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NASA NTRS1998

Human motion perception and smooth eye movements show similar directional biases for elongated apertures

Although numerous studies have examined the relationship between smooth-pursuit eye movements and motion perception, it remains unresolved whether a common motion-processing system subserves both perception and pursuit. To address this question, we simultaneously recorded perceptual direction judgments and the concomitant smooth eye-movement response to a plaid stimulus that we have previously shown generates systematic perceptual errors. We measured the perceptual direction biases psychophysically and the smooth eye-movement direction biases using two methods (standard averaging and oculometric analysis). We found that the perceptual and oculomotor biases were nearly identical, suggesting that pursuit and perception share a critical motion processing stage, perhaps in area MT or MST of extrastriate visual cortex.

NASA Center ARC↗

NASA NTRS1996

The barberplaid illusion: plaid motion is biased by elongated apertures

The perceived direction of motion of plaids windowed by elongated spatial Gaussians is biased toward the window's long axis. The bias increases as the relative angle between the plaid motion and the long axis of the window increases, peaks at a relative angle of approximately 45 deg, and then decreases. The bias increases as the window is made narrower (at fixed height) and decreases as the component spatial frequency increases (at fixed aperture size). We examine several models of human motion processing (cross-correlation, motion-energy, intersection-of-constraints, and vector-sum), and show that none of these standard models can predict our data. We conclude that spatial integration of motion signals plays a crucial role in plaid motion perception and that current models must be explicitly expanded to include such spatial interactions.

NASA Center ARC↗

NASA NTRS2003

Precise Heater Controller with rf-Biased Josephson Junctions

Paramagnetic susceptibility thermometers used in fundamental physics experiments are capable of measuring temperature changes with a precision of a part in 2 x 10(exp 10). However, heater controllers are only able to control open-loop power dissipation to about a part in 10(exp 5). We used an array of rf-biased Josephson junctions to precisely control the electrical power dissipation in a heater resistor mounted on a thermally isolated cryogenic platform. Theoretically, this method is capable of controlling the electrical power dissipation to better than a part in 10(exp 12). However, this level has not yet been demonstrated experimentally. The experiment consists of a liquid helium cell that also functions as a high-resolution PdMn thermometer, with a heater resistor mounted on it. The cell is thermally connected to a temperature-controlled cooling stage via a weak thermal link. The heater resistor is electrically connected to the array of Josephson junctions using superconducting wire. An rf-biased array of capacitively shunted Josephson junctions drives the voltage across the heater. The quantized voltage across the resistor is Vn = nf(h/2e), where h is Planck's constant, f is the array biasing frequency, e is the charge of an electron, and n is the integer quantum state of the Josephson array. This results in an electrical power dissipation on the cell of Pn = (Vn)(sup 2/R), where R is the heater resistance. The change of the quantum state of the array changes the power dissipated in the heater, which in turn, results in the change of the cell temperature. This temperature change is compared to the expected values based on the known thermal standoff resistance of the cell from the cooling stage. We will present our initial experimental results and discuss future improvements. This work has been funded by the Fundamental Physics Discipline of the Microgravity Science Office of NASA, and supported by a no-cost equipment loan from Sandia National Laboratories.

Green, Colin J.↗

NASA NTRS2006

On the Biases in AIRS Retrieval of Ozone (Work in Progress)

A viewgraph presentation on various biases in AIRS retrieval of ozone is given. The topics include: 1) AIRS captures UTLS ozone events; 2) Simplified AIRS retrieval of ozone; 3) How much is AIRS getting its skill in ozone from regression? biases are similar to ECMWF; 4) How does channel selection and damping affect the retrieval?; 5) Decreasing damping worsens results in upper"trop/lower"strat with current channel selection; 6) If results worse with decreased damping, let s give the retrieval more information; 7) Adding channels at current damping doesn't change anything.; 8) Adding channels and decreasing damping gives mixed results; 9) Let's look at the radiances and their uncertainties ; 10) Systematic biases in radiance uncertainties?; 11) Surface Temperature; 12) Temperature Profile; and 13) x vs Brightness Temperature Error Optimal Estimation Retrieval.

