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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Planning Bias: Planning as a Source of Sampling Bias

Many data-driven planning methods are trained on data generated by planners. It is well known that many statistical learning methods are sensitive to sampling bias, and yet there has been little or no attention to planning as a sampling method and its role in introducing sampling bias into planner-generated training data. Recently, it has been demonstrated that A**,* in the presence of problems with variable heuristic error, prefers some solutions over other equally cost-optimal solutions. But, as we discuss in this paper, mitigation may not be as simple as resolving arbitrary tie-breaking by sampling from ties uniformly at random. In this paper, we formalize an intuition of planning bias. We focus on problems which output a single solution. Diverse planning only complicates the problem by generalizing it to bias in the set of sets; we show how it is subject to bias in the single solution. We make some useful observations about deterministic algorithms in contrast to non-deterministic algorithms. We explain how information entropy may be a good way to measure planning bias, and discuss some issues in evaluating practical approaches to measurement. We address the intuition that uniform random tiebreaking should mitigate bias; and sketch a novel approach to constructing an appropriate random distribution for duplicate detection during forward search for unbiased A*. Finally, we suggest directions for future work.

Planning Scheduling Algorithms↗
NASA NTRS1974

Fore and aft elastic response characteristics of 34 x 9.9, type 7, 14 ply-rating aircraft tires of bias-ply, bias-belted, and radial-belted design

An investigation was conducted to determine the fore-and-aft elastic response characteristics of 34 x 9.9, type VII, 14 ply-rating aircraft tires of bias-ply, bias-belted, and radial-belted design. The investigation consisted of static and rolling tests on dry concrete pavements at the Langley aircraft landing loads and traction facility; statistical techniques which related the measured tire elastic characteristics to variations in the vertical load, inflation pressure, braking force and/or tire vertical deflection; and a semiempirical analysis which related the tire elastic behavior to measured wheel slippage during steady-state braking. The bias-belted tire developed the largest spring constant value for most loading conditions; the radial-belted tire, the smallest. The elastic response of the tire free periphery to static braking included both tread stretch and carcass torsional wind-up about the axle for the bias-ply and bias-belted tires and carcass wind-up alone for the radial-belted tire.

Tanner, J. A.↗
NASA NTRS2011

Study of the Dependence on Magnetic Field and Bias Voltage of an AC-Biased TES Microcalorimeter

At SRON we are studying the performance of a Goddard Space Flight Center single pixel TES microcalorimeter operated in the AC bias configuration. For x-ray photons at 6keV the AC biased pixel shows a best energy resolution of 3.7eV, which is about a factor of 2 worse than the energy resolution observed in identical DC-biased pixels. To better understand the reasons of this discrepancy, we investigated the detector performance as a function of temperature, bias working point and applied magnetic field. A strong periodic dependence of the detector noise on the TES AC bias voltage is measured. We discuss the results in the framework of the recent weak-link behaviour observed inTES microcalorimeters.

Bandler, Simon↗
NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 2: Estimation of the urban bias

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data -- the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data -- to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km x 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1 deg x 1 deg latitude-longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative (COOP) Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network (GHCN). Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40 C for monthly minimum temperatures, near 0.25 C for monthly mean temperatures, and near 0.10 C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near -1.1 C for rural stations to 2.4 C for stations from the largest urban areas. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available.

Epperson, David L.↗
NASA NTRS2012

Study of the Dependency on Magnetic Field and Bias Voltage of an AC-Biased TES Microcalorimeter

At SRON we are studying the performance of a Goddard Space Flight Center single pixel TES microcalorimeter operated in an AC bias configuration. For x-ray photons at 6 keV the pixel shows an x-ray energy resolution Delta E(sub FWHM) = 3.7 eV, which is about a factor 2 worse than the energy resolution observed in an identical DC-biased pixel. In order to better understand the reasons for this discrepancy we characterized the detector as a function of temperature, bias working point and applied perpendicular magnetic field. A strong periodic dependency of the detector noise on the TES AC bias voltage is measured. We discuss the results in the framework of the recently observed weak-link behaviour of a TES microcalorimeter.

