Ensemble Of Parameters Research Articles

Optimal Dimensional Synthesis of Ackermann Steering Mechanisms for Three-Axle, Six-Wheeled Vehicles

This study employs four metaheuristic optimization methods to optimize the dimensional synthesis of Ackermann steering mechanisms for three-axle, six-wheeled vehicles with front-axle steering mode and reverse-phase steering mode. The employed optimization methods include Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (HPSO), Differential Evolution with golden ratio (DE-gr), and Linearly Ensemble of Parameters and Mutation Strategies in Differential Evolution (L-EPSDE). With a front-wheel steering angle range of 70 degrees, two hundred optimization experiments were conducted for each method, and statistical analyses revealed that DE-gr and L-EPSDE methods outperformed PSO and HPSO methods in terms of standard deviation, mean value, and minimum error. These two methods exhibited superior convergence stability, faster convergence, and higher accuracy compared to PSO and HPSO. Reverse-phase (K = 1) steering mode outperformed front-axle steering mode, delivering reduced steering errors and turning radii. Considering the transmission ratio of front to rear axle (K) as a design variable in reverse-phase steering mode increased design flexibility and significantly lowered steering errors for the front and rear axle steering mechanisms. However, this comes with a slight increase in the turning radius of the vehicle’s front part compared to when K = 1. The optimized mechanism, designed using the DE-gr method, was validated through kinematic simulations and steering analyses using MSC-ADAMS v2015 software, further confirming the effectiveness and reliability of the proposed design.

Revisiting two-layer energy balance models for climate assessment

Abstract Given the pivotal role of probabilistic approaches with two-layer energy balance models in the latest climate assessment, this study aims to gain deeper insight into their advancement by comparing different approaches for generating constrained posterior ensembles. Several methodological improvements are possible both in the calibration of model parameters to the behavior of comprehensive Earth system models and in constraining the calibrated parameter ensemble with other lines of evidence. The results imply that a conventional single parameter representing evolving climate feedback characteristics is not a requirement for reliable climate projections; rather, there are potential improvements on the forcing side regarding the separation of forcing and feedbacks. Constraining the ensemble based on observational and expert-assessed climate metrics, which critically affects probabilistic climate assessment, needs to appropriately deal with different constraints on a multivariate space in a standardized and flexible way. The method introduced here is an option that fulfills the need.

Effect of resolution on simulated teleconnections to winter North Atlantic circulation inferred from a causal network derived from expert elicitation

When designing a set of climate projections for a given amount of supercomputing, the choice of resolution affects the ensemble size that can be afforded. The larger sample size helps to quantify uncertainties more robustly and provide better statistics to understand the projections. The trade-off is that the use of coarser resolution risks some compromise on quality in representing processes that could play a crucial role in the climate response. But what exactly is the added value of the enhanced resolution? We use two tools, which are applicable to a broader set of applications, to assess this for processes (teleconnections) that drive the year-to-year variability of late winter circulation over the North Atlantic. First, a causal network, which captures the teleconnections that relate key drivers of variability to late winter North Atlantic Oscillation (NAO), is elicited from experts in seasonal forecasting. Causal Inference theory is then used to estimate the teleconnection strengths. Second, we use two parallel perturbed parameter ensembles (PPEs) of coupled control simulations based on HadGEM3-GC3 at low and medium atmosphere/ocean resolutions. Combining the two tools allows us to identify teleconnections which have strengths (a) controlled by parameters, and (b) that are altered in a systematic way across parameter space by the enhanced resolution. Overall, the medium resolution is marginally better than the lower resolution, notably in the way El Niño-Southern Oscillation (ENSO) affects the Indian Ocean Dipole. For many of the teleconnections, most model configurations have weaker relationships than observed. Several teleconnections are shown to have modest parametric effects that produce correlated changes across the two PPEs. However, the ensemble size would need to be increased to better identify the key parameters and processes.

