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A novel aerosol activation scheme was developed and implemented into the WRF Thompson–Eidhammer Aerosol‐Aware microphysical module. While in most of the numerical weather prediction (NWP) models the activation is based on updraft velocity, even in the fog, the new parameterization considers the local rate of cooling and water vapor flux in the evaluation of the aerosol activation rate. In addition, diagnostic variables are added to evaluate the visibility reduction due to formation of haze droplets. The impact of changing the activation scheme is demonstrated by a case study of a well‐observed fog event. Data observed during the fog event campaign at Budapest and observed at standard meteorological stations are compared with the output of the numerical model. The results are summarized as follows: (i) the inhomogeneous spatial and temporal distribution of the number concentration of droplets is an inherent characteristic of the new parameterization scheme. (ii) Compared to the prior parameterization based on updraft velocity, the new parameterization scheme increases the number concentration of the droplets significantly, especially at the top of the fog. As a consequence, it reduces the downward short‐wave radiation flux prolonging the lifetime of the fog by about 30–60 min. (iii) Analyses reveal that earlier dissipation of the fog comparing to the observed data cannot be explained only by overestimation of the downward short‐wave radiation flux. (iv) The new method is able to evaluate the reduction of visibility due to haze. [ABSTRACT FROM AUTHOR]

In this study, we perform online sea ice bias correction within a Geophysical Fluid Dynamics Laboratory global ice‐ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., 2023, https://doi.org/10.1029/2023ms003757) for the purpose of predicting sea ice concentration (SIC) data assimilation (DA) increments. An initial implementation of the CNN shows systematic improvements in SIC biases relative to the free‐running model, however large summertime errors remain. We show that these residual errors can be significantly improved with a novel sea ice data augmentation approach. This approach applies sequential CNN and DA corrections to a new simulation over the training period, which then provides a new training data set to refine the weights of the initial network. We propose that this machine‐learned correction scheme could be utilized for generating improved initial conditions, and also for real‐time sea ice bias correction within seasonal‐to‐subseasonal sea ice forecasts. Plain Language Summary: Climate models contain errors which often lead to predictions which are consistently out of agreement with what we observe in reality. In some cases we know the origin of these errors, for example, predicting too much sea ice as a result of consistently cool ocean temperatures. In reality, however, there are typically numerous model errors interacting across the atmosphere, ocean and sea ice, and to manually parse through large volumes of climate model data in an attempt to isolate these errors in time and space is highly impractical. Machine learning on the other hand is a framework which is well‐suited to this task. In this work we take a machine learning model which, at any given moment, ingests information about a climate model's atmosphere, ocean and sea ice conditions, and predicts how much error there is in the climate model's representation of sea ice, without seeing any actual sea ice observations. We use this to adjust the sea ice conditions in one particular climate model as it is running forward in time making predictions, and we find that this significantly reduces the model's sea ice errors globally. Key Points: We use a convolutional neural network (CNN) to perform online sea ice bias correction within global ice‐ocean simulationsThe CNN systematically reduces the free‐running model bias in both the Arctic and AntarcticThe online performance can be improved by combining CNN and data assimilation corrections in order to iteratively augment the training data [ABSTRACT FROM AUTHOR]

Abstract In this study, we perform online sea ice bias correction within a Geophysical Fluid Dynamics Laboratory global ice‐ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., 2023, https://doi.org/10.1029/2023ms003757) for the purpose of predicting sea ice concentration (SIC) data assimilation (DA) increments. An initial implementation of the CNN shows systematic improvements in SIC biases relative to the free‐running model, however large summertime errors remain. We show that these residual errors can be significantly improved with a novel sea ice data augmentation approach. This approach applies sequential CNN and DA corrections to a new simulation over the training period, which then provides a new training data set to refine the weights of the initial network. We propose that this machine‐learned correction scheme could be utilized for generating improved initial conditions, and also for real‐time sea ice bias correction within seasonal‐to‐subseasonal sea ice forecasts.

Simulation and inferential modeling of sexual contact dynamics can be used to help predict the propagation of sexually transmitted infections (STI) such as HIV, by providing information on both cross-sectional network structure and how that structure changes over time. Researchers' choices regarding the temporal resolution of such network models is often driven by the resolution of the data collected to support model fitting and evaluation. These inherited temporal resolutions can become problematic if they differ from the resolution of other processes to which the network must be related. In such cases, a model "correction" is necessary: specifically, we would like to have a systematic method for adjusting a fitted model to allow it to make predictions at a different timescale than the one on which it was initially based. Here, we introduce a basic set of desiderata for such adjustments, and assess three very simple approaches to timescale adjustments for models of sexual contact networks (SCNs), with focus on models parameterized within the separable temporal exponential family random graph model (STERGM) framework. We also examine the impact of time-scale changes on SCN characteristics themselves, and outline a set of desiderata for what an adjustment strategy may be expected to achieve. Our findings have implications both for timescale correction of dynamic network models, and for dynamic network data collection. • Often, modeling and simulation of sexual contact networks inherit the timescale implicit in measurment during data collection. • We outline cases where timescale adjustment could be necessary and describe desiderata from such adjustments. • We tested the ability of three possible timescale 'corrections' to match the properties of appropriately adjusted data. • The adjustment that best-preserved statistics of adjusted data was joint adjustment the formation dissolution edge terms. • Forward-reachable vertices and concurrency duration varied with how one coarsened data, with implications for measurement. [ABSTRACT FROM AUTHOR]

Comprehensive surveillance systems are the key to provide accurate data for effective modeling. Traditional symptom-based case surveillance has been joined with recent genomic, serologic, and environment surveillance to provide more integrated disease surveillance systems. A major gap in comprehensive disease surveillance is to accurately monitor potential population behavioral changes in real-time. Population-wide behaviors such as compliance with various interventions and vaccination acceptance significantly influence and drive the overall epidemic dynamics in the society. Original infoveillance utilizes online query data (e.g., Google and Wikipedia search of a specific content topic such as an epidemic) and later focuses on large volumes of online discourse data about the from social media platforms and further augments epidemic modeling. It mainly uses number of posts to approximate public awareness of the disease, and further compares with observed epidemic dynamics for better projection. The current COVID-19 pandemic shows that there is an urgency to further harness the rich, detailed content and sentiment information, which can provide more accurate and granular information on public awareness and perceptions toward multiple aspects of the disease, especially various interventions. In this perspective paper, we describe a novel conceptual analytical framework of content and sentiment infoveillance (CSI) and integration with epidemic modeling. This CSI framework includes data retrieval and pre-processing; information extraction via natural language processing to identify and quantify detailed time, location, content, and sentiment information; and integrating infoveillance with common epidemic modeling techniques of both mechanistic and data-driven methods. CSI complements and significantly enhances current epidemic models for more informed decision by integrating behavioral aspects from detailed, instantaneous infoveillance from massive social media data.

Numerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid dynamical aspects (i.e., those represented by physical parameterizations such as subgrid-scale mixing), and nonfluid dynamical aspects such as radiation and microphysical processes. Typically, each component is developed, at least initially, independently.Once development ismature, the components are coupled to deliver a model of the required complexity. The implementation of the coupling can have a significant impact on the model.As the error associated with each component decreases, the errors introduced by the coupling will eventually dominate. Hence, any improvement in one of the components is unlikely to improve the performance of the overall system. The challenges associated with combining the components to create a coherentmodel are here termed physics-dynamics coupling. The issue goes beyond the coupling between the parameterizations and the resolved fluid dynamics. This paper highlights recent progress and some of the current challenges. It focuses on three objectives: to illustrate the phenomenology of the coupling problemwith references to examples in the literature, to show howthe problem can be analyzed, and to create awareness of the issue across the disciplines and specializations. The topics addressed are different ways of advancing full models in time, approaches to understanding the role of the coupling and evaluation of approaches, coupling ocean and atmosphere models, thermodynamic compatibility between model components, and emerging issues such as those that arise as model resolutions increase and/ormodels use variable resolutions.

Parametrization in bridge construction for the application of artificial intelligence For the application of digital methods in the execution phase, the acquisition of exisiting structures or the construction progress with the aid of LiDAR is a suitable method to subsequently detect components as automatically as possible and e. g., to evaluate them for construction progress monitoring. Machine learning methods are suitable for component detection and are used for various classification problems. One problem is the semantic segmentation of point clouds, in which the individual points are assigned to specific component classes. To solve the segmentation with sufficient accuracy and to make predictions on real data, a large amount of training data is needed. This process is also called "Scan to BIM". In this paper, synthetic data for training a convolutionalneural network (CNN) is provided in a highly parameterized and fully automatic way. An approach for generating variations of bridge models based with the visual programming interface Dynamo in Autodesk Revit is proposed. At the beginning, guidelines for the definition of parameters are evaluated to subsequently use them in adaptive templates. Attributes are assigned to each component and grouped into a feature structure when exported to the IFC data format. This approach resulted in numerous variations of bridge models that can be used for semantic segmentation of point clouds and training of a CNN. [ABSTRACT FROM AUTHOR]

How and to what extent does the environment affect people’s behavior? This study focuses on a narrow and specialized statistical indicator describing human sociability – social capital, and an easily replicable method of researching the urban environment – the skeleton analysis of the urban form. Social capital is a growing in popularity and important statistical indicator which describes social environment of the place. Shape skeleton analysis allow us to model how a person perceives shapes. In this study, social capital is measured by the Putnam methodology adapted to Lithuania, and the shape skeleton methodology of the urban form is adapted based on psychological research. Artificial intelligence methods using data from urban form generated prediction which had statistically weak relationship with social capital. Article in Lithuanian. Lietuvos socialinio kapitalo modeliavimas taikant urbanistinės formos skeleto metodą Santrauka Kaip ir kiek aplinka veikia žmonių elgesį? Šiame tyrime koncentruojamasi į siaurą ir specializuotą žmonių visuomeniškumą apibūdinantį statistinį rodiklį – socialinį kapitalą ir nesunkiai pakartojamą urbanistinės aplinkos tyrimo būdą – urbanistinės formos skeleto analizę. Socialinis kapitalas yra populiarėjantis ir svarbus statistinis vietovės rodiklis, apibūdinantis socialinę aplinką. Formos skeleto analizės tyrimai, supaprastintai galima sakyti, leidžia modeliuoti, kaip žmogus suvokia formą. Šiame tyrime socialinis kapitalas išmatuojamas adaptuota Lietuvai Putnamo metodika, o urbanistinės formos skeleto metodologija pritaikyta remiantis psichologijos tyrimais. Modeliuoti taikant dirbtinio intelekto metodus buvo rastas statistiškai silpnas ryšys tarp urbanistinės formos ir socialinio kapitalo. Reikšminiai žodžiai: socialinis kapitalas, urbanistinė forma, formos skeletas, parametrizacija, formos suvokimas, modeliavimas.

Pareto-type distributions are well-known distributions used to fit heavy-tailed data. However, the standard parameterizations used for Pareto-type distributions are poorly suited to modeling. On this note, we suggest new parameterizations that are better suited to the purpose. In addition, we propose many regression models where the response variable is Pareto-type distributed using new parameterizations that are indexed by mean and precision parameters. The main motivation for these new parametrizations is the useful interpretation of the regression coefficients in terms of the mean and precision, as is usual in the context of regression models. The parameter estimation of these new models is performed, based on the maximum likelihood paradigm. Some numerical illustrations of the estimators are presented with a discussion of the obtained results. Finally, we illustrate the practicality of the new models by means of two applications to real data sets.

Macromolecular signals are crucial constituents of short echo‐time 1H MR spectra with potential clinical implications in themselves as well as essential ramifications for the quantification of the usually targeted metabolites. Their parameterization, needed for general fitting models, is difficult because of their unknown composition. Here, a macromolecular signal parameterization together with metabolite signal quantification including relaxation properties is investigated by multidimensional modeling of interrelated 2DJ inversion‐recovery (2DJ‐IR) datasets. Simultaneous and iterative procedures for defining the macromolecular background (MMBG) as mono‐exponentially or generally decaying signals over TE are evaluated. Varying prior knowledge and restrictions in the metabolite evaluation are tested to examine their impact on results and fitting stability for two sets of three‐dimensional spectra acquired with metabolite‐cycled PRESS from cerebral gray and white matter locations. One dataset was used for model optimization, and also examining the influence of prior knowledge on estimated parameters. The most promising model was applied to a second dataset. It turned out that the mono‐exponential decay model appears to be inadequate to represent TE‐dependent signal features of the MMBG. TE‐adapted MMBG spectra were therefore determined. For a reliable overall quantification of implicated metabolite concentrations and relaxation times, a general fitting model had to be constrained in terms of the number of fitting variables and the allowed parameter space. With such a model in place, fitting precision for metabolite contents and relaxation times was excellent, while fitting accuracy is difficult to judge and bias was likely influenced by the type of fitting constraints enforced. In summary, the parameterization of metabolite and macromolecule contributions in interrelated MR spectra has been examined by using multidimensional modeling on complex 2DJ‐IR datasets. A tightly restricted model allows fitting of individual subject data with high fitting precision documented in small Cramér‐Rao lower bounds, good repeatability values and a relatively small spread of estimated concentration and relaxation values for a healthy subject cohort. [ABSTRACT FROM AUTHOR]

