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Building Hybrid Rover Models for NASA: Lessons Learned

Particle filters have recently become popular for diagnosis and monitoring of hybrid systems. In this paper we describe our experiences using particle filters on a real diagnosis problem, the NASA Ames Research Center's K-9 rover. As well as the challenge of modelling the dynamics of the system, there are two major issues in applying a particle filter to such a model. The first is the asynchronous nature of the system-observations from different subsystems arrive at different rates, and occasionally out of order, leading to large amounts of uncertainty in the state of the system. The second issue is data interpretation. The particle filter produces a probability distribution over the state of the system, from which summary statistics that can be used for control or higher-level diagnosis must be extracted. We describe our approaches to both these problems, as well as other modelling issues that arose in this domain.

Willeke, Thomas↗

OTD Observations of Continental US Ground Flashes Detected by NLDN

Lightning optical flash parameters (e.g., radiance, area, duration, number of optical groups, and number of optical events) derived from almost 5 yrs of Optical Transient Detector (OTD) data are compared with peak current and multiplicity observations derived from the US National Lightning Detection Networkm (NLDN). Despite the relatively low lightning geolocation accuracy afforded by OTD, a total of 48,870 NLDN cloud-to-ground (CG) flashes were correlated with OTD flashes, or about 10,000 CGs per year. The median values of the above OTD flash parameters for the 48,870 CGs were, respectively: 0.137 J/square meters/sr/micrometers, 313.7 square kilometers, 0.189 s, 4 optical groups per CG, and 8 optical events per CG. Invoking the multiplicity data, the median number of optical groups per stroke was 2.5, and the median number of optical events per stroke was 5.0. Median values of peak current for negative and positive CGs were -21.6 kA and 17.8 kA, respectively, and as expected, the negative CGs had a larger average multiplicity than the positive CGs. A statistical summary is provided for all CGs, for positive and negative CGs, and for CGs from different seasons. Standard two-distribution hypothesis tests were perfonned to intercompare the population means of the various lightning parameters. In particular, and to greater than the 99% confidence level, it was found that positive CGs are on average more radiant, of greater areal extent, and are longer lasting than negative CGs. Rankings from a complete set of hypothesis tests between CGs of different polarities and from different seasons are also provided. Most notably, wintertime positive CGs tend to be more radiant, of greater areal extent, and longer lasting than any other group of CGs (i.e., negative springtime CGs, positive summertime CGs, etc.).

Koshak, William J.↗

Ambiguity of Quality in Remote Sensing Data

This slide presentation reviews some of the issues in quality of remote sensing data. Data "quality" is used in several different contexts in remote sensing data, with quite different meanings. At the pixel level, quality typically refers to a quality control process exercised by the processing algorithm, not an explicit declaration of accuracy or precision. File level quality is usually a statistical summary of the pixel-level quality but is of doubtful use for scenes covering large areal extents. Quality at the dataset or product level, on the other hand, usually refers to how accurately the dataset is believed to represent the physical quantities it purports to measure. This assessment often bears but an indirect relationship at best to pixel level quality. In addition to ambiguity at different levels of granularity, ambiguity is endemic within levels. Pixel-level quality terms vary widely, as do recommendations for use of these flags. At the dataset/product level, quality for low-resolution gridded products is often extrapolated from validation campaigns using high spatial resolution swath data, a suspect practice at best. Making use of quality at all levels is complicated by the dependence on application needs. We will present examples of the various meanings of quality in remote sensing data and possible ways forward toward a more unified and usable quality framework.

Lynnes, Christopher↗

Using NASA Environmental Data to Enhance Public Health Decision Making

The Universities Space Research Association at the NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) to address issues of environmental health and enhance public health decision making by utilizing NASA remotely sensed data and products. The objectives of this collaboration are to develop high-quality spatial data sets of environmental variables, and deliver the data sets and associated analyses to local, state and federal end-user groups. These data can be linked spatially and temporally to public health data, such as mortality and disease morbidity, for further analysis and decision making. Three daily environmental data sets have been developed for the conterminous U.S. on different spatial resolutions for the time period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid utilizing the US Environmental Protection Agency (EPA) ground observations and NASA s MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Solar Insolation (SI) and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets will be linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline and other health outcomes. These environmental datasets and public health linkage analyses will be made available to public health professionals, researchers and the general public through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system and through peer reviewed publications. To date, two of the data sets have been released to the public in CDC WONDER, Daily Air Temperature and Heat Index for years 1979-2010, and Daily Fine Particulate Matter (PM2.5) air quality measures for years 2003-2008. These data in CDC WONDER can be aggregated to the county-level, state-level, or regional-level as per users need and downloaded in tabular, graphical, and map formats. The summary statistical output are available to web and app developers via the WONDER Application Programming Interface (API). The linkage of these data with the CDC WONDER system provides a significant addition to CDC WONDER, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER online system. It also substantially expands public access to NASA environmental data, making their use by a wide range of decision makers feasible.

