Physical Uncertainties Research Articles

The experience of women following first acute coronary syndrome: An integrative literature review

There is lack of evidence and research understanding among women's lived experiences following first acute coronary syndrome, thus their recovery process remains poorly understood. To date research has largely focused on men's experience of acute coronary syndrome while this area of health care and recovery has considerable impact on women's health and quality of life. Our aim was to review the literature exploring lived experience of women following first acute coronary syndrome. Integrative review of the literature. We searched PubMed, MEDLINE, EMBASE, CINAHL and Scopus from 2008-2018 for articles published in English. Of 1675 publications identified, 18 qualitative, quantitative, and mixed method studies met our inclusion criteria. Quality of included studies was assessed using Joanna Briggs Institute quality assessment tools. Findings were integrated using thematic synthesis. Experiencing acute coronary syndrome was reported to have significant impacts on women's lives. The most common issues reported were physical limitations, fear, and uncertainties about the future, sexual dissatisfaction, and social isolation. Women also reported to have higher short- and long-term mortality rate, stroke, recurrent, and hospital readmissions compared with men. This review identified current knowledge and gaps about lived experience of women following first acute coronary syndrome. It is anticipated that the information gained from this literature review will support new research aimed at improving the care women receive following acute coronary syndrome and therefore enhance their recovery and quality of life. This review contributes to the current body of knowledge by addressing women's physical, psychosocial, and sexual state following acute coronary syndrome. Improvement in women's quality of life after acute coronary syndrome necessitates further research which ultimately results in better management and treatment of women and their recovery following first acute coronary syndrome.

Актуальні проблеми забезпечення теплогідравлічної надійності перспективних ядерних реакторів з надкритичними параметрами

There is a significant lack of reliable information on the physical characteristics of thermohydraulic processes in emergency heat transfer modes when cooling the surface of fuel rods with light water coolant with supercritical thermodynamic parameters, in particular, on the physics of heat transfer processes and hydromechanics in the critical area. It is shown that in these conditions there is physical uncertainty about the causes of deteriorating heat transfer, which limits the possibility of creating effective calculation techniques for reliable determination of the upper limit of safe forcing of the heat transfer process in the core. At present, the vast majority of theoretical and experimental studies of thermohydraulic processes in the near-critical area have been performed only for the socalled “normal” heat transfer, which corresponds to the heat removal conditions with mixed turbulent convection of superheated to “gas” state of light water coolant in its inertial mode. Attention is paid to the possible appearance of macromolecular ensembles on this surface in the form of pseudo-vapor formations, which are capable of causing an emergency mode of pseudo-film boiling. On the basis of the given experimental data of various authors existence of rather deep physical analogy between processes of heat exchange in supercritical thermodynamic system and unheated boiling at subcritical parameters of the heat carrier is proved. Existence of the pseudo-boiling process in the conditions of supercritical thermodynamic parameters makes it impossible to use in the thermohydraulic calculation the empirical dependences for “hot” gas for the range of active zones operational parameters.

Variance-based sensitivity analysis for uncertainties in proton therapy: A framework to assess the effect of simultaneous uncertainties in range, positioning, and RBE model predictions on RBE-weighted dose distributions.

Treatment plans in proton therapy are more sensitive to uncertainties than in conventional photon therapy. In addition to setup uncertainties, proton therapy is affected by uncertainties in proton range and relative biological effectiveness (RBE). While to date a constant RBE of 1.1 is commonly assumed, the actual RBE is known to increase toward the distal end of the spread-out Bragg peak. Several models for variable RBE predictions exist. We present a framework to evaluate the combined impact and interactions of setup, range, and RBE uncertainties in a comprehensive, variance-based sensitivity analysis (SA). The variance-based SA requires a large number (104 -105 ) of RBE-weighted dose (RWD) calculations. Based on a particle therapy extension of the research treatment planning system CERR we implemented a fast, graphics processing unit (GPU) accelerated pencil beam modeling of patient and range shifts. For RBE predictions, two biological models were included: The mechanistic repair-misrepair-fixation (RMF) model and the phenomenological Wedenberg model. All input parameters (patient position, proton range, RBE model parameters) are sampled simultaneously within their assumed probability distributions. Statistical formalisms rank the input parameters according to their influence on the overall uncertainty of RBE-weighted dose-volume histogram (RW-DVH) quantiles and the RWD in every voxel, resulting in relative, normalized sensitivity indices (S=0: noninfluential input, S=1: only influential input). Results are visualized as RW-DVHs with error bars and sensitivity maps. The approach is demonstrated for two representative brain tumor cases and a prostate case. The full SA including RWD calculations took 39, 11, and 55min, respectively. Range uncertainty was an important contribution to overall uncertainty at the distal end of the target, while the relatively smaller uncertainty inside the target was governed by biological uncertainties. Consequently, the uncertainty of the RW-DVH quantile D98 for the target was governed by range uncertainty while the uncertainty of the mean target dose was dominated by the biological parameters. The SA framework is a powerful and flexible tool to evaluate uncertainty in RWD distributions and DVH quantiles, taking into account physical and RBE uncertainties and their interactions. The additional information might help to prioritize research efforts to reduce physical and RBE uncertainties and could also have implications for future approaches to biologically robust planning and optimization.

