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Cold-Point Tropopause Temperature Bias in Reanalyses Modulated By Equatorial Waves

In this reanalysis inter-comparison study, we compare the modeled Cold-point tropopause temperature (CPT from model level data) near the equator to that of multi-mission GNSS-RO within a preliminary time period of 2007-2011. The known reanalysis CPT temperature warm bias is studied relative to the phase of equatorial waves filtered from GNSS-RO at tropopause level. This uncovers a common denominator in the CPT temperature bias behavior in all reanalyses: - When equatorial waves are present, within their cold phase the reanalysis bias is increased by over 1K, i.e. >1K on top of the reanalysis average warm bias. - Within an equatorial wave’s warm phase, the opposite is observed, with the reanalysis CPT temperature bias even being reversed at higher equatorial wave amplitudes, i.e. the modeled CPT becoming too cold in these cases. - With no equatorial wave anomalies present, the reanalysis bias remains at the average reported in previous inter-comparisons that looked at zonal-mean CPT biases. Inertia-Gravity and Mixed Rossby-Gravity waves show this behavior most clearly, Kelvin waves show a similar modulation but with half of the magnitude, and equatorial Rossby wave modulation of CPT reanalysis bias is still apparent but with further decreased magnitude. This hints on a time-space scale-dependence, with medium-smaller-scale and faster equatorial waves influencing CPT reanalysis bias the most. For comparison, the large-scale and slow-moving MJO does not show any of the effects described above. Current work focuses on the data assimilation response of the reanalysis systems, again relative to the equatorial wave’s phases.

Tropopause↗

NASA NTRS1999

An Analysis of Gravity-Field Estimation Based on Intersatellite Dual-1-Way Biased Ranging

The GRACE (Gravity Recovery And Climate Experiment) mission is designed to make global, highly accurate measurements of the Earth's gravity field with high spatial resolution. Ancillary GPS occultation measurements are also to be carried out for atmospheric monitoring. In the dual-1-way biased ranging of this mission, the range between two satellites separated by 100 to 200 km in nearly polar, coplanar, circular orbits, is measured to very high precision, to within an additive constant, through the exchange of K- and Ka-band sinusoidal signals. Such biased ranging data, along with GPS L-band range and phase data, can be processed and fit over successive multiday intervals to obtain accurate estimates of the Earth's gravity field. This report approximately models and analyzes this process, from the generation of the RF signals at the two satellites through the extraction of the geopotential. The steps include generation of the transmitted signals, processing the received signals to extract high-rate baseband phase, carrying out a dual-1-way combination of baseband phase to extract high-rate biased range for each band, combining K- and Ka-band ranges to correct for the ionosphere effect, and processing the resulting high-rate biased range values to extract three types of reduced-rate observables: biased range, range rate and range acceleration. The version of dual-1-way biased ranging developed by this report improves upon previous versions in a number of ways: highly accurate satellite-timetag corrections derived from concurrent GPS data, better baseband phase extraction using highly digital processing, highly accurate USO-rate calibration derived from concurrent GPS data, an improved method for extracting high-rate biased range from baseband phase, improved filtering for extracting reduced- rate observables from high-rate biased range, and parallel extraction of three observable types.

SINUSOIDAL SIGNALS↗

NASA NTRS1990

Bias and design in software specifications

Implementation bias in a specification is an arbitrary constraint in the solution space. Presented here is a model of bias in software specifications. Bias is defined in terms of the specification process and a classification of the attributes of the software product. Our definition of bias provides insight into both the origin and the consequences of bias. It also shows that bias is relative and essentially unavoidable. Finally, we describe current work on defining a measure of bias, formalizing our model, and relating bias to software defects.