Atmospheric Infrared Sounder (AIRS)↗

NASA NTRS2008

Optimization of Angular-Momentum Biases of Reaction Wheels

RBOT [RWA Bias Optimization Tool (wherein RWA signifies Reaction Wheel Assembly )] is a computer program designed for computing angular momentum biases for reaction wheels used for providing spacecraft pointing in various directions as required for scientific observations. RBOT is currently deployed to support the Cassini mission to prevent operation of reaction wheels at unsafely high speeds while minimizing time in undesirable low-speed range, where elasto-hydrodynamic lubrication films in bearings become ineffective, leading to premature bearing failure. The problem is formulated as a constrained optimization problem in which maximum wheel speed limit is a hard constraint and a cost functional that increases as speed decreases below a low-speed threshold. The optimization problem is solved using a parametric search routine known as the Nelder-Mead simplex algorithm. To increase computational efficiency for extended operation involving large quantity of data, the algorithm is designed to (1) use large time increments during intervals when spacecraft attitudes or rates of rotation are nearly stationary, (2) use sinusoidal-approximation sampling to model repeated long periods of Earth-point rolling maneuvers to reduce computational loads, and (3) utilize an efficient equation to obtain wheel-rate profiles as functions of initial wheel biases based on conservation of angular momentum (in an inertial frame) using pre-computed terms.

Lee, Clifford↗

NASA NTRS2009

Phase Retrieval with Signal Bias

The effect of a uniform measurement bias, due to background light, stray light, detector dark current, or detector offset, on phase retrieval wavefront sensing algorithms is analyzed. Simulation results indicate that the root-mean-square error of the retrieved phase can be more sensitive to an unaccounted-for signal bias than to random noise in practical scenarios. Three methods for reducing the impact of signal bias are presented

Thurman, Samuel T.↗

NASA NTRS2011

A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

Kumar, Sujay V.↗

NASA NTRS2010

Assessment of Biases in MODIS Surface Reflectance Due to Lambertian Approximation

Using MODIS data and the AERONET-based Surface Reflectance Validation Network (ASRVN), this work studies errors of MODIS atmospheric correction caused by the Lambertian approximation. On one hand, this approximation greatly simplifies the radiative transfer model, reduces the size of the look-up tables, and makes operational algorithm faster. On the other hand, uncompensated atmospheric scattering caused by Lambertian model systematically biases the results. For example, for a typical bowl-shaped bidirectional reflectance distribution function (BRDF), the derived reflectance is underestimated at high solar or view zenith angles, where BRDF is high, and is overestimated at low zenith angles where BRDF is low. The magnitude of biases grows with the amount of scattering in the atmosphere, i.e., at shorter wavelengths and at higher aerosol concentration. The slope of regression of Lambertian surface reflectance vs. ASRVN bidirectional reflectance factor (BRF) is about 0.85 in the red and 0.6 in the green bands. This error propagates into the MODIS BRDF/albedo algorithm, slightly reducing the magnitude of overall reflectance and anisotropy of BRDF. This results in a small negative bias of spectral surface albedo. An assessment for the GSFC (Greenbelt, USA) validation site shows the albedo reduction by 0.004 in the near infrared, 0.005 in the red, and 0.008 in the green MODIS bands.

Wang, Yujie↗

NASA NTRS2012

Fabrication of a Cryogenic Bias Filter for Ultrasensitive Focal Plane

A fabrication process has been developed for cryogenic in-line filtering for the bias and readout of ultrasensitive cryogenic bolometers for millimeter and submillimeter wavelengths. The design is a microstripline filter that cuts out, or strongly attenuates, frequencies (10 50 GHz) that can be carried by wiring staged at cryogenic temperatures. The filter must have 100-percent transmission at DC and low frequencies where the bias and readout lines will carry signal. The fabrication requires the encapsulation of superconducting wiring in a dielectric-metal envelope with precise electrical characteristics. Sufficiently thick insulation layers with high-conductivity metal layers fully surrounding a patterned superconducting wire in arrayable formats have been demonstrated. A degenerately doped silicon wafer has been chosen to provide a metallic ground plane. A metallic seed layer is patterned to enable attachment to the ground plane. Thick silicon dioxide films are deposited at low temperatures to provide tunable dielectric isolation without degrading the metallic seed layer. Superconducting wiring is deposited and patterned using microstripline filtering techniques to cut out the relevant frequencies. A low Tc superconductor is used so that it will attenuate power strongly above the gap frequency. Thick dielectric is deposited on top of the circuit, and then vias are patterned through both dielectric layers. A thick conductive film is deposited conformally over the entire circuit, except for the contact pads for the signal and bias attachments to complete the encapsulating ground plane. Filters are high-aspect- ratio rectangles, allowing close packing in one direction, while enabling the chip to feed through the wall of a copper enclosure. The chip is secured in the copper wall using a soft metal seal to make good thermal and electrical contact to the outer shield.

