Specific Parameterization Research Articles

The Gravitational Wave Universe Toolbox

Context:TheGW-Universe Toolboxis a software package that simulates observations of the gravitational wave (GW) Universe with different types of GW detectors, including Earth-based and space-borne laser interferometers and pulsar timing arrays. It is accessible as a website, and can also be imported and run locally as a Python package.Methods:We employ the method used by theGW-Universe Toolboxto generate a synthetic catalogue of detection of stellar-mass binary black hole (BBH) mergers. As an example of its scientific application, we study how GW observations of BBHs can be used to constrain the merger rate as a function of redshift and masses. We study advanced LIGO (aLIGO) and theEinsteinTelescope (ET) as two representatives of the second and third generation GW observatories, respectively. We also simulate the observations from a detector that is half as sensitive as the ET at its nominal designed sensitivity, which represents an early phase of the ET. We used two methods to obtain the constraints on the source population properties from the catalogues: the first uses a parameteric differential merger rate model and applies a Bayesian inference on the parameters; the other is non-parameteric and uses weighted Kernel density estimators.Results:Our results show the overwhelming advantages of the third generation detector over those of the second generation for the study of BBH population properties, especially at redshifts higher than ∼2, where the merger rate is believed to peak. With the simulated aLIGO catalogue, the parameteric Bayesian method can still give some constraints on the merger rate density and mass function beyond its detecting horizon, while the non-parametric method loses the constraining ability completely there. The difference is due to the extra information placed by assuming a specific parameterisation of the population model in the Bayesian method. In the non-parameteric method, no assumption of the general shape of the merger rate density and mass function are placed, not even the assumption of its smoothness. These two methods represent the two extreme situations of general population reconstruction. We also find that, despite the numbers of detected events of the half ET can easily be compatible with full ET after a longer observation duration, and the catalogue from the full ET can still give much better constraints on the population properties due to its smaller uncertainties on the physical parameters of the GW events.

Evaluation of WRF Cumulus Parameterization Schemes for the Hot Climate of Sudan Emphasizing Crop Growing Seasons

High spatiotemporal resolution climate data are essential for climate-related impact studies. The Weather Research and Forecasting (WRF) model is widely used to downscale climate data for different regions with regional-specific physics configurations. This study aimed to identify robust configurations of the WRF model, especially cumulus parameterization schemes, for different climatic zones of Sudan. We focused on wet season (June–September) rainfall and dry season (November–February) temperature, which are determinants of summer crop and irrigated wheat yields, respectively. Downscaling experiments were carried out to compare the following schemes: Betts–Miller–Janjic (BMJ), improved Kain–Fritch (KFT), modified Tiedtke (TDK), and Grell–Freitas (GF). Results revealed that the BMJ performed better for wet season rainfall in the hyper-arid and arid zones; KFT performed better for rainfall in July and August in the semi-arid zone where most summer crops are cultivated. For dry season temperature, the BMJ and TDK outperformed the other schemes in all three zones, except that the GF performed best for the minimum temperature in December and January in the arid zone, where irrigated wheat is produced, and in the semi-arid zone. Specific parameterization schemes therefore need to be selected for specific seasons and climatic zones of Sudan.

Implications of rootless geothermal models: Missing processes, parameter compensation, and imposter convection

Numerical models of geothermal systems commonly capture only the top of a reservoir. Deeper areas of the reservoir are simplified to a boundary at the base of the model domain. Commonly, the basal boundary is given either a heat source or a source of mass and enthalpy. We developed and present simple numerical experiments which demonstrate that these approaches do not produce the correct model behavior in comparison to a model that captures the entire convecting domain with a heat flux only. We describe a variety of incorrect types of model behavior that arise directly from the choice of boundary condition, independent of the specific parameterization of the model. Heat sources are sensitive to the thickness of the domain and parameters take unphysical values to compensate for the reduced height. The combined mass/heat boundary can produce temperatures that show similarities to circulating geothermal systems, but with incorrect fluid flow and a strong boundary layer focused at the base of the simulated clay cap. These errors likely cause parameters to adjust their values to compensate for the incorrect physics. We highlight these issues and show an example from a developed reservoir model. Initial calibrations to natural state temperature were unsuitable for history matching. The 3D model required parameter adjustments to achieve a more realistic production model. Appropriate mitigation measures should be considered to reduce parameter compensation and improve decision-support models.