Straub, Pablo A.↗

NASA NTRS2012

Adaptive Control in the Presence of Simultaneous Sensor Bias and Actuator Failures

The problem of simultaneously accommodating unknown sensor biases and unknown actuator failures in uncertain systems is considered in a direct model reference adaptive control (MRAC) setting for state tracking using state feedback. Sensor biases and actuator faults may be present at the outset or may occur at unknown instants of time during operation. A modified MRAC law is proposed, which combines sensor bias estimation with control gain adaptation for accommodation of sensor biases and actuator failures. This control law is shown to provide signal boundedness in the resulting system. For the case when an external asymptotically stable sensor bias estimator is available, an MRAC law is developed to accomplish asymptotic state tracking and signal boundedness. For a special case wherein biases are only present in the rate measurements and bias-free position measurements are available, an MRAC law is developed using a model-independent bias estimator, and is shown to provide asymptotic state tracking with signal boundedness.

Joshi, Suresh M.↗

NASA NTRS2018

A Realization of Bias Correction Method in the GMAO Coupled System

Over the past several decades, a tremendous effort has been made to improve model performance in the simulation of the climate system. The cold or warm sea surface temperature (SST) bias in the tropics is still a problem common to most coupled ocean atmosphere general circulation models (CGCMs). The precipitation biases in CGCMs are also accompanied by SST and surface wind biases. The deficiencies and biases over the equatorial oceans through their influence on the Walker circulation likely contribute the precipitation biases over land surfaces. In this study, we introduce an approach in the CGCM modeling to correct model biases. This approach utilizes the history of the model's short-term forecasting errors and their seasonal dependence to modify model's tendency term and to minimize its climate drift. The study shows that such an approach removes most of model climate biases. A number of other aspects of the model simulation (e.g. extratropical transient activities) are also improved considerably due to the imposed pre-processed initial 3-hour model drift corrections. Because many regional biases in the GEOS-5 CGCM are common amongst other current models, our approaches and findings are applicable to these other models as well.

Chang, Yehui↗

NASA NTRS2018

CALIPSO IIR Version 2 Level 1b Calibrated Radiances: Analysis and Reduction of Residual Biases in the Northern Hemisphere

Version 2 of the Level 1b calibrated radiances of the Imaging Infrared Radiometer (IIR) on board the Cloud-Aerosol Lidar and Infrared Satellite Observation (CALIPSO) satellite has been released recently. This new version incorporates corrections of small but systematic seasonal calibration biases previously revealed in Version 1 data products mostly north of 30° N. These biases – of different amplitudes in the three IIR channels 8.65 µm (IIR1), 10.6 µm (IIR2), and 12.05 µm (IIR3) – were made apparent by a striping effect in images of IIR inter-channel brightness temperature differences (BTDs) and through seasonal warm biases of nighttime IIR brightness temperatures in the 30–60° N latitude range. The latter were highlighted through observed and simulated comparisons with similar channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua spacecraft. To characterize the calibration biases affecting Version 1 data, a semi-empirical approach is developed, which is based on the in-depth analysis of the IIR internal calibration procedure in conjunction with observations such as statistical comparisons with similar MODIS/Aqua channels. Two types of calibration biases are revealed: an equalization bias affecting part of the individual IIR images and a global bias affecting the radiometric level of each image. These biases are observed only when the temperature of the instrument increases, and they are found to be functions of elapsed time since night-to-day transition, regardless of the season. Correction coefficients of Version 1 radiances could thus be defined and implemented in the Version 2 code. As a result, the striping effect seen in Version 1 is significantly attenuated in Version 2. Systematic discrepancies between nighttime and daytime IIR–MODIS BTDs in the 30–60° N latitude range in summer are reduced from 0.2 K in Version 1 to 0.1 K in Version 2 for IIR1–MODIS29. For IIR2–MODIS31 and IIR3–MODIS32, they are reduced from 0.4 K to close to zero, except for IIR3–MODIS32 in June, where the night-minus-day difference is around −0.1 K.

