Search NASA⌕ Search

SEARCH · Search NASA

Results for “Bias”

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.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

The Barberplaid illusion: Even Perceived Plaid Motion is Biased by Asymmetric Apertures

Our results show that the perceived direction of motion of plaids windowed by asymmetric spatial Gaussians is biased toward the long axis of the window. The bias increases as the relative angle between the plaid motion and the window increases, peaks at a relative angle of about 40 degrees, and then decreases. The peak bias was 14 degrees for a spatial frequency of 0.6 cpd and a window aspect ratio of 4.0. The biases increase as the window is elongated and decrease as the component spatial frequency increases. We tested the predictions of several models of human motion processing (cross correlation, motion energy, intersection of constraints, and vector sum), and show that none of these can predict our data. These results suggest that spatial integration of motion signals plays a crucial role in the perception of plaid motion.

Beutter, Brent↗
NASA NTRS1996

Decision Making In A High-Tech World: Automation Bias and Countermeasures

Automated decision aids and decision support systems have become essential tools in many high-tech environments. In aviation, for example, flight management systems computers not only fly the aircraft, but also calculate fuel efficient paths, detect and diagnose system malfunctions and abnormalities, and recommend or carry out decisions. Air Traffic Controllers will soon be utilizing decision support tools to help them predict and detect potential conflicts and to generate clearances. Other fields as disparate as nuclear power plants and medical diagnostics are similarly becoming more and more automated. Ideally, the combination of human decision maker and automated decision aid should result in a high-performing team, maximizing the advantages of additional cognitive and observational power in the decision-making process. In reality, however, the presence of these aids often short-circuits the way that even very experienced decision makers have traditionally handled tasks and made decisions, and introduces opportunities for new decision heuristics and biases. Results of recent research investigating the use of automated aids have indicated the presence of automation bias, that is, errors made when decision makers rely on automated cues as a heuristic replacement for vigilant information seeking and processing. Automation commission errors, i.e., errors made when decision makers inappropriately follow an automated directive, or automation omission errors, i.e., errors made when humans fail to take action or notice a problem because an automated aid fails to inform them, can result from this tendency. Evidence of the tendency to make automation-related omission and commission errors has been found in pilot self reports, in studies using pilots in flight simulations, and in non-flight decision making contexts with student samples. Considerable research has found that increasing social accountability can successfully ameliorate a broad array of cognitive biases and resultant errors. To what extent these effects generalize to performance situations is not yet empirically established. The two studies to be presented represent concurrent efforts, with student and professional pilot samples, to determine the effects of accountability pressures on automation bias and on the verification of the accurate functioning of automated aids. Students (Experiment 1) and commercial pilots (Experiment 2) performed simulated flight tasks using automated aids. In both studies, participants who perceived themselves as accountable for their strategies of interaction with the automation were significantly more likely to verify its correctness, and committed significantly fewer automation-related errors than those who did not report this perception.

Mosier, Kathleen L.↗
NASA NTRS2007

Bias Selectable Dual Band AlGaN Ultra-violet Detectors

Bias selectable dual band AlGaN ultra-violet (UV) detectors, which can separate UV-A and UV-B using one detector in the same pixel by bias switching, have been designed, fabricated and characterized. A two-terminal n-p-n photo-transistor-like structure was used. When a forward bias is applied between the top electrode and the bottom electrode, the detectors can successfully detect W-A and reject UV-B. Under reverse bias, they can detect UV-B and reject UV-A. The proof of concept design shows that it is feasible to fabricate high performance dual-band UV detectors based on the current AlGaN material growth and fabrication technologies.

Yan, Feng↗
NASA NTRS1999

Method and apparatus for sensing a desired component of an incident magnetic field using magneto resistive elements biased in different directions

A method and apparatus for sensing a desired component of a magnetic field using an isotropic magnetoresistive material. This is preferably accomplished by providing a bias field that is parallel to the desired component of the applied magnetic field. The bias field is applied in a first direction relative to a first set of magnetoresistive sensor elements, and in an opposite direction relative to a second set of magnetoresistive sensor elements. In this configuration, the desired component of the incident magnetic field adds to the bias field incident on the first set of magnetoresistive sensor elements, and subtracts from the bias field incident on the second set of magnetoresistive sensor elements. The magnetic field sensor may then sense the desired component of the incident magnetic field by simply sensing the difference in resistance of the first set of magnetoresistive sensor elements and the second set of magnetoresistive sensor elements.