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The major part of dissolved iron (DFe) in seawater is bound to organic matter, which prevents iron from adsorptive removal by sinking particles and essentially regulates the residence time of DFe and its availability for marine biota. Characteristics of iron‐binding ligands highly depend on their biological origin and physico‐chemical properties of seawater which may intensely alter under ongoing climate change. To understand environmental controls on the iron binding, we applied a function of pH and dissolved organic carbon (DOC) to describe changes in the binding strength of organic ligands in a global biogeochemical model (REcoM). This function was derived based on calculations using a thermodynamic chemical speciation model NICA. This parameterization considerably improved the modeled DFe distribution, particularly in the surface Pacific and the global mesopelagic and deep waters, compared to our previous model simulations with a constant ligand or one prognostic ligand. This indicates that the organic binding of iron is apparently controlled by seawater pH in addition to its link to organic matter. We tested further the response of this control to environmental changes in a simulation with future pH of a high emission scenario. The response of the binding potential shows a complex pattern in different regions and is regulated by factors that have opposite effects on the binding potential. The relative contributions of these factors can change over time by a continual change of environmental conditions. A dynamic feedback system therefore needs to be considered to predict the marine iron cycle under ongoing climate change. Key Points: Iron‐ligand binding is described in a global biogeochemical model with a function of pH and DOCpH control improved model performance, particularly in the surface Pacific, mesopelagic and deep oceanResponse of iron binding to future pH has high spatial variability and can change over time in a specific region [ABSTRACT FROM AUTHOR]

Groundwater is the world's most important freshwater resource. Despite this importance, groundwater flow and interactions between groundwater and other parts of the hydrological cycle are often neglected or simplified in large‐scale hydrological models. One of the challenges in simulating groundwater flow and continental to global scales is the lack of consistent globally available hydrogeological data. These input data are needed for a more realistic physical representation of the groundwater system, enabling the simulation of groundwater head dynamics and lateral flows. A realistic representation of the subsurface is especially important as large‐scale hydrological models move to finer resolutions and aim to provide accurate and locally relevant hydrologic information everywhere. In this study, we aim at improving and extending on current available large‐scale data sets providing information of the subsurface. We present a detailed aquifer representation for the continental United States and Canada at hyper resolution (250 × 250 m). We integrate local hydrogeological information, including observations of aquifer layer thickness, conductivity, and vertical structure, to obtain representative aquifer parameter values applicable to the continental scale. The methods used are simple and can be expanded to other parts of the world. Hydrological simulations were performed using the integrated hydrological model ParFlow and demonstrated improved model performance when using the new aquifer parameterization. Our results support that more detailed and accurate aquifer parameterization will advance our understanding of the groundwater system at larger scales. Plain Language Summary: Groundwater is the largest available freshwater resource humans can use for drinking and growing food. It is stored beneath our feet in thick layers of sand or rock; we call these layers aquifers. For a realistic estimation of how much groundwater is stored and flows through these aquifers, detailed information on the properties of these layers is essential. However, this information is often missing, or not detailed enough, at the larger scales. In this study, we introduce a new method to obtain this detailed data by including information from observations and local‐scale studies what we upscaled to the continent of North America. We estimate aquifer parameters at very high spatial resolution over a large domain. These data were not available previously. We evaluate our newly obtained aquifer parameterization against other existing data sets and tested model sensitivity to different horizontal and vertical resolutions and conductivity values. We used the integrated hydrological model ParFlow. Our results show that estimates of water table depth improved using our new parameterization. The methods we introduce here can be used by other modelers looking to improve their aquifer parameterization. We hope that our study can be used as a road map towards improved and more detailed aquifer parameterization used in current large‐scale hydrological models. Key Points: We integrate local hydrogeological information into a continental‐scale aquifer parameterization for the continental United States and CanadaAquifer thickness and vertical structure are estimated at a level of detail not available beforeHydrological modeling confirmed accurate large‐scale aquifer parameterization will improve our understanding of the hydrological system [ABSTRACT FROM AUTHOR]

The parameterization of the nonlinear energy transfer called Discrete Interaction Approximation (DIA) is optimized in the WAVEWATCH III wave model by the criterion of minimizing deviations of model predictions from the data of field measurements. Short fetches are considered for which the wind-input source function had been previously adjusted. The results of the numerical simulation and field experiment for the DIA built-in version and for the DIA with the proposed parameters are compared. The improvement of the reproduction of the main parameters of the wave spectra, significant wave height, and their mean period by the model is shown. [ABSTRACT FROM AUTHOR]

How and to what extent does the environment affect people’s behavior? This study focuses on a narrow and specialized statistical indicator describing human sociability – social capital, and an easily replicable method of researching the urban environment – the skeleton analysis of the urban form. Social capital is a growing in popularity and important statistical indicator which describes social environment of the place. Shape skeleton analysis allow us to model how a person perceives shapes. In this study, social capital is measured by the Putnam methodology adapted to Lithuania, and the shape skeleton methodology of the urban form is adapted based on psychological research. Artificial intelligence methods using data from urban form generated prediction which had statistically weak relationship with social capital. [ABSTRACT FROM AUTHOR]

We use a simple parameterization of the Embedded Atom Method (EAM), termed EAM-X, to explore the dependence of grain boundary (GB) energies in FCC metals on EAM parameters. We study a set of GBs with various geometries and calculate their energies, scanning through EAM-X parameter space. We find that variations in GB energy with EAM parameters can be larger than variations due to GB geometry. The atomistic data are used to determine a fit of the GB energy in EAM parameter space, which can be used to obtain boundary energies in real FCC elements without the need to perform new atomistic calculations. Our results provide broad confirmation of a proposed correlation between calculated GB energy and shear modulii among FCC metals. Our work also highlights the need to consider sensitivity to details of empirical potentials when performing quantitative studies of GB physics. [ABSTRACT FROM AUTHOR]

Abstract We present a novel approach to mesh deformation that enables simple context sensitive manipulation of 3D geometry. The method is based on locally anisotropic transformations and is extended to global control directions. This allows intuitive directional modeling within an easy to implement framework. The proposed method complements current sculpting paradigms by providing further possibilities for intuitive surface-based editing without the need for additional host geometries. We show the anisotropic deformation to be seamlessly transferable to free boundary parameterization methods, which allows us to solve the hard problem of flattening compression garments in the domain of apparel design.

