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At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Spatiotemporal Prediction of Fine Particulate Matter Using High-Resolution Satellite Images in the Southeastern US 2003-2011

Numerous studies have demonstrated that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM(sub 2.5) to assess personal exposure, however, induces measurement error. Land-use regression provides spatially resolved predictions but land-use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM(sub 2.5) exposures. In this paper, we used AOD data with other PM(sub 2.5) variables, such as meteorological variables, land-use regression, and spatial smoothing to predict daily concentrations of PM(sub 2.5) at a 1 sq km resolution of the Southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 to 2011. We divided the study area into three regions and applied separate mixed-effect models to calibrate AOD using ground PM(sub 2.5) measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R2 values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors of 2.89, 2.51, and 2.82 cu micrograms for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM(sub 2.5) concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM(sub 2.5). Our model results will also extend the existing studies on PM(sub 2.5) which have mostly focused on urban areas because of the paucity of monitors in rural areas.

PM2.5↗
NASA NTRS2017

A Ground-Based Study on Extruder Standoff Distance for the 3D Printing in Zero Gravity Technology Demonstration Mission

Analysis of phase I specimens produced as part of the 3D printing in zero G technology demonstration mission exhibited some differences in structure and performance for specimens printed onboard the International Space Station (ISS) and specimens produced on the ground with the same printer prior to its launch. This study uses the engineering test unit for the printer, identical to the unit on ISS, to conduct a ground-based investigation of the impact of the distance between the extruder tip and the build tray on material outcomes. This standoff distance was not held constant for the phase I flight prints and is hypothesized to be a major source of the material variability observed in the phase I data set.

Prater, T. J.↗
NASA NTRS2017

Manuel Johnson's Tide Record at St. Helena

The astronomer Manuel Johnson, a future President of the Royal Astronomical Society, recorded the ocean tides with his own instrument at St. Helena in 1826-1827, while waiting for an observatory to be built. It is an important record in the history of tidal science, as the only previous measurements at St. Helena had been those made by Nevil Maskelyne in 1761, and there were to be no other systematic measurements until the late 20th century. Johnsons tide gauge, of a curious but unique design, recorded efficiently the height of every tidal high and low water for at least 13 months, in spite of requiring frequent re-setting. These heights compare very reasonably with a modern tidal synthesis based on present-day tide gauge measurements from the same site.Johnsons method of timing is unknown, but his calculations of lunar phases suggest that his tidal measurements were recorded in Local Apparent Time. Unfortunately, the recorded times are found to be seriously and variably lagged by many minutes. Johnsons data have never been fully published, but his manuscripts have been safely archived and are available for inspection at Cambridge University. His data have been converted to computerfiles as part of this study for the benefit of future researchers.

Cartwright, David E.↗
NASA NTRS2018

Weathering in the Forelands of Two Rapidly Retreating Alpine Glaciers of Volcanic Bedrock in the Three Sisters, Oregon, USA

The glaciers of the Three Sisters volcanoes in Cascadia have retreated dramatically over the past century. In order to understand ongoing chemical weathering and solute transport in the proglacial valleys, waters were sampled from glacier outwash streams, local snowmelt, and proglacial springs and lakes at Collier and Diller Glaciers. To understand weathering and transport processes in the proglacial plains, infrared orbital remote sensing data was used to map compositional variability and highlight weathering products, which were then ground-truthed with laboratory mineralogical and chemical analyses of sediments. The hydrochemistry is significantly affected by a sub- and proglacial mafic weathering system lacking carbonate minerals. Here we report major ion concentrations in meltwaters for the summer 2016 and 2017 melt seasons. Total cation concentrations range from 3 to 250 μeq/l and dissolved bicarbonate concentrations range from 2 to 200 μeq/l. Other dissolved anions are negligible compared to bicarbonate. Dissolved silica concentrations range from 2 to 260 μmol/l, comparable to total dissolved cation concentrations. The highest cation and silica concentrations were measured in moraine-sourced springs. Compositional remote sensing analysis identified alteration zones in the proglacial plains at both Collier and Diller indicating potential hydrated silica. This analysis is consistent with laboratory analysis of sediment samples, which indicate the presence of poorly crystalline phases weathering products, including hydrated silica. Weathered materials are preferentially deposited on moraines due to aeolian and glacial transport, as well as intra-moraine alteration, and at abandoned stream terraces due to fluvial transport. Geochemical measurements indicate that the predominant form of chemical weathering in these periglacial mafic systems is the carbonation of feldspar as well as reactive volcanic glass. The presence of poorly crystalline silicates, as indicated by remote sensing datasets and laboratory analysis, is consistent with rapid weathering of feldspars and glass and formation of Fe-Al-Si-bearing mineraloids in these proglacial valleys. This weathering regime has wide-ranging implications for atmospheric CO2 drawdown due to cold-climate volcanic rock weathering.