Pant, Bharat B.↗
NASA NTRS2004

Self regulating body bias generator

The back bias voltage on a functional circuit is controlled through a closed loop process. A delay element receives a clock pulse and produces a delay output. The delay element is advantageously constructed of the same materials as the functional circuit so that the aging and degradation of the delay element parallels the degradation of the functional circuit. As the delay element degrades, the transistor switching time increases, increasing the time delay of the delay output. An AND gate compares a clock pulse to an output pulse of the delay element, the AND output forming a control pulse. A duty cycle of the control pulse is determined by the delay time between the clock pulse and the delay element output. The control pulse is received at the input of a charge pump. The charge pump produces a back bias voltage which is then applied to the delay element and to the functional circuit. If the time delay produced by the delay element exceeds the optimal delay, the duty cycle of the control pulse is shortened, and the back bias voltage is lowered, thereby increasing the switching speed of the transistors in the delay element and reducing the time delay. If the throughput of the delay element is too fast, the duty cycle of the control pulse is lengthened, raising the back bias voltage produced by the charge pump. This, in turn, lowers the switching speed of the transistors in both the delay element and the functional circuit. The slower switching speed in the delay element increases time delay. In this manner, the switching speed of the delay element, and of the functional circuit, is maintained at a constant level over the life of the circuit.

Hass, Kenneth↗
NASA NTRS2008

Hovering Dual-Spin Vehicle Groundwork for Bias Momentum Sizing Validation Experiment

Angular bias momentum offers significant stability augmentation for hovering flight vehicles. The reliance of the vehicle on thrust vectoring for agility and disturbance rejection is greatly reduced with significant levels of stored angular momentum in the system. A methodical procedure for bias momentum sizing has been developed in previous studies. This current study provides groundwork for experimental validation of that method using an experimental vehicle called the Dual-Spin Test Device, a thrust-levitated platform. Using measured data the vehicle's thrust vectoring units are modeled and a gust environment is designed and characterized. Control design is discussed. Preliminary experimental results of the vehicle constrained to three rotational degrees of freedom are compared to simulation for a case containing no bias momentum to validate the simulation. A simulation of a bias momentum dominant case is presented.

Rothhaar, Paul M.↗
NASA NTRS2012

Systematic Biases in Parameter Estimation of Binary Black-Hole Mergers

Parameter estimation of binary-black-hole merger events in gravitational-wave data relies on matched filtering techniques, which, in turn, depend on accurate model waveforms. Here we characterize the systematic biases introduced in measuring astrophysical parameters of binary black holes by applying the currently most accurate effective-one-body templates to simulated data containing non-spinning numerical-relativity waveforms. For advanced ground-based detectors, we find that the systematic biases are well within the statistical error for realistic signal-to-noise ratios (SNR). These biases grow to be comparable to the statistical errors at high signal-to-noise ratios for ground-based instruments (SNR approximately 50) but never dominate the error budget. At the much larger signal-to-noise ratios expected for space-based detectors, these biases will become large compared to the statistical errors but are small enough (at most a few percent in the black-hole masses) that we expect they should not affect broad astrophysical conclusions that may be drawn from the data.

Littenberg, Tyson B.↗
NASA NTRS2012

Biases in Long-term NO2 Averages Inferred from Satellite Observations Due to Cloud Selection Criteria

