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There are two distinct parameterizations for the restratification effect of mesoscale eddies: the Greatbatch and Lamb (1990, GL90, https://journals.ametsoc.org/view/journals/phoc/20/10/1520-0485%5f1990%5f020%5f1634%5fopvmom%5f2%5f0%5fco%5f2.xml?tab%5fbody=abstract-display) parameterization, which mixes horizontal momentum in the vertical, and the Gent and McWilliams (1990, GM90, https://journals.ametsoc.org/view/journals/phoc/20/1/1520-0485%5f1990%5f020%5f0150%5fimiocm%5f2%5f0%5fco%5f2.xml) parameterization, which flattens isopycnals adiabatically. Even though these two parameterizations are effectively equivalent under the assumption of quasi‐geostrophy, GL90 has been used much less than GM90, and exclusively in z‐coordinate models. In this paper, we compare the GL90 and GM90 parameterizations in an idealized isopycnal coordinate model, both from a theoretical and practical perspective. From a theoretical perspective, GL90 is more attractive than GM90 for isopycnal coordinate models because GL90 provides an interpretation that is fully consistent with thickness‐weighted isopycnal averaging, while GM90 cannot be entirely reconciled with any fully isopycnal averaging framework. From a practical perspective, the GL90 and GM90 parameterizations lead to extremely similar energy levels, flow and vertical structure, even though their energetic pathways are very different. The striking resemblance between the GL90 and GM90 simulations persists from non‐eddying through eddy‐permitting resolution. We conclude that GL90 is a promising alternative to GM90 for isopycnal coordinate models, where it is more consistent with theory, computationally more efficient, easier to implement, and numerically more stable. Assessing the applicability of GL90 in realistic global ocean simulations with hybrid coordinate schemes should be a priority for future work. Plain Language Summary: Ocean models are complex simulations run on large supercomputers, and are useful for predicting changes in ocean circulation and climate. Ocean models divide the globe into grid cells. Choosing many, very small grid cells is not feasible because the simulations would take too much time and would be too expensive. Therefore, the grid cells in most ocean models are not small enough to simulate mesoscale eddies. Mesoscale eddies are swirling motions that are less than 100 km wide and play an important role in transporting heat and carbon throughout the ocean. To still account for the effects of mesoscale eddies, one can use approximate "parameterizations." Which parameterization is "best" is an ongoing research question. This paper compares two parameterizations that simulate the effect of mesoscale eddies in two distinct ways: the commonly used Gent and McWilliams (1990, https://journals.ametsoc.org/view/journals/phoc/20/1/1520-0485%5f1990%5f020%5f0150%5fimiocm%5f2%5f0%5fco%5f2.xml) parameterization and the less commonly used Greatbatch and Lamb (1990, https://journals.ametsoc.org/view/journals/phoc/20/10/1520-0485%5f1990%5f020%5f1634%5fopvmom%5f2%5f0%5fco%5f2.xml?tab%5fbody=abstract-display) parameterization. This paper shows that the two parameterizations impact ocean circulation in a very similar way, and that for a certain class of models the Greatbatch and Lamb (1990, https://journals.ametsoc.org/view/journals/phoc/20/10/1520-0485%5f1990%5f020%5f1634%5fopvmom%5f2%5f0%5fco%5f2.xml?tab%5fbody=abstract-display) parameterization has advantages because it is more consistent with physical and mathematical theory, is easier to code, and leads to faster computations. Key Points: We compare the Greatbatch and Lamb (1990, GL90, https://journals.ametsoc.org/view/journals/phoc/20/10/1520-0485%5f1990%5f020%5f1634%5fopvmom%5f2%5f0%5fco%5f2.xml?tab%5fbody=abstract-display) and the Gent and McWilliams (1990, GM90, https://journals.ametsoc.org/view/journals/phoc/20/1/1520-0485%5f1990%5f020%5f0150%5fimiocm%5f2%5f0%5fco%5f2.xml) parameterizations in an isopycnal ocean modelGL90 leads to very similar flow as GM90, for non‐eddying through eddy‐permitting resolutionWe argue, however, that for isopycnal coordinate models GL90 is more consistent with theory than GM90 [ABSTRACT FROM AUTHOR]

In this paper, the vector extension operation is proposed to replace the de Boor‐Cox formula for a fast algorithm to B‐spline basis functions. This B‐spline basis function based on vector extending operation is implemented in the class and shape transformation (CST) parameterization method in place of the traditional Bézier polynomials to enhance the local ability of control and accuracy to represent an airfoil shape. To calculate the k‐degree B‐spline function's nonzero values, the algorithm can improve the computing efficiency by 2k+1 times. [ABSTRACT FROM AUTHOR]

This paper deals with finite‐horizon minimum‐variance and covariance steering problems subject to constraints. The goal of the minimum variance problem is to steer the state mean of an uncertain system to a prescribed vector while minimizing the trace of its terminal state covariance whereas the goal in the covariance steering problem is to steer the covariance matrix of the terminal state to a prescribed positive definite matrix. The paper proposes a solution approach that relies on a stochastic version of the affine disturbance feedback control parametrization. In this control policy parametrization, the control input at each stage is expressed as an affine function of the history of disturbances that have acted upon the system. It is shown that this particular parametrization reduces the stochastic optimal control problems considered in this paper into tractable convex programs or difference of convex functions programs with essentially the same decision variables. In addition, the paper proposes a variation of this control parametrization that relies on truncated histories of past disturbances, which allows for sub‐optimal controllers to be designed that strike a balance between performance and computational cost. The suboptimality of the truncated policies is formally analyzed and closed form expressions are provided for the performance loss due to the use of the truncation scheme. Finally, the paper concludes with a comparative analysis of the truncated versions of the proposed policy parametrization and other standard policy parametrizations through numerical simulations. [ABSTRACT FROM AUTHOR]

The responses of atmospheric kinetic energy (KE) spectra to three convective parameterizations (CPs) in global high‐resolution simulations are revealed. The results show that the KE spectra exhibit high sensitivity to the CPs, mainly at mesoscales in the middle and upper troposphere. The New Tiedtke scheme produces the steepest mesoscale slope, followed by the Kain‐Fritsch scheme and then the Grell‐Freitas scheme. In general, there is a compensating relationship between latent heat released by the CP and microphysics parameterization (MP). The less latent heat released by the CP is compensated by the more latent heat released by the MP. The shallowest mesoscale spectra for the Grell‐Freitas scheme are related to the strongest downscale cascade dominated by the rotational component of the flow, and this is attributed to more latent heat released from MP enhancing the intensity of vorticity in the troposphere and producing more gravity wave activities in the lower stratosphere. Plain Language Summary: At the current horizontal resolution level of the atmospheric models, convective parameterization (CP) is crucial for representing convective clouds unresolved by model mesh and thus is still an important model component. Exploring the impacts of different CP schemes on global high‐resolution simulations is an important subject of modeling. Energy spectra have become a useful diagnostic for validating and comparing atmospheric models. Their sensitivity to CP is an important and not‐well‐studied part of this subject. This paper investigates the impact of CPs on atmospheric mesoscale kinetic energy spectra with global simulations from the Model for Prediction Across Scales. We found that the energy spectral slope and energy cascade are sensitive to the CP schemes. This is related to the complementary relationship between CP and microphysical parameterization (MP), which is also important for moist convection development and evolution. The less latent heat released by the CP, the more released by the MP. This leads to stronger vertical motion, accompanied by stronger convergence/divergence in the troposphere, thereby enhancing vortex motion. As a result, more energy is transferred from the synoptic scale to the mesoscale and more gravity waves are vertically propagated into the lower stratosphere, leading to the shallower spectra at mesoscales. Key Points: The kinetic energy (KE) spectra exhibit high sensitivity to the convective parameterizations (CPs) mainly at mesoscalesThe more latent heat released from CP, the steeper KE spectra at mesoscalesThe shallowest mesoscale KE spectra generated by the Grell‐Freitas scheme are attributed to the strongest RKE downscale cascades [ABSTRACT FROM AUTHOR]

