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We discuss the various performance aspects of parallelizing our transient global-scale groundwater model at 30′′ resolution (30arcsec; °1/41km at the Equator) on large distributed memory parallel clusters. This model, referred to as GLOBGM, is the successor of our 5′ (5arcmin; °1/410km at the Equator) PCR-GLOBWB 2 (PCRaster Global Water Balance model) groundwater model, based on MODFLOW having two model layers. The current version of GLOBGM (v1.0) used in this study also has two model layers, is uncalibrated, and uses available 30′′ PCR-GLOBWB data. Increasing the model resolution from 5′ to 30′′ creates challenges, including increased runtime, memory usage, and data storage that exceed the capacity of a single computer. We show that our parallelization tackles these problems with relatively low parallel hardware requirements to meet the needs of users or modelers who do not have exclusive access to hundreds or thousands of nodes within a supercomputer. For our simulation, we use unstructured grids and a prototype version of MODFLOW 6 that we have parallelized using the message-passing interface. We construct independent unstructured grids with a total of 278 million active cells to cancel all redundant sea and land cells, while satisfying all necessary boundary conditions, and distribute them over three continental-scale groundwater models (168 million - Afro-Eurasia; 77 million - the Americas; 16 million - Australia) and one remaining model for the smaller islands (17 million). Each of the four groundwater models is partitioned into multiple non-overlapping submodels that are tightly coupled within the MODFLOW linear solver, where each submodel is uniquely assigned to one processor core, and associated submodel data are written in parallel during the pre-processing, using data tiles. For balancing the parallel workload in advance, we apply the widely used METIS graph partitioner in two ways: it is straightforwardly applied to all (lateral) model grid cells, and, Mathematical Physics

This paper describes a neural network cloud masking scheme from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) multi-angle polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of 1 year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns, but the PARASOL neural network (PARASOL-NN) retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags fewer clear pixels as cloudy than MODIS (∼ 3 % of the clear pixels versus ∼ 15 % by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 20 % by NN, versus ∼ 15 % by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from multi-angle polarimeter (MAP) aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager., Mathematical Physics

Super dust storms re-occurred over East Asia in 2021 spring and casted great health damages and property losses. It is essential to achieve an accurate dust forecast to reduce the damage for early warning. The forecasting system fundamentally relies on a numerical model which can forecast the full evolution of dust storms. However, large uncertainties exist in model forecasts. Meanwhile, various near-real-time observations are available that contain valuable dust information. A dust storm forecasting system is here developed through coupling a chemical transport model, LOTOS-EUROS, and Localized EnKF (LEnKF) assimilation approach. The assimilations are carried out via an interface of our self-designed assimilation toolbox, PyFilter v1.0. Ground-based PM10 measurements from air quality monitoring network are assimilated. Sequential assimilation tests are carried out over the 2021 spring super dust storms. The results show that the assimilation-based forecasting system produces a promising dust forecast than model-only forecast, and the improvements is also validated through comparing to the independent MODIS aerosol optical depth (AOD). Superior performance is obtained when LEnKF is implemented, as the localization helps EnKF in resolving the PM10 measurements that have a large spatial variability with limited ensemble members. In addition, sensitivity experiments are conducted to exploit the distance-dependent localization for the LEnKF. Considering both cases, the optimal choice of the distance is tested to be around 500 km: the larger distance is less effective in removing the spurious correction, while the smaller one easily falls into the local optimum and the model would become divergent rapidly., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Mathematical Physics