Rutledge, Alicia M.↗
NASA NTRS2015

Human Factors Assessment of Disturbances to Scheduled Performance-Based Navigation Arrival Operations

The introduction of Performance-Based Navigation (PBN) specifications to air traffic management has resulted in many benefits during nominal operations, including shorter flight paths, reduced fuel costs, and improved terminal area arrival rates. However, these benefits become less noticeable during off-nominal operations where aircraft are routinely interrupted from staying on PBN procedures due to disturbances such as missed approaches. This human-in-the-loop (HITL) study used multiple types of disturbance events to perturb the arrival schedule. Perturbed schedules were managed with different types of schedule adjustments, including a condition with no adjustments. The study collected data on a host of dependent variables, including human factors measures on controller workload and system performance measures such as schedule nonconformance (nc). Initial analyses showed strong correlations between aggregated controller workload and aggregated nc, as well as benefits of both automatic and manual schedule adjustments for increasing system performance, such as reduced PBN procedure interruptions. The goal of this paper is to further test these initial findings. The results indicated that an increase in schedule nonconformance correlated with an increase in controller workload at specific time intervals, and automated schedule adjustments consistently reduced controller workload associated with nonconformance.

Human factors↗
NASA NTRS2022

Landslide Likelihood Prediction using Machine Learning Algorithms

The supply of electricity via power plants is criticalto the operation of many critical infrastructure systems in mod-ern society. Natural hazards can disrupt the power supply, causepower outages that can halt economic growth, and impede emer-gency response until power is restored. The proposed work aimsto predict the landslides likelihood in these critical infrastructurelocations in the Northeastern USA using integrated databases ofexplanatory variables and machine learning algorithms. First,data related to landslides are obtained and merged, includingtopographic, soil moisture, and precipitation-related data. Fiveregression algorithms, namely: Random Forest, Extreme Gradi-ent Boosting (XGBoost), K-Nearest Neighbor regression (KNN),Linear Support Vector Regressor (SVR), and Linear regression,are utilized to predict the landslide probability and evaluatedon the dataset. The accuracy of the models is assessed by usingstatistical metrics such as mean absolute error (MAE), meansquared error (MSE), and root mean squared error (RMSE).The study results show that Random Forest outperformed othermodels with the mutual information feature selection method.It achieved an MSE of 0.0011 with mutual information-basedfeature selection and an MSE of 0.00157 without feature selection.KNN regressor outperformed the other models with an MSEof 0.00139 with correlation-based information selection. Theproposed landslide identification model with Random Forestalgorithm shows outstanding robustness and great potential intackling the landslide likelihood prediction by employing MLalgorithms.

Vasundhara Acharya↗
NASA NTRSDate not supplied

Turbulence Effects on Shaped Booms: Propagation Simulations Using KZKFourier

Upcoming X-59 aircraft flight tests as part of NASA’s Quesst Mission are expected to occur in a range of atmospheric conditions. Sensitivity of ground waveform acoustic metrics to turbulent perturbations during propagation complicates the determination of noise levels. A series of simulations through turbulence was executed using the KZKFourier model of Stout et al. to develop a database of results for expanding the functionality of NASA tools for estimating turbulence effects on shaped-boom ground waveforms. For 45 cases covering a seven-factor design space, multiple realizations of turbulence were generated to characterize statistical turbulence effects on levels of six acoustic metrics. An individual simulation in the study produced over one thousand waveforms across a virtual microphone array. Different measures were used to evaluate refinement of results with increasing number of simulations. Data suggest that mean effects and variability were most strongly influenced by propagation distance and velocity fluctuation intensity. Although local increases in acoustic metrics were common, the overall average result in all cases was a reduction in metric levels. Sensitivity of acoustic metrics varied, with mean reductions of up to 2 dB in Perceived Level occurring across the 45 cases.

