Sources Of Uncertainty Research Articles

JOYS+ study of solid-state 12C / 13C isotope ratios in protostellar envelopes. Observations of CO and CO_2 ice with the James Webb Space Telescope

The carbon isotope ratio is a powerful tool for studying the evolution of stellar systems due to its sensitivity to the local chemical environment. Recent detections of CO isotopologs in disks and exoplanet atmospheres revealed a high variability in the isotope abundance, ponting towards significant fractionation in these systems. In order to fully understand the evolution of this quantity in stellar and planetary systems, however, it is crucial to trace the isotope abundance from stellar nurseries to the time of planet formation. During the protostellar stage, the multiple vibrational modes of CO_2 and CO ice, which peak in the near- and mid-infrared, provide a unique opportunity to examine the carbon isotope ratio in the solid state. With the current sensitivity and wide spectral coverage of the James Webb Space Telescope, the multiple weak and strong absorption features of CO_2 and CO have become accessible at a high signal-to-noise ratio in solar-mass systems. We aim to study the carbon isotope ratio during the protostellar stage by deriving column densities and ratios from the various absorption bands of CO_2 and CO ice, and by comparing our results with the ratios derived in other astronomical environments. We quantify the CO_2 CO_2 and the CO CO isotope ratios in 17 class 0/I low-mass protostars from the CO_2 nu_1 nu_2 and 2 nu_1 nu_2 combination modes (2.70 mu m and 2.77 mu m), the CO_2 nu_3 stretching mode (4.27 mu m), the CO_2 nu_3 stretching mode (4.39 mu m), the CO_2 nu_2 bending mode (15.2 mu m), the CO 1-0 stretching mode (4.67 mu m), and the CO 1-0 stretching mode (4.78 mu m) using the James Webb Space Telescope NIRSpec and MIRI observations. We also report a detection of the 2-0 overtone mode of CO at 2.35 mu m. The column densities and CO_2 CO_2 ratios derived from the various CO_2 vibrational modes agree within the reported uncertainties, and we find mean ratios of 85 pm 23, 76 pm 12, and 97 pm 17 for the 2.70 mu m band, the 4.27 mu m band, and the 15.2 mu m band, respectively. The main source of uncertainty on the derived values stems from the error on the band strengths; the observational errors are negligible in comparison. Variation of the CO_2 CO_2 ratio is observed from one source to the next, which indicates that the chemical conditions of their envelopes might be genuinely different. The CO CO ratios derived from the 4.67 mu m band are consistent, albeit elevated with respect to the CO_2 CO_2 ratios, and we find a mean ratio of 165 pm 52. These findings indicate that ices leave the prestellar stage with elevated carbon isotope ratios relative to the overall values found in the interstellar medium, and that fractionation becomes a significant factor during the later stages of star and planet formation.

A framework for categorizing sources of uncertainty in in silico toxicology methods: considerations for chemical toxicity predictions.

Improving regulatory confidence and acceptance of in silico toxicology methods for chemical risk assessment requires assessment of associated uncertainties. Therefore, there is a need to identify and systematically categorize sources of uncertainty relevant to the methods and their predictions. In the present study, we analyzed studies that have characterized sources of uncertainty across commonly applied in silico toxicology methods. Our study reveals variations in the kind and number of uncertainty sources these studies cover. Additionally, the studies use different terminologies to describe similar sources of uncertainty; consequently, a majority of the sources considerably overlap. Building on an existing framework, we developed a new uncertainty categorization framework that systematically consolidates and categorizes the different uncertainty sources described in the analyzed studies. We then illustrate the importance of the developed framework through a case study involving QSAR prediction of the toxicity of five compounds, as well as compare it with the QSAR Assessment Framework (QAF). The framework can provide a structured (and potentially more transparent) understanding of where the uncertainties reside within in silico toxicology models and model predictions, thus promoting critical reflection on appropriate strategies to address the uncertainties.

