Uncertainties In Physical Parameters Research Articles

A Multilevel Surrogate Model-Based Precipitation Parameter Tuning Method for CAM5 Using Remote Sensing Data for Validation

The uncertainty of physical parameters is a major factor contributing to poor precipitation simulation performance in Earth system models (ESMs), particularly in tropical and Pacific regions. To address the high computational cost of repetitive ESM runs, this study proposes a multilevel surrogate model-based parameter optimization framework and applies it to improve the precipitation performance of CAM5. A top-level surrogate model using gradient boosting regression trees (GBRTs) was constructed, leveraging the candidate point (CAND) approach applied to balance exploration and exploitation. A bottom-level surrogate model was then built based on a small, selected dataset; we designed a trust region approach to adjust the sampling region during the bottom-level tuning process. Experimental results demonstrate that the proposed method achieves fast convergence and significantly enhances precipitation simulation accuracy, with an average improvement of 19% in selected regions. In integrating optimization results through a nonuniform parameterization scheme and parameter smoothing, substantial improvements were observed in the South Pacific, Niño, South America, and East Asia. Comparisons with remote sensing data confirm that the optimized precipitation simulations do not introduce significant biases to other variables, validating the effectiveness and robustness of the proposed method.

The critical role of c and φ in ensuring stability: A study on rockfill dams

Abstract Slope stability analysis is an important part of rockfill dam design, and the uncertainty of rock and soil physical and mechanical parameters has a significant impact on slope stability. In this article, based on physical and mechanical parameters c and φ, simplified Bishop’s method and mean clustering method are adopted to study the influence of parameter cohesion c and internal friction Angle φ on slope safety factor k of composite geomembrane rockfill dam, and the key and non-critical areas of the dam are preliminarily differentiated according to the different influences of the changes in different sections of the dam body on slope safety factor c and φ. The research results show that whether c and φ change ±5 and ±10% simultaneously with single parameter or double parameter, it shows that cohesion force c has little influence on slope stability, while internal friction Angle φ is the most sensitive factor in slope stability calculation, and its numerical accuracy has a great influence on the calculation result of safety factor. In addition, the influence of c and φ in key areas on the stability of the dam body is more significant. Therefore, in the construction of a composite geofilm rockfill dam, a relatively accurate φ value is required when selecting parameters, especially in key areas. This study not only has a certain guiding significance for the property requirements of the selected soil, engineering safety, and engineering optimization design, but also puts forward some new methods and ideas for optimizing the design scheme and improving the safety of the dam.

Fatigue Characteristics Analysis of Carbon Fiber Laminates with Multiple Initial Cracks

In the entire wind turbine system, the blade acts as the central load-bearing element, with its stability and reliability being essential for the safe and effective operation of the wind power unit. Carbon fiber, known for its high strength-to-weight ratio, high modulus, and lightweight characteristics, is extensively utilized in blade manufacturing due to its superior attributes. Despite these advantages, carbon fiber composites are frequently subjected to cyclic loading, which often results in fatigue issues. The presence of internal manufacturing defects further intensifies these fatigue challenges. Considering this, the current study focuses on carbon fiber composites with multiple pre-existing cracks, conducting both static and fatigue experiments by varying the crack length, the angle between cracks, and the distance among them to understand their influence on the fatigue life under various conditions. Furthermore, this study leverages the advantages of Paris theory combined with the Extended Finite Element Method (XFEM) to simulate cracks of arbitrary shapes, introducing a fatigue simulation method for carbon fiber composite laminates with multiple cracks to analyze their fatigue characteristics. Concurrently, the Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal weight configuration, and the Backpropagation neural network (BP) is used to train and adjust the weights and thresholds to minimize network errors. Building on this foundation, a surrogate model for predicting the fatigue life of carbon fiber composite laminates with multiple cracks under conditions of physical parameter uncertainty has been constructed, achieving modeling and assessment of fatigue reliability. This research offers theoretical insights and methodological guidance for the utilization of carbon fiber-reinforced composites in wind turbine blade applications.