We address the question of how to use a machine learned (ML) parameterization in a general circulation model (GCM), and assess its performance both computationally and physically. We take one particular ML parameterization (Guillaumin & Zanna, 2021, https://doi.org/10.1002/essoar.10506419.1) and evaluate the online performance in a different model from which it was previously tested. This parameterization is a deep convolutional network that predicts parameters for a stochastic model of subgrid momentum forcing by mesoscale eddies. We treat the parameterization as we would a conventional parameterization once implemented in the numerical model. This includes trying the parameterization in a different flow regime from that in which it was trained, at different spatial resolutions, and with other differences, all to test generalization. We assess whether tuning is possible, which is a common practice in GCM development. We find the parameterization, without modification or special treatment, to be stable and that the action of the parameterization to be diminishing as spatial resolution is refined. We also find some limitations of the machine learning model in implementation: (a) tuning of the outputs from the parameterization at various depths is necessary; (b) the forcing near boundaries is not predicted as well as in the open ocean; (c) the cost of the parameterization is prohibitively high on central processing units. We discuss these limitations, present some solutions to problems, and conclude that this particular ML parameterization does inject energy, and improve backscatter, as intended but it might need further refinement before we can use it in production mode in contemporary climate models. Plain Language Summary: This paper discusses how machine learning can be used to make climate models more accurate. Specifically, we import an existing machine learning model that predicts how small eddies (in the order of 10–100 km) in the ocean affect larger currents. We test this machine learning model in a different ocean circulation model than the one it was originally designed for, and found that it worked well. However, we also found some limitations: the model works differently at different depths in the ocean, and it does not work as well near the coasts of the ocean. We also found that the model takes a long time to run on normal computers. Overall, we concluded that the model is promising, but more work is needed to make it work well in realistic situations. Key Points: A stochastic‐deep learning model is implemented in an ocean circulation model, MOM6We evaluate the online performance of the stochastic‐deep learning model as a subgrid parameterizationWe identify certain limitations of the machine learned parameterization which otherwise has the potential to improve specific metrics [ABSTRACT FROM AUTHOR]

Summary: Grid‐forming (GFM) inverters offer stable frequency support for microgrid systems, even in the absence of synchronous generators. However, the GFM inverters have low inertia and vulnerability to system uncertainties and external disturbances. The conventional dual‐loop proportional integral (PI) control strategy, while widely used for its simplicity and robustness, suffers from poor dynamic performance. Motivated by this, this paper presents an observer‐based residual‐driven dynamic compensation (RDDC) strategy based on the coprime factorization technic and the Youla parameterization theory to achieve the primary control of the GFM inverter. The observer‐based RDDC strategy comprises four components: a PI controller for tracking control, a linear quadratic regulator (LQR) controller for dynamic adjustment, a residual generator based on the Kalman filter for state estimation and residual generation, and a residual compensation controller designed using model matching theory and solved through linear matrix inequality (LMI) methods for disturbance suppression. Simulation and experiment results consistently demonstrate that the observer‐based RDDC strategy ensures system robustness, enhances the dynamic and steady‐state performance of the GFM inverter system, and strengthens the ability of the GFM inverter to suppress disturbances. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we investigate the global adaptive stabilization problem via partial‐state feedback for a class of uncertain chained‐form nonholonomic systems with the dynamic uncertainty and nonlinear parameterization. The notions of Sontag's input‐to‐state stability (ISS) and ISS‐Lyapunov function, together with the changing supply rates technique are used to overcome the dynamic uncertainty. The nonlinear parameterization is well treated with the aid of the parameter separation technique. The discontinuous input‐to‐state scaling technique is employed in this procedure to derive the global stabilization controllers. Additionally, we develop a switching adaptive control strategy in order to get around the smooth stabilization burden associated with nonholonomic systems. The simulation results illustrate the efficacy of the presented algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces a novel multi‐variate volatility model that can accommodate appropriately defined network structures based on low‐frequency and high‐frequency data. The model offers substantial reductions in the number of unknown parameters and computational complexity. The model formulation, along with iterative multi‐step‐ahead forecasting and targeting parameterization are discussed. Quasi‐likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation studies are carried out to assess the performance of parameter estimation in finite samples. Furthermore, a real data analysis demonstrates that the proposed model outperforms the existing volatility models in prediction of future variances of daily return and realized measures. [ABSTRACT FROM AUTHOR]

This paper considers structural controllability of multi‐agent systems (MASs) with switching topologies, in which each interaction topology is modeled by a weighted digraph. Under the adopted relative protocol, the MASs with directed switching topologies are modeled by a switched system with parameter interdependence. The technique of linear parameterization, the graph theory, and so forth are applied to tackle the structural controllability of the switched system. A graph‐theoretic interpretation for structural controllability of the system is proposed. Examples with single leader and multiple leaders are presented to illustrate our work. [ABSTRACT FROM AUTHOR]

This paper presents a Youla‐Kucera based interpolation between a set of Linear Parameter‐Varying (LPV) controllers, each one being a gain‐scheduled of Linear Time‐Invariant (LTI) controllers designed separately for different operating points. The gain‐scheduling is achieved based on Youla‐Kucera (YK) parameterization. A generalized LPV‐YK control structure is designed to interpolate between various LPV controllers. The closed‐loop system is proved to guarantee the quadratic stability for any continuous/discontinuous interpolating signals in terms of a set of Linear Matrix Inequalities (LMIs). The proposed method can help multi‐variable and multi‐objective systems to achieve high performances at different operating conditions and different critical situations regardless of the interpolation rate. A numerical example is simulated to show the importance of the proposed method to achieve different objectives for lateral control of autonomous vehicles. In addition, the approach has been tested on a real Renault ZOE vehicle to validate its real performance, and compare it with a standard polytopic LPV controller. [ABSTRACT FROM AUTHOR]