Abstract: The objective of this work was to evaluate how the parameterization and the application of different allometric values affect the obtention of the most adequate fit of von Bertalanffy’s model, in the description of the growth curve of meat-producing mammals (bovine, pigs, rabbits, and sheep). Among the nonlinear models, von Bertalanffy’s has been very often applied in several areas, with different parameterizations. This model has been commonly used with an allometric value of m = 2/3; however, for mammals, it is believed that this value can be m = 3/4. The analyzed data referring to the mass of meat-producing mammals according to their age were obtained from research institutions and from the literature. The results showed that von Bertalanffy’s model, with the allometric value of m = 3/4 and the used parameterization, provided better adjustments to quality evaluators. Besides, the model softened the overestimation of parameter a, giving a direct interpretation of parameter b, with the lowest values for curvature measurements, mainly for the parametric ones, and provided more reliable adjustments. Von Bertalanffy’s model can be used in the description of the growth curves of meat-producing mammals.

Modern Earth system models (ESMs) disagree on the impacts of anthropogenic global warming on the distribution of oxygen and associated low‐oxygen waters. A sensitivity study using the GFDL CM2Mc model points to the representation of lateral mesoscale eddy transport as a potentially important factor in such disagreement. Because mesoscale eddies are smaller than the spatial scale of ESM ocean grids, their impact must be parameterized using a lateral mixing coefficient AREDI. The value of AREDI varies across modern ESMs and nonlinearly impacts oxygen distributions. This study shows that an increase in atmospheric CO2 results in a decline in productivity and a decrease in ventilation age in the tropics, increasing oxygen concentrations in the upper thermocline. In high latitudes global warming causes shallowing of deep convection, reducing the supply of oxygen to the deep. The net impact of these processes depends on AREDI, with an increase in hypoxic volume yet smaller total deoxygenation in the low‐mixing models, but a decrease in hypoxic volume yet larger total deoxygenation in the high‐mixing models. All models show decreases in suboxic volume, which are largest in the low‐mixing models. A subset of Coupled Model Intercomparison Project Phase 5 models exhibits a similar range of responses to global warming and similar decoupling between total deoxygenation and change in hypoxic volume. Uncertainty in lateral mixing remains an important contributor to uncertainty in projecting ocean deoxygenation. Plain Language Summary: Global warming is expected to change the amount of oxygen present in the oceans, both at the surface and at depth. Some of this is driven by oceanic warming due to an increase in atmospheric temperatures, as warmer water holds less oxygen. But changes in biological drawdown play an even bigger role. Because warmer water is less dense, it becomes more difficult for heavier deep waters rich in nutrients to surface. This reduces biological productivity and in turn means that less organic matter sinks into the deep ocean and rots. Because decomposition uses oxygen, a decrease in decomposition will result in more oxygen at depth. But as oxygen‐rich surface waters get lighter, they do not flow as easily into the deep ocean and deliver oxygen to these depths. This causes oxygen to fall. The balance between these three processes determines whether increasing fractions of the deep ocean will become inhospitable to fish and other organisms. The Earth System Models used to project the impacts of climate change predict different balances between these processes. We demonstrate that an important reason for this disagreement is uncertainty in how to represent mixing associated with the oceanic "storms" known as mesoscale eddies. Key Points: An intermodel comparison reveals that ocean deoxygenation is sensitive to the representation of lateral mixingStronger lateral mixing results in greater deoxygenation but largely in zones which have high oxygen in the base stateIntermodel differences of deoxygenation are driven by changes in biological drawdown, which is ultimately controlled by ventilation [ABSTRACT FROM AUTHOR]

Highlights • Albedo of H 2 O-CO 2 atmospheres found to follow simple analytical parameterization. • Clouds dominate albedo at lower surface temperatures • Albedo increases with CO 2 /H 2 O ratio. • Albedo increases with stellar temperature. Abstract During accretion, the young rocky planets are so hot that they become endowed with a magma ocean. From that moment, the mantle convective thermal flux control the cooling of the planet and an atmosphere is created by outgassing. This atmosphere will then play a key role during this cooling phase. Studying this cooling phase in details is a necessary step to explain the great diversity of the observed telluric planets and especially to assess the presence of surface liquid water. We used here a radiative-convective 1D atmospheric model (H 2 O, CO 2) to study the impact of the Bond albedo on the evolution of magma ocean planets. We derived from this model the thermal emission spectrum and the spectral reflectance of these planets, from which we calculated their Bond albedos. Compared to Marcq et al. (2017), the model now includes a new module to compute the Rayleigh scattering, and state of the art CO 2 and H 2 O gaseous opacities data in the visible and infrared spectral ranges. We show that the Bond albedo of these planets depends on their surface temperature and results from a competition between Rayleigh scattering from the gases and Mie scattering from the clouds. The colder the surface temperature is, the thicker the clouds are, and the higher the Bond albedo is. We also evidence that the relative abundances of CO 2 and H 2 O in the atmosphere strongly impact the Bond albedo. The Bond albedo is higher for atmospheres dominated by the CO 2 , better Rayleigh scatterer than H 2 O. Finally, we provide the community with an empirical formula for the Bond albedo that could be useful for future studies of magma ocean planets. [ABSTRACT FROM AUTHOR]

Abstract Existing parameterization of origami structures is based on angles, which is an inconvenience for the fabrication and calculation of the geometrical properties of an origami-engineering project. In this paper, a unified parameterization based on the dimensional parameters, including the length and height instead of the angles, is proposed. Various origami-based tubes reconstructed through the proposed parameterization process and compiled into a program are then described. Three types of tubes including non-flat foldable, locally flat-foldable, and entirely flat-foldable are systematically divided when forming a closed-loop tube, and a criterion is proposed for estimating the flat foldability of the tube. Finally, the proposed theory is validated based on physical prototypes folded using paperboard and fabricated using polymer through a blow molding process. The constructed origami-based tubes provide extensive guidance for the design of energy absorbing structures. Highlights • New general crease pattern consists of various isosceles trapezoids as the basic geometrical units. • The parameterized transformation from a crease pattern to its corresponding origami-based tube. • Classification of origami-based tubes. • The criterion used to estimate the flat foldability. [ABSTRACT FROM AUTHOR]