Estimator-based multi-target tracking of networked robotic systems via predefined-time hierarchical control method

In this paper, we study the multi-target tracking problem of networked robotic systems (NRSs) within predefined time under a directed interaction topology considering disturbances and physical parameter uncertainties. On the basis of non-singular sliding-mode surface, a new predefined-time estimator-based hierarchical control algorithm is put forward so as to address the tracking problem under the case of multiple moving targets. Using the time base generator (TBG), a distributed estimator algorithm is designed to ensure that the followers can estimate different target states within a predefined-time. Certain sufficient conditions of predefined-time multi-target tracking of NRSs are presented using the Lyapunov stability and neighbor information interaction principle. In the end, numerical simulation experiments are offered to indicate the availability and correctness of the theoretical results.

Evaluation of reactor pulse experiments

In the paper we validate theoretical models of the pulse against experimental data from the Jozef Stefan Institute TRIGA Mark II research reactor. Data from all pulse experiments since 1991 have been collected, analysed and are publicly available. This paper summarizes the validation study, which is focused on the comparison between experimental values, theoretical predictions (Fuchs-Hansen and Nordheim-Fuchs models) and calculation using computational program Improved Pulse Model. The results show that the theoretical models predicts higher maximum power but lower total released energy, full width at half maximum and the time when the maximum power is reached is shorter, compared to Improved Pulse Model.We evaluate the uncertainties in pulse physical parameters (maximum power, total released energy and full width at half maximum) due to uncertainties in reactor physical parameters (inserted reactivity, delayed neutron fraction, prompt neutron lifetime and effective temperature reactivity coefficient of fuel). It is found that taking into account overestimated correlation of reactor physical parameters does not significantly affect the estimated uncertainties of pulse physical parameters. The relative uncertainties of pulse physical parameters decrease with increasing inserted reactivity. If all reactor physical parameters feature an uncorrelated uncertainty of 10 % the estimated total uncertainty in peak pulse power at 3 $ inserted reactivity is 59 %, where significant contributions come from uncertainties in prompt neutron lifetime and effective temperature reactivity coefficient of fuel. In addition we analyse contribution of two physical mechanisms (Doppler broadening of resonances and neutron spectrum shift) that contribute to the temperature reactivity coefficient of fuel. The Doppler effect contributes around 30 %–15 % while the rest is due to the thermal spectrum hardening for a temperature range between 300 K and 800 K.

Application of polynomial chaos expansion in sensitivity analysis of towed cable parameters of the underwater towing system

The key to achieving the optimal design of towed cables, maintaining numerical simulation accuracy, and achieving precise control of the towed body lies in sensitivity analysis. However, the traditional global sensitivity analysis method presents challenges such as high calculation costs and low accuracy. To address these issues, this paper introduces polynomial chaos expansion (PCE) to quantitatively analyze the impact of uncertainties in physical and environmental parameters on the position and attitude of the towed cable. Latin hypercube sampling is employed to obtain sample sets of input parameters, and these samples are applied to the lumped mass method to calculate the end position coordinates of the towed cable, which serves as the output response. PCE is utilized to quantitatively compute the Sobol global sensitivity index of the towed cable parameters. The accuracy of the PCE model is verified, and the optimal degree of basis functions is selected using the bias-variance trade-off. The advantages of PCE are demonstrated by comparing it with the Monte Carlo and Morris methods. The results indicate that PCE accurately calculates the global sensitivity index of towed cable parameters even with a limited sample size. Under the condition of a fixed cable length, the position and attitude of the towed cable are sensitive to the current rate, liquid density, cable diameter, normal drag coefficient, and specific gravity. The feasibility and efficiency of PCE applied to the sensitivity analysis of towed cable parameters is verified, and recommendations for the engineering application of towed cables are summarized.