Retrievals of atmospheric trace gas column densities from space are compromised by the presence of clouds, requiring most studies to exclude observations with significant cloud fractions in the instrument's field of view. Using NO2 observations at three ground stations representing urban, suburban, and rural environments, and tropospheric vertical column densities measured by the Ozone Monitoring Instrument (OMI) over each site, we show that the observations from space represent monthly averaged ground-level pollutant conditions well (R=0.86) under relatively cloud-free conditions. However, by analyzing the ground-level data and applying the OMI cloud fraction as a filter, we show there is a significant bias in long-term averaged NO2 as a result of removing the data during cloudy conditions. For the ground-based sites considered in this study, excluding observations on days when OMI-derived cloud fractions were greater than 0.2 causes 12:00-14:00 mean summer mixing ratios to be underestimated by 12%+/-6%, 20%+/-7%, and 40%+/-10% on average (+/-1 standard deviation) at the urban, suburban, and rural sites respectively. This bias was investigated in particular at the rural site, a region where pollutant transport is the main source of NO2, and where longterm observations of NOy were also available. Evidence of changing photochemical conditions and a correlation between clear skies and the transport of cleaner air masses play key roles in explaining the bias. The magnitude of a bias is expected to vary from site to site depending on meteorology and proximity to NOx sources, and decreases when longer averaging times of ground station data (e.g. 24-h) are used for the comparison.

SEASONAL-VARIATIONS↗
NASA NTRS2014

Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of SST gradients, associated PBL baroclinicity enabled by turbulent mixing, the ensuing PBL moisture convergence, and how changes in these processes relate to convective precipitation bias growth over this short period.

Cohen, Charlie↗
NASA NTRS2010

A Flight-Calibrated Methodology for Determination of Cassini Thruster On-Times for Reaction Wheel Biases

This paper describes a methodology for accurate and flight-calibrated determination of the on-times of the Cassini spacecraft Reaction Control System (RCS) thrusters, without any form of dynamic simulation, for the reaction wheel biases. The hydrazine usage and the delta V vector in body frame are also computed from the respective thruster on-times. The Cassini spacecraft, the largest and most complex interplanetary spacecraft ever built, continues to undertake ambitious and unique scientific observations of planet Saturn, Titan, Enceladus, and other moons of Saturn. In order to maintain a stable attitude during the course of its mission, this three-axis stabilized spacecraft uses two different control systems: the RCS and the reaction wheel assembly control system. The RCS is used to execute a commanded spacecraft slew, to maintain three-axis attitude control, control spacecraft's attitude while performing science observations with coarse pointing requirements, e.g. during targeted low-altitude Titan and Enceladus flybys, bias the momentum of reaction wheels, and to perform RCS-based orbit trim maneuvers. The use of RCS often imparts undesired delta V on the spacecraft. The Cassini navigation team requires accurate predictions of the delta V in spacecraft coordinates and inertial frame resulting from slews using RCS thrusters and more importantly from reaction wheel bias events. It is crucial for the Cassini spacecraft attitude control and navigation teams to be able to, quickly but accurately, predict the hydrazine usage and delta V for various reaction wheel bias events without actually having to spend time and resources simulating the event in flight software-based dynamic simulation or hardware-in-the-loop simulation environments. The methodology described in this paper, and the ground software developed thereof, are designed to provide just that. This methodology assumes a priori knowledge of thrust magnitudes and thruster pulse rise and tail-off time constants for eight individual attitude control thrusters, the spacecraft's wet mass and its center of mass location, and a few other key parameters.

Earth Mean Equatorial↗
NASA NTRS2015

RACORO Continental Boundary Layer Cloud Investigations: 3. Separation of Parameterization Biases in Single-Column Model CAM5 Simulations of Shallow Cumulus

Climatically important low-level clouds are commonly misrepresented in climate models. The FAst-physics System TEstbed and Research (FASTER) Project has constructed case studies from the Atmospheric Radiation Measurement Climate Research Facility's Southern Great Plain site during the RACORO aircraft campaign to facilitate research on model representation of boundary-layer clouds. This paper focuses on using the single-column Community Atmosphere Model version 5 (SCAM5) simulations of a multi-day continental shallow cumulus case to identify specific parameterization causes of low-cloud biases. Consistent model biases among the simulations driven by a set of alternative forcings suggest that uncertainty in the forcing plays only a relatively minor role. In-depth analysis reveals that the model's shallow cumulus convection scheme tends to significantly under-produce clouds during the times when shallow cumuli exist in the observations, while the deep convective and stratiform cloud schemes significantly over-produce low-level clouds throughout the day. The links between model biases and the underlying assumptions of the shallow cumulus scheme are further diagnosed with the aid of large-eddy simulations and aircraft measurements, and by suppressing the triggering of the deep convection scheme. It is found that the weak boundary layer turbulence simulated is directly responsible for the weak cumulus activity and the simulated boundary layer stratiform clouds. Increased vertical and temporal resolutions are shown to lead to stronger boundary layer turbulence and reduction of low-cloud biases.