Full-grid bathymetry estimation in the numerical simulation of 8 tidal constituents using an ensemble adjustment Kalman filter-smoothing scheme

The spatially varying geographic-parameters introduce significant uncertainty into the ocean model. Due to the impracticality of manually tuning spatial varying parameters, data assimilation methods are widely used for geographic-parameter optimization (GPO). Practically, the limited observations do not contain enough information to perform GPO directly on the entire grid. Therefore, techniques are required to reduce the complexity of the parameters. A full-grid GPO scheme based on the ensemble adjustment Kalman filter (EAKF) is developed. Via smoothing the spatial distribution of posterior parameter members, the EAKF-smoothing (EAKF-S) introduces additional spatial correlations among parameters. Meanwhile, the small-scale correlation between the state and the parameters, which exhibit strong pseudo-correlations, is filtered out. A tide model of the Bohai Sea and Yellow Sea, considering 8 principal tidal constituents. is constructed using the Princeton Ocean Model with the generalized coordinate system (POMgcs). The EAKF-S is employed for optimizing the full-grid bathymetry. In twin experiment, based on idealized water level observations, EAKF-S effectively reduced model errors and approximately inverted the “true” bathymetry. After GPO, the lowest mean absolute error of the parameter ensemble is 0.83 m. A series of practical GPO experiments based on synthetic water level observations calculated from NAO.99Jb data are performed. First, the improvement of EAKF-S in accuracy and efficiency over standard EAKF is proven using an M2 tide model. After that, a practical experiment on an 8 constituents tide model is performed. The results show that the forecasting performance of all 8 constituents is improved after GPO, indicating the efficacy of EAKF-S.

Land Processes Can Substantially Impact the Mean Climate State

AbstractTerrestrial processes influence the atmosphere by controlling land‐to‐atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally‐informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.

The Interaction Between Climate Forcing and Feedbacks

AbstractA Perturbed Parameter Ensemble (PPE) with the Community Atmosphere Model version 6 (CAM6) is used to better understand the sensitivity of aerosol forcing and cloud feedbacks to changes in model processes. Aerosol forcing through aerosol‐cloud interactions is mostly negative (a cooling) due to shortwave radiation, while feedbacks are positive or negative in different regions due to contrasting longwave and shortwave effects. Both forcing and feedbacks are related to the mean climate state. Higher magnitude cloud radiative effects generally mean larger magnitude net negative forcing and larger magnitude net positive feedback. Aerosol forcing is broadly related to the susceptibility of clouds to drop number. Feedbacks also related to susceptibility, but to a lesser extent and in different regions to aerosol forcing. Aerosol forcing and cloud feedbacks are anti‐correlated in the CAM6 PPE such that stronger negative forcing is associated with stronger positive feedbacks. Even the processes governing forcing and feedback sensitivity in the PPE are similar. These include the warm rain formation process, ice loss processes and deep convective intensity.

Impact of Instantaneous Parameter Sensitivity on Ensemble‐Based Parameter Estimation: Simulation With an Intermediate Coupled Model

AbstractOn ensemble‐based coupled data assimilation, cross‐component parameter estimation (CPE), has not been as extensively developed and applied as weakly coupled state and parameter estimation along with cross‐component state estimation. This discrepancy is partially attributed to the lack of emphasis on the instantaneous response of coupled model states with respect to parameters across different components. We define so‐called response as the instantaneous parameter sensitivity (IPS). Under the framework of sequential assimilation, the prior information heavily relies on the IPS of coupled states with different time scales. Based on the IPS analysis for an intermediate coupled model, a series of twin experiments of state and parameter estimation are conducted, in which an IPS‐inspired adaptive inflation scheme for parameter ensemble is introduced. Results show that the success of a parameter estimation strategy is closely tied to the significant IPS of the observed state to the parameter targeted for optimization, as it maintains a high signal‐to‐noise ratio in the error covariance between parameter and prior state, thereby enhancing parameter estimation. An interesting finding in the context of IPS‐based CPE is: an atmospheric parameter can be successfully estimated by assimilating observations from slow‐varying oceanic component, but not vice versa. In comparison with cross‐component state estimation, successful CPE significantly enhances the estimation accuracy of coupled states by mitigating model bias.