High-Order Observer-Based Sliding Mode Control for the Isolated Microgrid with Cyber Attacks and Physical Uncertainties

System security is essential for the operation of the island microgrid. However, the system security is generally threatened due to the presence of physical uncertainties and cyber attacks. In this article, a novel sliding mode load control strategy is proposed for the microgrid to mitigate cyber attacks and physical uncertainties. Firstly, a high-order disturbance observer (HODO) is designed to estimate the unmeasurable factors in the microgrid. Secondly, a HODO-based sliding mode control (SMC) strategy is proposed where the estimated value observed by the HODO is applied to the sliding mode surface and control law. It can better guarantee the security of the isolated microgrid. Then, the stability of the HODO-based SMC is demonstrated by Lyapunov stability theory. Finally, simulation results show that the proposed control strategy has excellent control performance.

A Compact 2-D Stochastic FDTD Method for Uncertainty Analysis in Superconducting Transmission Lines

In this article, a stochastic finite-difference time-domain (S-FDTD) method is proposed for analyzing the influence of physical size uncertainty on the time-domain response of superconducting transmission lines. The formulations of the proposed method are, in detail, derived with the two-fluid model and London equation. A typical superconducting microstrip line is simulated to verify the effectiveness of the proposed algorithm. Compared with the Monte Carlo simulation, the proposed method has the same accuracy but much higher efficiency. The computation time of the proposed S-FDTD method is only about 3% of that of the Monte Carlo simulation. Therefore, the proposed method shows a promising prospect on predicting the variability in transmission line's performance caused by variation in superconducting manufacturing.

An Electrical Impedance Tomography Based Interface for Human–Robot Collaboration

Adaptation to the operator's intents and the external physical uncertainties is critical to the performances of the human–robot collaboration. In this study, we proposed a control interface that combined the electrical impedance tomography (EIT) based sensing approach with the robotic controllers to produce proper assistance with external uncertainties in the collaborative task. The interface first estimates the continuous forearm muscle contractions (represented by the grasp forces) by the optimized EIT features with an easily worn fabric band. The recognition decisions then serve as the control inputs to adjust the state transitions and the desired interaction forces in real time. We evaluated the interface in the tasks of grasp force estimation and human–robot sawing by recruiting more than 20 subjects in total. For grasp force estimation, the interface produced an average of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}\geq 0.9$</tex-math></inline-formula> in both offline and online validations with the feature optimization procedure and a sigmoid regression function. The interface was robust to external disturbances without retraining or manual calibrations. The average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}=0.86$</tex-math></inline-formula> with the untrained dynamic postures, and the average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> values ranged from 0.85 to 0.88 in the tests with redonning the front-end in interday and intraday uses. For human–robot sawing, the interface accomplished the tasks with a high success rate in controlling the states ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 96%) and intuitive adjusting of the sawing forces, being combined with the designed hybrid admittance/position controller. It was also adaptive to the sawing frequency changes and the sawing directions according to the operator's intents. The interface's performances are comparable, if not better, to the state of the art on both biological signal based grasp force estimation and human–robot sawing. Future efforts are worth being paid in this new direction to get more promising outcomes.