The chemical transport model LOTOS-EUROS uses a volatility basis set (VBS) approach to represent the formation of secondary organic aerosol (SOA) in the atmosphere. Inclusion of the VBS approximately doubles the dimensionality of LOTOS-EUROS and slows computation of the advection operator by a factor of two. This complexity limits SOA representation in operational forecasts. We develop a mass-conserving dimensionality reduction method based on matrix factorization to find latent patterns in the VBS tracers that correspond to a smaller set of superspecies. Tracers are reversibly compressed to superspecies before transport, and the superspecies are subsequently decompressed to tracers for process-based SOA modeling. This physically interpretable data-driven method conserves the total concentration and phase of the tracers throughout the process. The superspecies approach is implemented in LOTOS-EUROS and found to accelerate the advection operator by a factor of 1.5–1.8. Concentrations remain numerically stable over model simulation times of 2 weeks, including simulations at higher spatial resolutions than the data-driven models were trained on. The reversible compression of VBS tracers enables detailed, process-based SOA representation in LOTOS-EUROS operational forecasts in a computationally efficient manner. Beyond this case study, the physically consistent data-driven approach developed in this work enforces conservation laws that are essential to other Earth system modeling applications, and generalizes to other processes where computational benefit can be gained from a two-way mapping between detailed process variables and their representation in a reduced-dimensional space., Mathematical Physics

We aim to increase the ability of coupled phase oscillators to maintain synchronization when the system is affected by stochastic disturbances. We model the disturbances by Gaussian noise and use the mean first hitting time when the state hits the boundary of a secure domain, that is a subset of the basin of attraction, to measure synchronization stability. Based on the invariant probability distribution of a system of phase oscillators subject to Gaussian disturbances, we propose an optimization method to increase the mean first hitting time and, thus, increase synchronization stability. In this method, a new metric for synchronization stability is defined as the probability of the state being absent from the secure domain, which reflects the impact of all the system parameters and the strength of disturbances. Furthermore, by this new metric, one may identify those edges that may lead to desynchronization with a high risk. A case study shows that the mean first hitting time is dramatically increased after solving corresponding optimization problems, and vulnerable edges are effectively identified. It is also found that optimizing synchronization by maximizing the order parameter or the phase cohesiveness may dramatically increase the value of the metric and decrease the mean first hitting time, thus decrease synchronization stability., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Mathematical Physics

The synchronization of power generators is an important condition for the proper functioning of a power system, in which the fluctuations in frequency and the phase angle differences between the generators are sufficiently small when subjected to stochastic disturbances. Serious fluctuations can prompt desynchronization, which may lead to widespread power outages. Here, we model the stochastic disturbance by a Brownian motion process in the linearized system of the non-linear power systems and characterize the fluctuations by the variances of the frequency and the phase angle differences in the invariant probability distribution. We propose a method to calculate the variances of the frequency and the phase angle differences. For the system with uniform disturbance-damping ratio, we derive explicit formulas for the variance matrices of the frequency and the phase angle differences. It is shown that the fluctuation of the frequency at a node depends on the disturbance-damping ratio and the inertia at this node only, and the fluctuations of the phase angle differences in the lines are independent of the inertia. In particular, the synchronization stability is related to the cycle space of the network. We reveal the influences of constructing new lines and increasing capacities of lines on the fluctuations in the phase angle differences in the existing lines. The results are illustrated for the transmission system of Shandong Province of China. For the system with non-uniform disturbance-damping ratio, we further obtain bounds of the variance matrices., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Mathematical Physics

Influence maximization (IM) is a very important issue in social network diffusion analysis. The topology of real social network is large-scale, dynamic, and heterogeneous. The heterogeneity, and continuous expansion and evolution of social network pose a challenge to find influential users. Existing IM algorithms usually assume that social networks are static or dynamic but homogeneous to simplify the complexity of the IM problem. We propose a community-based influence maximization algorithm using network embedding in dynamic heterogeneous social networks. We use DyHATR algorithm to obtain the propagation feature vectors of network nodes, and execute k-means cluster algorithm to transform the original network into a coarse granularity network (CGN). On CGN, we propose a community-based three-hop independent cascade model and construct the objective function of IM problem. We design a greedy heuristics algorithm to solve the IM problem with approximation guarantee and use community structure to quickly identify seed users and estimate their influence value. Experimental results on real social networks demonstrated that compared with existing IM algorithms, our proposed algorithm had better comprehensive performance with respect to the influence value, more less execution time and memory consumption, and better scalability., Mathematical Physics

Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmental monitoring stations and their operational forecasts in advance using inputs of the real-time ambient pollutant observations. Therefore, these high-quality machine learning models only provide site-available predictions and cannot solely be used as the operational forecast. In contrast, deterministic chemical transport models (CTMs), which simulate the full life cycles of air pollutants, provide predictions that are continuous in the 3D field. Despite their benefits, CTM predictions are typically biased, particularly on a fine scale, owing to the complex error sources due to the emission, transport, and removal of pollutants. In this study, we proposed a fusion of site-available machine learning prediction, which is from our regional feature selection-based machine learning model (RFSML v1.0), and a CTM prediction. Compared to the normal pure machine learning model, the fusion system provides a gridded prediction with relatively high accuracy. The prediction fusion was conducted using the Bayesian-theory-based ensemble Kalman filter (EnKF). Background error covariance was an essential part in the assimilation process. Ensemble CTM predictions driven by the perturbed emission inventories were initially used for representing their spatial covariance statistics, which could resolve the main part of the CTM error. In addition, a covariance inflation algorithm was designed to amplify the ensemble perturbations to account for other model errors next to the uncertainty in emission inputs. Model evaluation tests were conducted based on independent measurements. Our EnKF-based prediction fusion presented superior performance compared to the pure CTM. Moreover, covariance inflation further enhanced the fused prediction, particu, Mathematical Physics

The synchronization stability of a complex network system of coupled phase oscillators is discussed. In case the network is affected by disturbances, a stochastic linearized system of the coupled phase oscillators may be used to determine the fluctuations of phase differences in the lines between the nodes and to identify the vulnerable lines that may lead to desynchronization. The main result is the derivation of the asymptotic variance matrices of the phase differences which characterizes the severity of the fluctuations. It is found that the cycle space of the graph of the system plays a role in this characterization. With theory of the cycle space, the effect of forming small cycles on the fluctuations is evaluated. It is proven that adding a new line or increasing the coupling strength of a line affects the fluctuations in the lines in any cycle including this line, while it does not affect the fluctuations in the other lines. In particular, if the phase differences at the synchronous state are not changed by these actions, then the affected fluctuations reduce., Mathematical Physics

Atmospheric ammonia has been hazardous to the environment and human health for decades. Current inventories are usually constructed in a bottom-up manner and subject to uncertainties and incapable of reproducing the spatiotemporal characteristics of ammonia emission. Satellite measurements, for example, Infrared Atmospheric Sounder Interferometer (IASI) and Cross-Track Infrared Sounder, which provide global coverage of ammonia distribution, have gained popularity in ammonia emission estimation through data assimilation methods. However, satellite-based emission inversion studies on China are limited. In this study, we propose a four-dimensional ensemble variational-based ammonia emission inversion system to optimize ammonia emissions in China. It was developed by assimilating the IASI ammonia retrievals onboard Meteorological Operational satellite A and B into a chemical transport model Goddard Earth Observing System Chemical model (GEOS-Chem). Monthly inversion experiments were conducted in April, July, and October 2016 to test the performance. The inversion result indicated that the prior inventory from the MEIC model captured ammonia spreads in general; however, it heterogeneously underrated the emission intensity. The increments obtained in the assimilation were as high as 50% in North, East, and Northwest China. The posterior emission inventory presented a regional emission flux consistent with relevant studies. Driven by the optimized source estimate, GEOS-Chem provides superior results than using the prior in the evaluation of the assimilated IASI retrievals and the surface ammonia concentration measured by the ground-based Ammonia Monitoring Network in China., Mathematical Physics

