Quantitative Uncertainty Estimates Research Articles

Uncertainties and sensitivities in the quantification of future tropical cyclone risk

Tropical cyclone risks are expected to increase with climate change and socio-economic development and are subject to substantial uncertainties. We thus assess future global tropical cyclone risk drivers and perform a systematic uncertainty and sensitivity analysis. We combine synthetic tropical cyclones downscaled from CMIP6 global climate models for several emission scenarios with economic growth factors derived from the Shared Socioeconomic Pathways and a wide range of vulnerability functions. We highlight non-trivial effects between climate change and socio-economic development that drive future tropical cyclone risk. Furthermore, we show that the choice of climate model affects the output uncertainty most among all varied model input factors. Finally, we discover a positive correlation between climate sensitivity and tropical cyclone risk increase. We assert that quantitative estimates of uncertainty and sensitivity to model parameters greatly enhance the value of climate risk assessments, enabling more robust decision-making and offering a richer context for model improvement.

Can We Estimate the Uncertainty Level of Satellite Long-Term Precipitation Records?

Abstract Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.

Uncertainty: qualitative and quantitative aspects

The analysis the concept of uncertainty, its origin, interpretation in various scientific fields, sources of uncertainty, qualitative and quantitative aspects of this concept, as well as approaches to quantifying uncertainty are presented. Methods of historical and comparative analysis were used in the study. The main conclusion is the necessity of taking into account the presence of uncertainty and quantification of uncertainty of the results of mathematical modeling of objects and phenomena of the surrounding world in the same way as we do when assessing measurement uncertainty of experimental data. In is shown that the interval approach to estimating uncertainty of modeling results currently seems to be the most promising. The concept of uncertainty first proposed at the beginning of the last century refers to epistemic situations involving imperfect or unknown information. This concept had only a qualitative aspect for a long time. In the second half of the twentieth century, almost simultaneously with the development of a risk-based approach in the field of technological security, there was an interest in understanding uncertainty, its origin and typification. We are indebted to metrology for giving uncertainty a quantitative aspect, in which, instead of the measurement error paradigm, a measurement uncertainty paradigm was developed, and approaches (partly controversial) to its quantitative assessment were proposed. Uncertainty is an attribute of any data obtained both experimentally or theoretically (currently, usually by mathematical modeling). In the field of experimental research, specifying the uncertainty interval of the result has long been a scientific standard and routine. The time has come to make it mandatory for the results of theoretical research. To date, three alternative methods of quantitative estimation of uncertainty have been developed: probabilistic, fuzzy and interval methods. Methods for leveling the negative features of its initial «naive» version have been proposed in modern interval analysis. It seems to be the most promising method of quantifying uncertainty of the results of mathematical modeling today.

Ensemble scenario‐based inversion: A new approach for estimating the uncertainty of resistivity models derived from 3D controlled source electromagnetic data

AbstractNon‐linear inversion of controlled source electromagnetic data is non‐unique. Inversion ambiguity and uncertainty grow with model complexity and limitations in sensitivity, for example when imaging deep targets. Uncertainties should be estimated. The best way to do that is by statistical inversion techniques. Because of the large number of parameters of 3D models, these methods are too costly for 3D applications. But neglecting or underestimating uncertainties often leads to erroneous interpretation and poor exploration decisions. The number of inversion parameters can be dramatically reduced while still being able to describe a complex 3D subsurface. To obtain that, I represent prior information by an input ensemble of geologically reasonable prior models. The model space is defined by the principal directions of this ensemble, where the inversion parameters are the coefficients of the different components. In this model space, inversion becomes much more efficient and can produce an updated ensemble of posterior models at the same computational cost as a controlled‐source electromagnetic inversion project using conventional non‐linear inversion techniques. I present examples where unique Gauss–Newton inversions lead to difficult interpretations and poor conclusions about hydrocarbon presence and saturation. The new approach provides a quantitative estimation of resistivity ambiguities and uncertainties leading to better interpretation and safer decisions. The examples use 2D synthetic controlled‐source electromagnetic data and 3D controlled‐source electromagnetic field data.

