance?

While there exists extensive literature on software cost estimation techniques, industry practice continues to rely upon standard regression-based algorithms. These software effort models are typically calibrated or tuned to local conditions using local data. This paper cautions that current approaches to model calibration often produce sub-optimal models because of the large variance problem inherent in cost data and by including far more effort multipliers than the data supports. Building optimal models requires that a wider range of models be considered while correctly calibrating these models requires rejection rules that prune variables and records and use multiple criteria for evaluating model performance. The main contribution of this paper is to document a standard method that integrates formal model identification, estimation, and validation. It also documents what we call the large variance problem that is a leading cause of cost model brittleness or instability.

data mining↗
NASA NTRS2010

Projecting the Local Impacts of Climate Change on a Central American Montane Avian Community

Significant changes in the climates of Central America are expected over the next century. Lowland rainforests harbor high alpha diversity on local scales (<1 km2), yet montane landscapes often support higher beta diversity on 10-100 km2 scales. Climate change will likely disrupt the altitudinal zonation of montane communities that produces such landscape diversity. Projections of biotic response to climate change have often used broad-scale modelling of geographical ranges, but understanding likely impacts on population viability is also necessary for anticipating local and global extinctions. We model species abundances and estimate range shifts for birds in the Tilaran Mountains of Costa Rica, asking whether projected changes in temperature and rainfall could be sufficient to imperil high-elevation endemics and whether these variables will likely impact communities similarly. We find that nearly half of 77 forest bird species can be expected to decline in the next century. Almost half of species projected to decline are endemic to Central America, and seven of eight species projected to become locally extinct are endemic to the highlands of Costa Rica and Panam . Logistic-regression modelling of distributions and similarity in projections produced by temperature and rainfall models suggest that changes in both variables will be important. Although these projections are probably conservative because they do not explicitly incorporate biological or climate variable interactions, they provide a starting point for incorporating more realistic biological complexity into community-change models. Prudent conservation planning for tropical mountains should focus on regions with room for altitudinal reorganization of communities comprised of ecological specialists.

Gasner, Matthew R.↗
NASA NTRS2012

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.↗
NASA NTRS1995

Spectrometric Estimation of Total Nitrogen Concentration in Douglas-Fir Foliage

Spectral measurements of fresh and dehydrated Douglas-fir foliage, from trees cultivated under three fertilization treatments, were acquired with a laboratory spectrophotometer. The slope (first-derivative) of the fresh- and dry-leaf absorbance spectra at locations near known protein absorption features was strongly correlated with total nitrogen (TN) concentration of the foliage samples. Particularly strong correlation was observed between the first-derivative spectra in the 2150-2170 nm region and TN, reaching a local maximum in the fresh-leaf spectra of -0.84 at 2 160 nm. Stepwise regression was used to generate calibration equations relating first derivative spectra from fresh, dry/intact, and dry/ground samples to TN concentration. Standard errors of calibration were 1.52 mg g-1 (fresh), 1.33 (dry/intact), and 1.20 (dry/ground), with goodness-of-fit 0.94 and greater. Cross-validation was performed with the fresh-leaf dataset to examine the predictive capability of the regression method; standard errors of prediction ranged from 1.47 - 2.37 mg g(exp -1) across seven different validation sets, prediction goodness of fit ranged from .85-.94, and wavelength selection was fairly insensitive to the membership of the calibration set. All regressions in this study tended to select wavelengths in the 2100-2350 nm region, with the primary selection in the 2142-2172 nm region. The study provides positive evidence concerning the feasibility of assessing TN status of fresh-leaf samples by spectrometric means. We assert that the ability to extract biochemical information from fresh-leaf spectra is a necessary but insufficient condition regarding the use of remote sensing for canopy-level biochemical estimation.

Johnson, Lee F.↗
NASA NTRS2003

Seasonal Variation of Martian Polar Caps: 1999 and 2001 MOC Data

The seasonal Martian polar caps wax and wane in response to condensation and sublimation of carbon dioxide resulting from seasonal insolation changes on Mars. Numerous data exist on observations of the recession or sublimation phases in the visible portion of the spectrum for the last two centuries. William Herschel published the first quantitative observations of the seasonal recession of the Martian polar caps in 1784. During the next 180 years, ground based observers used a variety of techniques to observe recessions; Slipher summarized these observations in 1962, on the eve of the first space exploration of Mars. Portions of the seasonal cycles of the surface caps that were observed by Mariners 7 and 9 and by Viking as well as ground based studies from 1971-1988 by the International Planetary Patrol were summarized in a review article following the Fourth International Conference on Mars in 1989. Hubble Space Telescope observed points in the seasonal recessions of the south and north caps during the 1990 s. Differences between different Martian regressions have been reported in the past; but, because many of the relevant data sets are localized in longitude, at least some of these results could be an artifact introduced by the considerable longitudinal asymmetry that observed during recessions.