It is difficult for coarse‐resolution global models of the atmosphere to accurately simulate the observed distribution of low clouds. In particular, it is difficult for moist turbulence closure models to simulate sufficiently bright near‐coastal stratocumulus (Sc) without simulating overly bright marine shallow cumuli (Cu). To parameterize bright Sc, a turbulence parameterization must damp the turbulent fluxes of heat and moisture above cloud top in order to prevent excessive entrainment of dry air into cloud top. To parameterize dim shallow Cu, the subgrid variances of temperature and moisture must remain large, in order to permit partial cloudiness. However, damping the fluxes but not the variances just above cloud top is difficult if a parameterization uses a single "master" time scale to damp both. In nature, the above‐cloud fluxes are damped by pressure fluctuations, whereas scalar variances are damped by a different process, namely, turbulent dissipation. In a stably stratified inversion above cloud, pressure damping is large but turbulent dissipation is small. To avoid this problem, a multitime scale parameterization for damping has been developed. The damping parameterization has been implemented in a global model and evaluated. The parameterization is capable of dimming shallow Cu while producing adequately bright Sc. Plain Language Summary: This paper describes an assumption in turbulence modeling that is known to be questionable but is still sometimes made. To avoid making this assumption, the formulation of a particular turbulence model is generalized. The generalized formulation, when implemented in a global model of the atmosphere, changes the pattern of low‐altitude clouds. Key Points: Some turbulence parameterizations use a single master turbulence length/time scale even though this assumption is an oversimplificationThis paper parameterizes turbulence by use of multiple turbulent time scales, including one for dissipation and another for pressureUse of the new multiscale parameterization in a global model significantly alters the distribution of stratocumulus and shallow cumulus clouds [ABSTRACT FROM AUTHOR]

A new version of the stochastic multiplume Jet Propulsion Laboratory Eddy‐Diffusivity/Mass‐Flux (JPL‐EDMF) parameterization which consistently couples the simplified Khairoutdinov and Kogan (2000), https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2, warm phase cloud microphysical parameterization with the parameterization of cloud macrophysical and subgrid scale dynamical processes is described. The new parameterization combines the EDMF approach with an assumed shape of a joint probability density function of thermodynamic and kinematic variables which provide the basis for the computation of all parameterized processes. As far as we are aware this is the first attempt to consistently couple all of these parameterized processes in the EDMF framework. This paper is part one of a two paper series. Here, the JPL‐EDMF parameterization is described and benchmark simulations of precipitating stratocumulus and cumulus convection are performed in a single‐column‐model framework. The parameterization results compare favorably to the reference large‐eddy‐simulation results. In the second part (Smalley et al., 2022, https://doi.org/10.1029/2021MS002729) the JPL‐EDMF parameterization is validated for a wide range of observation‐based scenarios covering the continuous transition from subtropical stratocumulus to cumulus convection derived from global reanalysis, and parameterization uncertainties are studied in detail. Plain Language Summary: Parameterizations are components of atmospheric models that represent the impact of unresolved processes on the resolved scale flow and are crucial for shaping simulated dynamics and thermodynamics. In many atmospheric models, multiple parameterizations are used in parallel and each of them represents subgrid processes only in part, or only for a certain atmospheric regime. Recent parameterization developments focus on unified approaches which represent key unresolved processes in a mathematically consistent framework that allows to capture important interactions among unresolved processes, which is hard to achieve with modularized approaches. We describe a new unified parameterization which represents subgrid‐scale dynamical, cloud and rain processes, thus removing key shortcomings of traditional parameterizations. In this new parameterization, the subgrid‐scale dynamical processes are represented by the stochastic multiplume Eddy‐Diffusivity/Mass‐Flux framework from our previous works. Cloud and rain processes are represented by well‐known parameterizations developed for high‐resolution models. The key features of the new parameterization are a set of consistent assumptions about subgrid variability which is used to evaluate grid mean rates of these three processes, and representation of interactions between them. Our new parameterization is tested against benchmark simulations of marine stratocumulus and cumulus convection and the results compare favorably to the reference large‐eddy‐simulation results. Key Points: We describe a novel unified parameterization that represents interactive subgrid scale dynamical and warm cloud and rain processesThe new parameterization is based on a stochastic multiplume Eddy‐Diffusivity/Mass‐Flux approachThe parameterization successfully reproduces benchmark cases of precipitating marine cumulus and stratocumulus convection [ABSTRACT FROM AUTHOR]

This paper presents a turbulent orographic form drag (TOFD) parameterization scheme that takes into account the directional effects stemming from the angle between the low‐level wind and the principal axis of small‐scale orography. Suitable for models with both kilometer and subkilometer horizontal resolutions, this scheme builds upon prior theoretical and numerical studies to formulate surface TOFD based on the slope and direction of the sinusoidal hills. In this study, we proposed a straightforward function to calculate the surface TOFD for various orographic aspect ratios and directional parameters. The vertical decay of the drag is modeled with a scale twice the standard deviation of the sub‐grid orography. A comparison with numerous large‐eddy simulations featuring a single ellipsoidal hill demonstrates that our scheme effectively captures the dependence of drag on factors such as maximum slope, aspect ratio, the angle between low‐level wind and the principle axis, and hill height. We recommend calculating the sub‐grid orographic parameters using a well‐established method and digital terrain elevation data with a horizontal resolution less than a 100 m. This will allow for representation on orographic scales of both kilometers and subkilometers. Real‐case numerical weather prediction tests are conducted and verified with dense surface wind observations. The proposed scheme improves surface wind simulation compared to a renowned TOFD scheme, and also effectively exhibits the wind response to orographic anisotropy. [ABSTRACT FROM AUTHOR]

We develop a doubly periodic version of the Simple Convection‐Permitting E3SM Atmosphere Model (SCREAM) to provide an efficient configuration for this global convection permitting model (GCPM), akin to a single column model often found in conventional general circulation models. The design details are explained, in addition to the extensive case library associated with the doubly periodic SCREAM (DP‐SCREAM) configuration. We demonstrate that doubly periodic cloud resolving models are useful tools to explore the horizontal resolution sensitivity of GCPMs, in addition to replicating biases seen in the global models. Using DP‐SCREAM, we show that SCREAM exhibits behaviors of a scale aware model as it is able to naturally partition between sub‐grid scale (SGS) and resolved vertical transport across the gray zone of turbulence. We show that SCREAM is reasonably scale insensitive when run at resolutions from 1 to 5 km, but can exhibit sensitivity, particularly for the shallow convective regime, when run at resolutions approaching that of large eddy simulations. We conclude that SGS parameterization improvements are likely needed to reduce this scale sensitivity. Plain Language Summary: Advances in computational resources have allowed climate simulations to be performed with very high resolution, which provides higher quality results. However, these simulations require a lot of time and computer resources to perform, which makes these models difficult to use for the common scientist. In this paper we develop a high‐resolution configuration which focuses on a specific point on the globe, enabling it to run fast and to use minimal computational resources. This allows users and developers to gauge how the model may perform before doing a computationally intensive global simulation. We show that this faster configuration is a useful tool to replicate problems that are found in the global model and is a valuable way to assess sensitivities of the model, particularly pertaining to choices made in its resolution. Key Points: Doubly periodic configurations represent an efficient framework to supplement global cloud resolving modelsShallow convection is sensitive to horizontal resolution in Simple Convection‐Permitting E3SM Atmosphere Model (SCREAM) but stratiform clouds are more robustSCREAM exhibits the behaviors of a scale aware model and can represent a range of cloud regimes [ABSTRACT FROM AUTHOR]