Highlights • Upstream flow properties are not representative of iceberg basal flow properties. • A Taylor column of reduced flow forms under large icebergs. • Two conflicting representations of the heat transfer coefficient exist in ice melt parameterizations. • A new parameterization of tabular iceberg basal melt is proposed, accounting for these factors. Abstract In this study, we consider the influence of icebergs on the ocean when they are modeled as occupying physical space, to answer the question of how the melting of icebergs and subsequent distribution of meltwater in the water column might be accurately parameterized in climate models. Iceberg melt is analyzed by comparing in-situ melt rates calculated via the three-equation parameterization, which was developed for application under ice shelves, with the commonly used bulk parameterization of iceberg basal melt. Our results suggest an updated velocity-independent version of the basal melt parameterization for tabular icebergs for use in calculating the basal melt rate of icebergs that are large (relative to the deformation radius), to account for the changes in ocean properties caused by the physical presence of a large iceberg in the ocean. [ABSTRACT FROM AUTHOR]

In 2010, the Natural Resources and Environmental Systems Hydrology Committee (NRES-21) of ASABE initiated a long-term process to develop guidelines to improve modeling practice through better understanding of the calibration, validation, and evaluation process across applications and more effective interpretation and communication of model performance. This effort generated a compilation of 23 articles with model-specific descriptions and guidance (2012), a position paper outlining guidance for evaluating, interpreting, and communicating performance of hydrologic and water quality models considering intended use (2014), and a compilation of ten articles addressing key topics related to model calibration and validation (2015). In 2016, the first draft of ASABE Engineering Practice 621 (EP621), "Guidelines for Calibrating, Validating, and Evaluating Hydrologic and Water Quality (H/WQ) Models," was developed, subsequently revised, and ultimately approved by the ASABE Standards Committee in 2017. EP621 provides guidelines, not prescriptive requirements, and as such recommends "good" modeling practices to enhance calibration, validation, evaluation, and communication of H/WQ models through establishment of consistent terminology; model selection; compilation and processing of input data and calibration, validation, and evaluation data; determination of model performance measures; model parameterization and calibration; re-examination of input and calibration data and/or consideration of model refinement; re-evaluation of model performance; and documentation of modeling process and results. EP621 can be obtained from the ASABE Technical Library at https://elibrary.asabe.org/abstract.asp?aid=47804. The objectives of this technical note are to review the process and rationale used to develop EP621 and to briefly summarize its major components. [ABSTRACT FROM AUTHOR]

Purpose: To introduce and evaluate the use of stable distributions as a methodology to quantify the behavior of proton pencil beams in a medium. Methods: The proton pencil beams of a clinically commissioned proton treatment facility are replicated in a Monte Carlo simulation system (FLUKA). For each available energy, the beam deposition in water medium is characterized by the dose deposition. Using a stable distribution methodology, each beam with a nominal energy E is characterized by the lateral spread at depth z: S(z; α, γ, E) and a total energy deposition ID(z, E). The parameter α describes the tailedness of the distributions, while γ is used to scale the size of the function. The beams can then be described completely by a function of the variation of the parameters with depth. Results: Quantitatively, the fit of the stable distributions, compared to those implemented in some standard treatment planning systems, are equivalent for all but the highest energies (i.e., 230 MeV/u). The decrease in goodness of fit makes this methodology comparable to a double Gaussian approach. The introduction of restricted linear combinations of stable distributions also resolves that particular case. More importantly, the meta‐parameterization (i.e., the description of the dose deposition by only providing the fitted parameters) allows for interpolation of nonmeasured data. In the case of the clinical commissioning data used in this paper, it was possible to only commission one out of five nominal energies to obtain a viable dataset, valid for all energies. An additional parameter β allows to describe asymmetric beam profiles as well. Conclusions: Stable distributions are intrinsically suited to describe proton pencil beams in a medium and provide a tool to quantify the propagation of proton beams in a medium. [ABSTRACT FROM AUTHOR]

We present a novel approach to mesh deformation that enables simple context sensitive manipulation of 3D geometry. The method is based on locally anisotropic transformations and is extended to global control directions. This allows intuitive directional modeling within an easy to implement framework. The proposed method complements current sculpting paradigms by providing further possibilities for intuitive surface-based editing without the need for additional host geometries. We show the anisotropic deformation to be seamlessly transferable to free boundary parameterization methods, which allows us to solve the hard problem of flattening compression garments in the domain of apparel design. [ABSTRACT FROM AUTHOR]

Parameterizations of normal atmospheric modes (NAMs) and orographic gravity waves (OGWs) are implemented into the mechanistic general circulation model of the middle and upper atmosphere (MUA). Numerical experiments of sudden stratospheric warming (SSW) events are performed for climatological conditions typical for January and February using meteorological reanalysis data from the UK MET Office in the MUA model averaged over the years 1992-2011 with the easterly phase of quasi-biennial oscillation (QBO). The simulation shows that an increase in the OGW amplitudes occurs at altitudes higher than 30 km in the Northern Hemisphere after SSW. The OGW amplitudes have maximums at altitudes of about 50 km over the North American and European mountain systems before and during SSW, as well as over the Himalayas after SSW. At high latitudes of the Northern Hemisphere, significant (up to 50-70%) variations in the amplitudes of stationary planetary waves (SPWs) are observed during and after the SSW. Westward travelling NAMs have local amplitude maximums not only in the Northern Hemisphere, but also in the Southern Hemisphere, where there are waveguides for the propagation of these modes. Calculated variations of SPW and NAM amplitudes correspond to changes in the mean temperature and wind fields, as well as the Eliassen-Palm flux and atmospheric refractive index for the planetary waves, during SSW. Including OGW thermal and dynamical effects leads to an increase in amplitude (by 30-70%) of almost all SPWs before and during SSW and to a decrease (up to 20-100%) after the SSW at middle and high latitudes of the Northern Hemisphere. [ABSTRACT FROM AUTHOR]