On the consistent classification and treatment of uncertainties in structural health monitoring applications

Abstract In this paper, we provide a comprehensive definition and classification of various sources of uncertainty within the fields of Structural Dynamics, System Identification, and Structural Health Monitoring (SHM), with a primary focus on the latter. Utilizing the classical input-output system representation as a main contextual framework, we present a taxonomy of uncertainties, intended for consistent classification of uncertainties in SHM applications: (i) input uncertainty; (ii) model form uncertainty; (iii) model parameter/variable uncertainty; (iv) measurement uncertainty; and (v) inherent variability. We then critically review methods and algorithms that address these uncertainties in the context of key SHM tasks: system identification and model inference, model updating, accounting for environmental and operational variability, virtual sensing, damage identification, and prognostic health management. A benchmark shear frame model with hysteretic links is employed as a running example to illustrate the application of selected methods and algorithmic tools. Finally, we discuss open challenges and future research directions in uncertainty quantification for SHM.

Trimming the UCERF3-TD logic tree: Model order reduction for an earthquake rupture forecast considering loss exceedance

The Uniform California Earthquake Rupture Forecast version 3-Time Dependent depicts California’s seismic faults and their activity. Its logic tree has 5760 leaves. Considering 30 more model combinations related to ground motion produces 172,800 distinct models representing so-called epistemic uncertainties. To calculate risk to a portfolio of buildings, one also considers millions of earthquakes and spatially correlated ground-motion variability. We offer a tree-trimming technique that retains the probability distribution of portfolio loss and identifies the leading sources of uncertainty for further study. We applied it to a California statewide building portfolio and various levels of nonexceedance probability between one in 100 and one in 2500. We trimmed the logic tree from 172,800 leaves to as few as 15. The result: a supercomputer that would otherwise run 24 h to estimate the distribution of one-in-250-year loss can calculate it in moments with the reduced-order model. Others can use the reduced-order model to calculate risk to different California portfolios, and scientists can prioritize study to reduce the remaining epistemic uncertainty.

Metatheoretical Distinctions in Theories of Functional Differentiation: Delineating Alternative Traditions

While the concept of functional differentiation is one of sociology’s oldest analytic tools, there is significant confusion about its meaning and purpose in the contemporary discipline. This article addresses one source of uncertainty: the conflicting array of ontological and methodological positions which are currently attached to the differentiation term. Drawing on Laudan’s philosophy of science, I argue that sociology does not house a unified program of differentiation theory, but is instead marked by at least two discrete traditions of differentiation thinking. Disentangling these traditions will, I contend, clarify our thinking, and help reduce the prevalence of internally contradictory conceptual models.

Sensitivity Analysis of a Nuclear Hybrid Energy System With Thermal Energy Storage in Deregulated Electricity Markets Considering Time Series Uncertainty in Electricity Price

ABSTRACTAdding thermal energy storage to nuclear power plants has been proposed as a way to allow nuclear plants to operate more flexibly and potentially be more competitive in deregulated electricity markets. The economics of these systems in deregulated markets are subject to uncertainties in capital costs, operating costs, and revenue. This study quantifies the uncertainty in the net present value of a nuclear power plant with integrated thermal energy storage in three U.S. deregulated electricity markets considering these sources of uncertainty and quantifies, for the first time, the relative contributions each source makes to the overall uncertainty. To accomplish this, a computationally efficient block bootstrap method is introduced to quantify uncertainty contributions from the stochastic time series of electricity prices, achieving a two order of magnitude decrease computational time compared to the model‐based methods used in previous works while also relaxing several strict assumptions made by the model‐based approach. Up to 18.5% of the overall variance in net present value is attributable to variance in the electricity price stochastic process, with this sensitivity varying significantly across markets.