TFMKC: Tuning-Free Multiple Kernel Clustering Coupled With Diverse Partition Fusion.

Clustering is a popular research pipeline in unsupervised learning to find potential groupings. As a representative paradigm in multiple kernel clustering (MKC), late fusion-based models learn a consistent partition across multiple base kernels. Despite their promising performance, a common concern is the limited representation capacity caused by the inflexible fusion mechanism. Concretely, the representations are constrained by truncated- k Eigen-decomposition (EVD) without fully exploiting potential information. An intuitive idea to alleviate this concern is to generate a set of augmented partitions and then select the optimal partition by fine-tuning. However, this is overlimited by: 1) introducing undesired hyperparameters and dataset-related consequences; 2) neglecting rich information across diverse partitions; and 3) expensive parameter-tuning costs. To address these problems, we propose transforming the challenging problem of directly determining the optimal partition (optimal parameter) into a diverse partition fusion (parameter ensemble) problem. We design a novel flexible fusion mechanism called tuning-free multiple kernel clustering coupled with diverse partition fusion (TFMKC) by reweighting diverse partitions through optimization, achieving an optimal consensus partition by integrating diverse and complementary information rather than traditional fine-tuning, and distinguishing our work from existing methods. Extensive experiments verify that TFMKC achieves competitive effectiveness and efficiency over comparison baselines. The code can be accessed at https://github.com/ZJP/TFMKC.

Nonideality in Arrayed Carbon Nanotube Field Effect Transistors Revealed by High-Resolution Transmission Electron Microscopy.

High density and high semiconducting-purity single-walled carbon nanotube array (A-CNT) have recently been demonstrated as promising candidates for high-performance nanoelectronics. Knowledge of the structures and arrangement of CNTs within the arrays and their interfaces to neighboring CNTs, metal contacts, and dielectrics, as the key components of an A-CNT field effect transistor (FET), is essential for device mechanistic understanding and further optimization, particularly considering that the current technologies for the fabrication of A-CNT wafers are mainly laboratory-level solution-based processes. Here, we conduct a systematic investigation into the microstructures of A-CNT FETs mainly via cross-sectional high-resolution transmission electron microscopy and tentatively establish a framework consisting of up to 11 parameters which can be used for structure-side quality evaluation of the A-CNT FETs. The parameter ensemble includes the diameter, length (or terminal), and density distribution of CNTs, radial deformation of CNTs, array alignment defects, surface crystallography facets of contact metal, thickness distribution of high-k dielectrics (HfO2), and the contact ratios for the CNT-CNT, CNT-metal, CNT-dielectric, and CNT-substrate interfaces. Enriched array alignment defects, i.e., bundle, stacking, misorientation, and voids, are observed with a total ratio sometimes up to ∼90% in pristine A-CNTs and even up to ∼95% after the device fabrication process. Thus, they are suggested as the prevalent performance-limiting factors for A-CNT FETs. Complex interfacial structures are observed at the CNT-CNT, CNT-metal contact, and CNT-high-k dielectric interfaces, making the local environment and the property of each component CNT involved in an A-CNT FET distinct from others in terms of the diameters, radial deformation, and interactions with the local surroundings (mainly through van der Waals interactions). The present study suggests further improvements on the fabrication technology of A-CNT wafers and devices and mechanistic investigations into the impacts of complex array alignment defects and interface structures on the electrical performance of A-CNT FETs as well.

