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NASA NTRS1985

A comparison of in situ and airborne radar observations of ocean wave directionality

The directional spectrum of a fully arisen, about 3 m sea as measured by an experimental airborne radar, the NASA K(u)-band radar ocean wave spectrometer (ROWS), is compared to reference pitch-roll buoy data and to the classical SWOP (stereo wave observations project) spectrum for fully developed conditions. The ROWS spectrum, inferred indirectly from backscattered power measurements at 5-km altitude, is shown to be in excellent agreement with the buoy spectrum. Specifically, excellent agreement is found between the two nondirectional height spectra, and mean wave directions and directional spreads as functions of frequency. A comparison of the ROWS and SWOP spectra shows the two spectra to be very similar, in detailed shape as well as in terms of the gross spreading characteristics. Both spectra are seen to exhibit bimodal structures which accord with the Phillips' (1958) resonance mechanism. This observation is thus seen to support Phillips' contention that the SWOP modes were indeed resonance modes, not statistical artifacts.

Jackson, F. C.↗

NASA NTRS2008

Total Lightning Observations of Extreme Weather Events over the Contiguous United States

The overall objective is to investigate total lightning characteristics of extreme weather events over the contiguous United States (CONUS) using TRMM (Tropical Rainfall Measuring Mission) LIS (Lightning Image Sensor) and OTD (Optical Transient Detector) satellite observations. A large LIS (10+ years) and OTD (5 years) data base is available to study the instantaneous total or cloud-to-ground (CG) plus intracloud (IC) lightning characteristics of extreme weather events. More specifically, the LIS and OTD data are combined with National Lightning Detection Network (NLDN) observations to examine the total and CG lightning flash rate and density, the IC:CG ratio, and positive CG percentage. These instantaneous lightning characteristics can be used for basic science studies to better understand the physical and dynamical linkages between lightning and precipitation and their environmental controls. They can also provide a first-look of extreme weather events leading up to future satellite observations (e.g., NOAA GOES-R Geostationary Lightning Mapper [GLM]) for use in climate studies and the short-term prediction and warning process. Extreme weather events are defined by the NOAA Storm Data reports of tornadoes, large hail (at least 0.75 inch) and strong straight-line winds (at least 50 kts). Over CONUS, there are over 70,000 severe storm reports in the TRMM spatial domain (< 35 N) from 1998-2007 and over 100,000 storm reports in the OTD spatial domain (5/1995-4/2000). Temporal co-location is on the order of 1% (i.e., 1000 s of coincident overpasses), providing a statistically significant sample of instantaneous total lightning properties. This instantaneous behavior of lightning in extreme weather is then compared to that of typical thunderstorm events, or randomly sampled LIS/OTD events in which the extreme events have been eliminated from the population. Results describing the instantaneous behavior of total lightning within a large sample of extreme and typical thunderstorms over CONUS will be presented. When possible, coincident VHF lightning observations from the ground-based Northern Alabama Lightning Mapping Array (LMA) are compared to the LIS/OTD optical lightning signatures, providing a validation source for instantaneous space-based optical lightning properties and a means to extend lightning inferences over the life-cycle of extreme weather.

Carey, Lawrence D.↗

NASA NTRS2012

Turbulent Statistics from Time-Resolved PIV Measurements of a Jet Using Empirical Mode Decomposition

Empirical mode decomposition is an adaptive signal processing method that when applied to a broadband signal, such as that generated by turbulence, acts as a set of band-pass filters. This process was applied to data from time-resolved, particle image velocimetry measurements of subsonic jets prior to computing the second-order, two-point, space-time correlations from which turbulent phase velocities and length and time scales could be determined. The application of this method to large sets of simultaneous time histories is new. In this initial study, the results are relevant to acoustic analogy source models for jet noise prediction. The high frequency portion of the results could provide the turbulent values for subgrid scale models for noise that is missed in large-eddy simulations. The results are also used to infer that the cross-correlations between different components of the decomposed signals at two points in space, neglected in this initial study, are important.