Design computed torque control for Stewart platform with uncertainty to the rehabilitation of patients with leg disabilities

Physiotherapy is a treatment that may be required permanently by many patients. As a result, a robot that can execute physiotherapy exercises for the legs like a professional therapist with adequate performance and acceptable safety may be efficient and widely used. In this study, a robust control system for a Stewart platform with six degrees of freedom is provided. First, the Newton-Euler approach is used in conjunction with a methodology and some simplification tools to achieve explicit dynamics formulation for the Stewart platform. For the primary application of this research, which is to follow the specified trajectory of ankle rehabilitation, computed torque control law (CTCL) and polynomial chaos expansion (PCE) were used to examine and consider any uncertainty in geometric and physical parameters. In fact, this strategy integrated the uncertainties with CTCL using PCE. The suggested PCE-based CTCL eliminates the system’s nonlinearity by applying feedback linearization to evaluate generalized driving forces; hence, the nondeterministic multi-body system follows the desired direction. Uncertainties in the patient’s foot as well as the main diameter parameters of the moment of inertia of the upper platform of the Stewart robot with various uniform, beta, and normal distributions, have been analyzed. The PCE technique’s results were compared to the Monte Carlo method’s outcomes, and the strengths and weaknesses of each method were investigated. In brief, the PCE method operated far better than the Monte Carlo (MC) method in speed, accuracy, and numerical volume.

Multi-parameter nuclear data adjustment for sodium fast reactor

This paper studies the application of nuclear data adjustment method in the physics calculation of sodium cooled fast reactor. Based on Bayesian theory, the multi-parameter nuclear data adjustment method is derived, and then the experimental screening rules are given. The verification based on 47 candidate experiments, including the critical measurement experiments, control rod worth measurement experiments and sodium void reactivity measurement experiments of ZPPR-9、ZPPR-10A、ZPPR-10B and ZPPR-10C zero-power experimental facilities, is then carried out. The numerical results show that the adjustment based only on keff has obvious limitations, it can only reduce the calculation uncertainty of keff, but the uncertainties of other physical parameters cannot be improved effectively. However, the multi-parameter nuclear data adjustment method effectively reduces the calculation uncertainties and errors of different physical parameters besides keff. In the meantime, the screening rules for experimental data are analyzed to avoid the improper adjustment of nuclear data.

Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning Under Uncertainty

In this article, we present generalized polynomial chaos-based sequential convex programming (gPC-SCP) to compute a suboptimal solution for a continuous-time chance-constrained stochastic nonlinear optimal control (SNOC) problem. The approach enables motion planning for robotic systems under uncertainty. The gPC-SCP method involves two steps. The first step is to derive a surrogate problem of deterministic nonlinear optimal control (DNOC) with convex constraints by using gPC expansion and the distributionally robust convex subset of the chance constraints. The second step is to solve the DNOC problem using sequential convex programming for trajectory generation and control. We prove that in the unconstrained case, the optimal value of the DNOC converges to that of SNOC asymptotically and that any feasible solution of the constrained DNOC is a feasible solution of the chance-constrained SNOC. We also present the predictor–corrector extension (gPC-SCP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\text{PC}$</tex-math></inline-formula> ) for real-time motion trajectory generation in the presence of stochastic uncertainty. In the gPC-SCP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\text{PC}$</tex-math></inline-formula> method, we first predict the uncertainty using the gPC method and then optimize the motion plan to accommodate the uncertainty. We empirically demonstrate the efficacy of the gPC-SCP and the gPC-SCP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\text{PC}$</tex-math></inline-formula> methods for the following two test cases: first, collision checking under uncertainty in actuation and physical parameters and second, collision checking with stochastic obstacle model for 3DOF and 6DOF robotic systems. We validate the effectiveness of the gPC-SCP method on the 3DOF robotic spacecraft testbed.

