Uncertainty Analysis Research Articles

Impact-based forecasting of tropical cyclone-related human displacement to support anticipatory action

Tropical cyclones (TCs) displace millions every year. While TCs pose hardships and threaten lives, their negative impacts can be reduced by anticipatory actions like evacuation and humanitarian aid coordination. In addition to weather forecasts, impact forecast enables more effective response by providing richer information on the numbers and locations of people at risk of displacement. We introduce a fully open-source implementation of a globally consistent and regionally calibrated TC-related displacement forecast at low computational costs, combining meteorological forecast with population exposure and respective vulnerability. We present a case study of TC Yasa which hit Fiji in December 2020. We emphasise the importance of considering the uncertainties associated with hazard, exposure, and vulnerability in a global uncertainty analysis, which reveals a considerable spread of possible outcomes. Additionally, we perform a sensitivity analysis on all recorded TC displacement events from 2017 to 2020 to understand how the forecast outcomes depend on these uncertain inputs. Our findings suggest that for longer forecast lead times, decision-making should focus more on meteorological uncertainty, while greater emphasis should be placed on the vulnerability of the local community shortly before TC landfall. Our open-source codes and implementations are readily transferable to other users, hazards, and impact types.

Integrating Measurement Uncertainty Analysis into Laboratory Education for the Development of Critical Thinking and Practical Skills

Integrating Measurement Uncertainty Analysis into Laboratory Education for the Development of Critical Thinking and Practical Skills

EURAD state-of-the-art report: development and improvement of numerical methods and tools for modeling coupled processes in the field of nuclear waste disposal

The Strategic Research Agenda (SRA; https://www.ejp-eurad.eu/publications/eurad-sra) of the European Joint Programme on Radioactive Waste Management (EURAD; https://www.ejp-eurad.eu/) describes the scientific and technical domains and sub-domains and knowledge management needs of common interest between EURAD participant organizations. Theme number 7 is entitled “Performance assessment, safety case development and safety analyses.” A list of research and development priorities and activities of common interest to be addressed within EURAD for theme 7 have been established. Amongst others, the Understanding and modelling of multi-physical Thermo-Hydro-Mechanical-Chemical coupled processes (THMC) occurring in radioactive waste disposal is a major and permanent issue to support optimization of design and safety case abstraction. To tackle this challenge a research work package entitled “DONUT: Development and improvement of numerical methods and tools for modelling coupled processes” has been conducted within the EURAD join programming initiative. The purpose of this work package is to improve/develop methods or numerical tools in order to go a step further in development of (i) relevant, performant and cutting-edge numerical methods that can easily be implemented in existing or new tools, in order to carry out high-performance computing to facilitate the study of highly coupled processes in large systems, (ii) numerical scale transition schemes for coupled processes, (iii) innovative numerical methods to carry out uncertainty and sensitivity analyses. In this paper the work carried out within the DONUT work package is put in perspective regarding the existing concept and literature on the field. It does not pretend to be exhaustive but rather to put emphasis on particular issues tackled during the project.

Comparative Life Cycle Assessment and Carbon Footprint of Typical Hydrogen Energy Products