We study a family of lattice polarized K3 surfaces which is an extension of the family of Kummer surfaces derived from principally polarized Abelian surfaces. Our family has two special properties. First, it is coming from a resolution of a simple K3 singularity. Second, it has a natural parameterization by Hermitian modular forms of four complex variables. In this paper, we show two results: (1) we determine the transcendental lattice and the Néron–Severi lattice of a generic member of our family. (2) We give a detailed description of the double covering structure associated with our K3 surfaces. [ABSTRACT FROM AUTHOR]

The atmospheric radiative transfer calculations are among the most time‐consuming components of the numerical weather prediction (NWP) models. Deep learning (DL) models have recently been increasingly applied to accelerate radiative transfer modeling. Besides, a physical relationship exists between the output variables, including fluxes and heating rate profiles. Integration of such physical laws in DL models is crucial for the consistency and credibility of the DL‐based parameterizations. Therefore, we propose a physics‐incorporated framework for the radiative transfer DL model, in which the physical relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. It is also found that the prediction accuracy was improved with the physic‐incorporated layer. In addition, we trained and compared various types of DL model architectures, including fully connected (FC) neural networks (NNs), convolutional‐based NNs (CNNs), bidirectional recurrent‐based NNs (RNNs), transformer‐based NNs, and neural operator networks, respectively. The offline evaluation demonstrates that bidirectional RNNs, transformer‐based NNs, and neural operator networks significantly outperform the FC NNs and CNNs due to their capability of global perception. A global perspective of an entire atmospheric column is essential and suitable for radiative transfer modeling as the changes in atmospheric components of one layer/level have both local and global impacts on radiation along the entire vertical column. Furthermore, the bidirectional RNNs achieve the best performance as they can extract information from both upward and downward directions, similar to the radiative transfer processes in the atmosphere. Plain Language Summary: Numerical weather prediction models require a lot of computational resources and time to run. Calculating the atmospheric radiative transfer processes is one of the most computationally expensive parts of the NWP model. One alternative is to model the radiative transfer using deep learning (DL) models, but the DL models do not involve physical laws and may have physically inconsistent outputs. This paper proposes a DL model framework to ensure the thermal equilibrium between fluxes and heating rates, which are outputs of radiative transfer models. Also, the accuracy of DL‐based radiative transfer prediction is improved when using the framework. Various DL models have been trained and compared. The results demonstrate that model structures with global receptive fields work best for emulating radiative transfer calculations. Key Points: A physics‐incorporated deep learning (DL) framework for parameterization of atmospheric radiative transfer is proposedThe DL model structures with global receptive fields are more suitable for the radiative transfer problem [ABSTRACT FROM AUTHOR]

Despite recent advances in geometric modelling, 3D mesh modelling still involves a considerable amount of manual labour by experts. In this paper, we introduce Mesh Draping: a neural method for transferring existing mesh structure from one shape to another. The method drapes the source mesh over the target geometry and at the same time seeks to preserve the carefully designed characteristics of the source mesh. At its core, our method deforms the source mesh using progressive positional encoding (PE). We show that by leveraging gradually increasing frequencies to guide the neural optimization, we are able to achieve stable and high‐quality mesh transfer. Our approach is simple and requires little user guidance, compared to contemporary surface mapping techniques which rely on parametrization or careful manual tuning. Most importantly, Mesh Draping is a parameterization‐free method, and thus applicable to a variety of target shape representations, including point clouds, polygon soups and non‐manifold meshes. We demonstrate that the transferred meshing remains faithful to the source mesh design characteristics, and at the same time fits the target geometry well. [ABSTRACT FROM AUTHOR]

This paper presents efficient and practical implementations of sequential data assimilation methods for the Simplified Parameterizations, primitive‐Equation DYnamics (SPEEDY) Model, a well‐known numerical model, into the data assimilation community for climate prediction. In the SPEEDY model, the time evolution of dynamics is performed via the second‐order Leapfrog integration scheme; this time integrator relies on two steps: the position and the velocity. The computational implementation of SPEEDY blends the time integrator and the spatial discretization of dynamics to accelerate algebraic computations. Thus, there is no access to the right‐hand side function of the ordinary differential equations governing the time evolution of model dynamics. Consequently, the SPEEDY model is often treated as a black box wherein positions and velocities work as inputs and outputs. Since observations in operational data assimilation only match position states, we can exploit augmented vector states to propagate analysis innovations from positions to velocities. For this purpose, we formulate three variants of ensemble‐based filters and perform numerical experiments to assess their accuracies. We consider two scenarios for the experiments: an ideal case wherein positions and velocities can be observed and a more realistic one wherein measurements are only accessible for position states. Besides, we discuss the effects of the ensemble size on the accuracies of our formulations and, even more, the typical case in which velocities are not updated across assimilation steps. The results reveal that all filter formulations' accuracies remain the same in terms of Root‐Mean‐Square‐Error by neglecting observations from velocities (a realistic scenario) even for cases wherein the number of measurements decreases to 6% of model components. Furthermore, for all discussed filter implementations, the propagation of analysis increments from position to velocities improves up to 100% the performance of filter implementations wherein velocities are not updated, a typical operational scenario. [ABSTRACT FROM AUTHOR]

A self‐sustained charge neutrality parameterization scheme of intracloud (IC) lightning is presented in this paper. The scheme contains the following features: simultaneous development of multiple branches, polarity asymmetry of positive and negative leaders, nonlinear electrical parameters, complete charge neutrality, and channel decay and reactivation. Nonlinear electrical parameters and charge neutrality are the key factors that determinate channel decay and reactivation. To demonstrate the simulation capacity of this scheme, two simulated intracloud flashes by this scheme under the tripole charge structure are chosen for analysis. These two IC flashes show clearly the above features, where reactivation processes that initiated from different branches and the real‐time changes in nonlinear electrical parameters are mainly analyzed. The simulation results are in line with the current knowledge on lightning, which also suggests that the new scheme may become a useful tool to deeply explore the discharge phenomena involving channel decay and reactivation. Plain Language Summary: Lightning parameterization schemes are essential for the simulation of thunderstorm electrical evolution, and in particular, the parameterization scheme with explicit channels has developed into one of the most important tools for exploring the relationship between the thunderstorm electrical environment and lightning discharge characteristics. In this study, we present a new intracloud lightning parameterization scheme with explicit channels. The most significant improvement of this scheme over previous schemes is the addition of channel state variations including channel decay and reactivation. To enable the simulation of channel decay and reactivation, the new scheme replaces the fixed potential setting in the stochastic lightning model with time‐varying nonlinear electrical parameters, and preserves the charge neutrality of the entire discharge channel at any simulation moment. Moreover, the new scheme also takes the polarity asymmetry of the positive and negative leader into account and allows the simultaneous development of multiple branches. Implanting this scheme into the classical tripole charge structure, we perform the simulation of IC flashes. The simulation results reproduce the morphological characteristics of IC flashes, the evolution of nonlinear electrical parameters, and the channel decay and reactivation process, which are in good agreement with the existing knowledge on the lightning discharges. Key Points: Channel decay and reactivation are considered in lightning parameterization schemes with explicit channels for the first timeSelf‐sustained charge neutrality and simultaneous development of multiple branches are realizedTime‐varying nonlinear electrical parameters including channel electric field, current, and conductivity are provided during the simulation [ABSTRACT FROM AUTHOR]