Outer Approximation for Mixed-Integer Nonlinear Robust Optimization

Currently, few approaches are available for mixed-integer nonlinear robust optimization. Those that do exist typically either require restrictive assumptions on the problem structure or do not guarantee robust protection. In this work, we develop an algorithm for convex mixed-integer nonlinear robust optimization problems where a key feature is that the method does not rely on a specific structure of the inner worst-case (adversarial) problem and allows the latter to be non-convex. A major challenge of such a general nonlinear setting is ensuring robust protection, as this calls for a global solution of the non-convex adversarial problem. Our method is able to achieve this up to a tolerance, by requiring worst-case evaluations only up to a certain precision. For example, the necessary assumptions can be met by approximating a non-convex adversarial via piecewise relaxations and solving the resulting problem up to any requested error as a mixed-integer linear problem.In our approach, we model a robust optimization problem as a nonsmooth mixed-integer nonlinear problem and tackle it by an outer approximation method that requires only inexact function values and subgradients. To deal with the arising nonlinear subproblems, we render an adaptive bundle method applicable to this setting and extend it to generate cutting planes, which are valid up to a known precision. Relying on its convergence to approximate critical points, we prove, as a consequence, finite convergence of the outer approximation algorithm.As an application, we study the gas transport problem under uncertainties in demand and physical parameters on realistic instances and provide computational results demonstrating the efficiency of our method.

Receptance-based antiresonant frequency assignment of an uncertain dynamic system using interval multiobjective optimization method

This study addresses the antiresonant frequency assignment problem of vibrating systems with uncertainty, which is solved using an interval multi-objective optimization method. The study accounts for several uncertain factors, including in the structural parameters and test data, and defines them as interval variables. The bounds of interval variation of the receptance (frequency response function or FRF) are determined from a small number of numerical or experimental results; thus, it is unnecessary to collect a large amount of data to construct the sample space. The antiresonant frequency assignment problem of uncertain systems is formulated as an interval multi-objective optimization. Optimal solutions (the structural modifications that assign the desired antiresonant frequency frequencies) are determined through the MI-NSGA-II algorithm proposed in this work. It employs the basic framework of the interval multi-objective genetic algorithm INSGA-II and redefines the interval confidence level to evaluate the merits and demerits between individual feasible solutions and improve the stability of INSGA-II. The obtained modifications not only accurately achieve the goal of antiresonant frequency assignment but also stably maximize the robustness of the antiresonant frequency frequencies after modifications. From the perspective of enhancing the applicability of the receptance method, the proposed method can overcome the problems of unsuccessful antiresonant frequency assignment due to measurement inaccuracies and the uncertainty of physical parameters to a certain extent. Numerical and experimental examples have been provided to demonstrate the effectiveness of the proposed method.

Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis

The energy consumption of cities is increasing fast due to growing global population and rapid urbanization. Urban Building Energy Models (UBEMs) are promising tools to simulate the energy demand of buildings under different urban scenarios. Nowadays, the major barriers to the effective use of UBEMs are the uncertainty related to their input parameters and the lack of good-quality, open energy consumption data. The latter make deterministic UBEM simulations unreliable, and calibration approaches unsuitable for most cities in the world. The present work proposes to combine physics-based UBEMs with Uncertainty and Sensitivity Analysis on the main input parameters using aggregated data on energy use from regional/national statistics. The proposed procedure selects the most influential input parameters and characterizes their uncertainty through Forward Uncertainty Analysis and Sensitivity Analysis to obtain stochastic load profiles for space heating and cooling. The method was first tested against hourly thermal power profiles metered on a heterogeneous sample of buildings in Verona (Italy). The average heating load profile obtained is significantly improved compared to deterministic, archetype-based simulations in terms of energy needs and peak loads. The overestimation of residential buildings peak load is reduced from 80% to 25%, and the deviation in the energy needs calculation drops from 18% to 10%. The proposed simulation procedure was then applied to a district of Milan (Italy), including more than 600 buildings, resulting in similar variations. Overall, the results demonstrate that considering the uncertainty of operational, geometrical and physical parameters is of the utmost importance to obtain reliable urban simulations.