turbulence↗
NASA NTRS2025

A Summary of Results from Vertical Drop Testing of Hybrid III and WIAMan ATDs

With the development and maturation of the Urban Air Mobility (UAM) market, many new types of electric vertical take-off and landing (eVTOL) vehicles will be flying in the national airspace carrying goods, people or conducting operations for a variety of missions. These types of vehicles are unlike current aircraft due to their novel design and operational profile. Several considerations must be examined in areas including noise, comfort and safety in order for these vehicles to be utilized and accepted into the current airspace system. Researchers at NASA Langley Research Center (LaRC) have conducted sub-scale and full-scale tests on representative eVTOL airframes and seats under a variety of dynamic impact conditions. These tests were conducted to generate data necessary to inform the development of standards in the areas specific to crashworthiness of eVTOL vehicle systems and safety. The data in this report relates to occupant responses obtained during a test campaign utilizing various makes, models, and sizes of Anthropomorphic Test Devices (ATD’s, a.k.a. crash test dummies) undergoing vertical impacts in a variety of seats. The data is intended to provide occupant behavior response and injury metrics for several anticipated impact scenarios that may occur in eVTOL operations. This report will present test data highlighting the effects of several variables on the test results. Discussions on the ATD sizes, along with comparisons between different ATD makes and types will be included. The performance of an in-house developed energy absorbing seat will be detailed, and discussions pertaining to the applicability in various loading conditions will be presented. Finally, a discussion as to the applicability of the tested results to eVTOL full-scale conditions will be included.

Dynamic Drop Testing↗
NASA NTRSDate not supplied

Time-History Statistics of Soot Formation in A Model Gas Turbine Combustor

Soot formation is a complex dynamic and intermittent process determined by properties of the fuel, combustor design, and combustor operation. Although the major steps in soot formation (i.e., formation of precursors, inception, growth and evolution) are similar for a variety of carbonaceous fuels, applications, and operating conditions, it remains unclear when the temporal transition between these steps occurs. An engineering prediction tool coupled with computational fluid physics (CFD), therefore needs to accurately model all these complex steps. To develop such a model, we propose the time-history concept for understanding the time dependency of soot formation as a function of local properties (i.e., temperature, velocity, local fuel air ratio, etc.). We continue our previous work with modeling the DLR aero-combustor [1] with our updated in-house CFD code, Open National Combustion Code (OpenNCC), that now includes a Multiple Time-Scale Flamelet Progress Variable approach and a the semi-empirical two-equation soot model. We injected massless tracer particles upstream of the injector region of the combustor to collect time-history statistics of the solution variables. The correlations between the collected statistics with respect to the experimental soot volume fraction data showed that time-history effect of certain flow variables, including turbulent kinetic energy (TKE), and multiple species is indeed important for soot formation. We then conducted a time-history based correlation analysis to determine the key species and the concentration ranges critical for soot formation (C6H5-based nucleation, acetylene-based surface growth, and oxidation with OH and O2). Based on the time-history correlation coefficient (THCC) analysis, we propose possible modifications to improve the current two-equation model.

LES↗
NASA NTRS2026

Spatially Refined Satellite Gravimetry Captures Human Signatures in Global Terrestrial Water Storage Trends