This is a summary of the papers published in the Special Section titled "Fast Physics in Climate Models: Parameterization, Evaluation, and Observation" (https://agupubs.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)2169‐8996.FASTPHYS1), as requested by the JGR‐Atmosphere Chief Editor, Professor Minghua Zhang. Key Points: Motivation for this special section is presentedPapers in this special issue are summarizedFuture challenges facing fast physics in climate models are discussed [ABSTRACT FROM AUTHOR]

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper. [ABSTRACT FROM AUTHOR]

The development of parameterizations is a major task in the development of weather and climate models. Model improvement has been slow in the past decades, due to the difficulty of encompassing key physical processes into parameterizations, but also of calibrating or "tuning" the many free parameters involved in their formulation. Machine learning techniques have been recently used for speeding up the development process. While some studies propose to replace parameterizations by data‐driven neural networks, we rather advocate that keeping physical parameterizations is key for the reliability of climate projections. In this paper we propose to harness machine learning to improve physical parameterizations. In particular, we use Gaussian process‐based methods from uncertainty quantification to calibrate the model free parameters at a process level. To achieve this, we focus on the comparison of single‐column simulations and reference large‐eddy simulations over multiple boundary‐layer cases. Our method returns all values of the free parameters consistent with the references and any structural uncertainties, allowing a reduced domain of acceptable values to be considered when tuning the three‐dimensional (3D) global model. This tool allows to disentangle deficiencies due to poor parameter calibration from intrinsic limits rooted in the parameterization formulations. This paper describes the tool and the philosophy of tuning in single‐column mode. Part 2 shows how the results from our process‐based tuning can help in the 3D global model tuning. Key Points: We apply uncertainty quantification to single‐column model/large‐eddy simulation comparison to calibrate free parametersWe revisit model development strategy with an emphasis on processes for model calibrationThe proposed tuning tool allows to formalize the complementary use of multicases with various metrics [ABSTRACT FROM AUTHOR]

Accurately estimated reference evapotranspiration (ET0) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P‐M) model with the Ångström–Prescott (A‐P) formula as the standard method for ET0 estimation with missing Rs measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A‐P formula. This paper proposes a new method for estimating ET0 with missing Rs by combining machine learning with physical‐based P‐M models (PM‐ET0). The benchmark values of the A‐P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in Rs and ET0 estimates at 80 national Rs measuring stations. Then, three empirical models and four machine‐learning methods were evaluated in estimating the A‐P coefficients. Machine learning methods were also used to estimate ET0 (ML‐ET0) to compare with the PM‐ET0. Finally, the optimal estimation method was used to estimate the A‐P coefficients for the 839 regular weather stations for ET0 estimation without Rs measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest Rs and ET0 estimation errors. Additionally, the A‐P coefficients calibrated at the daily scale obtained the best estimation accuracy for both Rs and ET0, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A‐P coefficients, especially for the sites with extreme values. Further, ML‐ET0 outperformed the PM‐ET0 with yearly A‐P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET0. However, we found that using the A‐P coefficients with finer time scales is unnecessary to deal with the missing Rs measurements. [ABSTRACT FROM AUTHOR]

In this paper, we consider the local well‐posedness of the 1D Green–Naghdi system. This system describes the evolution of water waves over an uneven bottom in the shallow water regime in terms of the water depth h and the horizontal velocity u. Using a Lagrangian formulation of this system on a Sobolev‐type diffeomorphism group, we prove local well‐posedness for (h,u)$(h,u)$ in the Sobolev space ([1−ξ]+Hs(R))×Hs+1(R),s>1/2$([1-\xi ]+H^s(\mathbb {R})) \times H^{s+1}(\mathbb {R}),\; s {>} 1/2$, where ξ:R→R$\xi :\mathbb {R}\rightarrow \mathbb {R}$ is the parameterization of the bottom and where we assume that the water surface has an equilibrium at height 1. This improves the present local well‐posedness range by one degree. [ABSTRACT FROM AUTHOR]

With the recent surge in big data analytics for hyperdimensional data, there is a renewed interest in dimensionality reduction techniques. In order for these methods to improve performance gains and understanding of the underlying data, a proper metric needs to be identified. This step is often overlooked, and metrics are typically chosen without consideration of the underlying geometry of the data. In this paper, we present a method for incorporating elastic metrics into the t‐distributed stochastic neighbour embedding (t‐SNE) and Uniform Manifold Approximation and Projection (UMAP). We apply our method to functional data, which is uniquely characterized by rotations, parameterization and scale. If these properties are ignored, they can lead to incorrect analysis and poor classification performance. Through our method, we demonstrate improved performance on shape identification tasks for three benchmark data sets (MPEG‐7, Car data set and Plane data set of Thankoor), where we achieve 0.77, 0.95 and 1.00 F1 score, respectively. [ABSTRACT FROM AUTHOR]

Bayesian lasso mimics the regular lasso penalty by placing a double‐exponential prior on the regression coefficients. It automatically provides integrated interval estimates that the regular lasso does not do. Bayesian lasso has found many applications from genetics and genomics to text categorization to traffic safety. The difficulty in specifying a sensible prior for the rate of the double exponential distribution for a particular application, however, is a significant barrier for the wider use of this methodology. This paper proposes a new Bayesian lasso formulation. Instead of using the rate of the double exponential distribution, the new formulation uses a standardized total effect size as the parameter that determines the level of shrinkage with several significant advantages. First, an informative prior is more effectively constructed and understood for this practically meaningful parameter. Second, a weakly informative prior for this new parameter is derived, which allows a practicing statistician to carry out the analysis in an automated way when an informative prior is difficult to elucidate. Third, it is more flexible in modelling prior distributions. A parallel new formulation for Bayesian ridge regression is also provided. A simple and efficient Stan implementation is supplied that can be readily used. [ABSTRACT FROM AUTHOR]

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It is very well known that parameter tuning can have a major impact on the performance of Global Climate Models. However, parameter tuning is not the only implementation detail that can drastically affect model performance and the representation of various phenomena in models. "Minor‐looking treatments" often exert a critical control on model performance; they include lower and upper limits of physical variables, thresholds of variables that control the enabling or disabling of a specific process, whether two schemes can work together or only one scheme works exclusively, and numerical methods including the order of calling various physics schemes. The impacts of such minor‐looking treatments are sometimes comparable to or even larger than those obtained by introducing advanced parameterizations based on theory and observation. Because the importance of these treatments is often overlooked and not discussed in the literature, we comprehensively summarize examples of various minor‐looking treatments that can considerably affect model performance. Plain Language Summary: Global climate models (GCMs) are used for climate simulations, including global warming simulations. Global climate models (GCMs) simulate various physical processes such as convection, clouds, radiation, and turbulence. A lot of effort is devoted to improving the representation of these physical processes by introducing more sophisticated or more complicated and detailed processes. The values of parameters in such processes significantly affect the model performance, and many modelers carefully set such parameter values. However, other factors can also affect model performance. The lower or upper limits specified in the detailed implementation of such processes, threshold values for switching on a certain effect, how to calculate each effect, vertical resolutions, and so on can significantly affect the model performance. We call these "minor‐looking treatments" and show a wide range of examples in this paper. Key Points: Minor‐looking treatments crucially determine the performance of global climate modelsThey include lower limits, upper limits, thresholds for enabling physical processes, and numerical methodsMinor‐looking treatments should be documented in detail, shared, and discussed more in the climate community [ABSTRACT FROM AUTHOR]