Uncertainty Analysis Based on Kriging Meta-Model for Acoustic-Structural Problems

This paper consists of evaluating the performance of a vibro-acoustic model in the presence of uncertainties in the geometric and material parameters of the model using Monte Carlo simulations (MCS). The purpose of using a meta-model is to reduce the computational cost of finite element simulations. Uncertainty analysis requires a large sample of MCS to predict the effect of uncertain parameters on the system response. So, if this study is done through the finite element method (FEM), then the computational cost will be very important. Furthermore, for that, we use meta-models to be able to conduct an efficient uncertainty analysis more quickly. In the present contribution, the approximated meta-model is verified and validated using error measures and cross-validation (CV). Then, the uncertainty analysis is performed by Monte Carlo simulations using the computed Kriging meta-model. The developed methodology has been applied in two vibro-acoustic models. In these two models, the covariance of uncertainty of geometric and physical (elasticity and density) parameters are equal to 2% and 5% respectively. The obtained results prove that the suggested methodology of uncertainty propagation based on the Kriging meta-model can be considered as a very efficient and sufficiently accurate approach for the quantification of uncertainties in acoustic-structural systems.

Reliability based failure assessment of deteriorated cast iron pipes lined with polymeric liners

Defects may appear or further develop in cast iron pipes lined with polymeric liners in the form of surface corrosion pits/patches, through-wall holes, gaps, longitudinal cracks, circumferential cracks and/or failed joints etc. This paper examines the performance of deteriorated cast iron pipes lined with polymeric liners using probabilistic analysis. The deterioration of lined cast iron pipes includes corrosion of cast iron pipes together with creep and creep rupture of polymeric liners. Various potential failure modes of lined pipes and the uncertainties of key physical parameters, including geometries, deterioration models and material properties of the pipe and liner, dimensions of the defects in the pipe and loading conditions etc., in probabilistic failure assessment of lined pipes are considered. The probability of failure of each lined pipe is calculated over its lifetime and a sensitivity analysis is also conducted to identify the most influential parameters on the failure probability of the lined pipe. It is found that the coefficients for the rupture strength and the creep retention factors of the liners are most influential on the probability of failure of the lined pipes.

Analysis of lithium-ion battery thermal models inaccuracy caused by physical properties uncertainty

• Uncertainty of thermal conductivity, heat capacity and density is evaluated. • Inaccuracy of thermal model due to uncertainty of physical parameters is studied. • Uncertainty of thermal conductivity shows the largest influence on temperature. • The reliability and accuracy of a 1D model are analyzed. Thermal management is of upmost importance for the safe and efficient operation of lithium-ion batteries in electric vehicles. To this purpose it is required to develop reliable thermal models to assess the behavior of the battery under different operating and ambient conditions. In this work, it is proposed a three-dimensional thermal model of the 40Ah LiFePO 4 /graphite prismatic battery, which is a particular type of the lithium-ion battery (LIB), and it is analyzed the uncertainty related to the base data needed to run the model. The base data comprises, among others, the battery material physical properties and their dependence on the temperature, and the special “double-coated electrodes” structure. The charge and discharge processes of the 40Ah LiFePO 4 /graphite prismatic battery are tested experimentally to verify the reliability of the thermal model. The deviation of the thermal model predictions caused by the uncertainty of the physical parameters of the battery is fully investigated numerically. The influence of physical parameters on the predictions is verified for the battery surface temperature and temperature difference between the battery interior and the surface. For the range of the properties tested the highest deviations of the predicted surface temperature and the inside and surface temperature difference are 0.14 °C and 0.93 °C during discharge at room temperature, respectively. Based on this investigation, it is proposed a simplified one-dimensional thermal model, which has the potential of being an expedite way of calculating the temperature distribution inside most commercial prismatic batteries. The comparison of the temperature distribution predictions using the three-dimensional thermal model and the simplified model indicates that the temperature gradient predictions obtained with the two models are in close agreement; the maximum relative difference of the two models is only 0.5%. The simplified thermal model, in what concerns the uncertainty of the physical properties, may provide an easy-to-use preliminary tool to evaluate the temperature distribution related to prismatic batteries.