Dahl, Milo D.↗

NASA NTRS2012

Turbulent Statistics from Time-Resolved PIV Measurements of a Jet Using Empirical Mode Decomposition

Dahl, Milo D.↗

NASA NTRS2013

Turbulent Statistics From Time-Resolved PIV Measurements of a Jet Using Empirical Mode Decomposition

Dahl, Milo D.↗

NASA NTRS1987

Soil moisture estimation using GOES-VISSR infrared data - A case study with a simple statistical method

Five days of clear sky observations of Kansas and Nebraska are used to examine the statistical relationship between soil moisture and infrared surface temperature observations taken from a geosynchronous satellite. Linear regression is used to relate soil moisture to surface temperature and other variables that represent wind speed, vegetation cover, and low-level temperature advection. Results show good agreement between estimated and observed soil moisture features on each of the 5 days. The average coefficient of determination for five pseudoindependent tests in which the test day is held out of the regression is 0.71. It is shown that a depletion coefficient of 0.92, when used to compute antecedent precipitation index (API), produces the best correlation between API and soil moisture as inferred from GOES thermal infrared data. By averaging daily predicted values over the 5-day rain-free case study period, 92 percent of the variance of the morning surface temperature change is explained by a simple multiple linear regression with all independent variables, or, alternatively, 85 percent of the observed variance in API is explained. It is concluded that this approach can distinguish at least four classes of soil wetness, but the necessity for measurement of surface advection may limit its usefulness in remote areas.

Wetzel, Peter J.↗

NASA NTRS2010

Monitoring System for Storm Readiness and Recovery of Test Facilities: Integrated System Health Management (ISHM) Approach

Severe weather events are likely occurrences on the Mississippi Gulf Coast. It is important to rapidly diagnose and mitigate the effects of storms on Stennis Space Center's rocket engine test complex to avoid delays to critical test article programs, reduce costs, and maintain safety. An Integrated Systems Health Management (ISHM) approach and technologies are employed to integrate environmental (weather) monitoring, structural modeling, and the suite of available facility instrumentation to provide information for readiness before storms, rapid initial damage assessment to guide mitigation planning, and then support on-going assurance as repairs are effected and finally support recertification. The system is denominated Katrina Storm Monitoring System (KStorMS). Integrated Systems Health Management (ISHM) describes a comprehensive set of capabilities that provide insight into the behavior the health of a system. Knowing the status of a system allows decision makers to effectively plan and execute their mission. For example, early insight into component degradation and impending failures provides more time to develop work around strategies and more effectively plan for maintenance. Failures of system elements generally occur over time. Information extracted from sensor data, combined with system-wide knowledge bases and methods for information extraction and fusion, inference, and decision making, can be used to detect incipient failures. If failures do occur, it is critical to detect and isolate them, and suggest an appropriate course of action. ISHM enables determining the condition (health) of every element in a complex system-of-systems or SoS (detect anomalies, diagnose causes, predict future anomalies), and provide data, information, and knowledge (DIaK) to control systems for safe and effective operation. ISHM capability is achieved by using a wide range of technologies that enable anomaly detection, diagnostics, prognostics, and advise for control: (1) anomaly detection algorithms and strategies, (2) fusion of DIaK for anomaly detection (model-based, numerical, statistical, empirical, expert-based, qualitative, etc.), (3) diagnostics/prognostics strategies and methods, (4) user interface, (5) advanced control strategies, (6) integration architectures/frameworks, (7) embedding of intelligence. Many of these technologies are mature, and they are being used in the KStorMS. The paper will describe the design, implementation, and operation of the KStorMS; and discuss further evolution to support other needs such as condition-based maintenance (CBM).