parameterization↗
NASA NTRS2020

Impact of Bias in the Marine Air Temperature Observation Set on Atmospheric Reanalyses

Marine air temperature observations are collected by volunteer observing ships carrying automatic meteorological sensor packages. Close proximity of the package mount to a dark colored ship's deck is known to introduce a warm bias on sunny afternoons in summer under low wind conditions. This observation set is used as an influential constraint in the MERRA-2 reanalysis, as well as several other reanalyses, in the poorly sampled surface marine boundary layer. Here we explore the consequences of bias in the marine air temperature observation set during the 36 year period 1980-2015. Marine air temperature observations have mainly been collected along major shipping routes, with the densest coverage in the North Atlantic and North Pacific. In the first part of this presentation we review the historical observation coverage and compare air temperature and contemporaneous SST measurements to identify systematic differences. The comparison suggests the presence of diurnal bias in the air temperature observations peaking in subtropics and midlatitudes latitudes in mid-afternoon local time. In the second part we explore the impact of the marine air temperature observations on MERRA-2 through analysis of the terms in the temperature forecast tendency equation, the equation which describes the contributions to changes in temperature during each analysis cycle. Examination of the temperature forecast tendency equation allows us to separate systematic errors due to such processes and radiation, mixing, and advection and allows us to identify the major source of systematic forecast tendencies. The results show that the marine air temperature measurements introduce an error that can reach 0.4K/dy which also peaks in local afternoon at subtropical latitudes. This bias varies with season and has gradually reduced from the beginning of the MERRA-2 reanalysis in 1980. In the final part we conduct numerical experiments with a version of the MERRA-2 analysis system, comparing experiments when the marine air temperature observations are included or withheld from the analysis system.

Akella, Santharam↗
NASA NTRS2022

Seasonal Tidewater Glacier Terminus Oscillations Bias Multi-Decadal Projections of Ice Mass Change

Numerical, process-based simulations of tidewater glacier evolution are necessary to project future sea-level change under various climate scenarios. Previous work has shown that nonlinearities in tidewater glacier and ice stream dynamics can lead to biases in simulated ice mass change in the presence of noisy forcings. Ice sheet modeling projections that are used in the IPCC Assessment Report 6 (AR6) utilize atmospheric and oceanic forcings at annual temporal resolution, omitting any higher frequency forcings. Here, we quantify the effect of seasonal (<1 year) tidewater glacier terminus oscillations on decadal-scale (30 years) mass change. We use an idealized geometry to mimic realistic tidewater glacier geometries and investigate the impact of the magnitude of seasonal oscillations, bed slope at the glacier terminus, and basal friction law. We find that omitting seasonal terminus motion results in biased mass change projections, with up to an 39% underestimate or 25% overestimate of mass loss when seasonality is neglected, depending on bed slope and magnitude of oscillations. The bias is most sensitive to the magnitude of the seasonal terminus oscillations and exhibits very little sensitivity to choice of friction law. Although omitting seasonal terminus motion may bias mass change projections of individual glaciers, further work is needed to investigate the impact on ice sheet mass change projections. In order to achieve this, seasonality in atmospheric and oceanic forcings must be adequately represented and observations of seasonal terminus positions and tidewater glacier thickness changes must be acquired to evaluate numerical models. Note: Zip folder contains all videos listed in the supplemental document and can be downloaded for viewing.

D. Felikson↗
NASA NTRSDate not supplied

Does Collection Time Bias the Ecology of Cleanroom Air Samples?