Runoff prediction in ungauged and scarcely gauged catchments is a key research field in surface water hydrology. There have been numerous studies before and since the launch of the predictions in ungauged basins (PUB) initiative by the International Association of Hydrological Sciences in 2003. This study critically reviews and assesses the decadal progress in the regionalization of hydrological modeling, which is the major tool for PUB, from 2000 to 2019. This paper found that the journal publications have noticeably increased in terms of PUB in the past 7 years, and research countries have been expanded dramatically since 2013. The regionalization methods are grouped into three categories including similarity‐based, regression‐based, and hydrological signature‐based. There are more detailed researches focusing on the interdisciplinary and profound improvement of each regionalization method. Namely, tremendous efforts have been made and lots of improvements have been carried out in the parameterization domain for the post‐PUB period. However, there is still plenty of room to improve the prediction capability in data‐sparse regions (e.g., further verification and proof of multi‐modeling adaptation and uncertainties description). This paper also discusses possible research directions in the future, including PUB in a changing environment and better utilization of multi‐source remote‐sensing information. This article is categorized under:Science of Water > Science of Water [ABSTRACT FROM AUTHOR]

Summary: This paper aims to introduce a new approach to optimize the tunable controller parameters of linear parameterizable controllers. The presented approach is frequency‐domain based and can therefore directly be used to tune, among others, proportional integral derivative controllers, low/high‐pass filters, and notch filters, using a Frequency Response Function of the plant. The approach taken in this paper is to extract the tunable controller parameters into a diagonal matrix gain and absorb the remainder of the controller in the plant. Then, the generalized Nyquist stability criterion is exploited so as to impose stability and  H ∞ performance specifications on the closed‐loop system. It is shown that the approach results in a convex feasibility problem for certain controller cases and can be reformulated such that it can also be used for grey‐box system identification. Simulation and experimental examples demonstrate the efficacy of the approach. © 2016 The Authors. International Journal of Robust and Nonlinear Control published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates adaptive state feedback stabilization for a class of more general stochastic high‐order nonholonomic systems. By constructing the appropriate Lyapunov function, skillfully combining parameter separation, sign function, and backstepping design methods, an adaptive state feedback controller is designed to eliminate the phenomenon of uncontrollability and guarantee global asymptotic stability in probability of the closed‐loop system. Two simulation examples are used to demonstrate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non‐linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed‐forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: (a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation; (b) high optically thin cirrus‐like clouds are separated from low optically thick cumulus clouds; and (c) shallow convective processes are associated with large‐scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub‐grid‐scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties. Plain Language Summary: Deep neural nets are hard to interpret due to their hundred thousand or million trainable parameters without further postprocessing. We demonstrate in this paper the usefulness of a network type that is designed to drastically reduce this high dimensional information in a lower‐dimensional space to enhance the interpretability of predictions compared to regular deep neural nets. Our approach is, on the one hand, able to reproduce small‐scale cloud related processes in the atmosphere learned from a physical model that simulates these processes skillfully. On the other hand, our network allows us to identify key features of different cloud types in the lower‐dimensional space. Additionally, the lower‐order manifold separates tropical samples from polar ones with a remarkable skill. Overall, our approach has the potential to boost our understanding of various complex processes in Earth System science. Key Points: A Variational Encoder Decoder (VED) can predict sub‐grid‐scale thermodynamics from the coarse‐scale climate stateThe VED's latent space can distinguish convective regimes, including shallow/deep/no convectionThe VED's latent space reveals the main sources of convective predictability at different latitudes [ABSTRACT FROM AUTHOR]

To facilitate the calculation of the fourth‐order crystal‐field (CF) splitting moment, which in a complex way depends on the split state |J⟩, a time saving formula is elaborated, which is positively verified for several cubic systems RE3+: Cs2NaLaCl6. The appropriate crystal‐field parameters (CFPs) have to be substituted to the presented formula. However, the question is which ones. The clear answer proven in the paper is that single‐state CFPs should necessarily be used. To test the approach, a few cubic systems, for which such single‐state CFPs are available, have been studied. Conventionally fitted CFPs, averaged over the entire electron configuration, do not satisfy the equation, which contradicts the fundamental principle of the CF theory. Such verification significantly limits the physical meaning and applicability of the conventional HCF fitted parameterizations. [ABSTRACT FROM AUTHOR]

The article explores a paper on the methods for planning repeated measures accelerated degradation tests when the degradation process satisfies general conditions and unit-to-unit variability obeys a random effect model. It offers various responses from the authors on comments submitted regarding their paper including the idea using implicit differentiation followed by numerical evaluation of derivatives. The authors note the importance of using a stable parameterization for statistical models.

Traditional parameterizations of convection are a large source of error in weather and climate prediction models, and the assumptions behind them become worse as resolution increases. Multifluid modeling is a promising new method of representing subgrid‐scale and near‐grid‐scale convection allowing for net mass transport by convection and nonequilibrium dynamics. The air is partitioned into two or more fluids, which may represent, for example, updrafts and the nonupdraft environment. Each fluid has its own velocity, temperature, and constituents with separate equations of motion. This paper presents two‐fluid Boussinesq equations for representing subgrid‐scale dry convection with sinking and w = 0 air in Fluid 0 and rising air in Fluid 1. Two vertical slice test cases are developed to tune parameters and to evaluate the two‐fluid equations: a buoyant rising bubble and radiative convective equilibrium. These are first simulated at high resolution with a single‐fluid model and conditionally averaged based on the sign of the vertical velocity. The test cases are next simulated with the two‐fluid model in one column. A model for entrainment and detrainment based on divergence leads to excellent representation of the convective area fraction. Previous multifluid modeling of convection has used the same pressure for both fluids. This is shown to be a bad approximation, and a model for the pressure difference between the fluids based on divergence is presented. Plain Language Summary: Clouds and buoyant convection are often smaller than the grid size in weather and climate prediction models, but they are of central importance. Therefore, their effects are parameterized—their interactions with the larger scales are estimated based on properties of the larger scales. Convection parameterizations are a large source of error in models of the atmosphere despite decades of effort to improve them. Some unrealistic assumptions remain such as that convection does not provide a net upward transport of mass, convection is in equilibrium with the surroundings, and that the variations in the horizontal direction are not relevant. These assumptions make it much simpler to incorporate convection parameterizations into existing models. Multifluid modeling does not rely on these assumptions, but the approach means a whole new atmospheric model rather than a stand‐alone routine that can be incorporated into an existing model. This paper provides some closures necessary for multifluid modeling and evaluation of the technique for two‐dimensional dry convection. Key Points: Multifluid modeling enables net mass transport by subgrid‐scale convection and nonequlibrium dynamicsMultifluid modeling means a new dynamical core rather than a parameterization for an existing coreClosures for the pressure difference between fluids and entrainment and detrainment are provided [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptive neural network control strategy for image‐based visual servoing (IBVS) of robotic manipulators with both eye‐in‐hand and eye‐to‐hand camera configurations in the presence of unknown dynamics and external disturbances. The IBVS method is combined with the adaptive neural network to construct the proposed adaptive neural network controller to solve the visual servoing control problem of robots. The adaptive neural network based IBVS controller is designed based on the depth‐independent interaction matrix, which can be trained on‐line to identify the visual servoing robotic system modeling errors. Moreover, the proposed method can approach the unknown nonlinear dynamics for both eye‐in‐hand and eye‐to‐hand camera configurations without requiring the robot dynamics to be linearly parameterizable, and the exact knowledge of the robot structure is not needed. On the basis of the nonlinear robot dynamics, the Lyapunov stability analysis is given to prove the asymptotical convergence of the image position and velocity errors. Simulation results for both camera configurations are provided to demonstrate the performance of the proposed adaptive neural network based approach. [ABSTRACT FROM AUTHOR]