Garnier, Anne↗

NASA NTRS2020

Attribution of Chemistry-Climate Model Initiative (CCMI) Ozone Radiative Flux Bias from Satellites

The top-of-atmosphere (TOA) outgoing longwave flux over the 9.6-μm ozone band is a fundamental quantity for understanding chemistry-climate coupling. However, observed TOA fluxes are hard to estimate as they exhibit considerable variability in space and time that depend on the distributions of clouds, ozone (O3), water vapor (H2O), air temperature (Ta), and surface temperature (Ts). Benchmarking present day fluxes and quantifying the relative influence of their drivers is the first step for estimating climate feedbacks from ozone radiative forcing and predicting radiative forcing evolution. To that end, we constructed observational instantaneous radiative kernels (IRKs) under clear-sky conditions, representing the sensitivities of the TOA flux in the 9.6-μm ozone band to the vertical distribution of geophysical variables, including O3, H2O, Ta, and Ts based upon the Aura Tropospheric Emission Spectrometer (TES) measurements. Applying these kernels to present-day simulations from the Chemistry-Climate Model Initiative (CCMI) project as compared to a 2006 reanalysis assimilating satellite observations, we show that the models have large differences in TOA flux, attributable to different geophysical variables. In particular, model simulations continue to diverge from observations in the tropics, as reported in previous studies of the Atmospheric Chemistry Climate Model Inter-comparison Project (ACCMIP) simulations. The principal culprits are tropical mid and upper tropospheric ozone followed by tropical lower tropospheric H2O. Five models out of the eight studied here have TOA flux biases exceeding 100 mWm-2 attributable to tropospheric ozone bias. Another set of five models have flux biases over 50 mWm-2 due to H2O. On the other hand, Ta radiative bias is negligible in all models (no more than 30 mWm-2). We found that AM3 and CMAM have the lowest TOA flux biases globally but are a result of cancellation of opposite biases due to difference processes. Overall, the multi-model ensemble mean bias is –133±98 mWm-2, indicating that they are too atmospherically opaque due to trapping too much radiation in the atmosphere by overestimated tropical tropospheric O3 and H2O. Having too much O3 and H2O in the troposphere would have different impacts on the sensitivity of TOA flux to O3 and these competing effects add more uncertainties on the ozone radiative forcing. We find that the inter-model TOA outgoing longwave radiation (OLR) difference is well anti-correlated with their ozone band flux bias. This suggests that there is significant radiative compensation in the calculation of model outgoing longwave radiation.

Aura Tropospheric Emission Spectrometer (TES) meas↗

NASA NTRS2012

Cognitive Bias in Systems Verification

Working definition of cognitive bias: Patterns by which information is sought and interpreted that can lead to systematic errors in decisions. Cognitive bias is used in diverse fields: Economics, Politics, Intelligence, Marketing, to name a few. Attempts to ground cognitive science in physical characteristics of the cognitive apparatus exceed our knowledge. Studies based on correlations; strict cause and effect is difficult to pinpoint. Effects cited in the paper and discussed here have been replicated many times over, and appear sound. Many biases have been described, but it is still unclear whether they are all distinct. There may only be a handful of fundamental biases, which manifest in various ways. Bias can effect system verification in many ways . Overconfidence -> Questionable decisions to deploy. Availability -> Inability to conceive critical tests. Representativeness -> Overinterpretation of results. Positive Test Strategies -> Confirmation bias. Debiasing at individual level very difficult. The potential effect of bias on the verification process can be managed, but not eliminated. Worth considering at key points in the process.

Cognitive bias↗

NASA NTRS2012

Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)

A procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within +/- 50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation s of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (s/m, where m is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%-15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (s) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a different number of input products. For the globe the calculated relative error estimate from this study is about 9%, which is also probably a slight overestimate. These tropical and global estimated bias errors provide one estimate of the current state of knowledge of the planet's mean precipitation.

Global Precipitation Climatology Project (GPCP)↗

NASA NTRS1991

On the sea-state bias of the Geosat altimeter

The sea-state bias in a satellite altimeter's range measurement is caused by the influence of ocean waves on the radar return pulse; it results in an estimate of sea level that is too low according to some function of the wave height. This bias is here estimated for Geosat by correlating collinear differences of altimetric sea-surface heights with collinear differences of significant wave heights (H1/3). Corrections for satellite orbit error are estimated simultaneously with the sea-state bias. Based on twenty 17-day repeat cycles of the Geosat Exact Repeat Mission, the solution for the sea-state bias is 2.6 + or - 0.2 percent of H1/3. The least-squares residuals, however, show a correlation with wind speed U, so the traditional model of the bias has been supplemented with a second term: H1/3 + alpha-2H1/3U. This second term produces a small, but statistically significant, reduction in variance of the residuals. Both systematic and random errors in H1/3 and U tend to bias the estimates of alpha-1 and alpha-2, which complicates comparisons of the results with ground-based measurements of the sea-state bias.