Assessing dimensions of thought disorder with large language models: The tradeoff of accuracy and consistency

Natural Language Processing (NLP) methods have shown promise for the assessment of formal thought disorder, a hallmark feature of schizophrenia in which disturbances to the structure, organization, or coherence of thought can manifest as disordered or incoherent speech. We investigated the suitability of modern Large Language Models (LLMs - e.g., GPT-3.5, GPT-4, and Llama 3) to predict expert-generated ratings for three dimensions of thought disorder (coherence, content, and tangentiality) assigned to speech samples collected from both patients with a diagnosis of schizophrenia (n = 26) and healthy control participants (n = 25). In addition to (1) evaluating the accuracy of LLM-generated ratings relative to human experts, we also (2) investigated the degree to which the LLMs produced consistent ratings across multiple trials, and we (3) sought to understand the factors that impacted the consistency of LLM-generated output. We found that machine-generated ratings of the level of thought disorder in speech matched favorably those of expert humans, and we identified a tradeoff between accuracy and consistency in LLM ratings. Unlike traditional NLP methods, LLMs were not always consistent in their predictions, but these inconsistencies could be mitigated with careful parameter selection and ensemble methods. We discuss implications for NLP-based assessment of thought disorder and provide recommendations of best practices for integrating these methods in the field of psychiatry.

Twenty-first century marine climate projections for the NW European shelf seas based on a perturbed parameter ensemble

Twenty-first century marine climate projections for the NW European shelf seas based on a perturbed parameter ensemble

Buffering of Aerosol‐Cloud Adjustments by Coupling Between Radiative Susceptibility and Precipitation Efficiency

AbstractAerosol‐cloud interactions (ACI) in warm clouds are the primary source of uncertainty in effective radiative forcing (ERF) during the historical period and, by extension, inferred climate sensitivity. The ERF due to ACI (ERFaci) is composed of the radiative forcing due to changes in cloud microphysics and cloud adjustments to microphysics. Here, we examine the processes that drive ERFaci using a perturbed parameter ensemble (PPE) hosted in CAM6. Observational constraints on the PPE result in substantial constraints in the response of cloud microphysics and macrophysics to anthropogenic aerosol, but only minimal constraint on ERFaci. Examination of cloud and radiation processes in the PPE reveal buffering of ERFaci by the interaction of precipitation efficiency and radiative susceptibility.

Foreseeing of Next-day Large Forecast Error of Solar Irradiance by Parameter Ensemble Forecasting using WRF

Foreseeing of Next-day Large Forecast Error of Solar Irradiance by Parameter Ensemble Forecasting using WRF

Matilda v1.0: An R package for probabilistic climate projections using a reduced complexity climate model

A primary advantage to using reduced complexity climate models (RCMs) has been their ability to quickly conduct probabilistic climate projections, a key component of uncertainty quantification in many impact studies and multisector systems. Providing frameworks for such analyses has been a target of several RCMs used in studies of the future co-evolution of the human and Earth systems. In this paper, we present Matilda, an open-science R software package that facilitates probabilistic climate projection analysis, implemented here using the Hector simple climate model in a seamless and easily applied framework. The primary goal of Matilda is to provide the user with a turn-key method to build parameter sets from literature-based prior distributions, run Hector iteratively to produce perturbed parameter ensembles (PPEs), weight ensembles for realism against observed historical climate data, and compute probabilistic projections for different climate variables. This workflow gives the user the ability to explore viable parameter space and propagate uncertainty to model ensembles with just a few lines of code. The package provides significant freedom to select different scoring criteria and algorithms to weight ensemble members, as well as the flexibility to implement custom criteria. Additionally, the architecture of the package simplifies the process of building and analyzing PPEs without requiring significant programming expertise, to accommodate diverse use cases. We present a case study that provides illustrative results of a probabilistic analysis of mean global surface temperature as an example of the software application.

