Some recent atmospheric dynamics research at MSFC
Atmospheric dynamics, and statistical description of turbulence
SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Atmospheric dynamics, and statistical description of turbulence
Commercially available software packages today allow users to quickly perform the routine evaluations of (1) descriptive statistics to numerically and graphically summarize both sample and population data, (2) inferential statistics that draws conclusions about a given population from samples taken of it, (3) probability determinations that can be used to generate estimates of reliability allowables, and finally (4) the setup of designed experiments and analysis of their data to identify significant material and process characteristics for application in both product manufacturing and performance enhancement. This paper presents examples of analysis and experimental design work that has been conducted using Statgraphics®(Registered Trademark) statistical software to obtain useful information with regard to solid rocket motor propellants and internal insulation material. Data were obtained from a number of programs (Shuttle, Constellation, and Space Launch System) and sources that include solid propellant burn rate strands, tensile specimens, sub‐scale test motors, full‐scale operational motors, rubber insulation specimens, and sub‐scale rubber insulation analog samples. Besides facilitating the experimental design process to yield meaningful results, statistical software has demonstrated its ability to quickly perform complex data analyses and yield significant findings that might otherwise have gone unnoticed. One caveat to these successes is that useful results not only derive from the inherent power of the software package, but also from the skill and understanding of the data analyst.
Our program of research aims at developing a stochastic description of the residual acceleration field onboard spacecraft (g-jitter) to describe in quantitative detail its effect on fluid motion. Our main premise is that such a statistical description is necessary in those cases in which the characteristic time scales of the process under investigation are long compared with the correlation time of g-jitter. Although a clear separation between time scales makes this approach feasible, there remain several difficulties of practical nature: (i), g-jitter time series are not statistically stationary but rather show definite dependences on factors such as active or rest crew periods; (ii), it is very difficult to extract reliably the low frequency range of the power spectrum of the acceleration field. This range controls the magnitude of diffusive processes; and (iii), models used to date are Gaussian, but there is evidence that large amplitude disturbances occur much more frequently than a Gaussian distribution would predict. The lack of stationarity does not constitute a severe limitation in practice, since the intensity of the stochastic components changes very slowly during space missions (perhaps over times of the order of hours). A separate analysis of large amplitude disturbances has not been undertaken yet, but it does not seem difficult a priori to devise models that may describe this range better than a Gaussian distribution. The effect of low frequency components, on the other hand, is more difficult to ascertain, partly due to the difficulty associated with measuring them, and partly because they may be indistinguishable from slowly changing averages. This latter effect is further complicated by the lack of statistical stationarity of the time series. Recent work has focused on the effect of stochastic modulation on the onset of oscillatory instabilities as an example of resonant interaction between the driving acceleration and normal modes of the system, and on cavity flow as an example of how an oscillatory response under periodic driving becomes diffusive if the forcing is random instead. This paper describes three different topics that illustrate behavior that is peculiar to a stochastic acceleration field. In the first case, we show that g-jitter can induce effective attractive or repulsive forces between a pair of spherical particles that are suspended in an incompressible fluid of different density provided that the momentum diffusion length is larger than the interparticle separation (as in the case in most colloidal suspensions). Second, a stochastic modulation of the control parameter in the vicinity of a pitchfork or supercritical bifurcation is known not to affect the location of the threshold. We show, however, that resonance between the modulation and linearly stable modes close to onset can lead to a shift in threshold. Finally, we discuss the classical problem of vorticity diffusion away from a plane boundary that is being vibrated along its own plane. Periodic motion with zero average vorticity production results in an exponential decay of the vorticity away from the boundary. Random vibration, on the other hand, results in power law decay away from the boundary even if vorticity production averages to zero.
A new scheme is developed for describing interception at spatial scales comparable to the typical resolution of climate models. The scheme is based on the Rutter model of interception and statistical description of the subgrid-scale spatial variability of canopy storage and rainfall. The interception loss simulated by the new scheme is significantly smaller than those simulated by other schemes that do not include considerations for spatial variability. The explanation of this result is partly in the enhancement of spatially averaged canopy drainage due to the large local drainage from the few buckets of large canopy storage. The relative reduction in interception loss simulated by the new scheme may explain the overestimation of interception loss by climate models that do not include the effects of spatial variability on interception processes.
Recent developments related to microprocessor-based personal computers have made low-cost digital image processing systems a reality. Image analysis systems built around these microcomputers provide color image displays for images as large as 256 by 240 pixels in sixteen colors. Descriptive statistics can be computed for portions of an image, and supervised image classification can be obtained. The systems support Basic, Fortran, Pascal, and assembler language. A description is provided of a system which is representative of the new microprocessor-based image processing systems currently on the market. While small systems may never be truly independent of larger mainframes, because they lack 9-track tape drives, the independent processing power of the microcomputers will help alleviate some of the turn-around time problems associated with image analysis and display on the larger multiuser systems.