Bayesian Fitting of Multi-Gaussian Expansion Models to Galaxy Images

Abstract Fitting parameterized models to images of galaxies has become the standard for measuring galaxy morphology. This forward-modeling technique allows one to account for the point-spread function to effectively study semi-resolved galaxies. However, using a specific parameterization for a galaxy’s surface brightness profile can bias measurements if it is not an accurate representation. Furthermore, it can be difficult to assess systematic errors in parameterized profiles. To overcome these issues we employ the Multi-Gaussian expansion (MGE) method of representing a galaxy’s profile together with a Bayesian framework for fitting images. MGE flexibly represents a galaxy’s profile using a series of Gaussians. We introduce a novel Bayesian inference approach that uses pre-rendered Gaussian components, which greatly speeds up computation time and makes it feasible to run the fitting code on large samples of galaxies. We demonstrate our method with a series of validation tests. By injecting galaxies, with properties similar to those observed at z ∼ 1.5, into deep Hubble Space Telescope observations we show that it can accurately recover total fluxes and effective radii of realistic galaxies. Additionally we use degraded images of local galaxies to show that our method can recover realistic galaxy surface brightness and color profiles. Our implementation is available in an open source python package imcascade, which contains all methods needed for the preparation of images, fitting, and analysis of results.

Modeling persistence of coarse woody debris residuals in boreal forests as an ecological property

AbstractThere is a trade‐off between leaving coarse woody debris (CWD) in the stand, providing desirable ecosystem services, and harvesting it. To consider this trade‐off, forest management needs to model describing the decomposition of CWD. When a trunk is lying on the ground, it can be attacked by microorganisms faster than when it is still standing. Current decomposition models fail to account for these local differences in processes, which may give rise to errors in the estimation of stand C balance. We extended the Q decomposition model to represent the influences of tree species and the local position of the wood. We utilized data from two studies on long‐term deadwood decomposition in forests. We first calibrated the model on the whole dataset, and then divided the data into different CWD decomposition classes, and then allowed some of the parameters to vary between different CWD decomposition classes. The calibrations were performed within a Bayesian framework, which allowed for a statistically sound comparison of the calibration results. The difference between the remaining C mass predicted by the two versions of the model, one considering one single calibration for all decomposition classes and one specific to decomposition classes, depended on the CWD class but was in general substantial. Some classes, when modeled with a specific parameterization, resulted in C stocks after 50 yr 1–5 times less than that predicted by the single parameterization model. Logs decayed faster than snags, and birch wood much faster than pine and spruce wood, with little difference between the two conifers. Russian spruce wood decomposed somewhat faster than Finnish spruce wood. Incorporating our calibration, describing specifically the processes driving the wood decay locally, into a C balance model of forests may change model estimates substantially.

Specific Heat Capacity Measurements of Selected Meteorites for Planetary Surface Temperature Modeling

AbstractSpecific heat capacity Cp(T) is an intrinsic regolith property controlling planetary surface temperatures along with the albedo, density, and thermal conductivity. Cp(T) depends on material composition and temperature. Generally, modelers assume a fixed specific heat capacity value, or a standard temperature dependence derived from lunar basalts, mainly because of limited composition‐specific data at low temperatures relevant to planetary surfaces. In addition, Cp(T) only appears to vary by a small factor across various materials, in contrast with the bulk regolith thermal conductivity, which ranges over ∼3–4 orders of magnitude as a function of the regolith physical state (grain size, cementation, sintering, etc.). For these reasons, the impact of the basaltic assumption on modeled surface temperature is often considered unimportant although this assumption is not particularly well constrained. In this paper, we present specific heat capacity measurements and parameterizations from ∼90 to ∼290 K of 28 meteorites including those possibly originating from Mars and Vesta, and covering a wide range of planetary surface compositions. Planetary surface temperatures calculated using composition‐specific Cp(T) are within 2 K of model runs assuming a basaltic composition. This 2 K range approaches or exceeds typical instrumental noise or other sources of modeling uncertainties. These results suggest that a basaltic assumption for Cp(T) is generally adequate for the thermal characterization of a wide range of planetary surfaces, but possibly inadequate when looking at leveraging subtle trends to constrain subsurface layering, roughness, or seasonal/diurnal volatile transfer.