Gottardi, L.↗
NASA NTRS1973

Fore and aft elastic response characteristics of size 34x9.9, type 7, 14 ply-rated aircraft tires of bias ply, bias belted, and radial belted design

An investigation was conducted to determine the fore-and-aft elastic response characteristics of aircraft tires of bias ply, bias-belted, and radial-belted design. The investigation consisted of: (1)static and rolling tests, (2)a statistical analysis which related the measured tire elastic characteristics to variations in the vertical load, inflation pressure, braking force and/or tire vertical deflection, and (3) a semi-empirical analysis which related the tire elastic behavior to measured wheel slippage during a steady-state braking. The results of this investigation indicate that the bias-belted tire has the largest spring constant value for most loading conditions and the radial-belted tire has the smallest spring constant value.

Tanner, J. A.↗
NASA NTRS2013

Implications of CO Bias for Ozone and Methane Lifetime in a CCM

A low bias in carbon monoxide compared to observations at high latitudes is a common feature of chemistry climate models. CO bias can both indicate and contribute to a bias in modeled OH and methane lifetime. This study examines possible causes of CO bias in the ACCMIP simulation of the GEOSCCM, and considers how attributing the CO bias to uncertainty in CO emissions versus biases in other constituents impacts the relationship between CO bias and methane lifetime. We use a simplified model of CO tagged by source with specified OH to quantify the sensitivity of the CO bias to changes in CO emissions or OH concentration, comparing the modeled CO to surface and MOPITT observations. The simplified model shows that decreasing OH in the northern hemisphere removes most of the global mean and inter-hemispheric bias in surface CO. We then use results from this analysis to explore how adjusting CO sources in the CCM impacts the concentrations of ozone, OH and methane. The CCM simulation also exhibits biases in ozone and water vapor compared to observations. We use a parameterized CO-OH-CH4 model that takes ozone and water vapor as inputs to the parameterization to examine whether correcting water and ozone biases can alter OH enough to remove the CO bias. Through this analysis, we aim to better quantify the relationship between CO bias and model biases in ozone concentrations and methane lifetime.

carbon monoxide↗
NASA NTRS2020

Examining Biases in Diurnally-Integrated Shortwave Irradiances due to Two- and Four-Stream Approximations in Cloudy Atmosphere

Shortwave irradiance biases due to two- and four-stream approximations have been studied for the last couple of decades, but biases in estimating Earth’s radiation budget have not been examined in earlier studies. In order to quantify biases in diurnally-averaged irradiances, we integrate the two- and four-stream biases using realistic diurnal variations of cloud properties from Clouds and the Earth’s Radiant Energy System (CERES) synoptic (SYN) hourly product. Three approximations are examined in this study, delta-two-stream-Eddington (D2strEdd), delta- two-stream-quadrature (D2strQuad), and delta-four-stream-quadrature (D4strQuad). Irradiances computed by the Discrete Ordinates Radiative Transfer (DISORT) and Monte Carlo (MC) methods are used as references. The MC noises are further examined by comparing with DISORT results. When the biases are integrated with a one-day of solar zenith angle variation, regional biases of D2strEdd and D2strQuad reach up to 8 W/sq.m, while biases of D4strQuad reach up to 2 W/sq.m. When the biases are further averaged monthly or annually, regional biases of D2strEdd and D2strQuad can reach –1.5 W/sq.m in SW top-of-atmosphere (TOA) upward irradiances and +3 W/sq.m in surface downward irradiances. In contrast, regional biases of D4strQuad are within +0.9 for TOA irradiances and –1.2 W/sq.m for surface irradiances. Except for polar regions, monthly and annual global mean biases are similar, suggesting that the biases are nearly independent to season. Biases in SW heating rate profiles are up to –0.008 K/d for D2strEdd and –0.016 K/d for D2strQuad, while the biases of the D4strQuad method are negligible.