Research Scholar, English Department, North-Eastern Hill University,

While COVID-19 has unleashed waves of reverse-migration from the cities back to the hometowns due to economic and physical uncertainties that accompanied the pandemic, queer people from the North-Eastern Region of India choose to stay in the cities. Defying the reverse-migration trend, they choose the freedom that the city gives them over the prejudice of their hometown toward their sexuality. This paper will theorize how pandemic affects and at the same time affirms queer sexualities This paper will re-interpret metronormativity in the context of North-East queer people and will co-opt Judith Halberstam’s theory while at the same time reworking it to fit the local context of queer North-East. Unlike Halberstam’s theory that the metro offers a continuum of free existence to the queer people, this paper will examine certain queer narratives both online and through interviews in order to understand how the city offers not a “freer” existence ( in the sense that freedom is given and implied upon) but rather a relatively anonymous existence which allows them to live freely in anonymity but never silent. It will problematize the concept of home and space and how queer subjectivities are (un)formed depending upon certain variables that the home offers which eventually affects queer existence. This existence will then juxtapose with Kosofsky’s “gesture of silence” of the home and the closet and in doing so, it will attempt to understand North East queer’s preference for the danger of COVID 19 and the “insecurities” of the city over the “security” of home.

Uncertainty quantification of percolating electrical conductance for wavy carbon nanotube-filled polymer nanocomposites using Bayesian inference

This research focuses on the uncertainty quantification of electrical percolation behavior in wavy carbon nanotube (CNT)-filled polymer nanocomposites with a three-dimensional representative volume element accounting for both tunneling resistance (quantum carrier tunneling) and stochasticity in CNT waviness. The developed percolation model is validated with existing experimental data, and model parameters for electrical conductance converge to the optimal value with Markov Chain Monte Carlo (MCMC) based on Bayesian inference. The predicted 95% confidence interval of electrical conductance indicates a different trend between two-and three-parameters of the electrical conductance model. The main trend of correlation between the percolation threshold (φc) and a parameter of the phase transition (critical exponent, t) indicates a statistically linear relationship via evaluation of the Pearson correlation coefficient. Moreover, the correlation between intrinsic conductance of CNTs (σo) and t also strongly affect the magnitude and slope of electrical conductance in uncertainty quantification. This work can contribute to a robust and reliable design of the PNC considering the physical uncertainty satisfying the target electrical performance through controlling φc, σo, and t.

‘I go to school to survive’: Facing physical, moral and economic uncertainties in rural Lesotho

ABSTRACT In Lesotho, when children or adults talk about the importance of schooling, they frequently use the term ho iphelisa. This is usually translated as ‘to survive’, reflecting the uncertainties that people in this small country have confronted over recent decades: rapidly diminishing employment opportunities, extremely high HIV prevalence and environmental crises. Based on nine months of ethnographic fieldwork in two rural primary schools and their neighbouring communities, we examine how the idea of survival motivates engagement with education. We find that ‘survival’ permeates the school curriculum and the discourse of children, parents and teachers, and encompasses three distinct but related dimensions: economic, moral and physical. We also highlight how these aspects of survival are both individual and collective, and operate across different temporalities. Through this, we contribute to understanding the complexities of educational aspiration and motivation in contexts of uncertainty.

Computational and Experimental Modal Analysis of a Three Storied Building Model

Vibration-based health monitoring assists in determining the modal parameters and damage identification of a structure. This paper presents the computational and experimental modal analysis of a single bay three-storied building with steel slabs and aluminium columns. Three-dimensional finite element (FE) model of the structure is analysed using ABAQUS, and natural frequencies in the first three translational modes are determined. These results are compared with those obtained from field impact testing. A small variation in analytical and experimental results may be due to the simplified assumptions in FEA, physical and numerical uncertainties.

Exploring the Lower Mass Gap and Unequal Mass Regime in Compact Binary Evolution

Abstract On 2019 August 14, the LIGO and Virgo detectors observed GW190814, a gravitational-wave signal originating from the merger of a black hole (BH) with a compact object. GW190814's compact-binary source is atypical both in its highly asymmetric masses and in its lower-mass component lying between the heaviest known neutron star (NS) and lightest known BH in a compact-object binary. If formed through isolated binary evolution, the mass of the secondary is indicative of its mass at birth. We examine the formation of such systems through isolated binary evolution across a suite of assumptions encapsulating many physical uncertainties in massive-star binary evolution. We update how mass loss is implemented for the neutronization process during the collapse of the proto-compact object to eliminate artificial gaps in the mass spectrum at the transition between NSs and BHs. We find it challenging for population modeling to match the empirical rate of GW190814-like systems while simultaneously being consistent with the rates of other compact binary populations inferred from gravitational-wave observations. Nonetheless, the formation of GW190814-like systems at any measurable rate requires a supernova engine model that acts on longer timescales such that the proto-compact object can undergo substantial accretion immediately prior to explosion, hinting that if GW190814 is the result of massive-star binary evolution, the mass gap between NSs and BHs may be narrower or nonexistent.