Earth system responses to carbon dioxide removal as exemplified by ocean alkalinity enhancement: tradeoffs and lags

Carbon dioxide removal (CDR) is discussed for offsetting residual greenhouse gas emissions or even reversing climate change. All emissions scenarios of the Intergovernmental Panel on Climate Change that meet the ‘well below 2 °C’ warming target of the Paris Agreement include CDR. Ocean alkalinity enhancement (OAE) may be one possible CDR where the carbon uptake of the ocean is increased by artificial alkalinity addition. Here, we investigate the effect of OAE on modelled carbon reservoirs and fluxes in two observationally-constrained large perturbed parameter ensembles. OAE is assumed to be technically successful and deployed as an additional CDR in the SSP5-3.4 temperature overshoot scenario. Tradeoffs involving feedbacks with atmospheric CO2 result in a low efficiency of an alkalinity-driven atmospheric CO2 reduction of −0.35 [−0.37 to −0.33] mol C per mol alkalinity addition (skill-weighted mean and 68% c.i.). The realized atmospheric CO2 reduction, and correspondingly the efficiency, is more than two times smaller than the direct alkalinity-driven enhancement of ocean uptake. The alkalinity-driven ocean carbon uptake is partly offset by the release of carbon from the land biosphere and a reduced ocean carbon sink in response to lowered atmospheric CO2 under OAE. In a second step we use the Bern3D-LPX model in CO2 peak-decline simulations to address hysteresis and temporal lags of surface air temperature change (ΔSAT) in an idealized scenario where ΔSAT increases to ~2 °C and then declines to ~1.5 °C as result of CDR. ΔSAT lags the decline in CO2-forcing by 18 [14–22] years, depending close to linearly on the equilibrium climate sensitivity of the respective ensemble member. These tradeoffs and lags are an inherent feature of the Earth system response to changes in atmospheric CO2 and will therefore be equally important for other CDR methods.

Prediction of slowdown of the Atlantic Meridional Overturning Circulation in coupled model simulations

In coupled perturbed parameter ensemble (PPE) experiments or for development of a single coupled global climate model (GCM) in general, models can exhibit a slowdown in the Atlantic Meridional Overturning Circulation (AMOC) that can result in unrealistically reduced transport of heat and other tracers. Here we propose a method that researchers running PPE experiments can apply to their own PPE to diagnose what controls the AMOC strength in their model and make predictions thereof. As an example, using data from a 25-member coupled PPE experiment performed with HadGEM3-GC3.05, we found four predictors based on surface heat and freshwater fluxes in four critical regions from the initial decade of the spinup phase that could accurately predict the AMOC transport in the later stage of the experiment. The method, to our knowledge, is novel in that it separates the effects of the drivers of AMOC change from the effects of the changed AMOC. The identified drivers are shown to be physically credible in that the PPE members exhibiting AMOC weakening possess some combination of the following characteristics: warmer ocean in the North Atlantic Subpolar Gyre, fresher Arctic and Tropical North Atlantic Oceans and larger runoff from the Amazon and Orinoco Rivers. These characteristics were further traced to regional responses in atmosphere-only experiments. This study suggests promising potential for early stopping rules for parameter perturbations that could end up with an unrealistically weak AMOC, saving valuable computational resources. Some of the four drivers are likely to be relevant to other climate models so this study is of interest to model developers who do not have a PPE.

Describing future UK winter precipitation in terms of changes in local circulation patterns