Ham, Seung-Hee↗
NASA NTRS2011

Effect of Reverse Bias Stress on Leakage Currents and Breakdown Voltages of Solid Tantalum Capacitors

The majority of solid tantalum capacitors are produced by high-temperature sintering of a fine tantalum powder around a tantalum wire followed by electrolytic anodization that forms a thin amorphous Ta2O5 dielectric layer and pyrolysis of manganese nitrite on the oxide to create a conductive manganese dioxide electrode. A contact to tantalum wire is used as anode terminal and to the manganese layer as a cathode terminal of the device. This process results in formation of an asymmetric Ta -- Ta2O5 -- MnO2 capacitor that has different characteristics at forward (positive bias applied to tantalum) and reverse (positive bias applied to manganese cathode) voltages. Reverse bias currents might be several orders of magnitude larger than forward leakage currents so I-V characteristics of tantalum capacitors resemble characteristics of semiconductor rectifiers. Asymmetric I-V characteristics of Ta -- anodic Ta2O5 systems have been observed at different top electrode materials including metals, electrolytes, conductive polymers, and manganese oxide thus indicating that this phenomenon is likely related to the specifics of the Ta -- Ta2O5 interface. There have been multiple attempts to explain rectifying characteristics of capacitors employing anodic tantalum pentoxide dielectrics. A brief review of works related to reverse bias (RB) behavior of tantalum capacitors shows that the mechanism of conduction in Ta -- Ta2O5 systems is still not clear and more testing and analysis is necessary to understand the processes involved. If tantalum capacitors behave just as rectifiers, then the assessment of the safe reverse bias operating conditions would be a relatively simple task. Unfortunately, these parts can degrade with time under reverse bias significantly, and this further complicates analysis of the I-V characteristics and establishing safe operating areas of the parts. On other hand, time dependence of reverse currents might provide additional information for investigation of the processes under reverse bias conditions. In practice, there were instances when, due to unforeseen events, the system operated at conditions when capacitors experience periodically a relatively small reverse bias for some time followed by normal, forward bias conditions. In such a case an assessment should be made on the degree to which these capacitors are degraded by application of low-voltage reverse bias, and whether this degradation can be reversed by normal operating conditions. In this study, reverse currents in different types of tantalum capacitors were monitored at different reverse voltages below 15%VR and temperatures in the range from room to 145 C for up to 150 hours to get better understanding of the degradation process and determine conditions favorable to the unstable mode of operation. The reversibility of RB degradation has been evaluated after operation of the capacitors at forward bias conditions. The effect of reverse bias stress (RBS) on reliability at normal operating conditions was evaluated using highly accelerated life testing at voltages of 1.5VR and 2 VR and by analysis of changes in distributions of breakdown voltages. Possible mechanisms of RB degradation are discussed.

Teverovsky, Alexander A.↗
NASA NTRSDate not supplied

Evaluating Retrieval Algorithm Climate Stability: Estimating 3D Optical Thickness Bias Distributions by Cloud Type

Detecting climate trends on large spatiotemporal scales requires accurate, stable measurements and stable retrieval algorithms. We strive to estimate how time-variant retrieval algorithm biases may impact trend detection. Here we focus on the 3D cloud optical thickness (τc) bias, which is among the largest in passive cloud retrieval algorithms. If this bias is time dependent, a possibility with potential decadal changes in cloud morphology, it may obscure genuine trends in τc. Although previous studies have evaluated the cloud- and sun-view geometry-dependent 3D τc bias on small spatial scales, before our current study none have evaluated the stability of this well-known bias on climate-relevant large spatiotemporal scales. These studies must estimate large scale distributions of the 3D τc bias by cloud type and estimate how cloud type amount may change between two climate states. We employ a novel approach to estimate large scale distributions of 3D τc using a proxy of the bias that quantifies the departure of clouds from satisfying the 1D radiative transfer assumption used in passive τc retrievals. This existing globally-distributed proxy is an angular consistency metric that was developed using fused Moderate-Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) measurements. Calculating the 3D τc bias and the proxy, for known cloud fields enables us to establish statistical relationships between these two quantities, which can be used to calculate large-scale distributions of the 3D τc bias. This approach limits the number of 3D radiative transfer simulations required to only those needed to estimate a statistical relationship between the 3D τc bias for known cloud fields and a proxy of the bias. It is likely that future studies will be needed to evaluate retrieval algorithm bias stability for other geophysical variables as the community develops climate data records from satellite observations and their retrievals. This must be done in addition to monitoring and correcting measurement errors and uncertainties and understanding their impact on retrieved essential climate variables.