Statistical Analysis Toolset is a collection of eight Microsoft Excel spreadsheet programs, each of which performs calculations pertaining to an aspect of statistical analysis. These programs present input and output data in user-friendly, menu-driven formats, with automatic execution. The following types of calculations are performed: Descriptive statistics are computed for a set of data x(i) (i = 1, 2, 3 . . . ) entered by the user. Normal Distribution Estimates will calculate the statistical value that corresponds to cumulative probability values, given a sample mean and standard deviation of the normal distribution. Normal Distribution from two Data Points will extend and generate a cumulative normal distribution for the user, given two data points and their associated probability values. Two programs perform two-way analysis of variance (ANOVA) with no replication or generalized ANOVA for two factors with four levels and three repetitions. Linear Regression-ANOVA will curvefit data to the linear equation y=f(x) and will do an ANOVA to check its significance.
Statistical description of a photoelectric detector is given. The photosensitive surface of the detector is divided into many small areas, and the moment generating function of the photo-counting statistic is derived for large time-bandwidth product. The detection of a specified optical image in the presence of the background light by using the hypothesis test is discussed. The ideal detector based on the likelihood ratio from a set of numbers of photoelectrons ejected from many small areas of the photosensitive surface is studied and compared with the threshold detector and a simple detector which is based on the likelihood ratio by counting the total number of photoelectrons from a finite area of the surface. The intensity of the image is assumed to be Gaussian distributed spatially against the uniformly distributed background light. The numerical approximation by the method of steepest descent is used, and the calculations of the reliabilities for the detectors are carried out by a digital computer.
Actual tissue nitrogen (N2) kinetics are complex; the uptake and elimination is often approximated with a single half-time compartment in statistical descriptions of denitrogenation [prebreathe (PB)] protocols. Air breaks during PB complicate N2 kinetics. A comparison of symmetrical versus asymmetrical N2 kinetics was performed using the time to onset of hypobaric decompression sickness (DCS) as a surrogate for actual venous N2 tension. Published results of 12 tests involving 179 hypobaric exposures in altitude chambers after PB, with and without air breaks, provide the complex protocols from which to model N2 kinetics. DCS survival time for combined control and air breaks were described with an accelerated log logistic model where N2 uptake and elimination before, during, and after the air break was computed with a simple exponential function or a function that changed half-time depending on ambient N2 partial pressure. P1N2-P2 = delta P defined DCS dose for each altitude exposure, where P2 was the test altitude and P1N2 was computed N2 pressure at the beginning of the altitude exposure. The log likelihood (LL) without DCS dose (null model) was -155.6, and improved (best-fit) to -97.2 when dose was defined with a 240 min half-time for both N2 elimination and uptake during the PB. The description of DCS survival time was less precise with asymmetrical N2 kinetics, for example, LL was -98.9 with 240 min half-time elimination and 120 min half-time uptake. The statistical regression described survival time mechanistically linked to symmetrical N2 kinetics during PBs that also included air breaks. The results are data-specific, and additional data may change the conclusion. The regression is useful to compute additional PB time to compensate for an air break in PB within the narrow range of tested conditions.
Actual tissue nitrogen (N2) kinetics are complex; the uptake and elimination is often approximated with a single half-time compartment in statistical descriptions of denitrogenation [prebreathe(PB)] protocols. Air breaks during PB complicate N2 kinetics. A comparison of symmetrical versus asymmetrical N2 kinetics was performed using the time to onset of hypobaric decompression sickness (DCS) as a surrogate for actual venous N2 tension. METHODS: Published results of 12 tests involving 179 hypobaric exposures in altitude chambers after PB, with and without airbreaks, provide the complex protocols from which to model N2 kinetics. DCS survival time for combined control and airbreaks were described with an accelerated log logistic model where N2 uptake and elimination before, during, and after the airbreak was computed with a simple exponential function or a function that changed half-time depending on ambient N2 partial pressure. P1N2-P2 = (Delta)P defined decompression dose for each altitude exposure, where P2 was the test altitude and P1N2 was computed N2 pressure at the beginning of the altitude exposure. RESULTS: The log likelihood (LL) without decompression dose (null model) was -155.6, and improved (best-fit) to -97.2 when dose was defined with a 240 min half-time for both N2 elimination and uptake during the PB. The description of DCS survival time was less precise with asymmetrical N2 kinetics, for example, LL was -98.9 with 240 min half-time elimination and 120 min half-time uptake. CONCLUSION: The statistical regression described survival time mechanistically linked to symmetrical N2 kinetics during PBs that also included airbreaks. The results are data-specific, and additional data may change the conclusion. The regression is useful to compute additional PB time to compensate for an airbreak in PB within the narrow range of tested conditions.