To compare the environmental impact and carbon footprint of gray hydrogen, blue hydrogen, and green hydrogen, inventories were obtained through literature research. Some inventories that were not available in China were obtained through foreign inventories combined with localized power conversion. The localized end-point destructive life cycle impact assessment method was used to calculate the environmental impact potential of the raw material acquisition, transportation, and hydrogen production stages of five hydrogen products. The carbon footprint was calculated, and the sensitivity analysis and uncertainty analysis were carried out and compared with the ReCiPe method. The results showed that： ① The environmental impact from large to small was： gray hydrogen （coal） （1 203 mPt·kg-1） > blue hydrogen （coal） （876 mPt·kg-1） > gray hydrogen （gas） （492 mPt·kg-1） > green hydrogen （323 mPt·kg-1） > blue hydrogen （gas） （252 mPt·kg-1）. The environmental impacts of gray hydrogen and blue hydrogen were mainly concentrated in climate change, fine particulate matter formation, and fossil fuels. The environmental impacts of green hydrogen were mainly concentrated in climate change, fine particulate matter formation, fossil fuels, and mineral resources. ② The carbon footprint from large to small was： gray hydrogen （coal） （23.79 kg·kg-1, measured by CO2eq, the same below） > blue hydrogen （coal） （11.07 kg·kg-1） > gray hydrogen （gas） （10.97 kg·kg-1） > blue hydrogen （gas） （3.47 kg·kg-1） > green hydrogen （1.97 kg·kg-1）. Direct carbon emissions in the production process of gray hydrogen and blue hydrogen accounted for the largest proportion, whereas that of green hydrogen accounted for a large proportion of power input. ③ Measures to reduce environmental impact and carbon emissions include reducing direct emissions of pollutants and greenhouse gases, reducing power consumption, and strengthening raw material substitution and reduction.

Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using ensemble rainfall–runoff analysis (ERRA): proof of concept

Abstract. A classical approach to understanding hydrological behavior is the unit hydrograph and its many variants, but these often assume linearity (runoff response is proportional to effective precipitation), stationarity (runoff response to a given unit of rainfall is identical, regardless of when it falls), and spatial homogeneity (runoff response depends only on spatially averaged precipitation). In the real world, by contrast, runoff response is typically nonlinear, nonstationary, and spatially heterogeneous. Quantifying this nonlinearity, nonstationarity, and spatial heterogeneity is essential to unraveling the mechanisms and subsurface properties controlling hydrological behavior. Here, I present proof-of-concept demonstrations illustrating how nonlinear, nonstationary, and spatially heterogeneous rainfall–runoff behavior can be quantified, directly from data, using ensemble rainfall–runoff analysis (ERRA), a data-driven, model-independent method for quantifying rainfall–runoff relationships across a spectrum of time lags. I show how ERRA uses nonlinear deconvolution to quantify how catchments' runoff responses vary with precipitation intensity and to estimate their precipitation-weighted runoff response distributions. I further illustrate how ERRA combines nonlinear deconvolution with de-mixing techniques to reveal how runoff response depends jointly on precipitation intensity and nonstationary ambient conditions, including antecedent wetness and vapor pressure deficit. I demonstrate how ERRA's de-mixing techniques can be used to quantify spatially heterogeneous runoff responses in different parts of a catchment, even if those subcatchments are not separately gauged. I also illustrate how ERRA's broken-stick deconvolution capabilities can be used to quantify multiscale runoff responses that combine hydrograph peaks lasting for hours and recessions lasting for weeks, well beyond the average spacing between storms. ERRA can unscramble these multiple effects on runoff response even if they are overprinted on each other through time and even if they are corrupted by autoregressive moving average (ARMA) noise. Results from this approach may be informative for catchment characterization, process understanding, and model–data comparisons; they may also lead to a better understanding of storage dynamics and landscape-scale connectivity. An R script is provided to perform the necessary calculations, including uncertainty analysis.

Standardised reporting of burden of disease studies: the STROBOD statement.

The burden of disease (BOD) approach, originating with the Global Burden of Disease (GBD) study in the 1990s, has become a cornerstone for population health monitoring. Despite the widespread use of the Disability-Adjusted Life Year (DALY) metric, variations in methodological approaches and reporting inconsistencies hinder comparability across studies. To tackle this issue, we set out to develop guidelines for reporting DALY calculation studies to improve the transparency and comparability of BOD estimates. The development of the STROBOD statement began within the European Burden of Disease Network, evolving from initial concepts discussed in workshops and training sessions focused on critical analysis of BOD studies. In 2021, a working group was formed to refine the preliminary version into the final Standardised Reporting of Burden of Disease studies (STROBOD) statement, consisting of 28 items structured across six main sections. These sections cover the title, abstract, introduction, methods, results, discussion, and open science, aiming to ensure transparency and standardization in reporting BOD studies. Notably, the methods section of the STROBOD checklist encompasses aspects such as study setting, data inputs and adjustments, DALY calculation methods, uncertainty analyses, and recommendations for reproducibility and transparency. A pilot phase was conducted to test the efficacy of the STROBOD statement, highlighting the importance of providing clear explanations and examples for each reporting item. The inaugural STROBOD statement offers a crucial framework for standardizing reporting in BOD research, with plans for ongoing evaluation and potential revisions based on user feedback. While the current version focuses on general BOD methodology, future iterations may include specialized checklists for distinct applications such as injury or risk factor estimation, reflecting the dynamic nature of this field.