The mobile element variant is a very important structural variant, accounting for a quarter of structural variants, and it is closely related to many issues such as genetic diseases and species diversity. However, few detection algorithms of mobile element variants have been developed on third-generation sequencing data. We propose an algorithm ricME that combines sequence realignment and identity calculation for detecting mobile element variants. The ricME first performs an initial detection to obtain the positions of insertions and deletions, and extracts the variant sequences; then applies sequence realignment and identity calculation to obtain the transposon classes related to the variant sequences; finally, adopts a multi-level judgment rule to achieve accurate detection of mobile element variants based on the transposon classes and identities. Compared with a representative long-read based mobile element variant detection algorithm rMETL, the ricME improves the F1-score by 11.5 and 21.7% on simulated datasets and real datasets, respectively., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Mathematical Physics

Accurate parameter estimation for the Global Tide and Surge Model (GTSM) benefits from observations with long time-series. However, increasing the number of measurements leads to a large computation demand and increased memory requirements, especially for the ensemble-based methods that assimilate the measurements at one batch. In this study, a memory-efficient parameter estimation scheme using model order reduction in time patterns is developed for a high-resolution global tide model. We propose using projection onto empirical time-patterns to reduce the model output time-series to a much smaller linear subspace. Then, to further improve the estimation accuracy, we introduce an outer-loop, similar to Incremental 4D-VAR, to evaluate model-increments at a lower resolution and subsequently reduce the computational cost. The inner-loop optimizes parameters using the lower-resolution model and an iterative least-squares estimation algorithm called DUD. The outer-loop updates the initial output from the high-resolution model with updated parameters from the converged inner-loop and then restarts the inner-loop. We performed experiments to adjust the bathymetry with observations from the FES2014 dataset. Results show that the time patterns of the tide series can be successfully projected to a lower dimensional subspace, and memory requirements are reduced by a factor of 22 for our experiments. The estimation is converged after three outer iterations in our experiment, and tide representation is significantly improved, achieving a 34.5% reduction of error. The model's improvement is not only shown for the calibration dataset, but also for several validation datasets consisting of one year of time-series from FES2014 and UHSLC tide gauges., Mathematical Physics

Last spring, super dust storms reappeared in East Asia after being absent for one and a half decades. The event caused enormous losses in both Mongolia and China. Accurate simulation of such super sandstorms is valuable for the quantification of health damage, aviation risks, and profound impacts on the Earth system, but also to reveal the climatic driving force and the process of desertification. However, accurate simulation of dust life cycles is challenging, mainly due to imperfect knowledge of emissions. In this study, the emissions that lead to the 2021 spring dust storms are estimated through assimilation of MODIS AOD and ground-based PM10 concentration data simultaneously. With this, the dust concentrations during these super storms could be reproduced and validated with concentration observations. The multi-observation assimilation is also compared against emission inversion that assimilates AOD or PM10 concentration measurements alone, and the added values are analyzed. The emission inversion results reveal that wind-blown dust emissions originated from both China and Mongolia during spring 2021. Specifically, 19.9×106 and 37.5×106ĝ€¯t of particles were released in the Chinese and Mongolian Gobi, respectively, during these severe dust events. By source apportionment it was revealed that the Mongolian Gobi poses more severe threats to the densely populated regions of the Fenwei Plain (FWP) and the North China Plain (NCP) located in northern China than does the Chinese Gobi. It was estimated that 63ĝ€¯% of the dust deposited in FWP was due to transnational transport from Mongolia. For NCP, the long-distance transport dust from Mongolia contributes about 69ĝ€¯% to the dust deposition., Mathematical Physics