Ray, Richard D.↗

NASA NTRS1994

Bias and high-order galaxy correlation functions in the APM galaxy survey

On large scales, the higher order moments of the mass distribution, S(sub J) = bar-zeta(sub J)/bar-zeta(sup J-1)(sub 2), e.g., the skewness S(sub 3) and kurtosis S(sub 4), can be predicted using nonlinear perturbation theory. Comparison of these predictions with moments of the observed galaxy distribution probes the bias between galaxies and mass. Applying this method to models with initially Gaussian fluctuations and power spectra P(k) similar to that of galaxies in the Automatic Plate Measuring (APM) survey, we find that the predicted higher order moments S(sub J)(R) are in good agreement with those directly inferred from the APM survey in the absence of bias. We use this result to place limits on the linear and nonlinear bias parameters. Models in which the extra power observed on large scales (with respect to the standard cold dark matter (CDM) model) is produced by scale-dependent bias match the APM higher order amplitudes only if nonlinear bias (rather than nonlinear gravity) generates the observed higher order moments. When normalized to Cosmic Background Explorer Differential Microwave Radiometer (COBE DMR), these models are siginificantly ruled out by the S(sub 3) observations. The cold plus hot dark matter model normalized to COBE can reproduce the APM higher order correlations if one introduces nonlinear bias terms, while the low-density CDM model with a cosmological constant does not require any bias to fit the large-scale amplitudes.

Gaztanaga, Enrique↗

NASA NTRS2000

Sound Absorption of a 2DOF Resonant Liner with Negative Bias Flow

This report describes an experimental study conducted to determine the effect of negative bias flow on the sound absorption of a two degree-of-freedom liner. The backwall for the liner was designed to act as a double-Helmholtz resonator so as to act as a hard wall at all frequencies except at its resonant frequencies. All normal incident impedance data presented herein was acquired in an impedance tube. The effect of bias flow is investigated for a buried septum porosity of 2% and 19.5% for bias flow orifice mach numbers up to 03 11. As a porous backwall is needed for the flow to pass through, the effect of bias flow on this backwall all had to be evaluated first. The bias flow appears to modify the resistance and reactance of the backwall alone at lower frequencies up to about 2 kHz, with marginal effects at higher frequencies. Absorption coefficients close to unity are achieved for a frequency range of 500-4000 Hz for the overall liner for a septum porosity of 2% and orifice mach number of 0.128. Insertion loss tests performed in a flow duct facility for grazing flow Mach numbers up to 0.2 and septum mach numbers up to 0.15 showed that negative bias flow can increase insertion loss by as much as 10 dB at frequencies in the range of 500 - 1400 Hz compared to no grazing flow. The effectiveness of the negative bias flow is diminished as the grazing flow velocity is increased.

Ahuja, K. K.↗

NASA NTRS2005

AGCM Biases in Evaporation Regime: Impacts on Soil Moisture Memory and Land-Atmosphere Feedback

Because precipitation and net radiation in an atmospheric general circulation model (AGCM) are typically biased relative to observations, the simulated evaporative regime of a region may be biased, with consequent negative effects on the AGCM s ability to translate an initialized soil moisture anomaly into an improved seasonal prediction. These potential problems are investigated through extensive offline analyses with the Mosaic land surface model (LSM). We first forced the LSM globally with a 15-year observations-based dataset. We then repeated the simulation after imposing a representative set of GCM climate biases onto the forcings - the observational forcings were scaled so that their mean seasonal cycles matched those simulated by the NSIPP-1 (NASA Global Modeling and Assimilation Office) AGCM over the same period-The AGCM s climate biases do indeed lead to significant biases in evaporative regime in certain regions, with the expected impacts on soil moisture memory timescales. Furthermore, the offline simulations suggest that the biased forcing in the AGCM should contribute to overestimated feedback in certain parts of North America - parts already identified in previous studies as having excessive feedback. The present study thus supports the notion that the reduction of climate biases in the AGCM will lead to more appropriate translations of soil moisture initialization into seasonal prediction skill.