Statistical estimation of full-sky radio maps from 21cm array visibility data using Gaussian Constrained Realisations

Abstract An important application of next-generation wide-field radio interferometers is making high dynamic range maps of radio emission. Traditional deconvolution methods like CLEAN can give poor recovery of diffuse structure, prompting the development of wide-field alternatives like Direct Optimal Mapping and m-mode analysis. In this paper, we propose an alternative Bayesian method to infer the coefficients of a full-sky spherical harmonic basis for a drift-scan telescope with potentially thousands of baselines, that can precisely encode the uncertainties and correlations between the parameters used to build the recovered image. We use Gaussian Constrained Realisations (GCR) to efficiently draw samples of the spherical harmonic coefficients, despite the very large parameter space and extensive sky-regions of missing data. Each GCR solution provides a complete, statistically-consistent gap-free realisation of a full-sky map conditioned on the available data, even when the interferometer’s field of view is small. Many realisations can be generated and used for further analysis and robust propagation of statistical uncertainties. In this paper, we present the mathematical formalism of the spherical harmonic GCR-method for radio interferometers. We focus on the recovery of diffuse emission as a use case, along with validation of the method against simulations with a known diffuse emission component.

ФОРСАЙТ ЯК ІНСТРУМЕНТ ВІДПОВІДАЛЬНОГО ДЕРЖАВНОГО УПРАВЛІННЯ І СТРАТЕГУВАННЯ МІЖНАРОДНОЇ ЕКОНОМІЧНОЇ ПОЛІТИКИ В УМОВАХ ЗАГОСТРЕННЯ ГЛОБАЛЬНИХ ПРОБЛЕМ РОЗВИТКУ

The purpose of the article is to study strategic long-term planning tools that allow timely identification of the latest risks, challenges and threats to international economic development that make it difficult to solve global problems that are a consequence of failures in the coordination of international economic policy. It has been determined that strategic forecasting allows politicians to increase the efficiency of government decision-making by identifying opportunities and threats that may arise in the coming years and decades, and also involves the development of possible strategies to counter them by taking into account the characteristics of the identified trends. The phenomenon of the new economy is characterized. The influence of technological development on the asymmetry of international economic development is determined. It has been established that the problems of international economic development in foresight strategizing include geopolitical rivalry, resource confrontation, energy dependence, deglobalization, disintegration processes, trade fragmentation, gaps in value and supply chains, reinstitutionalization of the system of international economic relations, recession, population aging, unemployment. Foresight forecasting tools that are not aimed at predicting a single “correct future” include: analysis of long-term trends and uncertainties; horizon scanning); scenario planning; shaping a normative future; political games; futures wheels; causal multilevel analysis. Research into long-term trends and uncertainties reveals key factors in different possible futures. The comprehensive observation carried out allows us to identify risks for the macroeconomic environment and proactively formulate a pool of measures to minimize the destructive impact of instability on socio-economic development. Improving foresight research as a critical element of strategic forecasting for solving problems of long-term development affects both theoretical aspects (conceptual approach, conceptual apparatus) and applied aspects (technologies and methods of development) of the development and use of this tool. Strategic planning allows you to develop adequate tools to achieve goals and proactively respond to the challenges of modern development, minimizing costs and compensating for the losses that arise for this reason, which lead to the loss of existing competitive advantages. The difference between forecasts and foresights is noted: forecasts are usually associated with uncontrollable events, while foresights most often deal with events and processes that are clearly controllable, therefore they assess the probability of not only one or another outcome of future events, but also the adoption of certain strategic and management decisions. The ability to distinguish normative approaches (what we want) from pragmatic approaches (what we should) is an important component of thinking in terms of alternative futures