P B James↗
NASA NTRS2024

Parameterizing Spectral Surface Reflectance Relationships for the Dark Target Aerosol Algorithm Applied to A Geostationary Imager

Originally developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) in polar, sun-synchronous low-earth orbit (LEO), the Dark Target (DT) aerosol retrieval algorithm relies on the assumption of a Surface Reflectance Parameterization (SRP) over land surfaces. Specifically for vegetated and dark-soiled surfaces, values of surface reflectance in blue and red visible-wavelength bands are assumed to be nearly linearly related to each other and to the value in a shortwave infrared (SWIR) wavelength band. This SRP also includes dependencies on scattering angle and a normalized difference vegetation index computed from two SWIR bands (NDVISWIR). As the DT retrieval algorithm is being ported to new sensors to continue and expand the aerosol data record, we assess whether the MODIS-assumed SRP can be used for these sensors. Here, we specifically assess SRP for the Advanced Baseline Imager (ABI) aboard, the Geostationary Operational Environmental Satellite (GOES)-16/East (ABIE). First, we find that using MODIS-based SRP leads to higher biases and artificial diurnal signatures in aerosol optical depth (AOD)retrievals from ABIE. The primary reason appears to be that geostationary orbit (GEO) encounters an entirely different set of observation geometry than does LEO, primarily with regards to solar angles coupled with fixed view angles. Therefore, we have developed a new SRP for GEO that draws the angular shape of the surface bidirectional reflectance. We also introduce modifications to the parametrization of both red-SWIR and blue-red spectral relationships to include additional information.The revised Red-SWIR SRP includes solar zenith angle, NDVISWIR, and land-type percentage from an ancillary database. The blue-red SRP adds dependencies on the scattering angle and NDVISWIR. The new SRPs improve the AOD retrieval of ABIE in terms of overall less bias and mitigation of the overestimation around local noon. The average bias of DT AOD compared to AERONET AOD shows a reduction from 0.08 to 0.03, while the bias of local solar noon decreases from 0.12 to 0.03.The agreement between DT and AERONET AOD is established through regression slope of 1.06 and y-intercept of 0.01with correlation coefficient is0.74. By using the new SRP, the percentage of data falling within the expected error range (±0.05 + 15%) is notably risen from 54% to 78%.

Mijin Kim↗
NASA NTRS2020

Global Aerodynamic Modeling Using Automated Local Model Networks in Real Time

A novel method is presented for automated real-time global aerodynamic modeling using local model networks, known as Smoothed Partitioning with Localized Trees in Real Time (SPLITR), as part of NASA’s Learn-to-Fly technology development initiative. The global nonlinear aerodynamics are partitioned into several local regions known as cells, with the dimension, location, and timing of each partition automatically selected based on a residual characterization procedure, under the constraints of real-time operation. Regression trees represent the successive partitioning of the global flight envelope and describe the evolution of the cell structure. Recursive equation-error least-squares parameter estimation in the time domain is used to estimate a model that represents the local aerodynamics in each region, so that it can be updated independently with non-contiguous data in the range of each cell over time. A weighted superposition of these piecewise local models across the flight envelope forms a global nonlinear model that also accurately captures the local aerodynamics. The SPLITR approach is demonstrated using both simulation and flight data, and the results are analyzed in terms of model predictive capabilities as well as interpretability. The results show that SPLITR can be used to automatically partition complex nonlinear aerodynamic behavior, produce an accurate model, and provide valuable physical insight into the local and global aerodynamics.

Weinstein, Rose↗
NASA NTRS2014

Analyzing Source Apportioned Methane in Northern California During DISCOVER-AQ-CA Using Airborne Measurements and Model Simulations

This study analyzes source apportioned methane (CH4) emissions and atmospheric concentrations in northern California during the Discover-AQ-CA field campaign using airborne measurement data and model simulations. Source apportioned CH4 emissions from the Emissions Database for Global Atmospheric Research (EDGAR) version 4.2 were applied in the 3-D chemical transport model GEOS-Chem and analyzed using airborne measurements taken as part of the Alpha Jet Atmospheric eXperiment over the San Francisco Bay Area (SFBA) and northern San Joaquin Valley (SJV). During the time period of the Discover-AQ-CA field campaign EDGAR inventory CH4 emissions were 5.30 Gg/day (Gg 1.0 109 grams) (equating to 1.9 103 Gg/yr) for all of California. According to EDGAR, the SFBA and northern SJV region contributes 30 of total emissions from California. Source apportionment analysis during this study shows that CH4 concentrations over this area of northern California are largely influenced by global emissions from wetlands and local/global emissions from gas and oil production and distribution, waste treatment processes, and livestock management. Model simulations, using EDGAR emissions, suggest that the model under-estimates CH4 concentrations in northern California (average normalized mean bias (NMB) -5 and linear regression slope 0.25). The largest negative biases in the model were calculated on days when hot spots of local emission sources were measured and atmospheric CH4 concentrations reached values 3.0 parts per million (model NMB -10). Sensitivity emission studies conducted during this research suggest that local emissions of CH4 from livestock management processes are likely the primary source of the negative model bias. These results indicate that a variety, and larger quantity, of measurement data needs to be obtained and additional research is necessary to better quantify source apportioned CH4 emissions in California and further the understanding of the physical processes controlling them.

Methane↗