Figueroa, Fernando↗

NASA NTRS1990

Testing the Sun-climate Connection with Paleoclimate Data

If there is a significant sun-climate connection, it should be detectable in high-resolution paleoclimate records. Of particular interest is the last few thousand years, where we have both indices of solar variability (C-14 and Be-10) and climate variations (alpine glaciers, tree rings, ice cores, corals, etc.). Although there are a few exceptions, statistical analyses of solar and climate records generally indicates a flickering relationship between the two -- sometimes it seems to be present, sometimes not. The most repeatable solar climate periods occur at approx. 120 and approx. 56 yrs, although there is also evidence for approx. 420 and approx. 200 yrs. power in some records. However, coherence between solar and climate spectra is usually low, and occurrence of solar spectra in climate records is sometimes dependent on choice of analysis program. These results suggest in general a relatively weak sun-climate link on time scales of decades to centuries. This conclusion is consistent with previous studies and with the observation that inferred climate fluctuations of 1.0 to 1.5 C on this time scale would require solar constant variations of approximately 0.5 to 1.0 percent. This change in forcing is almost an order of magnitude greater than observed changes over the last solar cycle and appears to be on the far-outer limit of acceptable changes for a Maunder Minimum-type event.

Crowley, Thomas J.↗

NASA NTRS2001

The 3D Recognition, Generation, Fusion, Update and Refinement (RG4) Concept

This paper describes an active (real time) recognition strategy whereby information is inferred iteratively across several viewpoints in descent imagery. We will show how we use inverse theory within the context of parametric model generation, namely height and spectral reflection functions, to generate model assertions. Using this strategy in an active context implies that, from every viewpoint, the proposed system must refine its hypotheses taking into account the image and the effect of uncertainties as well. The proposed system employs probabilistic solutions to the problem of iteratively merging information (images) from several viewpoints. This involves feeding the posterior distribution from all previous images as a prior for the next view. Novel approaches will be developed to accelerate the inversion search using novel statistic implementations and reducing the model complexity using foveated vision. Foveated vision refers to imagery where the resolution varies across the image. In this paper, we allow the model to be foveated where the highest resolution region is called the foveation region. Typically, the images will have dynamic control of the location of the foveation region. For descent imagery in the Entry, Descent, and Landing (EDL) process, it is possible to have more than one foveation region. This research initiative is directed towards descent imagery in connection with NASA's EDL applications. Three-Dimensional Model Recognition, Generation, Fusion, Update, and Refinement (RGFUR or RG4) for height and the spectral reflection characteristics are in focus for various reasons, one of which is the prospect that their interpretation will provide for real time active vision for automated EDL.

Maluf, David A.↗

NASA NTRS2018

Remote Sensing, Uncertainty Quantification, and a Theory of Data Systems; Workshop Report

The purpose of the workshop was to invite statisticians, applied mathematicians, computer scientists, data system architects, experts in remote sensing technology, and Climate and Earth System scientists to review, discuss, and plan research on issues related to large-scale, efficient analysis of distributed data using spatial statistical methods. Our motivation in organizing this event was to catalyze interchange among experts on the fast-emerging problem of analysis of distributed data. As part of SAMSI's 2017-2018 Program on Mathematical and Statistical Methods for Climate and the Earth System, a Working Group on Remote Sensing was established to address statistical and mathematical research problems in the analysis of remote sensing data. The Working Group has five subgroups: 1) Spatial Retrieval Methodology (the so-called \Spatial-X" subgroup); 2) Spatial Analysis for Hyperspectral Data (the so-called \Spatial-Y" subgroup); 3) Emulators for Complex Forward Models; 4) Optimization for Remote Sensing Retrievals; and 5) Theory of Data Systems (ToDS). The ToDS subgroup spent the first half of this academic year formulating a framework in which to consider the joint problem of a) optimizing statistical methods for environments where data are distributed and too large to move to a central location, and b) the design of data system infrastructures within which to implement those statistical methods. To x ideas, the Workshop focused on spatial statistical methods. To date there are many new spatial statistical methods designed with massive data sets in mind, in the literature. However, very few have been implemented for remote sensing data, and none have been implemented in operational settings like those used by NASA and NOAA. A major impediment to their use in these cases is that the data are not only massive, but are stored in different physical locations. These data must be brought together in some way in order to estimate spatial covariance functions, but moving data to a central location for analysis is tedious at best and impossible at worst. Some remote data reduction is almost certainly necessary, but how much? What are the consequences for inference? The fundamental issue underlying these questions is how to navigate the trade-space between costs and uncertainty in the estimates or inferences that are ultimately produced.