Algorithm Analysis and Optimization of a Digital Image Correlation Method Using a Non-Probability Interval Multidimensional Parallelepiped Model

Digital image correlation (DIC), a widely used non-contact measurement technique, often requires empirical tuning of several algorithmic parameters to strike a balance between computational accuracy and efficiency. This paper introduces a novel uncertainty analysis approach aimed at optimizing the parameter intervals of a DIC algorithm. Specifically, the method leverages the inverse compositional Gauss–Newton algorithm combined with a prediction-correction scheme (IC-GN-PC), considering three critical parameters as interval variables. Uncertainty analysis is conducted using a non-probabilistic interval-based multidimensional parallelepiped model, where accuracy and efficiency serve as the reliability indexes. To achieve both high computational accuracy and efficiency, these two reliability indexes are simultaneously improved by optimizing the chosen parameter intervals. The optimized algorithm parameters are subsequently tested and validated through two case studies. The proposed method can be generalized to enhance multiple aspects of an algorithm’s performance by optimizing the relevant parameter intervals.

Evaluation of aerial thermography for measuring the thermal transmittance (U-value) of a building façade

In efforts to mitigate greenhouse gas emissions in the construction sector, policies promoting energy efficiency improvements in existing buildings are being implemented. Accurately assessing the impact of energy retrofit strategies requires precise determination of thermal envelope performance through energy audits. Aerial thermography, employing unmanned aerial vehicles (UAVs) equipped with infrared thermal cameras (IRT), is an underused method overcoming limitations in energy audits of large buildings or complexes. This approach offers an elevated perspective, providing an overview of the building’s thermal patterns, aiding in the identification of thermal envelope defects. By measuring temperature distribution, even in inaccessible envelope areas, the severity of identified defects can be estimated. Beyond detecting thermal bridges, air leaks, and moisture, aerial thermography is valid for estimating thermal transmittance (U-value) of enclosures. The aim of this research is to evaluate this method to calculate the U-value of the façade of an university building located in a Subtropical Mediterranean climate, during a typical full day in different seasons of the year. Concurrently with aerial thermography, the thermometric method (THM) was applied to compare results and refine the operational conditions during U-value measurement using different statistical indices and uncertainty analysis. The results demonstrated the validation of airborne thermography as a method for U-value calculation under specific meteorological and operational conditions, overcoming the common physical limitations of traditional building audit methods.

Uncertainty and sensitivity analysis of non-acoustic parameters on acoustic characteristics prediction models for porous foam materials

Porous foam materials are widely used in noise control applications. Their acoustic performance can be predicted by acoustic models composed of non-acoustic parameters such as flow resistivity, porosity, tortuosity, viscosity, and characteristic length. However, the influence of these parameters on model outputs varies across different frequency ranges. Therefore, when using a single model for predictions across the entire frequency range, parameters may not fully act within the frequency range where their influence is greatest. To reduce prediction errors and improve the correlation between predicted numerical results and experimental data, uncertainty and sensitivity analyses can be conducted to determine the extent of each parameter's influence at different frequency ranges. In this study, polyurethane foam materials are investigated, and uncertainty and sensitivity analyses are performed on two acoustic models. Firstly, the uncertainty of parameters is assessed using the Monte Carlo method. Secondly, Sobol index method is employed to reveal the influence of parameters across various frequency ranges. Finally, by comparing the results of both analyses, the optimal frequency ranges for each parameter can be summarized. This provides valuable reference for enhancing the accuracy of acoustic model predictions.