The focus of this research work is the elucidation of the physical origins of the observed extreme-rainfall variability over tropical oceans. The quantitative results of this work may be used to establish links between deterministic models of the mesoscale and synoptic scale with statistical descriptions of the temporal variability of local tropical oceanic rainfall. In addition, they may be used to quantify the influence of measurement error in large-scale forcing and cloud scale observations on the accuracy of local rainfall variability inferences, important for hydrologic studies. A simple statistical-dynamical model, suitable for use in repetitive Monte Carlo experiments, is formulated as a diagnostic tool for this purpose. Stochastic processes with temporal structure and parameters estimated from observed large-scale data represent large-scale forcing.
Estimates of monthly average rainfall based on satellite observations from a low earth orbit will differ from the true monthly average because the satellite observes a given area only intermittently. This sampling error inherent in satellite monitoring of rainfall would occur even if the satellite instruments could measure rainfall perfectly. The size of this error is estimated for a satellite system being studied at NASA, the Tropical Rainfall Measuring Mission (TRMM). First, the statistical description of rainfall on scales from 1 to 1000 km is examined in detail, based on rainfall data from the Global Atmospheric Research Project Atlantic Tropical Experiment (GATE). A TRMM-like satellite is flown over a two-dimensional time-evolving simulation of rainfall using a stochastic model with statistics tuned to agree with GATE statistics. The distribution of sampling errors found from many months of simulated observations is found to be nearly normal, even though the distribution of area-averaged rainfall is far from normal. For a range of orbits likely to be employed in TRMM, sampling error is found to be less than 10 percent of the mean for rainfall averaged over a 500 x 500 sq km area.
Introduction: Chronic sleep restriction (CSR) has been associated with increased calorie intake and increased consumption of fats and carbohydrates, with inconsistent changes in protein. However, the majority of studies have either been observational field studies with no sleep intervention, or laboratory-based studies where food availability may not have reflected participants’ real-world choices. We hypothesized that calorie, fat, and carbohydrate intake would increase during a week of imposed CSR compared to a week of sleep satiation (SS) among individuals living in their home environment. Methods: Twelve healthy participants (6 females) kept a fixed sleep-wake schedule, with a constant waketime, at home for four weeks (actigraphy confirmed compliance). During weeks one and three, participants maintained 9 hours in bed. During weeks two and four, participants were randomly assigned to experimental weeks of 5 and 9 hours of time-in-bed in a crossover design. Participants documented their food consumption during both experimental weeks using a picture-based meal logging application (MealLogger). Intake of calories and macronutrients were classified by two blinded evaluators. Descriptive statistics were calculated in SAS (Cary, NC). Results: Participants averaged 4.43 ± 0.33 (SD) hours of sleep per night during CSR compared to 7.42 ± 0.42 hours during SS. Participants consumed a daily average of 1812 ± 672 kilocalories, 71 ± 31 grams of total fat, 217 ± 69 grams of carbohydrates, and 84 ± 40 grams of protein during CSR, compared to 1682 ± 514 kilocalories, 68 ± 23 grams of total fat, 198 ± 61 grams of carbohydrates, and 77 ± 32 grams of protein during SS. Conclusion: Preliminary descriptive findings suggest that, on average, participants consumed more calories, from an increase in consumption of each macronutrient group, during a week of sleep restriction compared to a week of sleep satiation. Further analysis is needed to determine whether these differences are statistically different and to identify when calories were consumed in each of the experimental conditions.
It was the objective of the work reported here, and in the companion paper 1 . A broader examination of wind turbine dynamic response to turbulence, and attempts to ascertain the features of turbulence that wind turbines are most sensitive to were made. A statistical description of the wind input including all three wind components and allowing linear wind gradients across the rotor disk, was used together with quasi-static aerodynamic theory and an elementary structural model involving only a few degrees of freedom. The idea was to keep the turbine model simple and show the benefits of this type of statistical wind representation before attempting to use a more complex turbine model. As far as possible, the analysis was kept in the simplest form, while still preserving key physical responses.
When performing research using digital geographic information it is often useful to produce quantitative characterizations of the data, usually within some constraints. In the research environment the different combinations of required data and constraints can often become quite complex. This paper describes a technique that gives the researcher a powerful and flexible way to set up many possible combinations of data and constraints without having to perform numerous intermediate steps or create temporary data bands. This method provides an efficient way to produce descriptive statistics in such situations.