Microbial monitoring of astromaterials collections has taken on increased importance with the return of biologically sensitive samples from the asteroids Ryugu and Bennu and the initiation of the Mars Sample Return Program. Terrestrial bacteria and fungi can alter the mineralogy and organic composition of our collections causing irreversible contamination of pristine samples and increasing the risk of false positives for life detection measurements. NASA has conducted routine microbial monitoring of its existing collections since 20181. Initial monitoring focused on surface samples collected with foam swabs. Although, airborne microbiology is often decoupled from surface microbiology in the built environment2 culture-based air sampling techniques like impactors were not compliant with existing contamination control requirements. Bringing organic rich media, gelatin or liquids into curation cleanrooms presents an unacceptable risk to pristine samples. In 2022 NASA purchased a materials complaint air sampler and began collecting air samples from the cleanrooms in addition to surface samples3. The new instrument uses an electret filter to collect samples that are suitable for cultivating organisms or for direct DNA sequencing. Preliminary DNA sequencing results appeared to indicate that longer sampling times biased the microbial community in favor of hearty, spore-forming bacteria3. We present the results of a study comparing overnight sampling (17 hours) to short (1 hour) sampling of unoccupied curation cleanrooms. The results will help us optimize our monitoring protocols and develop a more detailed inventory of the ecology of astromaterials curation cleanrooms. Methods: We analyzed 72 paired air samples from six different cleanrooms including the meteorite processing lab (ISO 7 equivalent, 16 samples), the lunar lab (ISO 6 equivalent, 10 samples), the stardust lab (ISO 5 equivalent 14 samples), the OSIRIS-REx lab (ISO 5 equivalent, 12 samples), the Hayabusa2 lab (ISO 5 equivalent, 14 samples), and the Genesis lab (ISO 4 equivalent, 6 samples). All the samples were collected with an InnovaPrep Bobcat air sampler operating at a sampling rate of 200 L/min. The sampler operates for 5 minutes out of every 20 minute period. Half of the samples were collected by filtering 3,000L (15 min. of active sampling) of air across an electret filter for one hour. The rest of the samples were collected by filtering approximately 51,000 L air across the filter overnight (~17 hours, 255 min. of active sampling). Cells were eluted from the filter using 6-7 ml of pressurized 0.15% tween 20 in PBS (phosphate buffered saline). This liquid was used to cultivate bacteria according to previously published methods1,4,5 and for DNA extraction and next generation sequencing. DNA was extracted with a Qiagen MagAttract PowerMicrobiome kit6. To identify bacteria and archaea, the 16S rRNA gene was amplified using Earth Microbiome primers for the V4 region 7. The amplified DNA was sequenced on an Illumina MiSeq using a V3 reagent kit. The resulting sequences were processed using DADA2 and QIIME2 as implemented on the EDGE bioinformatics platform8–10. Results: Only two of the 72 samples had no amplifiable DNA. Amplified DNA concentrations ranged from 2.67 – 0.272 ng/µl. The median concentration of amplified DNA for the 1 hour samples was 0.770 ± 0.368 ng/µl. The median concentration of amplified DNA for the overnight samples was 0.877 ± 0.434 ng/µl. On average the overnight samples had slightly more sequences (58,960 vs. 59,456) and ASV’s (amplicon sequence variants) (60 vs 64.5) than the one hour samples, but these differences are not statistically significant. The most abundant ASV in every sample mapped to the genus Cupravidus. ASV’s mapping to the genuses Bacillus, Schlegelella, Thermus, and Staphylococcus were also common. Discussion and Future Work: Alpha diversity statistics like Shannon Entropy and Faith Phylogenetic Diversity are used to describe the diversity of organisms in a single sample. If a longer sampling time was biasing the data, we would expect to see a change in these diversity statistics vs. sample time. However, we did not observe this in our data. The median Shannon entropy was slightly higher for the overnight samples (3.773 vs 3.611) as was the Faith Phylogenetic Diversity (4.042 vs 3.596), but both values were within a standard deviation of each other for the two sampling times (Fig. 1). It is unlikely, that the longer sampling time is introducing bias into our data. We do observe a significant decrease in diversity when comparing the air samples by lab. The Genesis lab (ISO 4 equivalent) has a lower median number of ASV’s (45.5) than the other labs (62). Median values for Shannon Entropy (3.717 vs. 3.430) and Faith Phylogenetic Diversity (3.796 vs. 3.548) are also lower for Genesis, but those values are with one standard deviation of each other for the different sampling times. This is consistent with previous culture-based results suggesting that the environment in cleanrooms tends to select for a core group of organisms capable of surviving under dry, low nutrient, conditions. The presence of the ASV’s mapping to Cupravidus and Thermus in our sequencing blanks and controls suggests that several of the most common organisms in our samples represent contaminants from the reagents used to perform the DNA extractions and sequencing. Further work is needed to identify these contaminants, remove them from our data and recalculate the diversity statistics. This is a systematic error. Therefore, we do not expect removing the sequencing contaminants to change our conclusions. Longer air sample collection times appear to result in slightly higher diversity and do not bias the results towards “hardy” bacteria like spore-formers. Based on these preliminary results we conclude that sampling at least 3,000 liters of air is sufficient to capture the microbial diversity of cleanrooms, and that air samples can also be collected overnight without negatively impacting diversity. These results allow us to be flexible when designing microbial monitoring plans so that they do not interfere with routine lab activity. References: 1. Regberg, A. B. et al. 49th Lunar and Planetary Science Conference (2018). 2. The United States Pharmacopeial Convention. USP General Chapter <1116> (2013). 3. Regberg, A. B., et al. 54th Lunar and Planetary Science Conference (2023). 4. Regberg, A. B. et al. 53rd Lunar and Planetary Science Conference ( 2022). 5. Davis, R. E.,et al. 50th Lunar and Planetary Science Conference (2019). 6. Qiagen. MagAttract® PowerMicrobiome® DNA/RNA EP Kit Handbook. (2018). 7. Walters, W. et al. mSystems 1, (2015). 8. Callahan, B. J. et al. Nat. Methods 13, 581–583 (2016). 9. Hall, M. & Beiko, R. G. Microbiome Analysis: Methods and Protocols113–129 (Springer, 2018). 10. Philipson, C. et al. Bio-Protoc. 7, e2622 (2017).