Application of a regularised Coulomb sliding law to Jakobshavn Isbræ, western Greenland

Abstract. Reliable projections of future sea level rise from the polar ice sheets depend on the ability of ice sheet models to accurately reproduce flow dynamics in an evolving ice sheet system. Ice sheet models are sensitive to the choice of the basal sliding law, which remains a significant source of uncertainty. In this study we apply a range of sliding laws to a hindcast model of Jakobshavn Isbræ, western Greenland, from 2009 to 2018. We demonstrate that a linear viscous sliding law requires the assimilation of regular velocity observations into the model in order to reproduce the observed large seasonal and inter-annual variations in flow speed. This requirement introduces a major limitation for producing accurate future projections. A regularised Coulomb friction law, in which basal traction has an upper limit, is able to more accurately reproduce the range of speeds from 2012 to 2015, the period of peak flow and maximal retreat, without the requirement for assimilating regular observations. Additionally, we find evidence that the speed at which sliding transitions between power-law and Coulomb regimes may vary spatially and temporally. These results point towards the possible form of an ideal sliding parameterisation for accurately modelling fast-flowing glaciers and ice streams, although determining this is beyond the scope of this study.

"What does this mean for our future?" uncertainty management in mothers' narratives about the diagnosis and birth of their child with Down syndrome.

Pregnancy and childbirth are uncertain experiences that become even more so when parents receive an unexpected medical diagnosis for their child. In the present study, we document sources of uncertainty and the tools used to manage uncertainty in 44 mothers' narratives about the birth and diagnosis of their child with Down syndrome (DS); we also explore variability in the sources of uncertainty and uncertainty management tools as a function of whether mothers received a prenatal or postnatal diagnosis of DS for their child. Across our sample, thematic analysis revealed four sources of uncertainty in mothers' narratives: navigating dissonance between parents and providers during diagnosis, managing disclosure of the diagnosis to others, anticipating child-centered challenges and adjusting developmental expectations, and anticipating family-centered challenges and adjusting familial expectations. Analyses also revealed four ways that uncertainty was managed: finding balance between parents and providers during diagnosis, reducing knowledge gaps by seeking information, pursuing support and building positive interpersonal relationships, and pursuing support and building positive relationships in DS communities. These findings, along with potential nuance based on whether mothers received a prenatal or postnatal diagnosis of DS for their child, have important implications for healthcare providers and medical professionals regarding how to best communicate DS diagnoses to families as a means of understanding and ultimately reducing the uncertainty they experience.

Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements

Abstract. Accurately modelling optical snow properties like snow albedo and specific surface area (SSA) are essential for monitoring the cryosphere in a changing climate and are parameters that inform hydrologic and climate models. These snow surface properties can be modelled from spaceborne imaging spectroscopy measurements but rely on digital elevation models (DEMs) of relatively coarse spatial scales (e.g. Copernicus at 30 m), which degrade accuracy due to errors in derived products such as slope and aspect. In addition, snow deposition and redistribution can change the apparent topography, and thereby static DEMs may not be considered coincident with the imaging spectroscopy dataset. Testing in three different snow climates (tundra, maritime, alpine), we established a new method that simultaneously solves snow, atmospheric, and terrain parameters, enabling a solution that is more unified across sensors and introduces fewer sources of uncertainty. We leveraged imaging spectroscopy data from Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) (collected within 1 h) to validate this method and showed a 25 % increase in performance for the radiance-based method over the static method when estimating SSA. This concept can be implemented in missions such as Surface Biology and Geology (SBG), the Environmental Mapping and Analysis Program (EnMap), and the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME).

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainty with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of classification of winter precipitation type and regression of surface layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. In order to encourage broader adoption of evidential deep learning, we have developed a new Python package, MILES-GUESS (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.