In this paper, a framework for analyzing the motion resulting from the interaction between a snake robot and an object is shown. Metrics are derived to study the motion of the object and robot, showing that the addition of passive wheels to the snake robot helps to minimize slippage. However, the passive wheels do not have a significant impact on the force exerted onto the object. This puts snake robots in a similar framework as robotic arms, while considering special properties exclusive to snake robots (e.g., lack of a fixed-base, interaction with the environment through friction). It is also shown that the configuration (shape) of the snake robot, parameterized with the polar coordinates of the robot's COM, plays an important role in the interaction with the object. Two examples, a snake robot with two joints and another with three joints, are studied to show the applicability of the model. [ABSTRACT FROM AUTHOR]

An empirical relationship describing the bulk etch rate is formulated. The equation involves two free fitting parameters, which reproduce the bulk etch rate for CR-39 by alpha particles at different normalities of the etching solution. The values of the fitting parameters were obtained from the experimental data. This relationship is used to predict the bulk etch rate at different normalities. [ABSTRACT FROM AUTHOR]

We study three dimensional parametric agile design of gears and related software platforms in terms of: parameterization, modularization, and the software being easy to use, expand and maintain. We base our study on SolidWorks platform, aiming to generate 3-D solid models of gears automatically in terms of: i) inputting parameters to improve the modeling speed, ii) build up foundations for structure design, iii) do dynamic simulation, and iv) interference checks and v) use finite element analysis to improve design efficiency. Our results include: i) 3-D accurate description for involute gear is realized, ii) accurate specification of involute gear in SolidWorks has been achieved, iii) parameterized agile modeling system modules of involute cylindrical spur gear, helical gear and internal gear have been developed using SolidWorks as support platform, and iv) success in adopting object oriented technology for involute coordinate equations. [ABSTRACT FROM AUTHOR]

The environmental adversities and the global concern about the conservation of non-renewable natural resources have stimulated a search for environmentally friendly energy sources. In this context, biodiesel has emerged as an important alternative to replace fossil fuels, due to its renewability, non-toxicity and biodegradability. Modeling, simulation and design of unit operations involved in the production of edible oils and biodiesel require knowledge of phase equilibrium. Several versions of the UNIFAC model are frequently used for process design when experimental determination of phase equilibrium data is difficult or time-consuming. In this work, the original UNIFAC model parameters are first checked for their predictive capability and then modified in terms of new readjusted binary interaction parameters. It was noted that the UNIFAC model without any changes in its parameters results in inadequate predictions. Thus, in order to obtain a good predictive tool, a comprehensive liquid-liquid equilibrium data bank of systems present in biodiesel production was organized and new UNIFAC interaction parameters were adjusted. At first, the molecules were divided into UNIFAC traditional structural groups. However, this first approach resulted in poor prediction, probably as a consequence of the strongly polar hydroxyl groups bonded to the consecutive carbon atoms of glycerol and acylglycerol molecules. Thus, a new group (‘OHgly’) was introduced and two matrices of parameters were adjusted. In general, satisfactory predictions were obtained and a significant improvement in the performance of this group contribution model has been achieved. [ABSTRACT FROM AUTHOR]

Summary Process-based hydrological models have a long history dating back to the 1960s. Criticized by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is that these tools are necessary in many situations and, in a certain class of problems, they are the most appropriate type of hydrological model. This is especially the case in situations where knowledge of flow paths or distributed state variables and/or preservation of physical constraints is important. Examples of this include: spatiotemporal variability of soil moisture, groundwater flow and runoff generation, sediment and contaminant transport, or when feedbacks among various Earth’s system processes or understanding the impacts of climate non-stationarity are of primary concern. These are situations where process-based models excel and other models are unverifiable. This article presents this pragmatic view in the context of existing literature to justify the approach where applicable and necessary. We review how improvements in data availability, computational resources and algorithms have made detailed hydrological simulations a reality. Avenues for the future of process-based hydrological models are presented suggesting their use as virtual laboratories, for design purposes, and with a powerful treatment of uncertainty. [ABSTRACT FROM AUTHOR]

Many interesting characteristics of sea ice drift depend on the atmospheric drag coefficient ( C) and oceanic drag coefficient ( C). Parameterizations of drag coefficients rather than constant values provide us a way to look insight into the dependence of these characteristics on sea ice conditions. In the present study, the parameterized ice drag coefficients are included into a free-drift sea ice dynamic model, and the wind factor α and the deflection angle θ between sea ice drift and wind velocity as well as the ratio of C to C are studied to investigate their dependence on the impact factors such as local drag coefficients, floe and ridge geometry. The results reveal that in an idealized steady ocean, C/ C increases obviously with the increasing ice concentration for small ice floes in the marginal ice zone, while it remains at a steady level (0.2-0.25) for large floes in the central ice zone. The wind factor α increases rapidly at first and approaches a steady level of 0.018 when A is greater than 20%. And the deflection angle θ drops rapidly from an initial value of approximate 80° and decreases slowly as A is greater than 20% without a steady level like α. The values of these parameters agree well with the previously reported observations in Arctic. The ridging intensity is an important parameter to determine the dominant contribution of the ratio of skin friction drag coefficient (Cs'/Cs) and the ratio of ridge form drag coefficient ( C'/ C) to the value of C/ C, α, and θ, because of the dominance of ridge form drag for large ridging intensity and skin friction for small ridging intensity among the total drag forces. Parameterization of sea ice drag coefficients has the potential to be embedded into ice dynamic models to better account for the variability of sea ice in the transient Arctic Ocean. [ABSTRACT FROM AUTHOR]

We modified the rumen submodel of the Molly dairy cow model to simulate the rumen of a sheep and predict its methane emissions. We introduced a rumen hydrogen (H2) pool as a dynamic variable, which (together with the microbial pool in Molly) was used to predict methane production, to facilitate future consideration of thermodynamic control of methanogenesis. The new model corrected a misspecification of the equation of microbial H2 utilization in Molly95, which could potentially give rise to unrealistic predictions under conditions of low intake rates. The new model included a function to correct biases in the estimation of net H2 production based on the default stoichiometric relationships in Molly95, with this function specified in terms of level of intake. Model parameters for H2 and methane production were fitted to experimental data that included fresh temperate forages offered to sheep at a wide range of intake levels and then tested against independent data. The new model provided reasonable estimates relative to the calibration data set, but a different parameterization was needed to improve its predicted ability relative to the validation data set. Our results indicate that, although feedback inhibition on H2 production and methanogen activity increased with feeding level, other feedback effects that vary with diet composition need to be considered in future work on modeling rumen digestion in Molly. [ABSTRACT FROM AUTHOR]