Exponentially convergence for the regressor-free adaptive fuzzy impedance control of robots by gradient descent algorithm

Having the capability to estimate parametric and non-parametric uncertainties, this paper investigates a regressor-free adaptive model-reference scheme for impedance control of robotic systems interacting with an environment. Using the gradient descent algorithm for designing a fuzzy estimator makes the tune of controller’s parameters possible in different physical situations and uncertainties. Thanks to the voltage control strategy and fuzzy systems, there is no need for designers to have the knowledge of robots and actuators’ dynamics. In addition, the force feedback is not employed in the structure of control law. Using the Lyapunov-like stability analysis, not only the exponential convergence of error signal and its time derivative to zero are guaranteed in the decentralised form but also the boundedness of all signals is ensured. To confirm the efficiency of proposed control algorithm, several simulations as well as a comparison with a positioned-based impedance controller and a regressor-based adaptive impedance controller are conducted on a two-link planar robot manipulator considering a tunable desired trajectory and different stiffness of environment. Additionally, the proposed adaptive impedance controller is applied to a six degree of freedom revolute-joint manipulator to provide better evaluation.

The elusive topic of rainfall-induced surge waves in rivers: lessons from canyon accidents

ABSTRACT Rainfall-induced surge waves in rivers are still today considered as an elusive and non-realistic phenomenon that has continued to mystify researchers and practitioners alike since the kinematic shock theory introduced in 1955. This is mainly due to scarce sightings of real cases, as well as physical and numerical uncertainties and sporadic calculations. In order to overcome such indefiniteness, a first set of documented real cases of surge waves in canyon rivers has been collected and their dreadful consequences highlighted. Then, the physical mechanism of surge wave development and propagation and the role of wave celerity differences are examined. A novel parameter (relating wave celerity and cross-section geometry) is introduced and used to highlight few relevant characteristics of rainfall-induced surge waves. The role of floods faster than ordinary flash floods (here defined as instant floods) is demonstrated. The conclusions are confirmed with a series of numerical tests for ideal and natural channels. Finally, differences from ordinary flash floods are identified in the framework of risk assessment, as well as the perspective to revisit real cases of surge waves in canyons and other streams.

Optimal design of redundant structural systems: fundamentals

In spite of extended recent interest in System Reliability-Based Design Optimization (System RBDO) and life-cycle cost or Risk Optimization (RO), there is a lack of published studies on optimal design of redundant hyperstatic systems with objective consideration of (a) progressive collapse and (b) the impact of epistemic uncertainties. This paper investigates the fundamental aspects of the problem, by addressing the optimal design of simple two-bar active and passive redundant systems. Progressive collapse is objectively addressed, differentiating consequences of direct collapse of statically determinate structures, and progressive collapse of redundant, statically indeterminate structures. A comprehensive study is performed, considering material post-failure behavior (fragile-ductile), strength correlation, dynamic amplification factors in load re-distribution, and material strength ratios. Physical uncertainty in material strengths and loads is considered. However, it is well known that reliability of a structural system also depends on nonstructural factors, or factors beyond structural design, such as unanticipated loading, manufacturing quality, quality of workmanship and human errors. These factors can be taken into account in an encompassing risk analysis, which accounts for physical and epistemic uncertainties, and which contributes a fixed latent failure probability to the structural optimization problem. Results presented herein show that the latent failure probability is the single most important parameter in determining optimal solutions, in System RBDO and in RO solutions. When the latent failure probability is smaller than target (System RBDO) or optimal (RO) failure probabilities, there is an equivalence between redundant and non-redundant (hyperstatic and isostatic) designs. However, when the latent reliability is smaller than target or optimal reliabilities, optimal designs become necessarily redundant (hyperstatic); as the only way to make system reliability larger than the latent reliability is by making structural systems redundant. This result is widely known in context of system reliability, but has been overlooked in past studies involving structural System RBDO and Risk Optimization.