Yolanda Shea↗
NASA NTRS1995

Measurements of electromagnetic bias at Ku and C bands

The electromagnetic (EM) bias epsilon is an error present in radar altimetry of the ocean surface due to nonuniform reflection with surface displacement. The electromagnetic bias is defined as the difference in height between the mean reflecting surface and the mean sea surface. A knowledge of the electromagnetic bias is required for reducing errors in mean sea level measurements by satellite radar altimeters. Direct measurements of the EM bias at 14 GHz (Ku band) and 5 GHz (C band) were made from an oil production platform in the Gulf of Mexico over a 6-month period during 1989 and 1990. A total of 1280 hours of usable data was collected. During the experiment the significant wave height (SWH) varied from 0.6 to 3.2 m; the wind speed at 25 m above the surface varied from 0.1 to 14.3 m/s; the Ku band bias varied from -1.0 to -13.8 cm, or from -1.6% to -5.3% of the SWH; and the C band bias varied from -0.4 to -19.9 cm, or from -0.6% to -6.3% of the SWH. The biases has mean values of -3.7% and -3.6% of SWH with standard deviations of the variability about the mean of 0.7% and 1.0% of the SWH for Ku and C bands, respectively. We found a nonlinear relationship between dimensionless bias (bias/SWH) and wind speed at both low and high wind speeds. For wind speeds less than 3-4 m/s but less than 10 m/s, both biases were found to increase linearly with wind speed. For wind speeds greater than 11-12 m/s, the C band bias reaches a maximum. The Ku band bias reaches a maximum and then begins to decrease for wind speeds greater than 9-10 m/s.

Arnold, D. V.↗
NASA NTRS2014

Estimating Sampling Biases and Measurement Uncertainties of AIRS-AMSU-A Temperature and Water Vapor Observations Using MERRA Reanalysis

We use MERRA (Modern Era Retrospective-Analysis for Research Applications) temperature and water vapor data to estimate the sampling biases of climatologies derived from the AIRS/AMSU-A (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A) suite of instruments. We separate the total sampling bias into temporal and instrumental components. The temporal component is caused by the AIRS/AMSU-A orbit and swath that are not able to sample all of time and space. The instrumental component is caused by scenes that prevent successful retrievals. The temporal sampling biases are generally smaller than the instrumental sampling biases except in regions with large diurnal variations, such as the boundary layer, where the temporal sampling biases of temperature can be +/- 2 K and water vapor can be 10% wet. The instrumental sampling biases are the main contributor to the total sampling biases and are mainly caused by clouds. They are up to 2 K cold and greater than 30% dry over mid-latitude storm tracks and tropical deep convective cloudy regions and up to 20% wet over stratus regions. However, other factors such as surface emissivity and temperature can also influence the instrumental sampling bias over deserts where the biases can be up to 1 K cold and 10% wet. Some instrumental sampling biases can vary seasonally and/or diurnally. We also estimate the combined measurement uncertainties of temperature and water vapor from AIRS/AMSU-A and MERRA by comparing similarly sampled climatologies from both data sets. The measurement differences are often larger than the sampling biases and have longitudinal variations.

MERRA↗
NASA NTRS2013

Simultaneous Estimation of Model State Variables and Observation and Forecast Biases Using a Two-Stage Hybrid Kalman Filter

In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Data Assimilation↗
NASA NTRS2021

Seasonal Bias in Global Ocean Color Observations

In this study we identify a seasonal bias in the ocean color satellite derived remote sensing reflectances (Rrs(λ); sr-1) at the ocean color validation site, MOBY (Marine Optical BuoY). The seasonal bias in Rrs(λ) is present to varying degrees in all ocean color satellites examined, including VIIRS (Visible Infrared Imaging Radiometer Suite), SeaWiFS (Sea-viewing Wide Field-of-view Sensor), and MODIS (Moderate Resolution Imaging Spectrometer). The relative bias in Rrs has spectral dependence. Products derived from Rrs(λ) are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The propagation of Rrs(λ) bias into derived products is broadly confirmed on regional and global scales using Argo floats and data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder) satellite. The artifactual seasonality in ocean color is prominent in areas of low biomass (i.e., subtropical gyres) and is not easily discerned in areas of high biomass. While we have eliminated several candidates that could cause the biases in Rrs(λ), there are still outstanding questions regarding potential contributions from atmospheric corrections. Specifically, we provide evidence that the aquatic bidirectional reflectance distribution function may in part cause the observed seasonal bias, but this does not preclude an additional effect of the aerosol estimation. Our investigation highlights the contributions that atmospheric correction schemes can make in introducing biases in Rrs (λ) and we recommend more simulations to discern these influence Rrs (λ) biases. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data are or will be affected until a solution is implemented.