Chervenak, James↗

NASA NTRS2012

A Nonlinear Adaptive Filter for Gyro Thermal Bias Error Cancellation

Deterministic errors in angular rate gyros, such as thermal biases, can have a significant impact on spacecraft attitude knowledge. In particular, thermal biases are often the dominant error source in MEMS gyros after calibration. Filters, such as J\,fEKFs, are commonly used to mitigate the impact of gyro errors and gyro noise on spacecraft closed loop pointing accuracy, but often have difficulty in rapidly changing thermal environments and can be computationally expensive. In this report an existing nonlinear adaptive filter is used as the basis for a new nonlinear adaptive filter designed to estimate and cancel thermal bias effects. A description of the filter is presented along with an implementation suitable for discrete-time applications. A simulation analysis demonstrates the performance of the filter in the presence of noisy measurements and provides a comparison with existing techniques.

Galante, Joseph M.↗

NASA NTRS2012

Non-Uniform Bias Enhancement of a Varactor-Tuned FSS used with a Low Profile 2.4 GHz Dipole Antenna

In this paper a low profile antenna using a nonuniformly biased varactor-tuned frequency selective surface (FSS) is presented. The tunable FSS avoids the use of vias and has a simplified DC bias network. The voltages to the DC bias ports can be varied independently allowing adjustment in the frequency response and enhanced radiation properties. The measured data demonstrate tunability from 2.15 GHz to 2.63 GHz with peak efficiencies that range from 50% to 90% and instantaneous bandwidths of 50 MHz to 280 MHz within the tuning range. The total antenna thickness is approximately lambda/45.

Cure, David↗

NASA NTRS2011

A Critical Examination of Spatial Biases Between MODIS and MISR Aerosol Products - Application for Potential AERONET Deployment

AErosol RObotic NETwork (AERONET) data are the primary benchmark for evaluating satellite-retrieved aerosol properties. However, despite its extensive coverage, the representativeness of the AERONET data is rarely discussed. Indeed, many studies have shown that satellite retrieval biases have a significant degree of spatial correlation that may be problematic for higher-level processes or inverse-emissions-modeling studies. To consider these issues and evaluate relative performance in regions of few surface observations, cross-comparisons between the Aerosol Optical Depth (AOD) products of operational MODIS Collection 5.1 Dark Target (DT) and operational MODIS Collection 5.1 Deep Blue (DB) with MISR version 22 were conducted. Through such comparisons, we can observe coherent spatial features of the AOD bias while side-stepping the full analysis required for determining when or where either retrieval is more correct. We identify regions where MODIS to MISR AOD ratios were found to be above 1.4 and below 0.7. Regions where lower boundary condition uncertainty is likely to be a dominant factor include portions of Western North America, the Andes mountains, Saharan Africa, the Arabian Peninsula, and Central Asia. Similarly, microphysical biases may be an issue in South America, and specific parts of Southern Africa, India Asia, East Asia, and Indonesia. These results help identify high-priority locations for possible future deployments of both in situ and ground based remote sensing measurements. The Supplement includes a km1 file.

Shi, Y.↗

NASA NTRS2012

Evaluation of the Relative Contribution of Observing Systems in Reanalyses: Aircraft Temperature Bias and Analysis Innovations

Reanalyses have become important sources of data in weather and climate research. While observations are the most crucial component of the systems, few research projects consider carefully the multitudes of assimilated observations and their impact on the results. This is partly due to the diversity of observations and their individual complexity, but also due to the unfriendly nature of the data formats. Here, we discuss the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) and a companion dataset, the Gridded Innovations and Observations (GIO). GIO is simply a post-processing of the assimilated observations and their innovations (forecast error and analysis error) to a common spatio-temporal grid, following that of the MERRA analysis fields. This data includes in situ, retrieved and radiance observations that are assimilated and used in the reanalysis. While all these disparate observations and statistics are in a uniform easily accessible format, there are some limitations. Similar observations are binned to the grid, so that multiple observations are combined in the gridding process. The data is then implicitly thinned. Some details in the meta data may also be lost (e.g. aircraft or station ID). Nonetheless, the gridded observations should provide easy access to all the observations input to the reanalysis. To provide an example of the GIO data, a case study evaluating observing systems over the United States and statistics is presented, and demonstrates the evaluation of the observations and the data assimilation. The GIO data is used to collocate 200mb Radiosonde and Aircraft temperature measurements from 1979-2009. A known warm bias of the aircraft measurements is apparent compared to the radiosonde data. However, when larger quantities of aircraft data are available, they dominate the analysis and the radiosonde data become biased against the forecast. When AMSU radiances become available the radiosonde and aircraft analysis and forecast error take on an annual cycle. While this supports results of previous work that recommend bias corrections for the aircraft measurements, the interactions with AMSU radiances will also require further investigation. This also provides an example for reanalysis users in examining the available observations and their impact on the analysis. GIO data is presently available alongside the MERRA reanalysis.