Prompt gamma spectroscopy for absolute range verification of 12C ions at synchrotron-based facilities

The physical range uncertainty limits the exploitation of the full potential of charged particle therapy. In this work, we face this issue aiming to measure the absolute Bragg peak position in the target. We investigate p, 4He, 12C and 16O beams accelerated at the Heidelberg Ion-Beam Therapy Center. The residual range of the primary 12C ions is correlated to the energy spectrum of the prompt gamma radiation. The prompt gamma spectroscopy method was demonstrated for proton beams accelerated by cyclotrons and is developed here for the first time for heavier ions accelerated by a synchrotron. We develop a detector system that includes (i) a spectroscopic unit based on cerium(III) bromide and bismuth germanium oxide scintillating crystals, (ii) a beam trigger based on an array of scintillating fibers and (iii) a data acquisition system based on a FlashADC. We test the system in two different scenarios. In the first series of experiments, we detect and identify 19 independent spectral lines over a wide gamma energy spectrum in the presence of the four ion species for different targets, including a water target with a titanium insert. In the second series of experiments, we introduce a collimator aiming to relate the spectral information to the range of the primary particles. We perform extensive measurements for a 12C beam and demonstrate submillimetric precision for the measurement of its Bragg peak position in the experimental setup. The features of the energy and time spectra for gamma radiation induced by p, 4He and 16O are investigated upstream and downstream from the Bragg peak position. We conclude the analysis by extrapolating the required future developments, which would be needed to achieve range verification with a 2 mm accuracy during a single fraction delivery of physical dose.

Relationships between depression, family function, physical symptoms, and illness uncertainty in female patients with chronic kidney disease.

This study investigated the relationship between depression, family function, physical symptoms, and illness uncertainty in women with chronic kidney disease. Data were collected through structured questionnaire that was completed by 120 women undergoing hemodialysis. Assessment instruments consisted of the Family Adaptability, Partnership, Growth, Affection, Resolve Scale, Symptom Experience Scale, Mishel's Uncertainty in Illness Scale for Adults, and the Center for Epidemiological Studies-Depression Scale. The higher the uncertainty about illness and physical symptoms, the higher is the level of depression, and the better the family function, the lower is the level of depression. Greater uncertainty was associated with poorer family function and worsening physical symptoms. A regression model explained 41% of the variance in depression. Significant predictors of depression were physical symptoms, living alone, illness uncertainty, and "poor" subjective health status. To improve depression of women with chronic kidney disease, nurses need to reduce physical symptoms and illness uncertainty in these patients and improve their subjective health status. In addition, the establishment of a therapeutic support system considering living arrangement will help to reduce depression in women with chronic kidney disease.

Comparison of the use of a physical-based model with data assimilation and machine learning methods for simulating soil water dynamics

Soil moisture plays a critical role as an essential component of the global water resources by regulating mass and energy exchange between land surface and atmosphere. Quantification of these exchange processes requires accurate characterization and simulation of soil water movement. Physically-based models (PBMs) and machine learning methods (MLMs) can both be used in soil moisture simulation. However, their performances in soil water simulation have only been compared in a limited number of cases. Moreover, almost all of them are conducted in field studies each with fixed soil, initial condition, and boundary condition. Here, we developed three artificial neural network (ANN) frameworks, and made clearer and more systematic comparisons between them and a PBM—Ross numerical model solving Richards equation and parameter estimation using a data assimilation approach (iterative ensemble smoother, Ross-IES) in synthetic and real-world conditions. Compared with the ANNs, Ross-IES is more significantly affected by physical model uncertainties such as soil heterogeneity, initial and boundary conditions, while both methods are affected by observation noise. For Ross-IES, the errors from boundary conditions and hydraulic parameter conceptualization are found to be more prominent than that of observation noise and therefore are suggested to be identified first. Meanwhile, the ANNs have difficulty in simulating the peaks and troughs of the soil water time series as well as in situations where the soil moisture is constantly saturated. ANNs yield a superior simulation when the nonlinear relationship between the response variables and driving data is weak, while the performance of Ross-IES is governed by the prior soil hydraulic information. In addition, Ross-IES approach requires much higher computational cost than the ANNs. ANN-MS performs best among the three ANN-based machine learning models and demonstrates great data mining ability and robustness against overfitting.