Optimization of emergency frequency control strategy for power systems considering both source and load uncertainties

With the increasing integration of renewable energy sources and the presence of numerous controllable loads such as electric vehicles and energy storage in the modern power system, higher nonlinearities and uncertainty both sources and loads are introduced. These factors pose challenges in achieving fast and accurate emergency frequency control. Therefore, this paper addresses the issue of dual source-load uncertainties in power system and presents an optimization strategy based on the Soft Actor Critic (SAC) algorithm that involves the participation of controllable loads in emergency frequency control. Firstly, the spatio-temporal uncertainties of wind farm power output on power supply side and power demand on the load side are described using Weibull and normal probability distributions, respectively. Secondly, an improved Markov Decision Process (MDP) model for emergency frequency control is established, which considers the characteristics of the dual source-load uncertainties. Finally, an optimization of the SAC algorithm is conducted based on Deep Reinforcement Learning (DRL), aiming to achieve rapid system frequency recovery and minimize the cost of removing controllable loads. The presented approach in the paper enhances the emergency frequency control strategy for uncertain power systems and effectively addresses the issue of source-load uncertainty compounded by fault power shortages.

ENKIE: A package for predicting enzyme kinetic parameter values and their uncertainties.

Relating metabolite and enzyme abundances to metabolic fluxes requires reaction kinetics, core elements of dynamic and enzyme cost models. However, kinetic parameters have been measured only for a fraction of all known enzymes, and the reliability of the available values is unknown. The ENzyme KInetics Estimator (ENKIE) uses Bayesian Multilevel Models to predict value and uncertainty of KM and kcat parameters. Our models use five categorical predictors and achieve prediction performances comparable to deep learning approaches that use sequence and structure information. They provide calibrated uncertainty predictions and interpretable insights into the main sources of uncertainty. We expect our tool to simplify the construction of priors for Bayesian kinetic models of metabolism. Code and Python package are available at https://gitlab.com/csb.ethz/enkie and https://pypi.org/project/enkie/. Supplementary data are available at Bioinformatics online.

Advantages of base isolation in reducing the reliability sensitivity to structural uncertainties of buildings

Base isolation is among the most effective techniques for safeguarding buildings against earthquakes. Similar to non-isolated buildings, the seismic performance of a base-isolated building can vary not only due to the inherent record-to-record variability in earthquake ground motions but also the non-negligible influence of uncertainties associated with structural properties. Introducing an isolation layer of isolation bearings and energy dissipators to the building further adds to the source of uncertainties, raising the question regarding the reliability of base isolated buildings. This study proposed a methodology that employs the probability density evolution method (PDEM) with local time grid refinement to evaluate the reliability of a base-isolated structure and its non-isolated counterpart under earthquakes of various magnitudes and fault distances. It allows for simultaneously considering the uncertainties of various sources in the reliability analyses with affordable computational cost, including those of the ground motions, of the structural nonlinearity, and of the ultimate states that define the reliability. The results show that the base-isolated building exhibits much higher reliability than its non-isolated counterpart in all cases even if the ultimate behavior such as the isolator fracture or the impact to the retaining wall is considered. The added source of uncertainties in the isolation layer does not exhibit a notable impact on the reliability. In contrast, the seismic reliability of base-isolated buildings is much less sensitive to structural uncertainties than that of their non-isolated counterparts.

Evaluating the chain of uncertainties in the 3D geological modelling workflow

Geological models are used for a range of applications relevant for engineering geology and the demands for reliable geological models with realistic uncertainty assessments are therefore increasing. The geological modelling workflow is divided into multiple steps, each associated with uncertainties. Often however, many of these sources of uncertainty are overlooked, which risks underestimating the uncertainties of the final model. The main reason is that most of the steps in the geological modelling workflow are subjective to some degree. Thus, the possible sources of uncertainty in geological modelling are theoretically infinite, and without a trustworthy conceptual model to outline the expected geological structures and lithologies, the uncertainty assessment of the resulting model will likewise be unreliable. In this paper, we describe the chain of uncertainties in the geological modelling workflow and showcase some of the most important sources of uncertainties through practical modelling examples from two different model areas. The paper also presents and discuss a method to conduct qualitative uncertainty assessment, which is conducted by the modeler and based on expert evaluation and prioritization of the different sources of uncertainty. The practical use of the uncertainty assessment method is exemplified in the last two examples, representing a local-scale and a large-scale model, respectively. All four examples are from Danish geological models that have been constructed as interpretation-based layer-models. However, the considerations regarding the uncertainties in the chain of the geological modelling workflow are useful regardless of the modelling method.