What to expect from alternative management strategies to conserve seabirds? Hints from a dynamic modelling framework applied to an endangered population

AbstractThe worldwide decline of seabird populations due to the combined effects of global and regional changes is creating immense challenges for managers and conservationists. Predicting population responses to proposed management strategies could provide the most effective tools to prevent, halt and reverse ongoing declines. System dynamic modelling frameworks are considered particularly relevant to interrelate biological, ecological and environmental characteristics and to predict population trends. A system dynamics model was designed, compiling diverse information concerning a relict population of the European Shag located in western Iberia, to outline the most effective management options for its conservation. The simulations demonstrate that mortality caused by invasive animals and bycatch mortality were the main reasons for the current population decline. Without management interventions, a decrease of 8% was projected for the next decade, which could be mitigated by specific conservation actions. The results show the usefulness of dynamic modelling frameworks to understand local cause‐effect relationships and species responses to ecosystem management under changing environmental conditions. We highlight that the framework proposed, after specific parameterization, could be easily adaptable to other species within similar socio‐ecological systems.

Rational design of complex phenotype via network models

We demonstrate a modeling and computational framework that allows for rapid screening of thousands of potential network designs for particular dynamic behavior. To illustrate this capability we consider the problem of hysteresis, a prerequisite for construction of robust bistable switches and hence a cornerstone for construction of more complex synthetic circuits. We evaluate and rank most three node networks according to their ability to robustly exhibit hysteresis where robustness is measured with respect to parameters over multiple dynamic phenotypes. Focusing on the highest ranked networks, we demonstrate how additional robustness and design constraints can be applied. We compare our results to more traditional methods based on specific parameterization of ordinary differential equation models and demonstrate a strong qualitative match at a small fraction of the computational cost.

Design and development of a web-based EPANET model catalogue and execution environment

ABSTRACT Within the field of environmental and hydrologic modelling, there is a growing recognition of the scientific and educational value of sharing both model programs (i.e. the codes that formulate a model) and model instances (i.e. specific input files and model parameterizations). Indeed, numerous cyberinfrastructure tools have been created in recent years to simplify linking, executing, and sharing models. Multiple challenges hinder the adoption of such systems by modelling communities. These can include technological barriers such as installing and managing software and internet systems as well as social barriers such as a culture of not sharing models due to concerns over private or otherwise proprietary information. In some cases, model-sharing platforms are not easily replicated or implemented to fit the needs of a specific modelling community. This paper presents the design and implementation of a model-sharing repository and a model-viewing application, specifically for the EPANET modelling community – the pattern and structure of which could be easily adopted by any modelling community – using existing open source cyberinfrastructure. We used HydroShare as the backend data store for the EPANET model program, model instances, and metadata, and we used the rapid app development capabilities of Tethys Platform framework to create a web-based front-end for the repository and viewer. Results of this experimental work include a functional model repository based on less than 700 lines of code and a light-weight model viewer application that encompasses nearly 100% of the legacy EPANET desktop GUI’s functionality.

Methodology for modelling the new COVID-19 pandemic spread and implementation to European countries

After the outbreak of the new COVID-19 disease, the mitigation stage has been reached in most of the countries in the world. During this stage, a more accurate data analysis of the daily reported cases and other parameters became possible for the European countries and has been performed in this work. Based on a proposed parametrization model appropriate for implementation to an epidemic in a large population, we focused on the disease spread and we studied the obtained curves, as well as, investigating probable correlations between the country's characteristics and the parameters of the parametrization. We have also developed a methodology for coupling our model to the SIR-based models determining the basic and the effective reproductive number referring to the parameter space. The obtained results and conclusions could be useful in the case of a recurrence of this insidious disease in the future.

ParaMol: A Package for Automatic Parameterization of Molecular Mechanics Force Fields.

The ensemble of structures generated by molecular mechanics (MM) simulations is determined by the functional form of the force field employed and its parameterization. For a given functional form, the quality of the parameterization is crucial and will determine how accurately we can compute observable properties from simulations. While accurate force field parameterizations are available for biomolecules, such as proteins or DNA, the parameterization of new molecules, such as drug candidates, is particularly challenging as these may involve functional groups and interactions for which accurate parameters may not be available. Here, in an effort to address this problem, we present ParaMol, a Python package that has a special focus on the parameterization of bonded and nonbonded terms of druglike molecules by fitting to ab initio data. We demonstrate the software by deriving bonded terms' parameters of three widely known drug molecules, viz. aspirin, caffeine, and a norfloxacin analogue, for which we show that, within the constraints of the functional form, the methodologies implemented in ParaMol are able to derive near-ideal parameters. Additionally, we illustrate the best practices to follow when employing specific parameterization routes. We also determine the sensitivity of different fitting data sets, such as relaxed dihedral scans and configurational ensembles, to the parameterization procedure, and discuss the features of the various weighting methods available to weight configurations. Owing to ParaMol's capabilities, we propose that this software can be introduced as a routine step in the protocol normally employed to parameterize druglike molecules for MM simulations.