Since the mid-1980s, airborne and ground measurements have been widely used to provide comprehensive characterization of atmospheric composition and processes. Field campaigns have generated a wealth of insitu data and have grown considerably over the years in terms of both the number of measured parameters and the data volume. This can largely be attributed to the rapid advances in instrument development and computing power. The users of field data may face a number of challenges spanning data access, understanding, and proper use in scientific analysis. This tutorial is designed to provide an introduction to using data sets, with a focus on airborne measurements, for atmospheric research. The first part of the tutorial provides an overview of airborne measurements and data discovery. This will be followed by a discussion on the understanding of airborne data files. An actual data file will be used to illustrate how data are reported, including the use of data flags to indicate missing data and limits of detection. Retrieving information from the file header will be discussed, which is essential to properly interpreting the data. Field measurements are typically reported as a function of sampling time, but different instruments often have different sampling intervals. To create a combined data set, the data merge process (interpolation of all data to a common time base) will be discussed in terms of the algorithm, data merge products available from airborne studies, and their application in research. Statistical treatment of missing data and data flagged for limit of detection will also be covered in this section. These basic data processing techniques are applicable to both airborne and ground-based observational data sets. Finally, the recently developed Toolsets for Airborne Data (TAD) will be introduced. TAD (tad.larc.nasa.gov) is an airborne data portal offering tools to create user defined merged data products with the capability to provide descriptive statistics and the option to treat measurement uncertainty.
Aircraft-based observations of turbulence fields of velocity, moisture, and temperature are used to study coherent turbulent structures that dominate turbulent transfer of moisture and heat above three different eco-systems. Flux traces are defragmented, to reconstruct the presumed full size (along the sampled transect) of these structures, and flux traces are simplified by elimination of those that contribute negligibly to the flux estimate. Structures are analyzed in terms of size, spatial distribution, and contribution to the flux, in the four 'quadrant' modes of eddy-covariance transfer (excess up/down and deficit up/down). The effect of nonlinear detrending of moisture and temperature data on this 'structural analysis,' over surfaces with heterogeneous surface wetness, is also examined. Results over grassland, wetland, and moist and dry agricultural land, show that nonlinear detrending may provide a more physically realistic description of structures. Significant differences are observed between structure size and associated relative flux contribution, between moist and dry areas, with smaller structures playing a more important role over the moist areas. Structure size generally increases with height, as spatial reorganization from smaller structures into larger ones takes place. This coincides with a gradual loss of surface 'signature' (position and clustering of plumes above localized source areas). The data are expected to provide a basis for an eventual statistical description of boundary-layer transfer events , and help to interpret the link between boundary-layer transfer and hydrological surface conditions.
This work demonstrates the development and implementation of a Fully Constrained Least Squares (FCLS) unmixing model developed in C++ programming language with OpenCV package and boost C++ libraries in the NASA Earth Exchange (NEX). Visualization of the results is supported by GRASS GIS and statistical analysis is carried in R in a Linux system environment. FCLS was first tested on computer simulated data with Gaussian noise of various signal-to-noise ratio, and Landsat data of an agricultural scenario and an urban environment using a set of global end members of substrate (soils, sediments, rocks, and non-photosynthetic vegetation), vegetation that includes green photosynthetic plants and dark objects which encompasses absorptive substrate materials, clear water, deep shadows, etc. For the agricultural scenario, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data of Fresno, California, USA were unmixed and the results were validated with the corresponding ground data. To study an urbanized landscape, a clear sky Landsat-5 TM data were unmixed and validated with coincident World View-2 abundance maps (of 2 m spatial resolution) for an area of San Francisco, California, USA. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success, boxplot and bivariate distribution function. Finally, FCLS was used for sub-pixel land cover analysis of the monthly WELD (Wen-enabled Landsat data) repository from 2008 to 2011 of North America. The abundance maps in conjunction with DMSP-OLS nighttime lights data were used to extract the urban land cover features and analyze their spatial-temporal growth.
A model of a phase-locked loop has been developed which is valid for all signal-to-noise ratios. The model is in the form of a nonlinear feedback system with randomly time-varying parameters. The analysis considers two operating regions. In low signal-to-noise ratio regions, the important consideration is stability. We want to study the asymptotic stability in the mean of a nonlinear system. It follows directly that a necessary condition for asymptotic stability of any nonlinear system is that a linearized model about some equilibrium point be asymptotically stable. By considering all possible equilibrium points, we can find an upper bound on the value of noise density which makes the system unstable. This upper bound represents a threshold value for system operation. In high signal-to-noise ratio regions, our results provide an exact statistical. description of system behavior. Therefore, knowledge of the spectrum of the signal and noise may be used to optimize the system configuration.