Application of Indicator Kriging to hydraulic head data to test alternative conceptual models for spring source aquifers

Hydraulic head distributions can inform spring source-water characterisation by determining whether aquifers meet the thresholds required to sustain spring flow. Assessing hydraulic head data can be challenging in areas where data are sparsely distributed and subject to variable measurement uncertainty. Geostatistical methods can be used to estimate hydraulic head values at unmeasured locations with quantitative uncertainty estimates. While these methods have been applied extensively for hydraulic head estimation in management contexts, no studies have used these approaches for spring source-water conceptual model testing. In this study, an investigation into the hydraulic head distribution was conducted through the application of Ordinary Indicator Co-Kriging. Interpolated hydraulic head estimates were used to quantitatively assess the plausibility of source-aquifer conceptual models for the Doongmabulla Springs Complex (DSC), Queensland, Australia. The results offer insights into the likelihood of alternative source aquifers having adequate head to support flow to eight springs within the DSC. Probabilities of adequate head to support the springs ranged from 0.03 to 0.12 for the Permian Formations, compared to <0.01 to 0.49 for the Triassic Formations. Analyses indicated that the Triassic Formations are more likely to have adequate hydraulic head to support spring flow. However, significant uncertainty exists in the conceptual model assessment due to hydraulic head measurement scarcity, particularly in the vicinity of the springs. These findings have important implications given that the Permian Formations will be dewatered by the operation of the nearby Carmichael coal mine. The techniques employed here can inform conceptual model uncertainties arising from the interpretation of sparsely distributed hydraulic head datasets, a major benefit over traditional interpolation methods.

Precise values of running quark and lepton masses in the standard model

The precise values of the running quark and lepton masses $m^{}_f(\mu)$, which are defined in the modified minimal subtraction scheme ($\overline{\rm MS}$) with $\mu$ being the renormalization scale and the subscript $f$ referring to all the charged fermions in the Standard Model (SM), are very useful for the model building of fermion masses and flavor mixing and for the precision calculations in the SM or its new-physics extensions. In this paper, we calculate the running fermion masses by taking account of the up-to-date experimental results collected by Particle Data Group and the latest theoretical higher-order calculations of relevant renormalization-group equations and matching conditions in the literature. The emphasis is placed on the quantitative estimation of current uncertainties on the running fermion masses, and the linear error propagation method is adopted to quantify the uncertainties, which has been justified by the Monte-Carlo simulations. We identify two main sources of uncertainties, i.e., one from the experimental inputs and the other from the truncations at finite-order loops. The correlations among the uncertainties of running parameters can be remarkable in some cases. The final results of running fermion masses at several representative energy scales are tabulated for further applications.

Methods of quantitative estimation and reduction of uncertainty of the accidental risk in fire explosive facilities