Bosilovich, Michael G.↗

NASA NTRS2010

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

The Cassini spacecraft, the largest and most complex interplanetary spacecraft ever built, continues to undertake unique scientific observations of planet Saturn, Titan, Enceladus, and other moons of the ring world. In order to maintain a stable attitude during the course of its mission, this three-axis stabilized spacecraft uses two different control systems: the Reaction Control System (or RCS) and the Reaction Wheel Assembly (RWA) control system. In the course of its mission, Cassini performs numerous reaction wheel momentum biases (or unloads) using its reaction control thrusters. The use of the RCS thrusters often imparts undesired velocity changes (delta Vs) on the spacecraft and it is crucial for Cassini navigation and attitude control teams to be able to, quickly but accurately, predict the hydrazine usage and delta V vector in Earth Mean Equatorial (J2000) inertial coordinates for reaction wheel bias events, without actually having to spend time and resources simulating the event in a dynamic or hardware-in-the-loop simulation environments. The flight-calibrated methodology described in this paper, and the ground software developed thereof, are designed to provide the RCS thruster on-times, with acceptable accuracy and without any form of dynamic simulation, for reaction wheel biases, along with the hydrazine usage and the delta V in EME-2000 inertial frame.

Reaction Control System↗

NASA NTRS2017

The North Pacific Summer Jet and Climate Extremes over North America: Mechanisms and Model Biases

The North Pacific summer jet (NPSJ) plays a critical role as a waveguide for weather systems and other sub-seasonal Rossby waves entering North America and therefore has a controlling influence on the warm season weather and climate extremes over much of the continent. In particular, much of the warm season precipitation that occurs over the central United States depends on subseasonal transients that are able to tap moisture from the Gulf of Mexico as they propagate across the continent. The GEOS-5 atmospheric general circulation model (AGCM), like many AGCMs, is deficient in the simulation of the NPSJ. It is shown that the deficiency is composed of: 1) a stunted jet in which the strongest winds are confined to the Asian continent, failing to extend across the North Pacific into the Gulf of Alaska as observed, and 2) a zonally symmetric poleward shift in the jet. These biases combine to impede the eastward propagation of the weather systems into the continent (the stunted jet), and deprive those systems that do enter the continent access to the moisture from the Gulf (the northward shift), leading to a dry bias over the central US. It is shown that the stunted jet bias is the result of too strong heating that occurs just south of the jet core over and near Tibet. Furthermore, it is shown that the poleward shift of the NPSJ can be corrected in the current GEOS-5 AGCM by increasing the vertical resolution. The implications of these results for improving warm season forecasts of extreme events will be discussed.

NPS↗

NASA NTRS2018

On the Limitations of Variational Bias Correction

Satellite radiances are the largest dataset assimilated into Numerical Weather Prediction (NWP) models, however the data are subject to errors and uncertainties that need to be accounted for before assimilating into the NWP models. Variational bias correction uses the time series of observation minus background to estimate the observations bias. This technique does not distinguish between the background error, forward operator error, and observations error so that all these errors are summed up together and counted as observation error. We identify some sources of observations errors (e.g., antenna emissivity, non-linearity in the calibration, and antenna pattern) and show the limitations of variational bias corrections on estimating these errors.

Moradi, Isaac↗

NASA NTRS2018

Downscaling Atmosphere-Ocean Global Climate Model Precipitation Simulations over Africa Using Bias-Corrected Lateral and Lower Boundary Conditions

A prequel study showed that dynamic downscaling using a regional climate model (RCM) over Africa improved the Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model (GISS AOGCM: ModelE) simulation of June–September rainfall patterns over Africa. The current study applies bias corrections to the lateral and lower boundary data from the AOGCM driving the RCM, based on the comparison of a 30-year simulation to the actual climate. The analysis examines the horizontal pattern of June–September total accumulated precipitation, the time versus latitude evolution of zonal mean West Africa (WA) precipitation (showing monsoon onset timing), and the latitude versus altitude cross-section of zonal winds over WA (showing the African Easterly Jet and the Tropical Easterly Jet). The study shows that correcting for excessively warm AOGCM Atlantic sea-surface temperatures (SSTs) improves the simulation of key features, whereas applying 30-year mean bias corrections to atmospheric variables driving the RCM at the lateral boundaries does not improve the RCM simulations. We suggest that AOGCM climate projections for Africa should benefit from downscaling by nesting an RCM that has demonstrated skill in simulating African climate, driven with bias-corrected SST.

West African monsoon↗

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.

Calle, Leonardo↗