Mahanama, Sarith P. P.↗

NASA NTRS2007

Sampling Biases in MODIS and SeaWiFS Ocean Chlorophyll Data

Although modem ocean color sensors, such as MODIS and SeaWiFS are often considered global missions, in reality it takes many days, even months, to sample the ocean surface enough to provide complete global coverage. The irregular temporal sampling of ocean color sensors can produce biases in monthly and annual mean chlorophyll estimates. We quantified the biases due to sampling using data assimilation to create a "truth field", which we then sub-sampled using the observational patterns of MODIS and SeaWiFS. Monthly and annual mean chlorophyll estimates from these sub-sampled, incomplete daily fields were constructed and compared to monthly and annual means from the complete daily fields of the assimilation model, at a spatial resolution of 1.25deg longitude by 0.67deg latitude. The results showed that global annual mean biases were positive, reaching nearly 8% (MODIS) and >5% (SeaWiFS). For perspective the maximum interannual variability in the SeaWiFS chlorophyll record was about 3%. Annual mean sampling biases were low (<3%) in the midlatitudes (between -40deg and 40deg). Low interannual variability in the global annual mean sampling biases suggested that global scale trend analyses were valid. High latitude biases were much higher than the global annual means, up to 20% as a basin annual mean, and over 80% in some months. This was the result of the high solar zenith angle exclusion in the processing algorithms. Only data where the solar angle is <75deg are permitted, in contrast to the assimilation which samples regularly over the entire area and month. High solar zenith angles do not facilitate phytoplankton photosynthesis and consequently low chlorophyll concentrations occurring here are missed by the data sets. Ocean color sensors selectively sample in locations and times of favorable phytoplankton growth, producing overestimates of chlorophyll. The biases derived from lack of sampling in the high latitudes varied monthly, leading to artifacts in the apparent seasonal cycle from ocean color sensors. A false secondary peak in chlorophyll occurred in May-August, which resulted from lack of sampling in the Antarctic.

Gregg, Watson W.↗

NASA NTRS2005

Mechanically Biased, Hinged Pairs of Piezoelectric Benders

The upper part of the figure depicts an actuator that comprises two mechanically biased piezoelectric benders hinged together at their ends and equipped with tabs at their mid-length points for attachment to the relatively moving objects that are to be actuated. In the example of the figure, the attachment tabs are labeled to indicate that the actuator is used to drive a pump piston relative to a base plate. Actuators of this type could be used to drive low-power, small-volume pumps in consumer, medical, and aerospace applications, and to generate and measure linear displacements in such robotic applications as teleoperation and tactile feedback. Each bender is a bimorph a unitary plate that comprises an upper and a lower piezoelectric layer plus electrode layers. Benders may also be made of several layers arranged to produce the same effect at the lower operating voltages. As stated above, each bender is mechanically biased; it is fabricated to have a small permanent curvature (the bias curvature) in the absence of applied voltage. As on other bimorphs, the electrical connections on each bender are arranged so that an applied voltage of suitable polarity causes the upper layer to expand and the lower layer to contract. In this case, the net effect of applying the voltage is that the plate becomes more concave as viewed from below. Conversely, an applied voltage of the opposite polarity causes the plate to become less concave as viewed from below. The benders in a hinged pair are oriented with their bias curvatures concave inward, so that there is a bias distance between the attachment tabs. The two benders are connected electrically in parallel, with their connection polarities chosen so that an applied voltage of one polarity causes both benders to become more convex inward (more bent), while an applied voltage of the opposite polarity causes both benders to become less convex inward (less bent). An increase or decrease in bend is accompanied by an increase or decrease in distance between the attachment tabs; this increase or decrease is the linear displacement desired for actuation. Because the displacement can be either positive or negative relative to the bias distance, depending on the polarity of the applied voltage, the overall stroke achievable for a given magnitude of applied voltage is double the stroke achievable in the absence of mechanical bias. Each hinged pair can be regarded as a unit cell that can serve as a building block for a larger actuator: Multiple unit cells can be stacked (mechanically connected in series), as shown in the lower part of the figure, and electrically connected in parallel to multiply the overall stroke achievable at a given applied voltage.