Abstract. Currently, ensuring the industrial safety of hazardous industrial facilities involves – along with conventional oversight – the risk-oriented approach that is significantly more flexible. The procedure of quantitative estimation of an accidental risk for hazardous industrial facilities is essentially one of the procedures of conformity assessment, as it includes the comparison of the risk indicators obtained by means of calculation (or expert assessment) with their standard values. The Aim of the paper is to define the problem of uncertainty that is associated with all the stages of quantitative estimation of an accidental risk, make a brief historical account, analyze its types and sources, describe the approaches employed as part of quantitative estimation of this uncertainty. Currently, it is accepted to identify the terminological, parametric and model types of uncertainty, whose examples are provided in the paper. Analysis shows that a fourth – computational – type should be added, whose contribution in many cases may be considerable. It is shown that, due to a number of circumstances, scalar numbers that are normally used for defining parameter values of the physical-mathematical models of failure processes are in reality mere indicators of the ranges of their value variation. Currently, uncertainties in the values of accidental risk parameters are accounted for using probabilistic and deterministic approaches, as well as fuzzy numbers. Methods. For the purpose of quantitative estimation of uncertainty, the paper employs the method of interval analysis. In the most general case, without using the hypothesis on the behaviour of a parameter value within the range of its possible variation, the parametric uncertainty can be defined with an interval number. In that case, all the required calculations are performed using interval methods. The natural (naive) version of interval analysis has a serious drawback that consists in an unjustified increase of the width of the interval number deduced by means of interval calculations, if one or more input parameters of the model enter into the calculation formula more than once, or the input parameters are functionally interdependent. Modern interval analysis employs methods allowing to alleviate this effect. They are briefly described in this paper. It is shown that if statistical information is available on the behaviour of parameter values within their variation intervals, the results of interval calculations of the accidental risk indicators can be significantly improved. The suggested method of reducing the computational uncertainty of quantitative estimation of the accidental risk in the interval setting is illustrated with a numerical example of risk indicator calculation for the “fireball” accident scenario. The paper sets forth the results of interval calculation of an individual accidental risk for an explosion and fire hazardous facility “reservoir with a flammable liquid” in three ways: a) naive; b) accounting for the effect of parameter correlation; c) additionally, accounting for available statistical information. Conclusions. Interval methods allow not only taking into consideration the presence of uncertainty in the accidental risk parameters, but evaluating it quantitatively. There are efficient methods of alleviating the negative.

Estimating changes and trends in ecosystem extent with dense time-series satellite remote sensing.

Quantifying trends in ecosystem extent is essential to understanding the status of ecosystems. Estimates of ecosystem loss are widely used to track progress toward conservation targets, monitor deforestation, and identify ecosystems undergoing rapid change. Satellite remote sensing has become an important source of information for estimating these variables. Despite regular acquisition of satellite data, many studies of change in ecosystem extent use only static snapshots, which ignores considerable amounts of data. This approach limits the ability to explicitly estimate trend uncertainty and significance. Assessing the accuracy of multiple snapshots also requires time-series reference data which is often very costly and sometimes impossible to obtain. We devised a method of estimating trends in ecosystem extent that uses all available Landsat satellite imagery. We used a dense time series of classified maps that explicitly accounted for covariates that affect extent estimates (e.g., time, cloud cover, and seasonality). We applied this approach to the Hukaung Valley Wildlife Sanctuary, Myanmar, where rapid deforestation is greatly affecting the lowland rainforest. We applied a generalized additive mixed model to estimate forest extent from more than 650 Landsat image classifications (1999-2018). Forest extent declined significantly at a rate of 0.274%/year (SE = 0.078). Forest extent declined from 91.70% (SE = 0.02) of the study area in 1999 to 86.52% (SE = 0.02) in 2018. Compared with the snapshot method, our approach improved estimated trends of ecosystem loss by allowing significance testing with confidence intervals and incorporation of nonlinear relationships. Our method can be used to identify significant trends over time, reduces the need for extensive reference data through time, and provides quantitative estimates of uncertainty.

Bayesian Inversion for Geoacoustic Parameters in Shallow Sea.

Geoacoustic parameter inversion is a crucial issue in underwater acoustic research for shallow sea environments and has increasingly become popular in the recent past. This paper investigates the geoacoustic parameters in a shallow sea environment using a single-receiver geoacoustic inversion method based on Bayesian theory. In this context, the seabed is regarded as an elastic medium, the acoustic pressure at different positions under low-frequency is chosen as the study object, and the theoretical prediction value of the acoustic pressure is described by the Fast Field Method (FFM). The cost function between the measured and modeled acoustic fields is established under the assumption of Gaussian data errors using Bayesian methodology. The Bayesian inversion method enables the inference of the seabed geoacoustic parameters from the experimental data, including the optimal estimates of these parameters, such as density, sound speed and sound speed attenuation, and quantitative uncertainty estimates. The optimization is carried out by simulated annealing (SA), and the Posterior Probability Density (PPD) is given as the inversion result based on the Gibbs Sampler (GS) algorithm. Inversion results of the experimental data are in good agreement with both measured values and estimates from Genetic Algorithm (GA) inversion result in the same environment. Furthermore, the results also indicate that the sound speed and density in the seabed have fewer uncertainties and are more sensitive to acoustic pressure than the sound speed attenuation. The sea noise could increase the variance of PPD, which has less influence on the sensitive parameters. The mean value of PPD could still reflect the true values of geoacoustic parameters in simulation.