Social scientists have argued that good communication around risks in climate hazards requires information to be presented in a user-relevant way, allowing people to better understand the factors controlling those risks. We present a potentially useful way of doing this by explaining future UK winter precipitation in terms of changes in the frequency, and associated average rainfall, of local pressure patterns that people are familiar with through their use in daily weather forecasts. We apply this approach to a perturbed parameter ensemble (PPE) of coupled HadGEM3-GC3.05 simulations of the RCP8.5 emissions scenario, which formed part of the UK Climate Projections in 2018. The enhanced winter precipitation by 2050–99 is largely due to an increased tendency towards westerly and south-westerly conditions at the expense of northerly/easterly conditions. Daily precipitation is generally more intense, most notably for the south-westerlies. In turn, we show that the changes in the frequency of the pressure patterns are consistent with changes in larger scale drivers of winter circulation and our understanding of how they relate to each other; this should build user confidence in the projections. Across the PPE, these changes in pressure patterns are largely driven by changes in the strength of the stratospheric polar vortex; for most members the vortex strengthens over the twenty-first century, some beyond the CMIP6 range. The PPE only explores a fraction of the CMIP6 range of tropical amplification, another key driver. These two factors explain why the PPE is skewed towards exploring the more westerly side of the CMIP6 range, so that the PPE’s description of UK winter precipitation changes does not provide a full picture.

Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification.

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Spatial transferability of the physically based model TRIGRS using parameter ensembles

AbstractThe development of better, more reliable and more efficient susceptibility assessments for shallow landslides is becoming increasingly important. Physically based models are well‐suited for this, due to their high predictive capability. However, their demands for large, high‐resolution and detailed input datasets make them very time‐consuming and costly methods. This study investigates if a spatially transferable model calibration can be created with the use of parameter ensembles and with this alleviate the time‐consuming calibration process of these methods. To investigate this, the study compares the calibration of the model TRIGRS in two different study areas. The first study area was taken from a previous study where the dynamic physically based model TRIGRS was calibrated for the Laternser valley in Vorarlberg, Austria. The calibrated parameter ensemble and its performance from this previous study are compared with a calibrated parameter ensemble of the model TRIGRS for the Passeier valley in South Tyrol, Italy. The comparison showed very similar model performance and large similarities in the calibrated geotechnical parameter values of the best model runs in both study areas. There is a subset of calibrated geotechnical parameter values that can be used successfully in both study areas and potentially other study areas with similar lithological characteristics. For the hydraulic parameters, the study did not find a transferable parameter subset. These parameters seem to be more sensitive to different soil types. Additionally, the results of the study also showed the importance of the inclusion of detailed information on the timing of landslide initiation in the calibration of the model.

An ensemble-based approach for pumping optimization in an island aquifer considering parameter, observation and climate uncertainty

Abstract. In coastal zones, a major objective of groundwater management is often to determine sustainable pumping rates which avoid well salinization. Understanding how model and climate uncertainties affect optimal management solutions is essential for providing groundwater managers with information about salinization risk and is facilitated by the use of optimization under uncertainty (OUU) methods. However, guidelines are missing for the widespread implementation of OUU in real-world coastal aquifers and for the incorporation of climate uncertainty into OUU approaches. An ensemble-based OUU approach was developed considering parameter, observation and climate uncertainty and was implemented in a real-world island aquifer in the Magdalen Islands (Quebec, Canada). A sharp-interface seawater intrusion model was developed using MODFLOW-SWI2 and a prior parameter ensemble was generated containing multiple equally plausible realizations. Ensemble-based history matching was conducted using an iterative ensemble smoother which yielded a posterior parameter ensemble conveying both parameter and observation uncertainty. Sea level and recharge ensembles were generated for the year 2050 and were then used to generate a predictive parameter ensemble conveying parameter, observation and climate uncertainty. Multi-objective OUU was then conducted, aiming to both maximize pumping rates and minimize the probability of well salinization. As a result, the optimal trade-off between pumping and the probability of salinization was quantified considering parameter, historical observation and future climate uncertainty simultaneously. The multi-objective, ensemble-based OUU led to optimal pumping rates that were very different from a previous deterministic OUU and close to the current and projected water demand for risk-averse stances. Incorporating climate uncertainty into the OUU was also critical since it reduced the maximum allowable pumping rates for users with a risk-averse stance. The workflow used tools adapted to very high-dimensional, nonlinear models and optimization problems to facilitate its implementation in a wide range of real-world settings.