satellite ocean color↗
NASA NTRSDate not supplied

Bias in Planning Algorithms

Does bias exist in planning algorithms? If so, how does bias manifest, and how important is this bias? Answering this question requires a formal, mathematical definition of bias. We formally define bias as the distance between the probability distributions of solutions returned by various algorithms, and the uniform distribution over solutions. We show in this paper that deterministic algorithms are inherently biased, as they don’t return all solutions, and that this property holds even when algorithms return a set of plans instead of just one plan. Exceptions are problem instances or problem classes for which only a single solution exists. We then discuss changing the definition of bias to compare the probability distributions of properties of sets of plans instead of individual plans. We show the property bias is smaller than the bias of actual plans. Finally, we show that entropy is a proxy for the more complex and more expensive distance measurement between pairs of probability distributions. We then describe a roadmap for future investigations of bias in planning.

Planning Scheduling Algorithms↗
NASA NTRS2024

Quantifying Volumetric Scattering Bias in ICESat-2 and Operation IceBridge Altimetry Over Greenland Firn and Aged Snow

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has collected surface elevation measurements for over 5 years. ICESat-2 carries an instrument that emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat-2 acquisitions during the melting season. We performed two case studies over the Greenland Ice Sheet to quantify bias in ICESat-2 signals over snow: one to validate Airborne Topographic Mapper (ATM) data against Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) grain sizes, and a second to estimate ICESat-2 bias relative to ATM. We used snow optical grain sizes derived from ATM and AVIRIS-NG to attribute altimetry bias to snowpack properties. For the first case study, the mean and standard deviation of optical grain sizes were 340 ± 65 µm (AVIRIS-NG) and 670 ± 420 µm (ATM). A mean altimetry bias of 4.81 ± 1.76 cm was found for ATM, with larger biases linked to increases in grain size. In the second case study, we found a mean grain size of 910 ± 381 µm and biases of 6.42 ± 1.77 cm (ICESat-2) and 9.82 ± 0.97 cm (ATM). The grain sizes and densities needed to recreate biases with a model are uncommon in nature, so we propose that additional surface attributes must be considered to characterize ICESat-2 bias over snow. The altimetry biases are within the accuracy requirements of the ICESat-2 mission, but we cannot rule out more significant errors over coarse-grained snow.

Zachary Fair↗
NASA NTRS2012

Cognitive Bias in the Verification and Validation of Space Flight Systems

Cognitive bias is generally recognized as playing a significant role in virtually all domains of human decision making. Insight into this role is informally built into many of the system engineering practices employed in the aerospace industry. The review process, for example, typically has features that help to counteract the effect of bias. This paper presents a discussion of how commonly recognized biases may affect the verification and validation process. Verifying and validating a system is arguably more challenging than development, both technically and cognitively. Whereas there may be a relatively limited number of options available for the design of a particular aspect of a system, there is a virtually unlimited number of potential verification scenarios that may be explored. The probability of any particular scenario occurring in operations is typically very difficult to estimate, which increases reliance on judgment that may be affected by bias. Implementing a verification activity often presents technical challenges that, if they can be overcome at all, often result in a departure from actual flight conditions (e.g., 1-g testing, simulation, time compression, artificial fault injection) that may raise additional questions about the meaningfulness of the results, and create opportunities for the introduction of additional biases. In addition to mitigating the biases it can introduce directly, the verification and validation process must also overcome the cumulative effect of biases introduced during all previous stages of development. A variety of cognitive biases will be described, with research results for illustration. A handful of case studies will be presented that show how cognitive bias may have affected the verification and validation process on recent JPL flight projects, identify areas of strength and weakness, and identify potential changes or additions to commonly used techniques that could provide a more robust verification and validation of future systems.

Larson, Steve↗