This paper is concerned with the adaptive leader-following consensus for first- and second-order uncertain nonlinear multi-agent systems (NMASs) with single- and double-integrator leader, respectively. Remarkably, the control coefficients of the followers need not belong to any known finite interval, which makes the systems in question essentially different from those in the related works. Moreover, parameterized unknowns exist in the nonlinearities of the followers, and unknown control input is imposed on the leader, which make the problems difficult to solve. To compensate for these uncertainties/unknowns, the leader-following consensus protocols are constructed by employing adaptive technique for the first-order and the second-order NMASs. Under the designed adaptive consensus protocols and the connected graph, the leader-following consensus is achieved. Finally, two examples are given to show the effectiveness of the proposed leader-following consensus protocols. [ABSTRACT FROM AUTHOR]

In this paper, we introduced a novel voxel‐wise UV parameterization and view‐dependent texture synthesis for the immersive rendering of a truncated signed distance field (TSDF) scene model. The proposed UV parameterization delegates a precomputed UV map to each voxel using the UV map lookup table and consequently, enabling efficient and high‐quality texture mapping without a complex process. By leveraging the convenient UV parameterization, our view‐dependent texture synthesis method extracts a set of local texture maps for each voxel from the multiview color images and separates them into a single view‐independent diffuse map and a set of weight coefficients for an orthogonal specular map basis. Furthermore, the view‐dependent specular maps for an arbitrary view are estimated by combining the specular weights of each source view using the location of the arbitrary and source viewpoints to generate the view‐dependent textures for arbitrary views. The experimental results demonstrate that the proposed method effectively synthesizes texture for an arbitrary view, thereby enabling the visualization of view‐dependent effects, such as specularity and mirror reflection. [ABSTRACT FROM AUTHOR]

Data‐driven parameterizations offer considerable potential for improving the fidelity of General Circulation Models. However, ensuring that these remain consistent with the governing equations while still producing stable simulations remains a challenge. In this paper, we propose a combined Variational‐Multiscale (VMS) Artificial Neural Network (ANN) discretization which makes no a priori assumptions on the model form, and is only restricted in its accuracy by the precision of the ANN. Using a simplified problem, we demonstrate that good predictions of the required closure terms can be obtained with relatively compact ANN architectures. We then turn our attention to the stability of the VMS‐ANN discretization in the context of a single implicit time step. It is demonstrated that the ANN parameterization introduces nonphysical solutions to the governing equations that can significantly affect or prevent convergence. We show that enriching the training data with nonphysical states from intra‐time step iterations is an effective remedy. This indicates that the lack of representative ANN‐induced errors in our original, exact training inputs underpin the observed instabilities. In turn, this suggests that data set enrichment might aid in resolving instabilities that develop over several time steps. Plain Language Summary: Computer models of weather and climate have coarse resolutions, which prevent them from accurately predicting the effect of small atmospheric motions, for instance in low‐lying clouds, on the global climate. Recent studies indicate that improvements might be had by using models for the small motions that are informed purely by data, for example Artificial Neural Network (ANN) models. However, capitalizing on this potential is challenging in practice, since ANNs can introduce instabilities to the numerical model for larger‐scale motions. In this study, we zero in on these instabilities by introducing a model framework in which prediction accuracy is only limited by an ANN's ability to predict the net influence of small atmospheric motions. We contextualize this framework by contrasting it to more commonly used approaches. Using a simple test case based on the motions underpinning the development of low clouds, we illustrate that standard training procedures do not prepare ANN models for their interaction with the rest of the numerical model, and that this can contribute to numerical instability. We demonstrate an effective remedy for a specific type of instability, and suggest how this technique might also be used to treat other types of instabilities prompted by ANN models. Key Points: Artificial Neural Networks (ANNs) can parameterize exact closure terms of a Variational Multiscale method without a priori model form assumptionsThe ANN parameterization outperforms state‐of‐the‐art schemes in offline testsInstabilities in the numerical model comprising the ANN parameterization are amendable through data set enrichment [ABSTRACT FROM AUTHOR]

This paper proposes a robust state observer for linear time‐invariant descriptor systems in which parametric uncertainty exists in the derivative, the system, and the input matrices of the system. The proposed approach is based on a new parameterization in state variables such that in the new model, the derivative matrix is known. An observer is suggested for estimating the state of the new model. Sufficient conditions are obtained for the convergence of the observer in the form of a linear matrix inequality (LMI), which can be solved by the YALMIP toolbox. Numerical examples accompanied by comparison are presented to demonstrate the efficient performance of the proposed observer. [ABSTRACT FROM AUTHOR]

Summary: The use Q/V band spectrum for the feeder links of high throughput satellites and the need to cope with the significant propagation impairments at those frequencies motivate the development of smart diversity techniques. Those techniques aim at improving the availability level of the overall feeder link with a limited level of redundancy. The combinatorial gain of availability provided by those techniques can be obtained only if efficient switching methodologies are developed, performing the best trade‐off between system flexibility and channel prediction accuracy. This paper proposes various propagation forecast mechanisms for the control of switching between gateways in smart diversity, corresponding to various system assumptions in terms of required anticipation time for the triggering of the switches. The performances of those algorithms are then assessed against measured attenuation and meteorological data. It enables to evaluate the performance degradation regarding an idealized case. This paper analyzes methods to control switches between gateways in diversity configuration for HTS to counteract propagation impairments. Methods based on extrapolation of link SNR, extrapolation of weather radar data, and numerical weather forecasts have been developed. The degradation of the performances regarding an idealized control is assessed for the different methods and different parameterization of the control logic. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes an improvement on underwater vehicles control strategy using an adaptive sliding‐mode method. This improvement is threefold. First, water current compensation is explicitly dealt within in the control law. Second, a nonlinear parameterization is developed by employing new methods for the design of the sliding surface, aiming at vehicles tracking performance. Third, parameters of the designed sliding surface in the control law are varied according to a nonlinear mapping of thruster forces aimed to energy saving of propulsion system. Three different methods are given for the design of the sliding surface. The proposed control law guarantees global asymptotic stability of the tracking error, and the stability proof is provided in the paper even in the presence of variable currents. Moreover, the tracking performance and energy saving are compared with conventional methodologies to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The accuracy of an explicit traveltime‐offset approximation affects the results of the velocity analysis that plays a crucial role in the seismic data processing. Seismic anisotropy is important to account for large offset and azimuth since it can provide detailed information comparing with the isotropic assumption. For the perturbation‐based method, different traveltime approximation forms result in different accuracy. It is necessary to find the optimal traveltime approximation for specific signs and magnitudes of the anellipticity and different offset ranges. In this paper, a series of perturbation‐based traveltime approximations from different acoustic assumptions and parameterizations are specified. The perturbation coefficients are derived from the corresponding acoustic eikonal equations. We test the accuracy of the defined perturbation approximations in four homogeneous transversely isotropic medium with vertical symmetry axis (VTI) medium with a different set of anisotropy parameters and one multilayered transversely isotropic medium with vertical symmetry axis medium. The sensitivity in anellipticity for different approximations is also analysed. We find that different approximation achieves different accuracy under the circumstance of specific offset range and the value of anellipticity. Therefore, the optimal approximation form with higher accuracy can be selected based on different offset ranges and the magnitudes of anellipticity. [ABSTRACT FROM AUTHOR]