A comparison of remotely-sensed and inventory datasets for burned area in Mediterranean Europe

Quantitative estimate of observational uncertainty is an essential ingredient to correctly interpret changes in climatic and environmental variables such as wildfires. In this work we compare four state-of-the-art satellite fire products with the gridded, ground-based EFFIS dataset for Mediterranean Europe and analyse their statistical differences. The data are compared for spatial and temporal similarities at different aggregations to identify a spatial scale at which most of the observations provide equivalent results. The results of the analysis indicate that the datasets show high temporal correlation with each other (0.5/0.6) when aggregating the data at resolution of at least 1.0° or at NUTS3 level. However, burned area estimates vary widely between datasets. Filtering out satellite fires located on urban and crop land cover classes greatly improves the agreement with EFFIS data. Finally, in spite of the differences found in the area estimates, the spatial pattern is similar for all the datasets, with spatial correlation increasing as the resolution decreases. Also, the general reasonable agreement between satellite products builds confidence in using these datasets and in particular the most-recent developed dataset, FireCCI51, shows the best agreement with EFFIS overall. As a result, the main conclusion of the study is that users should carefully consider the limitations of the satellite fire estimates currently available, as their uncertainties cannot be neglected in the overall uncertainty estimate/cascade that should accompany global or regional change studies and that removing fires on human-dominated land areas is key to analyze forest fires estimation from satellite products.

Bayesian joint inversion of controlled source electromagnetic and magnetotelluric data to image freshwater aquifer offshore New Jersey

SUMMARY Joint inversion of multiple electromagnetic data sets, such as controlled source electromagnetic and magnetotelluric data, has the potential to significantly reduce uncertainty in the inverted electrical resistivity when the two data sets contain complementary information about the subsurface. However, evaluating quantitatively the model uncertainty reduction is made difficult by the fact that conventional inversion methods—using gradients and model regularization—typically produce just one model, with no associated estimate of model parameter uncertainty. Bayesian inverse methods can provide quantitative estimates of inverted model parameter uncertainty by generating an ensemble of models, sampled proportional to data fit. The resulting posterior distribution represents a combination of a priori assumptions about the model parameters and information contained in field data. Bayesian inversion is therefore able to quantify the impact of jointly inverting multiple data sets by using the statistical information contained in the posterior distribution. We illustrate, for synthetic data generated from a simple 1-D model, the shape of parameter space compatible with controlled source electromagnetic and magnetotelluric data, separately and jointly. We also demonstrate that when data sets contain complementary information about the model, the region of parameter space compatible with the joint data set is less than or equal to the intersection of the regions compatible with the individual data sets. We adapt a trans-dimensional Markov chain Monte Carlo algorithm for jointly inverting multiple electromagnetic data sets for 1-D earth models and apply it to surface-towed controlled source electromagnetic and magnetotelluric data collected offshore New Jersey, USA, to evaluate the extent of a low salinity aquifer within the continental shelf. Our inversion results identify a region of high resistivity of varying depth and thickness in the upper 500 m of the continental shelf, corroborating results from a previous study that used regularized, gradient-based inversion methods. We evaluate the joint model parameter uncertainty in comparison to the uncertainty obtained from the individual data sets and demonstrate quantitatively that joint inversion offers reduced uncertainty. In addition, we show how the Bayesian model ensemble can subsequently be used to derive uncertainty estimates of pore water salinity within the low salinity aquifer.