The hardest-hit home run?

We present a problem to be assigned or done as an in-class activity in an upper-division undergraduate course on computational physics. The problem involves a home run hit by Mickey Mantle on May 22, 1963, which he famously called “the hardest ball I ever hit.” Is this home run truly one for the record books, or has it been eclipsed by players in the modern era? Modeling the trajectory of a baseball involves consideration of both wind resistance and the Magnus effect and is an interesting application of numerical solutions to ordinary differential equations. Ultimately, the answer is that Mantle would compare favorably to the most powerful batters currently playing, but to arrive at that conclusion we must reflect on the plausibility of results and sources of uncertainty.

Robust solar sail trajectories using proximal policy optimization

Reinforcement learning is used to design minimum-time trajectories of solar sails subject to the typical sources of uncertainty associated with such a propulsion system, i.e., inaccurate knowledge of the sail’s optical properties and the presence of wrinkles on the sail membrane. A proximal policy optimization (PPO) algorithm is used to train the agent and derive the control policy that associates the optimal sail attitude with each dynamic state. First, the agent is trained assuming deterministic unperturbed dynamics, and the results are compared with optimal solutions found by an indirect optimization method, thus demonstrating the effectiveness of this approach. Next, two stochastic scenarios are analyzed. In the first, the optical coefficients of the sail are assumed to be random variables with Gaussian distribution, which leads to random variations in the sail characteristic acceleration. In the second scenario, wrinkles on the sail membrane are taken into account, resulting in a misalignment of the thrust vector with respect to a perfectly smooth surface. Both phenomena are modeled based on experimental measurements available in the literature in order to perform realistic analyses. In the stochastic scenarios, Monte Carlo simulations are performed using the trained policies, demonstrating that the reinforcement learning approach is capable of finding near time-optimal solutions, while also being robust to the sources of uncertainty considered.

Quantifying Streamflow Prediction Uncertainty Through Process‐Aware Data‐Driven Models

ABSTRACTThe hydrological model simulation accompanied with uncertainty quantification helps enhance their overall reliability. Since uncertainty quantification including all the sources (input, model structure and parameter) is challenging, it is often limited to only addressing model parametric uncertainty, neglecting other uncertainty sources. This paper focuses on exploiting the potential of state‐of‐the‐art data‐driven models (or DDMs) in quantifying the prediction uncertainty of process‐based hydrological models. This is achieved by integrating the robust predictive ability of the DDMs with the process understanding ability of the hydrological models. The Bayesian‐based data assimilation (DA) technique is used to quantify uncertainty in process‐based hydrological models. This is accomplished by choosing two DDMs, random forest algorithm (RF) and support vector machine (SVM), which are distinctly integrated with two process‐based hydrological models: HBV and HyMOD. Particle filter algorithm (PF) is chosen for uncertainty quantification. All these combinations led to four different process‐aware DDMs: HBV‐PF‐RF, HBV‐PF‐SVM, HyMOD‐PF‐RF and HyMOD‐PF‐SVM. The assessment of these models on the Baitarani, Beas and Sunkoshi river basins exemplified an improvement in the accuracy of the daily streamflow simulations with a reduction in the prediction uncertainty across all the models for all the basins. For example, the nash‐sutcliffe efficiency improved by 54.69% and 10.61% in calibration and validation of the Baitarani river basin, respectively. Equivalently, average bandwidth improved by 79.37% and 71.59%, respectively. This signified the (a) potential of the DDMs in quantifying and reducing the prediction uncertainty of the hydrological model simulations, (b) transferability of the model with an appreciable performance irrespective of the choice of basins having varying topography and climatology and (c) ability to perform significantly irrespective of different process‐based and DDMs being involved, thereby ensuring generalizability. Thus, the framework is expected to assist in effective decision‐making, including various environmental management and disaster preparedness.