Braverman, Amy↗

NASA NTRS2019

Predictability of Ocean Heat Content From Electrical Conductance

Ocean heat content (OHC) is a key climate variable that needs to be monitored to know how Earth's energy imbalance is changing, yet observing OHC remains a challenge. The present study examines whether a depth integral of the ocean's electrical conductivity (“conductance”), which may be inferred from both in situ methods and satellite magnetometers over the global ocean, could help monitor OHC. The ocean's electrical conductivity locally depends on temperature, salinity, and pressure, but it is not as well known how the conductance depends on OHC and ocean salt content. By examining the output of an ocean state estimate shown to agree well with observations that have not been assimilated, this study evaluates the fundamental limitations of using perfectly known ocean conductance to predict OHC, rather than the challenges associated with accounting for observational error. It is found that the ocean's conductance and OHC fields are nonlinearly related but nevertheless highly correlated. A statistical framework tends to predict OHC more accurately than ocean salt content from ocean conductance in regions where conductivity is more sensitive to salinity than temperature. The annually (bidecadally) averaged OHC can be predicted from a combination of conductance and depth‐averaged conductivity ocean fields to within nearly 0.1% (1%) error globally and even more accurately in many poorly observed (e.g., ice‐covered) regions. Practical application of this statistical framework to monitor OHC requires examination of the effect of uncertainties in the observed bathymetry and ocean conductance, which vary with application.

Trossman, D. S.↗

NASA NTRS2019

Discovery of Activities via Statistical Clustering of Fixation Patterns

Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a pilot flying an airplane; in this case, activities with distinct visual signatures might be things like communicating, navigating, monitoring, etc. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. We compare, not individual fixations, but groups of fixations aggregated over a fixed time interval (Tau). We assume that the environment has been divided into a finite set of discrete areas-of-interest (AOIs). For a given time interval, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector, where N is the number of AOIs. These proportions can be converted to integer counts by multiplying by Tau divided by the average fixation duration, a parameter that we fix at 283 milliseconds. We compare different intervals by computing the chi-squared statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. We cluster the intervals, first by merging adjacent intervals that are sufficiently similar, optionally shifting the boundary between non-merged intervals to maximize the difference. Then we compare and cluster non-adjacent intervals. The method is evaluated using synthetic data generated by a hand-crafted set of activities. While the method generally finds more activities than put into the simulation, we have obtained agreement as high as 80 percent between the inferred activity labels and ground truth.

Eye Movements↗

NASA NTRS2002

Transcriptional profiling reveals regulated genes in the hippocampus during memory formation