Spatial parameter fields are required to model hydrological processes across diverse landscapes. Transfer functions are often used to relate parameters to spatial catchment attributes, introducing large uncertainties. Quantifying these uncertainties remains a key challenge for large‐scale modeling. This paper extends the multiscale parameter regionalization (MPR) technique to consider parameter uncertainties. We evaluate this method of producing nationally consistent parameter fields, which maintain a constant relationship between model parameters and catchment attributes, across 437 catchments in Great Britain (GB). By sampling multiple transfer function parameters, we produce thousands of possible model parameter fields which are constrained within an uncertainty framework. This is compared to spatially homogeneous parameter sets constrained for individual catchments. The nationally consistent MPR parameter fields perform well (KGE* > 0.75) across 60% of catchments. Performance is similar or better than catchment‐constrained parameters (KGE* drop < 0.1) across 82% of catchments. Advantages of our national parameter fields include (a) improved representation of flows within catchments, (b) more robust performance between calibration and evaluation periods, and (c) spatial parameter fields reflecting hydrologically meaningful variation in catchment characteristics. By including uncertainties, we show that hydrographs produced using MPR have smaller uncertainty bounds which are better able to encompass flows. As the first application of MPR to both the DECIPHeR modeling framework and GB, we developed transfer functions and identified key catchment attributes to constrain model parameters, which are transferrable to other models alongside the addition of uncertainty. Methodologies presented here are informative for future regionalization efforts in GB and elsewhere. Key Points: By including uncertainties in multiscale parameter regionalization (MPR), we generate and evaluate ensembles of national model parameter fieldsRegionalized parameter fields result in more consistent performance and hydrographs with smaller uncertainty bounds than Monte‐Carlo constrained parametersThe method of incorporating uncertainties, transfer functions, and selection of catchment attributes can be applied in future regionalization studies [ABSTRACT FROM AUTHOR]

The characterization and parameterization of processes that arise in many fields of science and technology are very crucial. Of particular importance are dynamic processes whose statistics are time-varying and are often modeled as stochastic processes. A typical example of such process is the wireless communication channel. Existing methods that are used to characterize and parameterize the dynamic stochastic wireless channel often consider short-term duration over which the channel statistics are invariant. Conversely, this paper presents the characterization of the dynamic wireless communication channel over a long-term duration where time/frequency channel realizations are obtained at sample intervals. To structure such channel realizations over a long duration, the idea of concatenating the 'instantaneous' channel realizations is presented. The resultant concatenated multivariable process is characterized using the concepts of process non-summability and piecewise separability. Based on these concepts, the second-order statistical parameterization of the concatenated stochastic process in both time and frequency domain is presented. The parameterization approach is based on fitting appropriate set of unit step functions that approximate the raw concatenated data using sets of evolutionary stationarity parameters. To illustrate the concepts developed in this paper, measurement-based experiments and analysis are presented and adaptively applied to improve wideband multicarrier system performance. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a bipartite consensus problem is considered for a high‐order multiagent system with cooperative‐competitive interactions and unknown time‐varying disturbances. A signed graph is used to describe the interaction network associated with the multiagent system. The unknown disturbances are expressed by linearly parameterized models, and distributed adaptive laws are designed to estimate the unknown parameters in the models. For the case that there is no exogenous reference system, a fully distributed adaptive control law is proposed to ensure that all the agents reach a bipartite consensus. For the other case that there exists an exogenous reference system, another fully distributed adaptive control law is also developed to ensure that all the agents achieve bipartite consensus on the state of the exogenous system. The stability of the closed‐loop multiagent systems with the 2 proposed adaptive control laws are analyzed under an assumption that the interaction network is structurally balanced. Moreover, the convergence of the parameter estimation errors is guaranteed with a persistent excitation condition. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed adaptive bipartite consensus control laws for the concerned multiagent system. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, I study the role of changes in the wage structure and expectations about marriage in explaining the college gender gap reversal. With strongly diminishing marginal utility of wealth and in the presence of a gender wage gap, single women have a greater incentive than single men to invest in education. Marriage‐market distortions tend to depress the overall benefit of education for women relative to men. I develop a tractable two‐period model and parameterize it using US census data for the cohort born in 1950. I then show that it can generate a reversal and that the most important driving force for this is the decline in marriage rates. [ABSTRACT FROM AUTHOR]

This paper extends the results of minimum entropy H∞ control from the state‐space system to the descriptor system. We first give an explicit definition for entropy of descriptor systems, and then derive a formula for calculating the entropy. Next, we find the parameterization of all H∞ controllers for descriptor systems. Among these H∞ controllers, we find the one resulting in the minimum entropy. Finally, a numerical example is provided to illustrate the obtained results. [ABSTRACT FROM AUTHOR]

Creating fine garment details requires significant efforts and huge computational resources. In contrast, a coarse shape may be easy to acquire in many scenarios (e.g., via low‐resolution physically‐based simulation, linear blend skinning driven by skeletal motion, portable scanners). In this paper, we show how to enhance, in a data‐driven manner, rich yet plausible details starting from a coarse garment geometry. Once the parameterization of the garment is given, we formulate the task as a style transfer problem over the space of associated normal maps. In order to facilitate generalization across garment types and character motions, we introduce a patch‐based formulation, that produces high‐resolution details by matching a Gram matrix based style loss, to hallucinate geometric details (i.e., wrinkle density and shape). We extensively evaluate our method on a variety of production scenarios and show that our method is simple, light‐weight, efficient, and generalizes across underlying garment types, sewing patterns, and body motion. Project page: http://geometry.cs.ucl.ac.uk/projects/2021/DeepDetailEnhance/ [ABSTRACT FROM AUTHOR]

This paper deals with the problem of adaptive control of a class of strict-feedback nonlinearly parameterized systems with quantized input signal. A hysteretic type of quantizer is used to avoid chattering. By using a power integrator technique, a new controller is designed to ensure the global finite-time stability of the nonlinear system. Finally, two simulation examples are given to demonstrate the effectiveness of our design method. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]