Global tide and surge models play a major role in forecasting coastal flooding due to extreme events or climate change. The model performance is strongly affected by parameters such as bathymetry and bottom friction. In this study, we propose a method that estimates bathymetry globally and the bottom friction coefficient in shallow waters for a global tide and surge model (GTSMv4.1). However, the estimation effect is limited by the scarcity of available tide gauges. We propose complementing sparse tide gauges with tide time series generated using FES2014. The FES2014 dataset outperforms the GTSM in most areas and is used as observations for the deep ocean and some coastal areas, such as Hudson Bay and Labrador, where tide gauges are scarce but energy dissipation is large. The experiment is performed with a computation- and memory-efficient iterative parameter estimation scheme (time–POD-based coarse incremental parameter estimation; POD: proper orthogonal decomposition) applied to the Global Tide and Surge Model (GTSMv4.1). Estimation results show that model performance is significantly improved for the deep ocean and shallow waters, especially in the European shelf, directly using the CMEMS tide gauge data in the estimation. The GTSM is also validated by comparing to tide gauges from UHSLC, CMEMS, and some Arctic stations in the year 2014., Mathematical Physics

History matching can play a key role in improving geological characterization and reducing the uncertainty of reservoir model predictions. Application of reservoir history matching is restricted by the huge computational cost by amongst others the many runs of the full model. Surrogate models with a reduced complexity are therefore used to reduce the computational demands. This paper presents an efficient surrogate-assisted deterministic inversion framework to primarily explore the possibility of applying deep neural network (DNN) surrogate to approximate the gradient of large-scale history matching by using auto-differentiation (AD). In combination with the deep neural network model, the AD enables us to evaluate the gradients efficiently in a parallel manner. Furthermore, the benefits of using stochastic gradient optimizers in the deep learning practice, instead of full gradient optimizers in conventional deterministic inversions, is investigated as well. Numerical experiments are conducted on a 3D benchmark reservoir model in the context of a water-flooding production scenario. The quantity of interest, e.g., dynamic saturation for an ensemble of test models, can be accurately predicted. The proposed surrogate-assisted inversion with stochastic gradient optimizer obtains a very quick convergence rate against the model and data noise for the high-dimensional history matching problem with a large number of data and parameters. In addition, we also conduct several comparisons and evaluations with our previously proposed projection-based subdomain POD-TPWL approach in terms of computational efficiency and accuracy. The subdomain POD-TPWL constructs a local surrogate model, which is repeatedly reconstructed a number of times for maintaining a satisfactory accuracy, while DNN constructs a global surrogate model based on the entire training data and generally does not require additional reconstructions. The subdomain POD-TPWL is very sensitive to how the domain is de, Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Mathematical Physics, Reservoir Engineering

Wind and solar photovoltaic (PV) power form vital parts of the energy transition toward renewable energy systems. The rapid development of these two renewables represents an enormous infrastructure construction task including both power generation and its associated electrical grid systems, which will generate demand for metal resources. However, most research on material demands has focused on their power generation systems (wind turbines and PV panels), and few have studied the associated electrical grid systems. Here, we estimate the global metal demands for electrical grid systems associated with wind and utility-scale PV power by 2050, using dynamic material flow analysis based on International Energy Agency's energy scenarios and the typical engineering parameters of transmission grids. Results show that the associated electrical grids require large quantities of metals: 27-81 Mt of copper cumulatively, followed by 20-67 Mt of steel and 11-31 Mt of aluminum. Electrical grids built for solar PV have the largest metal demand, followed by offshore and onshore wind. Power cables are the most metal-consuming electrical components compared to substations and transformers. We also discuss the decommissioning issue of electrical grids and their recovery potential. This study would deepen the understanding of the nexus between renewable energy, grid infrastructure, and metal resources., Mathematical Physics

With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models)., Mathematical Physics