Quantitative analysis of failure performance uncertainty based on multiscale mechanical analysis of PEMFCs’ C/C composite bipolar plates

In order to quantify the failure performance of PEMFCs’ C/C composite bipolar plates (BPs) under considering the uncertainty in multiscale sizes and loads, the failure probability function (FPF) of BPs at cold start is estimated. A three-stage multiscale simulation method is proposed for mechanical behavior analysis of multi-component heterogeneous composites. And the efficient estimation of FPF for C/C composite BPs is achieved via the proposed multiscale simulation method and decoupling sparse integration method, in which the unified density weight is constructed to decouple the double-loop framework of analyzing FPF, thereby reducing the computational complexity. Results indicate that the dangerous positions of BPs are around the gas flow channels and bolt holes. And the dangerous components are the carbon matrix and the interface between the matrix and fiber. The radius of elliptical fibers affects the distribution of failure factors on microscopic components, thereby affecting the variation of failure probability with fiber sizes significantly. The distribution parameter of composite layer thickness in BPs has a significant effect on structural safety, and the FPF result can be used to effectively guide the design of structure. Therefore, the proposed method is of great significance for guiding the rapid parameter design of composite BPs.

An uncertainty‐based evaluation of sulphuric compounds transport in reservoirs considering sensitivity analysis

AbstractDeepwater bodies such as dam reservoirs are among the most prominent water resources that supply societies' water demand. Hence, water quality in these resources requires continuous and meticulous monitoring. This study evaluated the water quality of Changuleh Dam, a dam under construction in western Iran, using the CE‐QUAL‐W2 model to predict fluctuations in sulphate (SO42−) and hydrogen sulphide (H2S) concentrations. A sensitivity analysis was conducted to assess the dependence of model outputs on input variables. Additionally, an uncertainty analysis was performed using MATLAB to address the inherent uncertainties of the input variables through an ARIMA (2, 2, 1) time series model and Monte Carlo simulations. The results indicated a high sulphate concentration averaging 955.6 mg/l, considerably exceeding the drinking water standard of 400 mg/l, identifying the reservoir as a sulphate‐rich water resource. Seasonal fluctuations of H2S concentrations were also observed in the reservoir's anaerobic bed sediments, peaking at 87 mg/l. The sensitivity analysis showed that nitrate and total dissolved solids (TDS) varied more noticeably than dissolved oxygen (DO) at different depths, with the greatest variation at 50 m. This study highlights the critical need for continuous and meticulous monitoring of water quality in dam reservoirs to meet water supply standards.

Model-based experiments as epistemic evidence in paleoecology

Where ordinary experiments are impossible and observational data scarce and indirect—particularly in paleoecosystems—computational experiments are often our only means to learn about reality. There are good arguments to count such model-based predictions as evidence, testing hypotheses and updating our beliefs about the world. However, the epistemic weight of computational experiments depends on an adequate model representation of the target system, transparency about predictive uncertainty, and the avoidance of confirmation bias. I argue that mechanistic models are particularly suited for paleoecological predictions but that iterative uncertainty analyses should guide their development. Using a Bayesian framework I propose preregistration and blinded analysis as tools to strengthen the epistemic value of computational experiments. Here, a preregistration marks the boundary between exploratory model development, which establishes credence in the model, and predictive model application, which tests hypotheses. As good modeling practice I suggest clarifying epistemic goals at the outset of a project and accordingly choose methods to maximize the epistemic weight of the computational experiment.

Uncertainty and sensitivity analysis of the fission product behaviour in the Phébus FPT1 test with the system code AC2