Ship-of-Opportunity Noise Inversions for Geoacoustic Profiles of a Layered Mud-Sand Seabed

This paper considers the use of broadband noise from a ship-of-opportunity in statistical inference for estimating geoacoustic parameters of a layered mud-sand seabed model via trans-dimensional (trans-D) Bayesian matched-field inversion, with applications to data collected with a bottom-moored horizontal array in the 2017 Seabed Characterization Experiment conducted on the New England Shelf. The trans-D approach applied here samples probabilistically over possible model parameterizations (different numbers of seabed layer interfaces), and provides quantitative uncertainty estimates of seabed geoacoustic profiles. Inversions are carried out for acoustic data sets collected both when the ship-of-opportunity (a container ship) was oriented with its bow and with its stern towards the array. A third inversion involved combining data from a series of segments along the ship track. Inversion results image an upper sediment layer 3–7 m thick with low-sound speed (close to the water sound speed) over higher speed sediment, with indication of a transition layer above the interface. Sediment parameter estimates from the inversions are in good agreement with direct measurements from sediment cores and other geophysical data collected in the experiment area.

Radar–Radiometer-Based Liquid Water Content Retrievals of Warm Low-Level Clouds: How the Measurement Setup Affects Retrieval Uncertainties

Here, we propose a new methodology that increases the understanding of uncertainty sources of liquid water content (LWC) retrievals, which are caused by the combination of instruments having different beam widths and are horizontally displaced. Furthermore, we give first quantitative uncertainty estimates. This paper is based on a case study of a single-layer, warm, stratiform cloud observed at the Julich Observatory for Cloud Evolution. The LWC profiles of this cloud have been forward-simulated with the passive and active radiative transfer model providing radar and microwave radiometer (MWR) observables for all cloud columns. These observables have been converted back into LWC profiles, whereas, in this case, the MWR and radar observables from different columns were combined, representing horizontal displacement. We investigate the influence of horizontal distance between a radar and an MWR on a commonly used retrieval for LWC, which scales radar reflectivity profiles with the liquid water path given by the MWR. We found that a displacement of only 10 m already introduces an additional relative uncertainty of 10%. At 100 m displacement, the relative error grows up to 30%. Additionally, different beam widths decrease the retrieval accuracy by a few percent; however, at large displacements, radiometers with larger beam widths slightly decrease the error due to the displacement. Finally, we show that cloud edge studies require optimally matched beams between the radar and the radiometer, and already a displacement of 10 m leads to unreasonable results.

Cost-effectiveness of financial incentives for improving diet and health through Medicare and Medicaid: A microsimulation study.

BackgroundEconomic incentives through health insurance may promote healthier behaviors. Little is known about health and economic impacts of incentivizing diet, a leading risk factor for diabetes and cardiovascular disease (CVD), through Medicare and Medicaid.Methods and findingsA validated microsimulation model (CVD-PREDICT) estimated CVD and diabetes cases prevented, quality-adjusted life years (QALYs), health-related costs (formal healthcare, informal healthcare, and lost-productivity costs), and incremental cost-effectiveness ratios (ICERs) of two policy scenarios for adults within Medicare and Medicaid, compared to a base case of no new intervention: (1) 30% subsidy on fruits and vegetables (“F&V incentive”) and (2) 30% subsidy on broader healthful foods including F&V, whole grains, nuts/seeds, seafood, and plant oils (“healthy food incentive”). Inputs included national demographic and dietary data from the National Health and Nutrition Examination Survey (NHANES) 2009–2014, policy effects and diet-disease effects from meta-analyses, and policy and health-related costs from established sources. Overall, 82 million adults (35–80 years old) were on Medicare and/or Medicaid. The mean (SD) age was 68.1 (11.4) years, 56.2% were female, and 25.5% were non-whites. Health and cost impacts were simulated over the lifetime of current Medicare and Medicaid participants (average simulated years = 18.3 years). The F&V incentive was estimated to prevent 1.93 million CVD events, gain 4.64 million QALYs, and save $39.7 billion in formal healthcare costs. For the healthy food incentive, corresponding gains were 3.28 million CVD and 0.12 million diabetes cases prevented, 8.40 million QALYs gained, and $100.2 billion in formal healthcare costs saved, respectively. From a healthcare perspective, both scenarios were cost-effective at 5 years and beyond, with lifetime ICERs of $18,184/QALY (F&V incentive) and $13,194/QALY (healthy food incentive). From a societal perspective including informal healthcare costs and lost productivity, respective ICERs were $14,576/QALY and $9,497/QALY. Results were robust in probabilistic sensitivity analyses and a range of one-way sensitivity and subgroup analyses, including by different durations of the intervention (5, 10, and 20 years and lifetime), food subsidy levels (20%, 50%), insurance groups (Medicare, Medicaid, and dual-eligible), and beneficiary characteristics within each insurance group (age, race/ethnicity, education, income, and Supplemental Nutrition Assistant Program [SNAP] status). Simulation studies such as this one provide quantitative estimates of benefits and uncertainty but cannot directly prove health and economic impacts.ConclusionsEconomic incentives for healthier foods through Medicare and Medicaid could generate substantial health gains and be highly cost-effective.