ABSTRACT The suitability of the Guggenheim-Anderson-De Boer (GAB) model for the parameterization of gas sorption isotherms and their dependences on temperature is explored. The GAB model implies that molecules adsorb on inner surfaces of the polymer in multilayers, which contrasts with the assumptions of the classical Dual Mode Sorption (DMS) model which implies the simultaneous occurrence of Henry-like dissolution and Langmuir's case I adsorption. The GAB model shows similar efficacy of the parameterization of the gas sorption isotherms in polymers as the DMS model. The isosteric heat of adsorption shows clear dependence on relative surface coverage for carbon dioxide sorption in cellulose acetate, polyethylene terephthalate, and the first polymer of intrinsic microporosity (PIM-1), thus allowing for the occurrence of adsorption multilayers. © 2014 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2014, 52, 1490-1495 [ABSTRACT FROM AUTHOR]

Root biomass, root production and lifespan, and root-mycorrhizal interactions govern soil carbon fluxes and resource uptake and are critical components of terrestrial models. However, limitations in data and confusions over terminology, together with a strong dependence on a small set of conceptual frameworks, have limited the exploration of root function in terrestrial models. We review the key root processes of interest to both field ecologists and modelers including root classification, production, turnover, biomass, resource uptake, and depth distribution to ask (1) what are contemporary approaches for modeling roots in terrestrial models? and (2) can these approaches be improved via recent advancements in field research methods? We isolate several emerging themes that are ready for collaboration among field scientists and modelers: (1) alternatives to size-class based root classifications based on function and the inclusion of fungal symbioses, (2) dynamic root allocation and phenology as a function of root environment, rather than leaf demand alone, (3) improved understanding of the treatment of root turnover in models, including the role of root tissue chemistry on root lifespan, (4) better estimates of root stocks across sites and species to parameterize or validate models, and (5) dynamic interplay among rooting depth, resource availability and resource uptake. Greater attention to model parameterization and structural representation of roots will lead to greater appreciation for belowground processes in terrestrial models and improve estimates of ecosystem resilience to global change drivers. [ABSTRACT FROM AUTHOR]

Channel models are important tools to evaluate the performance of new concepts in mobile communications. However, there is a tradeoff between complexity and accuracy. In this paper, we extend the popular Wireless World Initiative for New Radio (WINNER) channel model with new features to make it as realistic as possible. Our approach enables more realistic evaluation results at an early stage of algorithm development. The new model supports 3-D propagation, 3-D antenna patterns, time evolving channel traces of arbitrary length, scenario transitions and variable terminal speeds. We validated the model by measurements in a coherent LTE advanced testbed in downtown Berlin, Germany. We then reproduced the same scenario in the model and compared several channel parameters (delay spread, path gain, K-factor, geometry factor and capacity). The results match very well and we can accurately predict the performance for an urban macro-cell setup with commercial high-gain antennas. At the same time, the computational complexity does not increase significantly and we can use all existing WINNER parameter tables. These artificial channels, having equivalent characteristics as measured data, enable virtual field trials long before prototypes are available. [ABSTRACT FROM PUBLISHER]

The authors discusses the future of hydrological sciences, focusing on the use of models and data to inform the discovery and learning process. Topics include the role of modeling in the development of hydrological science, and model and system identification problem, the importance of systems theory to hydrology. They suggest that much can be gained by working directly with process models, while de-emphasizing system parameterization.

Mercury is a ubiquitous global environmental toxicant responsible for most US fish advisories. Processes governing mercury concentrations in rivers and streams are not well understood, particularly at multiple spatial scales. We investigate how insights gained from reach-scale mercury data and model simulations can be applied at broader watershed scales using a spatially and temporally explicit watershed hydrology and biogeochemical cycling model, VELMA. We simulate fate and transport using reach-scale (0.1 km2) study data and evaluate applications to multiple watershed scales. Reach-scale VELMA parameterization was applied to two nested sub-watersheds (28 km2 and 25 km2) and the encompassing watershed (79 km2). Results demonstrate that simulated flow and total mercury concentrations compare reasonably to observations at different scales, but simulated methylmercury concentrations are out-of-phase with observations. These findings suggest that intricacies of methylmercury biogeochemical cycling and transport are under-represented in VELMA and underscore the complexity of simulating mercury fate and transport. [Copyright &y& Elsevier]

Both the foliage and stem essentially influence the flow resistance of woody plants, but their different biomechanical properties complicate the parameterization of foliated vegetation for modeling. This paper investigates whether modeling of flow resistance caused by natural woody vegetation can be improved using explicit description of both the foliage and stem. For this purpose, we directly measured the drag forces of Alnus glutinosa, Betula pendula, Salix viminalis, and Salix x rubens twigs in a laboratory flume at four foliation levels, parameterized with the leaf-area-to-stem-area ratio AL/ AS. The species differed in the foliage drag but had approximately equal stem drag. For the foliated twigs, increasing AL/ AS was found to increase the reconfiguration and the share of the foliage drag to the total drag. The experiments provided new insight into the factors governing the flow resistance of natural woody vegetation and allowed us to develop a model for estimating the vegetative friction factor using the linear superposition of the foliage and stem drag. The model is novel in that the foliage and stem are separately described with physically based parameters: drag coefficients, reconfiguration parameters, and leaf area and frontal-projected stem area per ground area. The model could satisfactorily predict the flow resistance of twig to sapling-sized specimens of the investigated species at velocities of 0.05-1 m/s. As a further benefit, the model allows exploring the variability in drag and reconfiguration associated with differing abundance of the foliage in relation to the stem. [ABSTRACT FROM AUTHOR]

Reliable and accurate photovoltaic (PV) models are essential for simulation of PV power systems. A solar cell is typically represented by a single diode equivalent circuit. The circuit parameters need to be estimated accurately to get an accurate model. However, one circuit parameter was assumed because of the limited information provided by commercial manufacturing datasheets, and thus the model accuracy is affected. This paper proposes a parameterization approach for PV models to improve modeling accuracy and reduce implementation complexity. It develops a method to accurately estimate circuit parameters, and thus improving the overall accuracy, relying only on the points provided by all commercial modules datasheet. The proposed modeling approach results in two simplified models demonstrating the advantage of fast simulation. The effectiveness of the modeling approach is thoroughly evaluated by comparing the simulation results with experimental data of solar modules made of mono-crystalline, multi-crystalline, and thin film. [ABSTRACT FROM PUBLISHER]