The Phèbus FP (Fission Product) research program, conducted by IRSN and CEA, aimed to improve the understanding of the phenomena governing core melt down, fission product release and transport, as well as the fission product behaviour in the containment. The test facility represents a 900 MWe PWR at a 1/5000 scale. The second test, FPT1, was carried out in 1996 and utilised an Ag-In-Cd alloy control rod. There is a growing interest in Best Estimate Plus Uncertainty analysis both in the research, as well as in the licensing. It has a long-standing history in case of thermal–hydraulic system codes focusing on design basis accidents, but its application is limited on the severe accident domain. In recent times however an increasing attention has been paid to evaluating severe accident codes’ uncertainties. This study presents an uncertainty and sensitivity analysis (UaSA) of the FPT1 test, focusing on fission product behavior modeling within the severe accident code system AC2. The detailed application of the coupled codes ATHLET-CD and COCOSYS in this context is novelty. Detailed description of the methodological and practical approach is provided. The uncertain input parameters chosen and quantified are directly related to the phenomena describing the fission product transport and retention phenomena both in the primary circuit, as well as in the containment, while thermal–hydraulic conditions within the primary circuit were kept as much as possible constant. Finally, the study has been complemented by a sensitivity analysis deriving the Spearman's rank correlation coefficient for each uncertain input parameter.

COFUN: A new tool for evaluating containment failure frequency and uncertainty

The feature of the COntainment Failure probability and Uncertainty aNalysis program (COFUN) is the quantification of the containment state. The probability factors used in probabilistic safety assessment (PSA) and the resulting outputs contain probability bands, so it is necessary to present the uncertainty bands as results. The uncertainty analysis process of the level 2 PSA is that a level 1 PSA uncertainty propagates to a level 2 PSA uncertainty. The basic event data including probability distribution were used for the level 1 PSA uncertainty analysis and probability distribution and priority of the fractional branch point were used for uncertainty analysis in a level 2 PSA. Once the input is ready, uncertainty analysis is performed via Monte-Carlo sampling. Finally, the uncertainty analysis can be performed for source term category (STC) and large early release frequency (LERF). One of the outstanding characteristics of the COFUN is to perform level 2 PSA for multi-unit accidents. The one of formats of multi-unit level 2 PSA is the combination of STCs but the combinations could be too many to acquire insight by STC combination itself. The COFUN provides a user-defined combination that makes STC combinations classified and enables the derivation of multi-unit accident LERF result.

An instrumentation guide to measuring thermal conductivity using frequency domain thermoreflectance (FDTR).

Frequency Domain Thermoreflectance (FDTR) is a versatile technique used to measure the thermal properties of thin films, multilayer stacks, and interfaces that govern the performance and thermal management in semiconductor microelectronics. Reliable thermal property measurements at these length scales (≈10nm to ≈10μm), where the physics of thermal transport and phonon scattering at interfaces both grow in complexity, are increasingly relevant as electronic components continue to shrink. While FDTR is a promising technique, FDTR instruments are generally home-built; they can be difficult to construct, align, and maintain, especially for the novice. Our goal here is to provide a practical resource beyond theory that increases the accessibility, replicability, and widespread adoption of FDTR instrumentation. We provide a detailed account of unpublished insights and institutional knowledge that are critical for obtaining accurate and repeatable measurements of thermal properties using FDTR. We discuss component selection and placement, alignment procedures, data collection parameters, common challenges, and our efforts to increase measurement automation. In FDTR, the unknown thermal properties are fit by minimizing the error between the phase lag at each frequency and the multilayer diffusive thermal model solution. For data fitting and uncertainty analysis, we compare common numerical integration methods, and we compare multiple approaches for fitting and uncertainty analysis, including Monte Carlo simulation, to demonstrate their reliability and relative speed. The instrument is validated with substrates of known thermal properties over a wide range of isotropic thermal conductivities, including Borofloat silica, quartz, sapphire, and silicon.