Estimating the Spin of MAXI J1348–630 from Intermediate and Soft States Using Insight-HXMT Data

We present a broadband spectral analysis of the soft-intermediate and soft states using Insight-HXMT observations of the black hole binary MAXI J1348–630 during its outburst in 2019. We employ a combination of reflection and continuum fitting methods to measure the spin of the black hole and determine a spin of a = 0.79 ± 0.13, which is consistent with most of the previous results. Additionally, we investigate some sources of systematic uncertainty that could bias the measurement. The valid spectral hardening factor can partially account for the spin evolution observed during the outburst. Other potential factors, such as assumptions about the corona’s structure and their interaction with reflected photons, can also affect the accuracy of spin measurements. Furthermore, our analysis reveals that MAXI J1348–630 exhibits a significantly high disk density (∼1022 cm−3), with a moderate iron abundance of approximately 2.5 times solar.

The Challenge of Predicting the Solar Wind Speed near Sunspot Minimum

By applying potential-field source-surface and potential-field current-sheet extrapolations to photospheric field maps from three different observatories, we predict the solar wind speed at Earth for several Carrington rotations during 2018–2021 and compare the results with in situ observations. The predicted speeds are taken to be inversely correlated with the rate of flux-tube expansion inside the source surface, located at a heliocentric distance of 2.5 R ⊙. The results often differ markedly from one observatory to another and are very sensitive to the latitudinal position of the ecliptic relative to the narrow belt of slow wind that surrounds the source-surface neutral line. Our main conclusions are that (1) the magnetograph measurements themselves are a major source of uncertainty in solar wind predictions; (2) these uncertainties are especially large near solar minimum, when Earth is located near the rapid transition between slow and fast wind that occurs on either side of the heliospheric current sheet; (3) comparison of the derived open field regions with observed coronal holes provides a strong, underutilized constraint on wind speed predictions; and (4) the observed polarity of the interplanetary magnetic field provides another important constraint on the location of the source region.

Considerations for the use of laboratory-based and field-based estimates of environmental tolerance in water management decisions for an endangered salmonid

Water infrastructure development and operation can provide for human economic activity, health, and safety. However, this infrastructure can also impact native fish populations resulting in regulatory protections that can, in turn, alter operations. Conflict over water allocation for ecological function and human use has come to the forefront at Shasta Reservoir, the largest water storage facility in California, USA. Shasta Reservoir supports irrigation for a multibillion-dollar agricultural industry, provides water for urban and domestic use, provides flood protection for downstream communities, and power generation as part of the larger Central Valley Project in California. Additionally, an endangered run of Chinook Salmon relies on cold water management at the dam for successful spawning and egg incubation. Tradeoffs between these uses can be explored through application of models that assess biological outcomes associated with flow and temperature management scenarios. However, the utility of models for predicting outcomes of management decisions is contingent on the data used to construct them, and data collected to evaluate their predictions. We evaluated laboratory and field data currently available to parameterize temperature-egg survival models for winter run Chinook Salmon that are used to inform Shasta Dam operations. Models based on both laboratory and field data types had poor predictive performance which appears to limit their value for management decisions. The sources of uncertainty that led to poor performance were different for each data type (field or laboratory) but were rooted in the fact that neither data set was collected with the intention to be used in a predictive model. Our findings suggest that if a predictive model is desired to evaluate operational tradeoffs, data should be collected for the specific variables and life stages desired, over an appropriate range of values, and at sufficient frequency to achieve the needed level of precision to address the modeling objective.