Human activities have directly altered the water cycle through water management, aquifer pumping, agricultural irrigation, and land use change. Although satellite gravimetry has transformed global hydrological research, its coarse resolution limits attribution of freshwater change to human activities at many management-relevant scales. Here we assessed global terrestrial water storage (TWS) trends from April 2002 to November 2025 using “stacked” regression of Level-1B intersatellite ranging data, which leverages temporal information and variability to dramatically improve effective spatial resolution relative to standard approaches. We combined this refined product with rigorous uncertainty analysis, autocorrelation-robust geostatistical methods, and literature assessment to evaluate TWS trend associations with land and water use, climate variability, and glacial mass loss. We identified TWS trend hotspots exhibiting significant spatial associations with anthropogenic 40 drivers, including groundwater and surface-water irrigation, rainfed agriculture, deforestation, and reservoir impoundment. Across these regions, cumulative TWS losses (3,122 Gt) substantially exceeded gains (2,432 Gt). Compared with traditional regression of monthly mascons, our approach yielded regional trend magnitudes that are on average 33% larger, revealing that global freshwater depletion, particularly from groundwater pumping, is considerably more acute than previously estimated. Multivariate regression models show that humans account for a significant share of the spatial variability in TWS trends on every non-polar continent except Australia. We detected localized TWS gains linked to rainfed agriculture, surface water irrigation, and reservoir filling that were unresolved in earlier gravimetric studies. The methodology provides a foundation for future gravity missions to independently track decadal freshwater change with unprecedented spatial fidelity.

groundwater↗
NASA NTRS1999

Six Years of Short-Spaced Monitoring of the v=1 and v=2 J=1-0 (28)SiO Maser Emission in Evolved Stars

We present the results from a monitoring of the v=1 and v=2 J=1-0 (28)SiO maser emission in 21 objects, covering all types of known SiO maser emitters: 13 Mira variables, 2 long period semiregulars (SRGs), 3 variable supergiants (SGs), 2 OH/IR stars, and one young stellar object. This study has been carried out with the 13.7 meter radiotelescope of the Centro Astronomico de Yebes (Guadalajara, Spain), from July 1984 to May 1990, and represents the longest and most tightly sampled monitoring of SiO masers ever published. Our data show that for Mira-type (i.e. regular) variables, the SiO and optical light curves agree in period, and that the maxima of the SiO emission lag the optical ones by about 0.1-0.2 periods. Since a similar lag characterizes the near infrared (NIR) emission variability from these stars, we conclude that for regular variables SiO and NIR vary in phase. This result was confirmed in three objects for which NIR variability curves are available. For SRGs and SGs, we found a less systematic behavior, but when the SiO emission is periodic, its variability curve agrees with the optical one, also showing a lag between maximum epochs similar to that of Mira-type stars. The data clearly reveal other interesting details on the SiO maser variability, such as the strong intensity differences between different maxima and changes in the velocity distribution of the emission. Finally, the SiO masers associated to the young stellar object Orion IRc2 showed a double peaked spectrum with low amplitude, aperiodic variations.

Alcolea, J.↗
NASA NTRS2022

Unique NISTAR-Based Climate GCM Diagnostics of the Earth’s Planetary Albedo and Spectral Absorption Through Longitudinal Data Slicing

Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar and emitted thermal radiation permit a unique model/data comparison perspective that is not readily available from other satellite data. The key factor is the unique Lissajous orbital viewing geometry from the Lagrangian L1 point, which enables a continuous view of Earth’s sunlit hemisphere. The National Institute of Standards and Technology Advanced Radiometer (NISTAR) is the DSCOVR Mission energy budget instrument, which views the reflected and emitted radiation of the Earth’s sunlit hemisphere by means of single pixel active cavity full-spectrum (Band-A, 0.2–100 μm) and filtered solar wavelength (Band-B, 0.2–4.0 μm; and Band-C, 0.7–4.0 μm) radiometer measurements. An additional solar wavelength photodiode channel (0.3–1.1 μm) provides a calibration reference. The objective of this study is the assessment of climate GCM performance via direct model/data comparisons. Such comparisons are difficult due to quasi-chaotic natural variability present in real-world observational data and in climate GCM simulations. This is where the unique DSCOVR viewing geometry makes possible the longitudinal data slicing methodology for more direct model/data comparison. The key point of the longitudinal slicing approach is that data integration over the entire sunlit hemisphere eliminates the quasi-chaotic meteorological weather-scale noise, while preserving intra-seasonal and planetary-scale variability. The rotation of the Earth that retrieves this climate-style, large-scale longitudinal and seasonal variability. The hemispheric averaging is accomplished automatically in NISTAR measurements with its single-pixel view of the Earth. For climate GCMs, this requires implementing the Sunlit Hemisphere Sampling (SHS) scheme to operate on the GCM run-time output data, utilizing the DSCOVR Satellite Ephemeris data to assure precise viewing geometry between NISTAR measurements and GCM output data, while averaging out the meteorological weather noise. However, GCM generated data are radiative fluxes, while NISTAR (and EPIC) measurements are near-backscattered radiances. Conversing NISTSR measurements into radiative fluxes cannot be accomplished using NISTAR data alone, even with detailed support from conventional satellite data. But the identical viewing geometry of Earth’s sunlit hemisphere, and synergistic analyses of EPIC data make it feasible for this conversion of NISTAR near-backscatter radiances into radiative fluxes.