Al-Hamdan, Mohammad↗

Likelihood-Based Climate Model Evaluation

Climate models are deterministic, mathematical descriptions of the physics of climate. Confidence in predictions of future climate is increased if the physics are verifiably correct. A necessary, (but not sufficient) condition is that past and present climate be simulated well. Quantify the likelihood that a (summary statistic computed from a) set of observations arises from a physical system with the characteristics captured by a model generated time series. Given a prior on models, we can go further: posterior distribution of model given observations.

climate models↗

Satellite-Based Drought Reporting on the Navajo Nation

The Navajo Nation (NN) is the largest reservation in the US, and faces challenges related to water management during long-term and widespread drought episodes. The Navajo Nation is a federally recognized tribe, which has boundaries within Arizona, New Mexico, and Utah. The Navajo Nation has a land area of over 70,000 square kilometers. The Navajo Nation Department of Water Resources (NNDWR) reports on drought and climatic conditions through the use of regional Standardized Precipitation Index (SPI) values and a network of in-situ rainfall, streamflow, and climate data. However, these data sources lack the spatial detail and consistent measurements needed to provide a coherent understanding of the drought regime within the Nation's regional boundaries. This project, as part of NASA's Western Water Applications Office (WWAO), improves upon the recently developed Drought Severity Assessment Tool (DSAT) to ingest satellite-based precipitation data to generate SPI values for specific administrative boundaries within the reservation. The tool aims to: (1) generate SPI values and summary statistics for regions of interest on various timescales, (2) to visualize SPI values within a web-map application, and (3) produce maps and comparative statistical outputs in the format required for annual drought reporting. The co-development of the DSAT with NN partners is integral to increasing the sustained use of Earth Observations for water management applications. This tool will provide data to support the NN in allocation of drought contingency dollars to the regions most adversely impacted by declines in water availability.

Satellite-based↗

TPSAS-NF1676L-13037-DND

Mineral dust has a significant and uncertain role in the direct aerosol radiative forcing of climate. Spaceborne lidars such as CALIOP help reduce these uncertainties through vertical profile measurements of aerosol optical properties. One current limitation to the accurate retrieval of aerosol extinction and optical depth from CALIOP is the assumed relationship between the aerosol extinction to aerosol backscatter (i.e. the extinction-to-backscatter ratio, also referred to here as the lidar ratio or Sa). This problem is especially acute at 1064 nm, where few estimates of the lidar ratio exist. This study uses a dataset of eight underflights of CALIOP during August 2010 by the NASA Langley Research Center airborne High Spectral Resolution Lidar (HSRL) to study Saharan dust transported across the Atlantic Ocean. The standard HSRL profile products include aerosol backscatter coefficients and depolarization ratios at both 532 nm and 1064 nm, and aerosol extinction coefficients (and therefore also lidar ratios) at 532 nm only. In this work, we further derive estimates of aerosol lidar ratios and extinction coefficients at 1064 nm via application of a two-wavelength technique that uses the 532 nm aerosol backscatter coefficients and the 1064 nm attenuated total backscatter profile. Summary statistics of the dust lidar ratio and depolarization at 532 nm and 1064 nm from these eight flights are presented. Implications for the CALIOP dust and polluted dust aerosol types and lidar ratio selection are discussed. In addition to the two-wavelength retrievals of lidar ratio at 1064 nm, a demonstration case of a 1064 nm lidar ratio retrieval over the ocean from CALIOP using the CloudSat measurement of surface scattering cross section as a constraint is presented (Josset et al, 2010).

Raymond R Rogers↗

Treating gridded geospatial data as point data to simplify analytics

Gridded geospatial remote sensing (satellite) data has traditionally been stored in file-based multidimensional arrays to preserve the locality of data. Measurements from locations that are physically next to each other on earth remain next to each other in the arrays. Maintaining this locality is useful when running calculations like reprojection, but unnecessary for many other calculations. This talk will go through a real world example of a tool redesign at the Goddard Earth Sciences Data and Information Services Center (GES DISC), showing the advantages of using the data frame model for calculating summary statistics, where measurement proximity is unimportant.