Assessing uncertainties in sea ice extent climate indicators

The uncertainties in sea ice extent (total area covered by sea ice with concentration >15%) derived from passive microwave sensors are assessed in two ways. Absolute uncertainty (accuracy) is evaluated based on the comparison of the extent between several products. There are clear biases between the extent from the different products that are of the order of 500 000 to 1 × 106 km2 depending on the season and hemisphere. These biases are due to differences in the algorithm sensitivity to ice edge conditions and the spatial resolution of different sensors. Relative uncertainty is assessed by examining extents from the National Snow and Ice Data Center Sea Ice Index product. The largest source of uncertainty, ∼100 000 km2, is between near-real-time and final products due to different input source data and different processing and quality control. For consistent processing, the uncertainty is assessed using different input source data and by varying concentration algorithm parameters. This yields a relative uncertainty of 30 000–70 000 km2. The Arctic minimum extent uncertainty is ∼40 000 km2. Uncertainties in comparing with earlier parts of the record may be higher due to sensor transitions. For the first time, this study provides a quantitative estimate of sea ice extent uncertainty.

Экспертная оценка соотношения затрат при строительстве нефтяной транспортной инфраструктуры в Арктике

When assessing the cost of construction and operation of transport infrastructure in the early stages of the investment process, there is a problem in the formalization of some indicators of the factors influencing decision-making. The temporary impossibility to express them in monetary or comparable natural quantities leads to uncertainty in the ratio between these two groups of factors. On the example of one of the discussed objects of oil transport infrastructure in the territory of the Russian Arctic, quantitative estimates of such uncertainties are obtained, the value of which was very weighty and variable depending on the considered alternative. The authors propose an approach based on the vector representation of factors, fuzzy set theory and expert estimates, which allows developing a criterion of the limit value of uncertainty, allowing making management decisions in the problems of choice. The corresponding model giving the possibility of calculation of such criterion is constructed.

Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional "Upwind" Scheme.

Long lead‐time space‐weather forecasting requires accurate prediction of the near‐Earth solar wind. The current state of the art uses a coronal model to extrapolate the observed photospheric magnetic field to the upper corona, where it is related to solar wind speed through empirical relations. These near‐Sun solar wind and magnetic field conditions provide the inner boundary condition to three‐dimensional numerical magnetohydrodynamic (MHD) models of the heliosphere out to 1 AU. This physics‐based approach can capture dynamic processes within the solar wind, which affect the resulting conditions in near‐Earth space. However, this deterministic approach lacks a quantification of forecast uncertainty. Here we describe a complementary method to exploit the near‐Sun solar wind information produced by coronal models and provide a quantitative estimate of forecast uncertainty. By sampling the near‐Sun solar wind speed at a range of latitudes about the sub‐Earth point, we produce a large ensemble (N = 576) of time series at the base of the Sun‐Earth line. Propagating these conditions to Earth by a three‐dimensional MHD model would be computationally prohibitive; thus, a computationally efficient one‐dimensional “upwind” scheme is used. The variance in the resulting near‐Earth solar wind speed ensemble is shown to provide an accurate measure of the forecast uncertainty. Applying this technique over 1996–2016, the upwind ensemble is found to provide a more “actionable” forecast than a single deterministic forecast; potential economic value is increased for all operational scenarios, but particularly when false alarms are important (i.e., where the cost of taking mitigating action is relatively large).