A conjunctive management framework for the optimal design of pumping and injection strategies to mitigate seawater intrusion

Coastal aquifer management (CAM) considering conjunctive optimization of pumping and injection system for seawater intrusion (SI) mitigation poses significant decision-making challenges. CAM needs to pose multiple objectives and massive decision variables to explore tradeoff strategies between the conflicting resources, economic, and environmental requirements. Here, we investigate a joint artificial injection scheme for ameliorating SI by establishing an evolutionary multi-objective decision-making framework that combines simulation-optimization (S–O) modelling with a cost-benefit analysis, and demonstrate the framework on a large-scale CAM case in Baldwin County, Alabama. First, a SI numerical model, using SEAWAT, was configured to predict the vulnerable region as an SI encroachment area with the scenarios of minimum and maximum pumping capacity. As a result, a smaller number of candidate sites were selected in the SI encroachment area for implementing groundwater injection to avoid the computationally infeasible SI optimization with an inordinate number of injection related decision variables. Second, the effective S–O methodology of niched Pareto tabu search combined with a genetic algorithm (NPTSGA), which considers the moving-well option, was applied to discover optimal pumping/injection (P/I) strategies (including P/I rates and injection well locations) between three conflicting management objectives under complicated SI constraints. Third, for practical operation of the P/I schemes, a cost-benefit analysis provides judgment criteria to allow decision-makers to implement more sustainable P/I strategies to capture the different realistic preferences. The implementation of three extreme optimization solutions for the case study indicates that, compared to the initial unoptimized scheme, a maximum increase of a factor of 3 in groundwater extraction rates, a maximum reduction of 17% in extent of SI, and a maximum 82.3 million US dollars in comprehensive benefits are specifically achieved by conjunctive P/I optimization. The robustness in the decision alternatives attributed to the uncertainty in physical parameters of hydraulic conductivity was discovered through global sensitivity analysis. The proposed framework provides a decision support system for multi-objective CAM with combined pumping control and engineering measures for SI mitigation.

Identification of Key Physical Processes and Improvements for Simulating and Predicting Net Primary Production Over the Tibetan Plateau

AbstractThere are still considerable uncertainties related to the numerical simulation and prediction of net primary production (NPP) as an important part of terrestrial carbon sources and sinks over the Tibetan Plateau (TP). To reduce the uncertainty of numerical simulations and improve the ability of predictions, the key physical processes related to the uncertainty of simulated NPP are identified at nine observational stations over the TP. A sensitivity analysis of parameter combinations based on the Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP‐P) approach, which can be used to assess the sensitivity of a parameter subset, is conducted for 28 target physical parameters in the Lund‐Potsdam‐Jena (LPJ) Wetland Hydrology and Methane Dynamic Global Vegetation Model (LPJ‐WHyMe v1.3.1). Firstly, the numerical results show that the uncertainties of physical parameters do lead to a large error in the simulated NPP over the TP, and the range of error varies from 72.4 (MS 3478) to 150.5 g C m−2 year−1 (Ngari station). Secondly, in areas of moderate precipitation over the TP, the photosynthesis is the main factor leading to high uncertainty in NPP modeling. In areas of low and high precipitation over the TP, the combined influences of hydrological processes and photosynthesis play a key role. Finally, eliminating the errors associated with the most sensitive and important parameter combinations led to the maximum benefit in terms of reducing the uncertainty of simulated NPP, when compared to that obtained with the traditional method. This study suggests that we should prioritize reducing the uncertainty of relatively sensitive parameter combinations among all physical parameters to improve the prediction or simulation ability of NPP over the TP.