Analysis-ready data↗

Comparing Theoretically Scaled Biomechanical Models

BACKGROUND An investigation into incorporating space suit aspects required a 50th-percentile male model which was not part of the existing dataset of models. Previously, theoretical models for 5th-percentile female and 95th-percentile male were created utilizing a scaled test subject as close as possible to the target height and weight. Scaling factors were created by taking the height ratio and applying it uniformly to the model body segments, then fine tuning to ensure the theoretical model’s height is as expected. This study was initiated to examine the existing method of isometrically scaling in OpenSim [1,2] and to create alternative methods which do not rely on an existing subject being close in height and weight to the theoretical model of interest. Generating a theoretical biomechanical model provides additional abilities without the reliance on available OpenSim models or real subjects. Gained abilities include creating different percentile models and attaining representative anthropometry for any target height/weight, such as targeting specific crew populations or gaps within the current dataset. METHODS AND RESULTS This study includes 3 methods for generating the theoretical model; utilizing the modified unscaled OpenSim Full Body Rajagopal Model (FBRM) [3,4], a dataset of scaled OpenSim models, and existing scaling factors from Dumas et al. [5]. All methods use the Anthropometric Survey of US Army Personnel (ANSUR II) [6] as an input of necessary anthropometric measurements. The following measurements are retrieved from the ANSUR II collection: mass, stature, cervicale height, acromial height, axilla height, waist height, trochanterion height, lateral femoral epicondyle height, lateral malleolus height, acromion-radiale length, radiale-stylion length, palm length, ball of foot length, bicristal breadth, bimalleolar breadth. Some of the measurements are direct segment lengths and others are utilized to derive segment lengths. Height, weight, and age ranges are the required inputs to parse the collection for a mean value of the measurements. A simple iterative process may be necessary for the output of mean mass and stature consistent with the theoretical model of interest. In some cases, one may specify exact values instead of a range but that is dependent upon whether those exact values pertain to a single subject from the ANSUR II collection. Equations from Dumas et al. were used to calculate scaling factors for the two methods that utilize OpenSim scaled and unscaled models. In the third method, Dumas’ scaling factors were used directly instead of generating our own. After scaling factors are achieved, they are applied along with the mass and segment lengths retrieved from ANSUR II in order to calculate the segment mass, Center of Mass (CoM), mass Moment of Inertia (MoI), and the joint location in the parent frame. The theoretical models from the different methods were compared to the previous existing method of isometrically scaling in OpenSim, as well as the standards found in NASA STD-3000 [7] and the NASA Human Integration Design Handbook [8]. The whole-body center of mass is the main comparison performed between the models and the standards. The body segment mass properties were also compared when possible. In some cases, the standards do not provide complete data, or the center of mass location is not provided with respect to the appropriate joint coordinate system. For a 50th-percentile male subject, results from isometric scaling in OpenSim and the method utilizing the unscaled FBRM were compared to the standards in NASA STD-3000. According to the standard, the whole-body vertical CoM location was taken from the head vertex to the CoM. The standards provide 80.2 cm for the CoM vertical component whereas isometric scaling in OpenSim and utilizing the unscaled FBRM provide 82.2 and 82.3 cm, respectively. Through definition, the whole-body CoM in the medial/lateral direction agreed across the models. Investigation is on-going to clearly identify the reference point pertaining to dorsal/ventral whole-body CoM position in standards and relate that to the theoretical models. REFERENCES [1] Delp, S.L., et al., “OpenSim: Open-source Software to Create and Analyze Dynamic Simulations of Movement”, IEEE Transactions on Biomedical Engineering, (2007). [2] Seth, A., Hicks J.L., Uchida, T.K., Habib, A., Dembia, C.L., Dunne, J.J., Ong, C.F., DeMers, M.S., Rajagopal, A., Millard, M., Hamner, S.R., Arnold, E.M., Yong, J.R., Lakshmikanth, S.K., Sherman,n M.A., Delp, S.L. OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. Plos Computational Biology, 14(7). (2018) [3] R. K. Huffman, W. K. Thompson, C. A. Gallo, L. J. Quiocho, “Improvement of Scaling and Inverse Kinematic Results with Additional Upper Body Joints Added to Opensim Rajagopal Model”, NASA Human Research Program Investigator’s Workshop, (2019). [4] Huffman R.K., Thompson W., Gallo C., “Modified OpenSim Rajagopal Full Body Model”, New Technology Report, MSC-26872-1. (August 2020). [5] Dumas R., Cheze L. and Verriest J., 2007. “Adjustments to McConville et al. and Young et al. body segment inertial parameters”. Journal of Biomechanics 40, pp. 543-553 [6] Gordon C.C., 2012 “Anthropometric Survey of U.S. Army Personnel: Methods and Summary Statistics”, US Army Natick Soldier RD&E Center. [7] NASA STD-3000/REV-B, 1995. “The Man-System Integration Standards” [8] NASA/SP-2010-3407/REV-1, 2014. “The Human Integration Design Handbook (HIDH)”