A. B. Regberg↗
NASA NTRS2005

Bias Reduction and Filter Convergence for Long Range Stereo

We are concerned here with improving long range stereo by filtering image sequences. Traditionally, measurement errors from stereo camera systems have been approximated as 3-D Gaussians, where the mean is derived by triangulation and the covariance by linearized error propagation. However, there are two problems that arise when filtering such 3-D measurements. First, stereo triangulation suffers from a range dependent statistical bias; when filtering this leads to over-estimating the true range. Second, filtering 3-D measurements derived via linearized error propagation leads to apparent filter divergence; the estimator is biased to under-estimate range. To address the first issue, we examine the statistical behavior of stereo triangulation and show how to remove the bias by series expansion. The solution to the second problem is to filter with image coordinates as measurements instead of triangulated 3-D coordinates.

robotics↗
NASA NTRS1993

Instrumental Biases in Ionospheric Measurement Derived from GPS Data

Line-of-sight ionosphere measurements derived from differencing dual frequency GPS range data are corrupted by instrumental biases in both receiver and GPS satellite transmitters due to hardware delays in the L1 and L2 signal paths. Using a global ionospheric shell model to fit GPS based ionospheric delay data from a world wide network of 30 to 40 receivers, we can estimate, with a single fit, satellite biases for the entire GPS constellation and receiver biases for all the uncalibrated receivers.

GPS↗
NASA NTRS2019

A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: an Application to Xco2 to Estimate Benchmarks and Assess Model Bias

There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

segmentation algorithm↗
NASA NTRS1972

Some effects of bias errors in redundant flight control systems.

The controllability and steady-state response of parallel-redundant flight control systems are examined. It is found that state components which appear in the parallel signal paths, e.g., individual actuator commands, are not controllable, although the sum of the command signals is well-behaved. If the response modes associated with these components are not stable, bias errors can cause the components to diverge, leading to the possibility of 'nuisance trips' in failure detection/isolation logic and eventual control system lockup (at saturation). Combining the inputs to the control computers assures that sensor bias will not cause divergence, while cross-strapping control strings bounds divergent response to all bias error inputs. Results of numerical solutions confirm the problem and its solutions.

Stengel, R. F.↗