Robust tuning and sensitivity analysis of stochastic integer and fractional‐order PID control systems: application of surrogate‐based robust simulation‐optimization

AbstractThis paper aims to make a trade‐off between performance and robustness in stochastic control systems with probabilistic uncertainties. For this purpose, we develop a surrogate‐based robust simulation‐optimization approach for robust tuning and analyzing the sensitivity of stochastic controllers. Kriging surrogate is combined with robust design optimization to construct a robust simulation‐optimization model in the class of dual response surfaces. Randomness in simulation experiments due to uncertainty is analyzed through bootstrapping technique by computing confidence regions for the estimation of Pareto frontier. Results confirmed a proper trade‐off between the model's performance with the measure of expected Integral Squared Error (ISE) and robustness against uncertainty in the plant's physical parameters. Finally, the proposed method is evaluated in terms of accuracy, computational cost, and simplicity particularly in comparison with some common existed techniques in the tuning of the Proportional‐Integral‐Derivative (PID) and Fractional‐Order PID (FOPID) controllers.

OGLE-2018-BLG-1269Lb: A Jovian Planet with a Bright I = 16 Host

Abstract We report the discovery of a planet in the microlensing event OGLE-2018-BLG-1269 with a planet–host mass ratio q ∼ 6 × 10−4, i.e., 0.6 times smaller than the Jupiter/Sun mass ratio. Combined with the Gaia parallax and proper motion, a strong one-dimensional constraint on the microlens parallax vector allows us to significantly reduce the uncertainties of lens physical parameters. A Bayesian analysis that ignores any information about light from the host yields that the planet is a cold giant orbiting a Sun-like star at a distance of . The projected planet–host separation is . Using Gaia astrometry, we show that the blended light lies from the host and therefore must be either the host star or a stellar companion to the host. An isochrone analysis favors the former possibility at &gt;99.6%. The host is therefore a subgiant. For host metallicities in the range of , the host and planet masses are then in the range of and , respectively. Low host metallicities are excluded. The brightness and proximity of the lens make the event a strong candidate for spectroscopic follow-up both to test the microlensing solution and to further characterize the system.

Quantifying Contributions of Uncertainties in Physical Parameterization Schemes and Model Parameters to Overall Errors in Noah‐MP Dynamic Vegetation Modeling

AbstractQuantifying contributions of errors in model structure and parameters to biases in a land surface model (LSM) is critical for model improvement. This paper investigated the uncertainties in parameterizations and parameters in the Noah with multiparameterization (Noah‐MP) LSM with dynamic vegetation using eddy flux data. First, we conducted full factorial experiments of eight land subprocesses, followed by sensitivity analysis (SA) to identify the subprocesses for which possible parameterizations made significant difference. Then, based on the full factorial experiments and SA results, we selected the statistically optimal parameterizations combination and the most biased parameterizations combination. Lastly, we calibrated the parameters in two selected parameterizations combinations. The results showed that five subprocesses—surface exchange coefficient, soil moisture β threshold, radiation transfer, runoff and groundwater, and surface resistance to evaporation—had significant influence on the model performances, and the interactions were generally low but contributed up to 80% of the variation in the performance at some sites. In the optimization period, following the criterion by Moriasi et al. (2007, https://doi.org/10.13031/2013.23153), parameter optimization improved the performance of both parameterizations combinations at most sites to be satisfactory, and the superiority between two parameterizations combinations was preserved; in the validation period, adjusting the parameterizations was more robust than parameter optimization in improving LSMs. Finally, we found that uncertainty in soil parameters was much higher than that in vegetation parameters because the optimal soil parameters were significantly different among sites with the same soil types and recommended that spatially calibrating the soil parameters was a major issue for Noah‐MP dynamic vegetation modeling.