Transcriptional profiling (TP) offers a powerful approach to identify genes activated during memory formation and, by inference, the molecular pathways involved. Trace eyeblink conditioning is well suited for the study of regional gene expression because it requires the hippocampus, whereas the highly parallel task, delay conditioning, does not. First, we determined when gene expression was most regulated during trace conditioning. Rats were exposed to 200 trials per day of paired and unpaired stimuli each day for 4 days. Changes in gene expression were most apparent 24 h after exposure to 200 trials. Therefore, we profiled gene expression in the hippocampus 24 h after 200 trials of trace eyeblink conditioning, on multiple arrays using additional animals. Of 1,186 genes on the filter array, seven genes met the statistical criteria and were also validated by real-time polymerase chain reaction. These genes were growth hormone (GH), c-kit receptor tyrosine kinase (c-kit), glutamate receptor, metabotropic 5 (mGluR5), nerve growth factor-beta (NGF-beta), Jun oncogene (c-Jun), transmembrane receptor Unc5H1 (UNC5H1), and transmembrane receptor Unc5H2 (UNC5H2). All these genes, except for GH, were downregulated in response to trace conditioning. GH was upregulated; therefore, we also validated the downregulation of the GH inhibitor, somatostatin (SST), even though it just failed to meet criteria on the arrays. By during situ hybridization, GH was expressed throughout the cell layers of the hippocampus in response to trace conditioning. None of the genes regulated in trace eyeblink conditioning were similarly affected by delay conditioning, a task that does not require the hippocampus. These findings demonstrate that transcriptional profiling can exhibit a repertoire of genes sensitive to the formation of hippocampal-dependent associative memories.

Non-NASA Center↗

NASA NTRS2001

Radiation breakage of DNA: a model based on random-walk chromatin structure

Monte Carlo computer software, called DNAbreak, has recently been developed to analyze observed non-random clustering of DNA double strand breaks in chromatin after exposure to densely ionizing radiation. The software models coarse-grained configurations of chromatin and radiation tracks, small-scale details being suppressed in order to obtain statistical results for larger scales, up to the size of a whole chromosome. We here give an analytic counterpart of the numerical model, useful for benchmarks, for elucidating the numerical results, for analyzing the assumptions of a more general but less mechanistic "randomly-located-clusters" formalism, and, potentially, for speeding up the calculations. The equations characterize multi-track DNA fragment-size distributions in terms of one-track action; an important step in extrapolating high-dose laboratory results to the much lower doses of main interest in environmental or occupational risk estimation. The approach can utilize the experimental information on DNA fragment-size distributions to draw inferences about large-scale chromatin geometry during cell-cycle interphase.

Chromatin/radiation effects↗

NASA NTRS2013

The Global Land-Ocean Temperature Index in Relation to Sunspot Number, the Atlantic Multidecadal Oscillation Index, the Mauna Loa Atmospheric Concentration of CO2, and Anthropogenic Carbon Emissions

Global warming/climate change has been a subject of scientific interest since the early 19th century. In particular, increases in the atmospheric concentration of carbon dioxide (CO2) have long been thought to account for Earth's increased warming, although the lack of a dependable set of observational data was apparent as late as the mid 1950s. However, beginning in the late 1950s, being associated with the International Geophysical Year, the opportunity arose to begin accurate continuous monitoring of the Earth's atmospheric concentration of CO2. Consequently, it is now well established that the atmospheric concentration of CO2, while varying seasonally within any particular year, has steadily increased over time. Associated with this rising trend in the atmospheric concentration of CO2 is a rising trend in the surface-air and sea-surface temperatures (SSTs). This Technical Publication (TP) examines the statistical relationships between 10-year moving averages (10-yma) of the Global Land-Ocean Temperature Index (GLOTI), sunspot number (SSN), the Atlantic Multidecadal Oscillation (AMO) index, and the Mauna Loa CO2 (MLCO2) index for the common interval 1964-2006, where the 10-yma values are used to indicate trends in the data. Scatter plots using the 10-yma values between GLOTI and each of the other parameters are determined, both as single-variate and multivariate fits. Scatter plots are also determined for MLCO2 using single-variate and bivariate (BV) fits, based on the GLOTI alone and the GLOTI in combination with the AMO index. On the basis of the inferred preferential fits for MLCO2, estimates for MLCO2 are determined for the interval 1885-1964, thereby yielding an estimate of the preindustrial level of atmospheric concentration of CO2. Lastly, 10-yma values of MLCO2 are compared against 10-yma estimates of the total carbon emissions (TCE) to determine the likelihood that manmade sources of carbon emissions are indeed responsible for the recent warming now being experienced. (Parametric values used in this TP are those available prior to the end of 2012.)