Abstract: A new model for the dispersion of passive puffs is presented (puff model). The model is based on the GILTT (Generalized Integral Laplace Transform Technique) approach, a general technique for solving the advection–diffusion equation. It is a well-known hybrid method that had solved a wide class of direct and inverse problems mainly in the area of Heat Transfer and Fluid Mechanics and the solution is given in series form. It is used a boundary layer parameterization that allows the model to be applied routinely using as input simple ground-level meteorological data. A preliminary performance evaluation is shown in the case of continuous emission from an elevated source in a variable boundary layer (with a time resolution of 10min). [Copyright &y& Elsevier]

Abstract: Parameterization of dynamical and thermal effects of stationary orographic gravity waves (OGWs) generated by the Earth’s surface topography is incorporated into a numerical model of general circulation of the middle and upper atmosphere. Responses of atmospheric general circulation and characteristics of planetary waves at altitudes from the troposphere up to the thermosphere to the effects of OGWs propagating from the earth surface are studied. Changes in atmospheric circulation and amplitudes of planetary waves due to variations of OGW generation and propagation in different seasons are considered. It is shown that during solstices the main OGW dynamical and heat effects occur in the middle atmosphere of winter hemispheres, where changes in planetary wave amplitudes due to OGWs may reach up to 50%. During equinoxes OGW effects are distributed more homogeneously between northern and southern hemispheres. [Copyright &y& Elsevier]

Visualizing quantitative time-dependent changes in the topography requires relying on a series of discrete given multi-temporal topographic datasets that were acquired on a given time-line. The reality of physical phenomenon occurring during the acquisition times is complex when trying to mutually model the datasets; thus, different levels of spatial inter-relations and geometric inconsistencies among the datasets exist. Any straight forward simulation will result in a truncated, ill-correct and un-smooth visualization. A desired quantitative and qualitative modelling is presumed to describe morphologic changes that occurred, so it can be utilized to carry out more precise and true-to-nature visualization tasks, while trying to best describe the reality transition as it occurred. This research paper suggests adopting a fully automatic hierarchical modelling mechanism, hence implementing several levels of spatial correspondence between the topographic datasets. This quantification is then utilized for the datasets morphing and blending tasks required for intermediate scene visualization. The establishment of a digital model that stores the local spatial transformation parameterization correspondences between the topographic datasets is realized. Along with designated interpolation concepts, this complete process ensures that the visualized transition from one topographic dataset to the other via the quantified correspondences is smooth and continuous, while maintaining morphological and topological relations. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate animal performance, carcass characteristics and costs for pigs fed diets adjusted to Brazilian conditions, through the model InraPore"1. We used 36 barrows and 36 females, during the growing and finishing in a completely randomized design. The treatments were control diets and diets adjusted for both males and females. The feeding program and the animal profile were added to the model parameterization and define strategies and nutritional feed in the diets adjusted. The performance and carcass traits were not affected by treatments. The average cost to feed the animals that received the control diet was higher in 3.4% and 2.4% for the males andfemales respectively. Comparing the diets, the average cost to feed the animals that received the control diet was 2.9% higher. Net income was R$0.19 and R$0.66 higher for males and females which received the diet adjusted, respectively. Diets adjusted through InraPore® for the growing and finishing pigs did not influence the performance and carcass traits, however reducedfeed costs which generates higher net production. [ABSTRACT FROM AUTHOR]

Abstract: A novel approach for the development of a model for Li-ion batteries with the potential for application in on-board diagnostic is introduced. This approach is not tied to a particular electrochemistry, and has the advantages of scalability and automatable parameterization as well as feasibility regarding an implementation on microcontrollers. The dynamic behavior of the cell under test, a Li-ion pouch cell with a graphite anode and a blend cathode, is characterized in an extremely broad frequency range from 100 kHz to 17 μHz. The introduced model gives predictions consistent with the experimental data, its accuracy depends on the assumed number of relaxation times respectively model order. Recommendations concerning application are given and limitations of the approach are discussed. [Copyright &y& Elsevier]

Abstract: Morphodynamic models of coastal evolution require relatively simple parameterizations of sediment transport for application over larger scales. Calantoni and Thaxton (2008) [6] presented a transport parameterization for bimodal distributions of coarse quartz grains derived from detailed boundary layer simulations for sheet flow and near sheet flow conditions. The simulation results, valid over a range of wave forcing conditions and large- to small-grain diameter ratios, were successfully parameterized with a simple power law that allows for the prediction of the transport rates of each size fraction. Here, we have applied the simple power law to a two-dimensional cellular automaton to simulate sheet flow transport. Model results are validated with experiments performed in the small oscillating flow tunnel (S-OFT) at the Naval Research Laboratory at Stennis Space Center, MS, in which sheet flow transport was generated with a bed composed of a bimodal distribution of non-cohesive grains. The work presented suggests that, under the conditions specified, algorithms that incorporate the power law may correctly reproduce laboratory bed surface measurements of bimodal sheet flow transport while inherently incorporating vertical mixing by size. [Copyright &y& Elsevier]

Abstract: The efficacy of mathematical modeling as a tool for estimating greenhouse gas (GHG) emissions from soil depends on the uncertainty. Systematic evaluation of various sources of uncertainties in GHG emission models is limited. This paper reviews the state-of-the-art knowledge on the parameterization and uncertainty analysis of soil GHG emission models. Major recommendations and conclusions from this work include: (a) uncertainties due to model parameters and structure can be quantified by combining the Bayesian theorem and the Markov Chain Monte Carlo (MCMC) method; (b) uncertainty due to event-based model input may also be assessed by regarding each event as a latent variable; however, the necessity of the simultaneous evaluation of uncertainties from model input, parameters, and structure might be negotiable because strong correlations may exist between input errors and model parameters; (c) uncertainty analysis is essential for a successful model parameterization by reducing both the number of undetermined parameters and the parameter space; and (d) model parameterization (calibration) should be conducted on multiple sites towards multiple objectives. Case studies were presented for comparing the model uncertainties of the denitrification components of four models, DAYCENT, DNDC, ECOSYS, and COMP. The methods discussed in this paper can help to evaluate model uncertainties and performances, and to offer a critical guidance for model selection and parameterization. [Copyright &y& Elsevier]