deep space climate observatory↗
NASA NTRS1990

Analysis of AVHRR, CZCS and historical in situ data off the Oregon Coast

The original scientific objectives of this grant were to: (1) characterize the seasonal cycles and interannual variability for phytoplankton concentrations and sea surface temperature (SST) in the California Current using satellite data; and (2) to explore the spatial and temporal relationship between these variables and surface wind forcing. An additional methodological objective was to develop statistical methods for forming mean fields, which minimize the effects of random data gaps and errors in the irregularly sampled CZCS (Coastal Zone Color Scanner) and AVHRR (Advanced Very High Resolution Radiometer) satellite data. A final task was to evaluate the level of uncertainty in the wind fields used for the statistical analysis. Funding in the first year included part of the cost of an image processing system to enable this and other projects to process and analyze satellite data. This report consists of summaries of the major projects carried out with all or partial support from this grant. The appendices include a list of papers and professional presentations supported by the grant, as well as reprints of the major papers and reports.

Strub, P. Ted↗
NASA NTRS1983

Advanced microwave soil moisture studies

Comparisons of low level L-band brightness temperature (TB) and thermal infrared (TIR) data as well as the following data sets: soil map and land cover data; direct soil moisture measurement; and a computer generated contour map were statistically evaluated using regression analysis and linear discriminant analysis. Regression analysis of footprint data shows that statistical groupings of ground variables (soil features and land cover) hold promise for qualitative assessment of soil moisture and for reducing variance within the sampling space. Dry conditions appear to be more conductive to producing meaningful statistics than wet conditions. Regression analysis using field averaged TB and TIR data did not approach the higher sq R values obtained using within-field variations. The linear discriminant analysis indicates some capacity to distinguish categories with the results being somewhat better on a field basis than a footprint basis.

Dalsted, K. J.↗
NASA NTRS2012

NASA GLDAS Evapotranspiration Data and Climatology

Evapotranspiration (ET) is the water lost to the atmosphere by evaporation and transpiration. ET is a shared component in the energy and water budget, therefore, a critical variable for global energy and water cycle and climate change studies. However, direct ET measurements and data acquisition are difficult and expensive, especially at the global level. Therefore, modeling is one common alternative for estimating ET. With the goal to generate optimal fields of land surface states and fluxes, the Global Land Data Assimilation System (GLDAS) has been generating quality-controlled, spatially and temporally consistent, terrestrial hydrologic data, including ET and other variables that affect evaporation and transpiration, such as temperature, precipitation, humidity, wind, soil moisture, heat flux, and solar radiation. This poster presents the long-term ET climatology (mean and monthly), derived from the 61-year GLDAS-2 monthly 1.0 deg x 1.0 deg. NOAH model Experiment-1 data, and describes the basic characteristics of spatial and seasonal variations of the climatology. The time series of GLDAS-2 precipitation and radiation, and ET are also discussed to show the improvement of GLDAS-2 forcing data and model output over those from GLDAS-1.