Wilson, Robert M.↗

NASA NTRS2017

Comparative Study of Three Reconnection X Line Models at the Earth's Dayside Magnetopause Using in Situ Observations

This work examines the large-scale aspects of magnetic field reconnection at the Earth's dayside magnetopause. We use two sets of reconnection events, which are identified mostly by the in situ detection of accelerated and Alfvenic plasma flows. We intercompare three analytical models that predict the reconnection X line location and orientation, namely, the Trattner et al. (2007) and Swisdak and Drake (2007) models and also a modified version of the component merging model. In the first set of reconnection observations, we show three fortuitous, quasi-simultaneous dayside magnetopause crossing events where two widely separated spacecraft detect reconnection signatures, and the X line location and orientation can be inferred from the observations. We compare X line model predictions to those inferred from observations. These three reconnection events indicate the presence of an extended (greater than 7 Earth radii in length), component-type reconnection X line on Earth's dayside magnetopause connecting and structuring the reconnection signatures at locations far apart. In the second set of reconnection events, we analyze the X line models' performance in predicting the observed reconnection outflow direction, i.e., its north-south and/or east-west senses, in a total of 75 single, rather than multiple and quasi-simultaneous, magnetopause crossing events, where reconnection-associated plasma flows were clearly present. We found that the Swisdak and Drake's (2007) X line model performs slightly better, albeit not statistically significant, when predicting both accelerated plasma flow north-south and east-west components in 73% and 53% of the cases, respectively, as compared to the Trattner et al. (2007) model (70% north-south and 42% east-west) and the modified component merging model (66% north-south and 50% east-west).

Souza, V. M.↗

NASA NTRS2002

Use of Satellite Data Assimilation to Infer Land Surface Thermal Inertia

There are two important but observationally uncertain parameters in the grid averaged surface energy budgets of mesoscale models - surface moisture availability and thermal heat capacity. A technique has been successfully developed for assimilating Geostationary Operational Environmental Satellite (GOES) skin temperature tendencies during the mid-morning time frame to improve specification of surface moisture. In a new application of the technique, the use of satellite skin temperature tendencies in early evening is explored to improve specification of the surface thermal heat capacity. Together, these two satellite assimilation constraints have been shown to significantly improve the characterization of the surface energy budget of a mesoscale model on fine spatial scales. The GOES assimilation without the adjusted heat capacity was run operationally during the International H2O Project on a 12-km grid. This paper presents the results obtained when using both the moisture availability and heat capacity retrievals in concert. Preliminary results indicate that retrieved moisture availability alone improved the verification statistics of 2-meter temperature and dew point forecasts. Results from the 1.5 month long study period using the bulk heat capacity will be presented at the meeting.

Lapenta, William↗

NASA NTRS1982

A statistical examination of Nimbus 7 SMMR data and remote sensing of sea surface temperature, liquid water content in the atmosphere and surfaces wind speed

Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) brightness temperature measurements over the global oceans have been examined with the help of statistical and empirical techniques. Such analyses show that zonal averages of brightness temperature measured by SMMR, over the oceans, on a large scale are primarily influenced by the water vapor in the atmosphere. Liquid water in the clouds and rain, which has a much smaller spatial and temporal scale, contributes substantially to the variability of the SMMR measurements within the latitudinal zones. The surface wind not only increases the surface emissivity but through its interactions with the atmosphere produces correlations, in the SMMR brightness temperature data, that have significant meteorological implications. It is found that a simple meteorological model can explain the general characteristics of the SMMR data. With the help of this model methods to infer over the global oceans, the surface temperature, liquid water content in the atmosphere, and surface wind speed are developed. Monthly mean estimates of the sea surface temperature and surface winds are compared with the ship measurements. Estimates of liquid water content in the atmosphere are consistent with earlier satellite measurements.

Prabhakara, C.↗