Sager, Frank E.↗

NASA NTRS2012

System and circuitry to provide stable transconductance for biasing

An amplifier system can include an input amplifier configured to receive an analog input signal and provide an amplified signal corresponding to the analog input signal. A tracking loop is configured to employ delta modulation for tracking the amplified signal, the tracking loop providing a corresponding output signal. A biasing circuit is configured to adjust a bias current to maintain stable transconductance over temperature variations, the biasing circuit providing at least one bias signal for biasing at least one of the input amplifier and the tracking loop, whereby the circuitry receiving the at least one bias signal exhibits stable performance over the temperature variations. In another embodiment the biasing circuit can be utilized in other applications.

Garverick, Steven L.↗

NASA NTRS2012

Correction Technique for Raman Water Vapor Lidar Signal-Dependent Bias and Suitability for Water Wapor Trend Monitoring in the Upper Troposphere

The MOHAVE-2009 campaign brought together diverse instrumentation for measuring atmospheric water vapor. We report on the participation of the ALVICE (Atmospheric Laboratory for Validation, Interagency Collaboration and Education) mobile laboratory in the MOHAVE-2009 campaign. In appendices we also report on the performance of the corrected Vaisala RS92 radiosonde measurements during the campaign, on a new radiosonde based calibration algorithm that reduces the influence of atmospheric variability on the derived calibration constant, and on other results of the ALVICE deployment. The MOHAVE-2009 campaign permitted the Raman lidar systems participating to discover and address measurement biases in the upper troposphere and lower stratosphere. The ALVICE lidar system was found to possess a wet bias which was attributed to fluorescence of insect material that was deposited on the telescope early in the mission. Other sources of wet biases are discussed and data from other Raman lidar systems are investigated, revealing that wet biases in upper tropospheric (UT) and lower stratospheric (LS) water vapor measurements appear to be quite common in Raman lidar systems. Lower stratospheric climatology of water vapor is investigated both as a means to check for the existence of these wet biases in Raman lidar data and as a source of correction for the bias. A correction technique is derived and applied to the ALVICE lidar water vapor profiles. Good agreement is found between corrected ALVICE lidar measurments and those of RS92, frost point hygrometer and total column water. The correction is offered as a general method to both quality control Raman water vapor lidar data and to correct those data that have signal-dependent bias. The influence of the correction is shown to be small at regions in the upper troposphere where recent work indicates detection of trends in atmospheric water vapor may be most robust. The correction shown here holds promise for permitting useful upper tropospheric water vapor profiles to be consistently measured by Raman lidar within NDACC (Network for the Detection of Atmospheric Composition Change) and elsewhere, despite the prevalence of instrumental and atmospheric effects that can contaminate the very low signal to noise measurements in the UT.

MOHAVE-2009↗

NASA NTRS2017

Effects of Inventory Bias on Landslide Susceptibility Calculations

Many landslide inventories are known to be biased, especially inventories for large regions such as Oregon's SLIDO or NASA's Global Landslide Catalog. These biases must affect the results of empirically derived susceptibility models to some degree. We evaluated the strength of the susceptibility model distortion from postulated biases by truncating an unbiased inventory. We generated a synthetic inventory from an existing landslide susceptibility map of Oregon, then removed landslides from this inventory to simulate the effects of reporting biases likely to affect inventories in this region, namely population and infrastructure effects. Logistic regression models were fitted to the modified inventories. Then the process of biasing a susceptibility model was repeated with SLIDO data. We evaluated each susceptibility model with qualitative and quantitative methods. Results suggest that the effects of landslide inventory bias on empirical models should not be ignored, even if those models are, in some cases, useful. We suggest fitting models in well-documented areas and extrapolating across the study region as a possible approach to modeling landslide susceptibility with heavily biased inventories.

Oregon↗