South Atlantic paleobathymetry since early Cretaceous

We present early Cretaceous to present paleobathymetric reconstructions and quantitative uncertainty estimates for the South Atlantic, offering a strong basis for studies of paleocirculation, paleoclimate and paleobiogeography. Circulation in an initially salty and anoxic ocean, restricted by the topography of the Falkland Plateau, Rio Grande Ridge and Walvis Rise, favoured deposition of thick evaporites in shallow water of the Brazilian-Angolan margins. This ceased as seafloor spreading propagated northwards, opening an equatorial gateway to shallow and intermediate circulation. This gateway, together with subsiding volcano-tectonic barriers would have played a key role in Late Cretaceous climate changes. Later deepening and widening of the South Atlantic, together with gateway opening at Drake Passage would lead, by mid-Miocene (∼15 Ma) to the establishment of modern-style thermohaline circulation.

Structural data collection with mobile devices: Accuracy, redundancy, and best practices

Smart phones are equipped with numerous sensors that enable orientation data collection for structural geology at a rate up to an order of magnitude faster than traditional analog compasses. The rapidity of measurement enables field structural geologists, for the first time, to enjoy the benefits of data redundancy and quantitative uncertainty estimates. Recent work, however, has called into question the reliability of sensors on Android devices. We present here our experience with programming a new smart phone app from scratch, and using it and commercial apps on iOS devices along with analog compasses in a series of controlled tests and typical field use cases. Additionally, we document the relationships between iPhone measurements and visible structures in satellite, drawing on a database of 3700 iPhone measurements of coseismic surface cracks we made in northern Chile following the Mw8.1 Pisagua earthquake in 2014. By comparing phone-collected attitudes to orientations determined independently of the magnetic field, we avoid having to assume that the analog compass, which is subject to its own uncertainties, is the canonical instrument. Our results suggest that iOS devices are suitable for all but the most demanding applications as long as particular care is taken with respect to metal and electronic objects that could affect the magnetic field.

Virus removal by ultrafiltration: Understanding long-term performance change by application of Bayesian analysis

Ultrafiltration is an effective barrier to waterborne pathogens including viruses. Challenge testing is commonly used to test the inherent reliability of such systems. Performance validation seeks to demonstrate the adequate reliability of the treatment system. Appropriate and rigorous data analysis is an essential aspect of validation testing. In this study we used Bayesian analysis to assess the performance of a full-scale ultrafiltration system which was validated and revalidated after five years of operation. A hierarchical Bayesian model was used to analyse a number of similar ultrafiltration membrane skids working in parallel during the two validation periods. This approach enhanced our ability to obtain accurate estimations of performance variability, especially when the sample size of some system skids was limited. This methodology enabled the quantitative estimation of uncertainty in the performance parameters and generation of predictive distributions incorporating those uncertainties. The results indicated that there was a decrease in the mean skid performance after five years of operation of approximately 1 log reduction value (LRV). Interestingly, variability in the LRV also reduced, with standard deviations from the revalidation data being decreased by a mean 0.37 LRV compared with the original validation data. The model was also useful in comparing the operating performance of the various parallel skids within the same year. Evidence of differences was obtained in 2015 for one of the membrane skids. A hierarchical Bayesian analysis of validation data provides robust estimations of performance and the incorporation of probabilistic analysis which is increasingly important for comprehensive quantitative risk assessment purposes.