λ -point anomaly in view of the seven-loop hypergeometric resummation for the critical exponent ν of the O(2) ϕ4 model

In this work, we use a specific parameterization of the hypergeometric approximants ( the one by Mera et.al in Phys. Rev. Let. 115, 143001 (2015)) to approximate the seven-loop critical exponent $\nu$ for the $O(2)$-symmetric $\phi^4$ model. Our prediction gives the result $\nu=0.6711(7)$ which is compatible with the value $\nu=0.6709(1)$ from the famous experiment carried on the space shuttle Columbia. On the other hand, our result is also compatible with recent precise theoretical predictions that are excluding the experimental result. These theoretical results include non-perturbative renormalization group calculations ( $\nu=0.6716(6)$), the most precise result from Monte Carlo simulations ($\nu=0.67169(7)$) as well as the recent conformal bootstrap calculations ($\nu=0.67175(10)$). Although our result is compatible with experiment, the plot of renormalization group result versus the number of loops suggests that higher orders are expected to add significantly to accuracy and precision of the $\nu$ exponent in a way that may favor the theoretical predictions.

Describing Physics For Physical Reasoning: Force-Based Sequential Manipulation Planning

Physical reasoning is a core aspect of intelligence in animals and humans. A central question is what model should be used as a basis for reasoning. Existing work considered models ranging from intuitive physics and physical simulators to contact dynamics models used in robotic manipulation and locomotion. In this work we propose descriptions of physics which directly allow us to leverage optimization methods for physical reasoning and sequential manipulation planning. The proposed multi-physics formulation enables the solver to mix various levels of abstraction and simplifications for different objects and phases of the solution. As an essential ingredient, we propose a specific parameterization of wrench exchange between object surfaces in a path optimization framework, introducing the point-of-attack as decision variable. We demonstrate the approach on various robot manipulation planning problems, such as grasping a stick in order to push or lift another object to a target, shifting and grasping a book from a shelve, and throwing an object to bounce towards a target.

Selecting Efficient and Reliable Preservation Strategies

This article addresses the problem of formulating efficient and reliable operational preservation policies that ensure bit-level information integrity over long periods, and in the presence of a diverse range of real-world technical, legal, organizational, and economic threats. We develop a systematic, quantitative prediction framework that combines formal modeling, discrete-event-based simulation, hierarchical modeling, and then use empirically calibrated sensitivity analysis to identify effective strategies.
 Specifically, the framework formally defines an objective function for preservation that maps a set of preservation policies and a risk profile to a set of preservation costs, and an expected collection loss distribution. In this framework, a curator’s objective is to select optimal policies that minimize expected loss subject to budget constraints. To estimate preservation loss under different policy conditions optimal policies, we develop a statistical hierarchical risk model that includes four sources of risk: the storage hardware; the physical environment; the curating institution; and the global environment. We then employ a general discrete event-based simulation framework to evaluate the expected loss and the cost of employing varying preservation strategies under specific parameterization of risks.
 The framework offers flexibility for the modeling of a wide range of preservation policies and threats. Since this framework is open source and easily deployed in a cloud computing environment, it can be used to produce analysis based on independent estimates of scenario-specific costs, reliability, and risks.
 We present results summarizing hundreds of thousands of simulations using this framework. This exploratory analysis points to a number of robust and broadly applicable preservation strategies, provides novel insights into specific preservation tactics, and provides evidence that challenges received wisdom.

Evaluation of ECMWF Radiation Scheme Using Aircraft Observations of Spectral Irradiance above Clouds