F N Matari↗

U.S. GPM Status

The joint U.S.-Japan Global Precipitation Measurement (GPM) mission, now in its tenth year of operations, continues to pursue research on scientific and operational shortcomings in algorithms for retrieving global precipitation from satellite observations. A summary of operations, including the orbit boost, processing, science, and applications is provided. This includes key improvements to the GPM products for which NASA has lead responsibility. This includes refinements to ensure continuity for each product across the boundary in 2014 between the predecessor Tropical Rainfall Measuring Mission (TRMM) and the GPM Core Observatory. Early evaluations of the U.S. Science Team’s Integrated Multi-satellitE Retrievals for GPM (IMERG) in V07 show interesting behavior across the TRMM orbit boost that is highly relevant to the planned orbit boost for the GPM Core observatory. We will present summary statistics that illustrate the tremendous demand that exists in the user community for GPM precipitation products, which on the U.S. side is primarily focused on IMERG, and end by considering major issues that might be addressed in up-coming versions. These include the use of machine learning algorithms, the operational challenge of swarms of “small”, perhaps short-lived satellites, and continuation of precessing calibration satellites after GPM.

GPM↗

Jemez Pueblo Agriculture: Monitoring Rangeland Conditions to Inform Drought and Land Management in New Mexico

The Jemez Pueblo, a community located in the semi-arid high desert region of northcentral New Mexico, faces challenges with drought mitigation and livestock management. NASA DEVELOP partnered with the Pueblo of Jemez Natural Resources Department and The Nature Conservancy to provide recent and historical rangeland conditions to aid present decision-making, which relies on cultural practices, site familiarity, available resources, and physical data. This partnership offers the opportunity to support management practices by leveraging remote sensing and virtual fencing technologies. Our project explored Rangeland Analysis Platform (RAP) products incorporating NASA Earth observation Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2 remote-sensed imagery as an agricultural land management tool. We performed linear regression analysis to evaluate RAP data to field data and explored RAP annual herbaceous biomass, ground cover trends, and climatic factors at the pasture level. We then computed study area summary statistics, performed pixel-by-pixel trend analysis across the study period, and evaluated ground cover trends near water sources. Based on available data, RAP was poorly correlated to bare ground and herbaceous biomass, generally underestimating the former and overestimating the latter. To improve the evaluation of RAP products, we suggest increasing the spatiotemporal distribution of the field dataset. According to RAP, from 1986 to 2023, the site became hotter and drier, bare ground increased, and annual forbs and grasses increased while perennials decreased. RAP products did not agree with field-collected data; however, the trends over time may be useful for informing rangeland management actions.

cattle grazing management↗

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $\chi^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

State of the art astronomical simulations have provided datasets which enabled the training of novel deep learning techniques for constraining cosmological parameters. However, differences in subgrid physics implementation and numerical approximations among simulation suites lead to differences in simulated datasets, which pose a hard challenge when trying to generalize across diverse data domains and ultimately when applying models to observational data. Recent work reveals deep learning algorithms are able to extract more information from complex cosmological simulations than summary statistics like power spectra. We introduce Domain Adaptive Graph Neural Networks (DA-GNNs), trained on CAMELS data, inspired by CosmoGraphNet (Villanueva-Domingo et al 2023). By utilizing GNNs, we can capitalize on their capacity to capture both astrophysical and topological features of galaxy distributions. Mixing these capabilities with domain adaptation techniques such as Maximum Mean Discrepancy (MMD), which enable extraction of domain-invariant features, our framework demonstrates enhanced accuracy and robustness. We present experimental results, including the alignment of distributions across domains through data visualization. These findings suggest that DA-GNNs are an efficient way of extracting domain independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗

Determining Cs-137 Background body burdens for Wild Pigs At Savannah River Site

Savannah River Site (SRS) is a unique U.S. Department of Energy facility in that most of the 310-square miles of land has not be industrialized since operations began in 1953. This has allowed abundant, undisturbed vegetation structure to exists as well as an increase in herd sizes among wildlife populations onsite. Cs-137 is widespread across SRS due to the production of nuclear materials, but is mostly from global atmospheric fallout from nuclear weapons testing. Due to most of SRS being undisturbed land, the concentrations of Cs-137 are higher on site than off site. The SRS began conducting annual deer harvests that were open to the public beginning in 1965. All animals harvested during these hunts are monitored for Cs-137, which is measured using a Sodium Iodide detector. There has been an effective method for calculating the background Cs-137 body burden for the deer population at the SRS, developed by Gaines and Novak using data collected during hunts and gamma overflight data to determine the areas contaminated by site activities. For animals that have a Cs-137 concentration above the established background level, a dose is assigned to the hunter. By applying the same method for wild pigs using data collected from hunts, gamma overflight, and developing a site-specific home range of wild pigs, we were able to calculate the Cs-137 background body burden concentrations of wild pigs on site. Objectives: Develop site-specific home range for wild pigs at SRS; Determine the average concentrations of Cs-137 in wild pigs from background; Use Gaines and Novak method for determining background Cs-137 body burdens wild pigs on SRS. Methods and Materials: Development of site-specific home range of wild pigs was determined to be 2.5 miles using: Four studies conducted between 1969 and 1992 for the SRS wild pig population. (Mayer and Brisbin) The background compartments were determined using: Gamma overflight data showing contaminated areas; Site-specific home range of the wild pigs. Results and Conclusion: All data were generated through the JMP software to provide a histogram and summary statistics. The estimate of an upper tolerance limit for the background contamination with 95% confidence is 1.965 pCi/g for the wild pigs. This means that for wild pigs on SRS from the background compartments are expected to have Cs-137 concentrations of less than 1.965 pCi/g. The current background for the deer is 2.59 pCi/g. For the background compartments Cs-137 concentrations from 2012-2018: Mean: 0.815 pCi/g, Maximum: 2.341 pCi/g, Minimum: -0.704 pCi/g.

07 ISOTOPE AND RADIATION SOURCES↗

GASPS--A Herschel Survey of Gas and Dust in Protoplanetary Disks: Summary and Initial Statistics

We describe a large-scale far-infrared line and continuum survey of protoplanetary disk through to young debris disk systems carried out using the ACS instrument on the Herschel Space Observatory. This Open Time Key program, known as GASPS (Gas Survey of Protoplanetary Systems), targeted approx. 250 young stars in narrow wavelength regions covering the [OI] fine structure line at 63 micron the brightest far-infrared line in such objects. A subset of the brightest targets were also surveyed in [OI]145 micron, [CII] at 157 μm, as well as several transitions of H2O and high-excitation CO lines at selected wavelengths between 78 and 180 micron. Additionally, GASPS included continuum photometry at 70, 100 and 160 micron, around the peak of the dust emission. The targets were SED Class II– III T Tauri stars and debris disks from seven nearby young associations, along with a comparable sample of isolated Herbig AeBe stars. The aim was to study the global gas and dust content in a wide sample of circumstellar disks, combining the results with models in a systematic way. In this overview paper we review the scientific aims, target selection and observing strategy of the program. We summarize some of the initial results, showing line identifications, listing the detections, and giving a first statistical study of line detectability. The [OI] line at 63 micron was the brightest line seen in almost all objects, by a factor of ∼10. Overall [OI] 63 micron detection rates were 49%, with 100% of HAeBe stars and 43% of T Tauri stars detected. A comparison with published disk dust masses (derived mainly from sub-mm continuum, assuming standard values of the mm mass opacity) shows a dust mass threshold for [OI] 63 μm detection of approx.10(exp -5) Solar M.. Normalizing to a distance of 140 pc, 84% of objects with dust masses ≥10 (exp -5) Solar M can be detected in this line in the present survey; 32% of those of mass 10(exp -6) – 10 (exp -5) Solar M, and only a very small number of unusual objects with lower masses can be detected. This is consistent with models with a moderate UV excess and disk flaring. For a given disk mass, [OI] detectability is lower for M stars compared with earlier spectral types. Both the continuum and line emission was, in most systems, spatially and spectrally unresolved and centered on the star, suggesting that emission in most cases was from the disk. Approximately 10 objects showed resolved emission, most likely from outflows. In the GASPS sample, [OI] detection rates in T Tauri associations in the 0.3–4 Myr age range were approx. 50%. For each association in the 5–20 Myr age range, approx. 2 stars remain detectable in [OI] 63 micron, and no systems were detected in associations with age >20 Myr. Comparing with the total number of young stars in each association, and assuming a ISM-like gas/dust ratio, this indicates that approx. 18% of stars retain a gas-rich disk of total mass approx. Jupiter- M for 1–4 Myr, 1–7% keep such disks for 5–10 Myr, but none are detected beyond 10–20 Myr. The brightest [OI] objects from GASPS were also observed in [OI]145 micron, [CII]157 micron and CO J = 18- 17, with detection rates of 20–40%. Detection of the [CII] line was not correlated with disk mass, suggesting it arises more commonly from a compact remnant envelope.