Rui, Hualan↗
NASA NTRS2016

Minimizing the Standard Deviation of Spatially Averaged Surface Cross-Sectional Data from the Dual-Frequency Precipitation Radar

For an airborne or spaceborne radar, the precipitation-induced path attenuation can be estimated from the measurements of the normalized surface cross section, sigma 0, in the presence and absence of precipitation. In one implementation, the mean rain-free estimate and its variability are found from a lookup table (LUT) derived from previously measured data. For the dual-frequency precipitation radar aboard the global precipitation measurement satellite, the nominal table consists of the statistics of the rain-free 0 over a 0.5 deg x 0.5 deg latitude-longitude grid using a three-month set of input data. However, a problem with the LUT is an insufficient number of samples in many cells. An alternative table is constructed by a stepwise procedure that begins with the statistics over a 0.25 deg x 0.25 deg grid. If the number of samples at a cell is too few, the area is expanded, cell by cell, choosing at each step that cell that minimizes the variance of the data. The question arises, however, as to whether the selected region corresponds to the smallest variance. To address this question, a second type of variable-averaging grid is constructed using all possible spatial configurations and computing the variance of the data within each region. Comparisons of the standard deviations for the fixed and variable-averaged grids are given as a function of incidence angle and surface type using a three-month set of data. The advantage of variable spatial averaging is that the average standard deviation can be reduced relative to the fixed grid while satisfying the minimum sample requirement.

spaceborne meteorological radar↗
NASA NTRS2017

Personalized Risk Assesment for Adverse Drug Reactions and Treatment Failures

As NASA and other space agencies prepare for future long-term space missions beyond the LEO, the cumulative impact of risk factors encountered in space increases substantially rising concerns about astronauts health. Application of on-board medications to mitigate clinical symptoms associated with certain medical conditions and illnesses is the first line of response to ensure sustainable health and performance of crew. Unfortunately, very limited research has been conducted to determine efficacy of the earth-based pharmaceuticals in a microgravity environment. In some instances, orally administered medications taken during flight were reported to be less effective than expected. Evaluation of series of experiments involving astronauts from shuttle flights shows notable individual variability to several pharmaceuticals during flight. These data provide reasonable assumption of perturbation in CYP450 enzymes during spaceflight, which contribute to the hepatic metabolism of the majority of drugs and therefore may have significant effects on therapeutic efficacy and increase treatment-related toxicity. The genes encoding the CYP450 enzymes are highly variable in humans. Inheritable variations of CYP450 hepatic metabolizer enzymes and transport proteins play a crucial role in the inter-individual variability of drug efficiency and risks of adverse drug reactions. Additionally, there are some reports that document changes in the levels of production of drug-metabolizing enzymes in microgravity. Therefore, in order to provide a safe and effective pharmaceutical treatment in space, medications selection should be based not only on the specific efficacy of medications but also on the individual drug sensitivity and flight-induced changes in metabolism of astronauts chosen for a particular mission. To our knowledge, there was no pre-flight drug sensitivity testing on a genetic level for any of the previous manned NASA space missions. Therefore, technologies capable of predicting and managing medication efficacy, side effects, and toxicity of drugs based on individual genetic variability of crew members are increasingly needed. In this report, we present results of testing the market available Personalized Prescribing System (PPS), a comprehensive, non-invasive solution for safer, targeted medication management for every crew member. Statistical accuracy and simplicity of non-invasive sample analysis demonstrate the feasibility of drug sensitivity assessment and record-keeping tool for flight surgeons and astronauts in applying the recommended medications for situations arising in flight. The information on individual drug sensitivity will translate into personalized risk assessment for adverse drug reactions and treatment failures for each drug from the medication kit as well as predefined outcome. This will address the HHC’s raised “Concern of Clinically Relevant Unpredicted Effects of Medication” as recently updated.

medications↗
NASA NTRS1983

LANDSAT-4 horizon scanner full orbit data averages

Averages taken over full orbit data spans of the pitch and roll residual measurement errors of the two conical Earth sensors operating on the LANDSAT 4 spacecraft are described. The variability of these full orbit averages over representative data throughtout the year is analyzed to demonstrate the long term stability of the sensor measurements. The data analyzed consist of 23 segments of sensor measurements made at 2 to 4 week intervals. Each segment is roughly 24 hours in length. The variation of full orbit average as a function of orbit within a day as a function of day of year is examined. The dependence on day of year is based on association the start date of each segment with the mean full orbit average for the segment. The peak-to-peak and standard deviation values of the averages for each data segment are computed and their variation with day of year are also examined.

Stanley, J. P.↗