Clinical implementation in proton therapy of multi-field optimization by a hybrid method combining conventional PTV with robust optimization

To implement a robust multi-field optimization (MFO) technique compatible with the application of a Monte Carlo (MC) algorithm and to evaluate its robustness. Nine patients (three brain, five head-and-neck, one spine) underwent proton treatment generated by a novel robust MFO technique. A hybrid (hMFO) approach was implemented, planning dose coverage on isotropic PTV compensating for setup errors, whereas range calibration uncertainties are incorporated into PTV robust optimization process. hMFO was compared with single-field optimization (SFO) and full robust multi-field optimization (fMFO), both on the nominal plan and the worst-case scenarios assessed by robustness analysis. The SFO and the fMFO plans were normalized to hMFO on CTV to obtain iso-D95 coverage, and then the organs at risk (OARs) doses were compared. On the same OARs, in the normalized nominal plans the potential impact of variable relative biological effectiveness (RBE) was investigated. hMFO reduces the number of scenarios computed for robust optimization (from twenty-one in fMFO to three), making it practicable with the application of a MC algorithm. After normalizing on D95 CTV coverage, nominal hMFO plans were superior compared to SFO in terms of OARs sparing (p < 0.01), without significant differences compared to fMFO. The improvement in OAR sparing with hMFO with respect to SFO was preserved in worst-case scenarios (p < 0.01), confirming that hMFO is as robust as SFO to physical uncertainties, with no significant differences when compared to the worst case scenarios obtained by fMFO. The dose increase on OARs due to variable RBE was comparable to the increase due to physical uncertainties (i.e. 4–5 Gy(RBE)), but without significant differences between these techniques. hMFO allows improving plan quality with respect to SFO, with no significant differences with fMFO and without affecting robustness to setup, range and RBE uncertainties, making clinically feasible the application of MC-based robust optimization.

Reference Models for Lithospheric Geoneutrino Signal

AbstractDebate continues on the amount and distribution of radioactive heat producing elements (i.e., U, Th, and K) in the Earth, with estimates for mantle heat production varying by an order of magnitude. Constraints on the bulk‐silicate Earth's (BSE) radiogenic power also places constraints on overall BSE composition. Geoneutrino detection is a direct measure of the Earth's decay rate of Th and U. The geoneutrino signal has contributions from the local ( 40%) and global ( 35%) continental lithosphere and the underlying inaccessible mantle ( 25%). Geophysical models are combined with geochemical data sets to predict the geoneutrino signal at current and future geoneutrino detectors. We propagated uncertainties, both chemical and physical, through Monte Carlo methods. Estimated total signal uncertainties are on the order of 20%, proportionally with geophysical and geochemical inputs contributing 30% and 70%, respectively. We find that estimated signals, calculated using CRUST2.0, CRUST1.0, and LITHO1.0, are within physical uncertainty of each other, suggesting that the choice of underlying geophysical model will not change results significantly, but will shift the central value by up to 15%. Similarly, we see no significant difference between calculated layer abundances and bulk crustal heat production when using these geophysical models. The bulk crustal heat production is calculated as 7 2 TW, which includes an increase of 1 TW in uncertainty relative to previous studies. Combination of our predicted lithospheric signal with measured signals yield an estimated BSE heat production of 21.5 10.4 TW. Future improvements, including uncertainty attribution and near‐field modeling, are discussed.

Improved thermal structure simulation and optimized sampling strategy for Lake Erie using a data assimilative model

Lake Erie has experienced substantial environmental issues (e.g., hypoxia, harmful algal blooms) for decades, which are closely related to the lake’s thermal characteristics. While three-dimensional (3D) hydrodynamic models have been widely applied to Lake Erie, challenges remain due to model representation of physical processes, errors and uncertainty in boundary conditions and forcing terms. The Great Lakes region has a relatively dense and long-term observational record, and these observational data have been used for model initialization and verification, but have not been incorporated into 3D model simulations through data assimilation (DA) to create reanalysis products or improve short-term forecasts. In this work, we developed and evaluated DA to improve thermal structure simulation of Lake Erie. Moored instrument data and satellite data are incorporated into a data-assimilative hydrodynamic model for analysis and evaluation. Results show that DA can effectively improve the model performance to create reanalysis fields when the DA formulation is appropriately developed in recognition of the dynamic complexities and anisotropic error covariances of Lake Erie. The data assimilative model also improves forecasting accuracy and restrains forecasting uncertainty to an acceptable level on a timescale of 1–7 days after being unleashed from DA. Lastly, data sampling strategies based on an error correlation map are examined. Results show the method can effectively reduce the sampling effort while still achieving similar model skills with potential for optimal design of an observation network or field sampling strategy.