AbstractA novel approach to compare airborne observations of solar spectral irradiances measured above clouds with along-track radiative transfer simulations (RTS) is presented. The irradiance measurements were obtained with the Spectral Modular Airborne Radiation Measurement System (SMART) installed on the High Altitude and Long Range Research Aircraft (HALO). The RTS were conducted using the operational ecRad radiation scheme of the Integrated Forecast System (IFS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), and a stand-alone radiative transfer solver, the library for Radiative transfer (libRadtran). Profiles of observed and simulated radar reflectivity were provided by the HALO Microwave Package (HAMP) and the Passive and Active Microwave Transfer Model (PAMTRA), respectively. The comparison aims to investigate the capability of the two models to reproduce the observed radiation field. By analyzing spectral irradiances above clouds, different ice cloud optical parameterizations in the models were evaluated. Simulated and observed radar reflectivity fields allowed the vertical representation of the clouds modeled by the IFS to be evaluated, and enabled errors in the IFS analysis data (IFS AD) and the observations to be separated. The investigation of a North Atlantic low pressure system showed that the RTS, in combination with the IFS AD, generally reproduced the observed radiation field. For heterogeneously distributed liquid water clouds, an underestimation of upward irradiance by up to 27% was found. Simulations of ice-topped clouds, using a specific ice optics parameterization, indicated a systematic underestimation of broadband cloud-top albedo, suggesting major deficiencies in the ice optics parameterization between 1242 and 1941 nm wavelength.

Preliminary investigation of the relationship between differential phase shift and path-integrated attenuation at the X band frequency in an Alpine environment

Abstract. The RadAlp experiment aims at developing advanced methods for rainfall and snowfall estimation using weather radar remote sensing techniques in high mountain regions for improved water resource assessment and hydrological risk mitigation. A unique observation system has been deployed since 2016 in the Grenoble region of France. It is composed of an X-band radar operated by Météo-France on top of the Moucherotte mountain (1901 m above sea level; hereinafter MOUC radar). In the Grenoble valley (220 m above sea level; hereinafter a.s.l.), we operate a research X-band radar called XPORT and in situ sensors (weather station, rain gauge and disdrometer). In this paper we present a methodology for studying the relationship between the differential phase shift due to propagation in precipitation (Φdp) and path-integrated attenuation (PIA) at X band. This relationship is critical for quantitative precipitation estimation (QPE) based on polarimetry due to severe attenuation effects in rain at the considered frequency. Furthermore, this relationship is still poorly documented in the melting layer (ML) due to the complexity of the hydrometeors' distributions in terms of size, shape and density. The available observation system offers promising features to improve this understanding and to subsequently better process the radar observations in the ML. We use the mountain reference technique (MRT) for direct PIA estimations associated with the decrease in returns from mountain targets during precipitation events. The polarimetric PIA estimations are based on the regularization of the profiles of the total differential phase shift (Ψdp) from which the profiles of the specific differential phase shift on propagation (Kdp) are derived. This is followed by the application of relationships between the specific attenuation (k) and the specific differential phase shift. Such k–Kdp relationships are estimated for rain by using drop size distribution (DSD) measurements available at ground level. Two sets of precipitation events are considered in this preliminary study, namely (i) nine convective cases with high rain rates which allow us to study the ϕdp–PIA relationship in rain, and (ii) a stratiform case with moderate rain rates, for which the melting layer (ML) rose up from about 1000 up to 2500 m a.s.l., where we were able to perform a horizontal scanning of the ML with the MOUC radar and a detailed analysis of the ϕdp–PIA relationship in the various layers of the ML. A common methodology was developed for the two configurations with some specific parameterizations. The various sources of error affecting the two PIA estimators are discussed, namely the stability of the dry weather mountain reference targets, radome attenuation, noise of the total differential phase shift profiles, contamination due to the differential phase shift on backscatter and relevance of the k–Kdp relationship derived from DSD measurements, etc. In the end, the rain case study indicates that the relationship between MRT-derived PIAs and polarimetry-derived PIAs presents an overall coherence but quite a considerable dispersion (explained variance of 0.77). Interestingly, the nonlinear k–Kdp relationship derived from independent DSD measurements yields almost unbiased PIA estimates. For the stratiform case, clear signatures of the MRT-derived PIAs, the corresponding ϕdp value and their ratio are evidenced within the ML. In particular, the averaged PIA∕ϕdp ratio, a proxy for the slope of a linear k–Kdp relationship in the ML, peaks at the level of the copolar correlation coefficient (ρhv) peak, just below the reflectivity peak, with a value of about 0.42 dB per degree. Its value in rain below the ML is 0.33 dB per degree, which is in rather good agreement with the slope of the linear k–Kdp relationship derived from DSD measurements at ground level. The PIA∕ϕdp ratio remains quite high in the upper part of the ML, between 0.32 and 0.38 dB per degree, before tending towards 0 above the ML.

Unsupervised Machine Learning and Band Topology.