Herschel Space Observatory↗

Using the Bootstrap Method for a Statistical Significance Test of Differences between Summary Histograms

A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries-Matusita distance and the Kuiper distance. The data used in testing the bootstrap method are satellite measurements of cloud systems called cloud objects. Each cloud object is defined as a contiguous region/patch composed of individual footprints or fields of view. A histogram of measured values over footprints is generated for each parameter of each cloud object and then summary histograms are accumulated over all individual histograms in a given cloud-object size category. The results of statistical hypothesis tests using all three distances as test statistics are generally similar, indicating the validity of the proposed method. The Euclidean distance is determined to be most suitable after comparing the statistical tests of several parameters with distinct probability distributions among three cloud-object size categories. Impacts on the statistical significance levels resulting from differences in the total lengths of satellite footprint data between two size categories are also discussed.

Xu, Kuan-Man↗

Offset Stream Technology Test-Summary of Results

Statistical jet noise prediction codes that accurately predict spectral directivity for both cold and hot jets are highly sought both in industry and academia. Their formulation, whether based upon manipulations of the Navier-Stokes equations or upon heuristic arguments, require substantial experimental observation of jet turbulence statistics. Unfortunately, the statistics of most interest involve the space-time correlation of flow quantities, especially velocity. Until the last 10 years, all turbulence statistics were made with single-point probes, such as hotwires or laser Doppler anemometry. Particle image velocimetry (PIV) brought many new insights with its ability to measure velocity fields over large regions of jets simultaneously; however, it could not measure velocity at rates higher than a few fields per second, making it unsuitable for obtaining temporal spectra and correlations. The development of time-resolved PIV, herein called TR-PIV, has removed this limitation, enabling measurement of velocity fields at high resolution in both space and time. In this paper, ground-breaking results from the application of TR-PIV to single-flow hot jets are used to explore the impact of heat on turbulent statistics of interest to jet noise models. First, a brief summary of validation studies is reported, undertaken to show that the new technique produces the same trusted results as hotwire at cold, low-speed jets. Second, velocity spectra from cold and hot jets are compared to see the effect of heat on the spectra. It is seen that heated jets possess 10 percent more turbulence intensity compared to the unheated jets with the same velocity. The spectral shapes, when normalized using Strouhal scaling, are insensitive to temperature if the stream-wise location is normalized relative to the potential core length. Similarly, second order velocity correlations, of interest in modeling of jet noise sources, are also insensitive to temperature as well.

Brown, Clifford A.↗

Preliminary Survey of Icing Conditions Measured During Routine Transcontinental Airline Operation

Icing data collected on routine operations by four DC-4-type aircraft equipped with NACA pressure-type icing-rate meters are presented as preliminary information obtained from a statistical icing data program sponsored by the NACA with the cooperation of many airline companies and the United States Air Force. The program is continuing on a much greater scale to provide large quantities of data from many air routes in the United States and overseas. Areas not covered by established air routes are also being included in the survey. The four aircraft which collected the data presented in this report were operated by United Air Lines over a transcontinental route from January through May, 1951. An analysis of the pressure-type icing-rate meter was satisfactory for collecting statistical data during routine operations. Data obtained on routine flight icing encounters from.these four instrumented aircraft, although insufficient for a conclusive statistical analysis, provide a greater quantity and considerably more realistic information than that obtained from random research flights. A summary of statistical data will be published when the information obtained daring the 1951-52 icing season and that to be obtained during the 1952-53 season can be analyzed and assembled. The 1951-52 data already analyzed indicate that the quantity, quality, and range of icing information being provided by this expanded program should afford a sound basis for ice-protection-system design by defining the important meteorological parameters of the icing cloud.

Perkins, Porter J.↗