Nanoscale Os isotopic quantification of Wadi Tayin dunite platinum group minerals by atom probe tomography

Serpentinites record processes that redistribute major and trace elements between mantle and crust. Platinum Group Elements (PGEs) are trace elements in serpentinites hosted in sulphides and alloys. Alloys are challenging to find, and most analytical techniques lack the spatial resolution to analyse them; hence, this research adopts automatic mineral mapping technique to detect PGM grains in a sample from the Wadi Tayin (Oman) peridotite and uses atom probe tomography, a nanoscale quantitative analytical technique, to analyse the grain. This work applies and assesses the applicability of atom probe tomography to measure the 187Os/188Os isotopic ratio of natural alloys and uses the ratio to constrain the source of Os. A novel algorithm is used to automatically determine the number of counts of the 187Os and 188Os peaks, to calculate the isotopic 187Os/188Os ratio and the analytical uncertainty. The 187Os/188Os ratio is 0.126 ± 0.003, consistent with the isotopic composition reported by literature in the dunite of the Main Mantle Section of the Wadi Tayin ophiolite.The analytical uncertainty is one order of magnitude higher than conventional bulk rock techniques, such as negative-thermal ionisation mass spectrometry (N-TIMS) and inductively coupled plasma mass spectrometry (ICP-MS). However, the precision is sufficient to conclude that the non-radiogenic 187Os/188Os ratio is compatible with a mantle origin for the alloy. Decreasing whole-rock Re with increasing LOI and the overprinting of magmatic pentlandite by magnetite demonstrate that progressive serpentinisation may have modified the Re budget. The results indicate that atom probe tomography can analyse 187Os/188Os ratio quantitatively in micron-sized natural alloys and provide insights into natural processes.

Implementing Superresolution of Nonstationary Tides with Wavelets: An Introduction to CWT_Multi

Abstract Tides are often nonstationary due to nonastronomical influences. Investigating variable tidal properties implies a trade-off between separating adjacent frequencies (using long analysis windows) and resolving their time variations (short analysis windows). Previous continuous wavelet transform (CWT) tidal methods resolved tidal species. Here, we present CWT_Multi, a MATLAB code that 1) uses CWT linearity (via the “response coefficient method”) to implement superresolution, i.e., resolving tidal constituents beyond the Rayleigh criterion; 2) provides a Munk–Hasselmann constituent selection criterion appropriate for superresolution; and 3) introduces an objective, time-variable form of inference (“dynamic inference”) based on time-varying data properties. CWT_Multi resolves tidal species on time scales of days, and multiple constituents per species with fortnightly filters. It outputs astronomical phase lags and admittances, analyzes multiple records, and provides power spectra of the signal(s), residual(s), and reconstruction(s); confidence limits; and signal-to-noise ratios. Artificial data and water levels from the Lower Columbia River Estuary (LCRE) and San Francisco Bay Delta (SFBD) are used to test CWT_Multi and compare it to harmonic analysis programs NS_Tide and UTide. CWT_Multi provides superior reconstruction, detiding, dynamic analysis utility, and time resolution of constituents (but with broader confidence limits). Dynamic inference resolves closely spaced constituents (like K1, S1, and P1) on fortnightly time scales, quantifying impacts of diel power peaking (with a 24-h period, like S1) on water levels in the LCRE. CWT_Multi also helps quantify the impacts of high flows and a salt barrier closing on tidal properties in the SFBD. On the other hand, CWT_Multi does not excel at prediction, and results depend on analysis details, as for any method applied to nonstationary data. Significance Statement Ocean tides, especially in coastal and estuarine systems, are often nonstationary, in the sense that the mean and standard deviation of tidal properties vary over time, usually in response to some nontidal process. We introduce here a MATLAB code, CWT_Multi, that uses wavelet transforms to resolve both tidal species and constituents on time scales from a few days to months. Our code accommodates multiple scalar time series and has typical tidal analysis features like constituent selection and inference, plus two forms of uncertainty analyses. It is flexible, allowing the user to adapt analysis properties to diverse datasets. CWT_Multi is applicable to many problems involving time-variable tides, including sea level rise, compound flooding, sediment transport, and wetland habitat analyses. Application to vector data is a straightforward extension, but further development of our uncertainty analysis is merited. Because nonstationary tidal analysis is rapidly advancing, we also define the features of a “well-formed” analysis code.

Review on Uncertainty Analysis and Information Fusion Diagnosis of Aircraft Control System

Review on Uncertainty Analysis and Information Fusion Diagnosis of Aircraft Control System