The study of topological band structures is an active area of research in condensed matter physics and beyond. Here, we combine recent progress in this field with developments in machine learning, another rising topic of interest. Specifically, we introduce an unsupervised machine learning approach that searches for and retrieves paths of adiabatic deformations between Hamiltonians, thereby clustering them according to their topological properties. The algorithm is general, as it does not rely on a specific parametrization of the Hamiltonian and is readily applicable to any symmetry class. We demonstrate the approach using several different models in both one and two spatial dimensions and for different symmetry classes with and without crystalline symmetries. Accordingly, it is also shown how trivial and topological phases can be diagnosed upon comparing with a generally designated set of trivial atomic insulators.

Multi-Higgs boson production and unitarity in vector-boson fusion at future hadron colliders

We study multi-Higgs final states in vector boson fusion (VBF) processes at the LHC and at future proton-proton colliders, focusing on the prospects for measurements at 27 TeV and at 100 TeV. We use an effective Lagrangian which includes higher-dimensional operators in the mass eigenstates which are relevant to VBF processes and relate this to specific parameterizations and models for new physics in the Higgs sector. We derive theoretical constraints on the parameter space from the unitarity of $2\to n$ scattering amplitudes and apply the results to $V V \to hh$ and $h h h$ processes, where $V=W,Z$. As a result, we present constraints on differential distributions as appropriate to the study of $V V \to hh$ and $h h h$ processes.

Spatiotemporal Modeling and Real-Time Prediction of Origin-Destination Traffic Demand

Traffic demand prediction has been a crucial problem for the planning, scheduling, and optimization in transportation management. The prediction of traffic demand counts for origin-destination (OD) pairs has been considered challenging due to the high variability and complicated spatiotemporal correlations in the data. Though several articles have considered estimating traffic flows from counts observed at specific locations, existing traffic prediction models seldom dealt with spatiotemporal demand count data of certain OD pairs, or they failed to effectively consider domain knowledge of the traffic network to enhance the prediction accuracy of traffic demand. To tackle the aforementioned challenges, we formulate and propose a multivariate Poisson log-normal model with specific parameterization tailored to the traffic demand problem, which captures the spatiotemporal correlations of the traffic demand across different routes and epochs, and automatically clusters the routes based on the demand correlations. The model is further estimated using an expectation-maximization algorithm and applied for predicting future demand counts at the subsequent epochs. The estimation and prediction procedures incorporate Markov chain Monte Carlo sampling to overcome the computational challenges. Simulations as well as a real application on a New York yellow taxi data are performed to demonstrate the applicability and effectiveness of the proposed method. Supplementary materials for this article are available online.

Feature-based comparison of sea ice deformation in lead-permitting sea ice simulations

Abstract. The sea ice modeling community is progressing towards pan-Arctic simulations that explicitly resolve leads in the simulated sea ice cover. Evaluating these simulations against observations poses new challenges. A new feature-based evaluation of simulated deformation fields is introduced, and the results are compared to a scaling analysis of sea ice deformation. Leads and pressure ridges – here combined into linear kinematic features (LKFs) – are detected and tracked automatically from deformation and drift data. LKFs in two pan-Arctic sea ice simulations with a horizontal grid spacing of 2 km are compared with an LKF dataset derived from the RADARSAT Geophysical Processor System (RGPS). One simulation uses a five-class ice thickness distribution (ITD). The simulated sea ice deformation follows a multi-fractal spatial and temporal scaling, as observed from RGPS. The heavy-tailed distribution of LKF lengths and the scale invariance of LKF curvature, which points to the self-similar nature of sea ice deformation fields, are reproduced by the model. Interannual and seasonal variations in the number of LKFs, LKF densities, and LKF orientations in the ITD simulation are found to be consistent with RGPS observations. The lifetimes and growth rates follow a distribution with an exponential tail. The model overestimates the intersection angle of LKFs, which is attributed to the model's viscous-plastic rheology with an elliptical yield curve. In conclusion, the new feature-based analysis of LKF statistics is found to be useful for a comprehensive evaluation of simulated deformation features, which is required before the simulated features can be used with confidence in the context of climate studies. As such, it complements the commonly used scaling analysis and provides new useful information for comparing deformation statistics. The ITD simulation is shown to reproduce LKFs sufficiently well for it to be used for studying the effect of directly resolved leads in climate simulations. The feature-based analysis of LKFs also identifies specific model deficits that may be addressed by specific parameterizations, for example, a damage parameter, a grounding scheme, and a Mohr–Coulombic yield curve.