This paper addresses the problem of studying the impacts of the strategic formation of platoons in automated mobility-on-demand (AMoD) systems in future cities. Forming platoons has the potential to improve traffic efficiency, resulting in reduced travel times and energy consumption. However, in the platoon formation phase, coordinating the vehicles at formation locations for forming a platoon may delay travelers. In order to assess these effects, an agent-based model has been developed to simulate an urban AMoD system in which vehicles travel between service points transporting passengers either forming or not forming platoons. A simulation study was performed on the road network of the city of The Hague, Netherlands, to assess the impact on traveling and energy usage by the strategic formation of platoons. Results show that forming platoons could save up to 9.6% of the system-wide energy consumption for the most efficient car model. However, this effect can vary significantly with the vehicle types and strategies used to form platoons. Findings suggest that, on average, forming platoons reduces the travel times for travelers even if they experience delays while waiting for a platoon to be formed. However, delays lead to longer travel times for the travelers with the platoon leaders, similar to what people experience while traveling in highly congested networks when platoon formation does not happen. Moreover, the platoon delay increases as the volume of AMoD requests decreases; in the case of an AMoD system serving only 20% of the commuter trips (by private cars in the case-study city), the average platoon delays experienced by these trips increase by 25%. We conclude that it is beneficial to form platoons to achieve energy and travel efficiency goals when the volume of AMoD requests is high., Mathematical Physics, Transport and Planning

Automated Mobility-on-Demand (AMoD) systems, in which fleets of automated vehicles provide on-demand services, are expected to transform urban mobility systems. Motivated by the rapid development of AMoD services delivered by self-driving car companies, an agent-based model (ABM) has been developed to study the coexistence phenomena of multiple AMoD operators competing for customers. The ABM is used to investigate how changes in pricing strategies, assignment methods, and fleet sizes affect travelers’ choice of different AMoD services and the operating performance of competing operators in the case-study city of The Hague, in the Netherlands. Findings suggest that an optimal assignment algorithm can reduce the average waiting time by up to 24% compared to a simple heuristic algorithm. We also find that a larger fleet could increase demand but lead to higher waiting times for its users and higher travel times for competing operators’ users due to the added congestion. Notably, pricing strategies can significantly affect travelers’ choice of AMoD services, but the effect depends strongly on the time of the day. Low-priced AMoD services can provide high service levels and effectively attract more demand, with up to 64.7% of customers choosing the very early morning service [5:30 AM,7:20 AM]. In the subsequent morning hours, high-priced AMoD services are more competitive in attracting customers as more idle vehicles are available. Based on the quantitative analysis, policies are recommended for the government and service operators., Mathematical Physics, Transport and Planning

In general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM., Mathematical Physics, Delft Institute of Applied Mathematics

We present an approach which combines the sample regenerating particle filter (SRGPF) and unequal weight ensemble Kalman filter (UwEnKF) to obtain a more accurate forecast for nonlinear dynamic systems. Ensemble Kalman filter assumes that the model errors and observation errors are Gaussian distributed. Particle filter has demonstrated its ability in solving nonlinear and non-Gaussian problems. The main difficulty for the particle filter is the curse of dimensionality, a very large number of particles is needed. We adopt the idea of the unequal weight ensemble Kalman filter to define a proposal density for the particle filter. In order to keep the diversity of particles, we do not apply resampling as the traditional particle filter does, instead we regenerate new samples based on a posterior distribution. The performance of the combined sample regenerating particle filter and unequal weight ensemble Kalman filter algorithm is evaluated using the Lorenz 63 model, the results show that the presented approach obtains a more accurate forecast than the ensemble Kalman filter and weighted ensemble Kalman filter under Gaussian noise with dense observations. It still performs well in case of sparse observations though more particles are required. Furthermore, for non-Gaussian noise, with an adequate number of particles, the performance of the approach is much better than the ensemble Kalman filter and more robust to noise with nonzero bias., Mathematical Physics

Building stock growth around the world drives extensive material consumption and environmental impacts. Future impacts will be dependent on the level and rate of socioeconomic development, along with material use and supply strategies. Here we evaluate material-related greenhouse gas (GHG) emissions for residential and commercial buildings along with their reduction potentials in 26 global regions by 2060. For a middle-of-the-road baseline scenario, building material-related emissions see an increase of 3.5 to 4.6 Gt CO2eq yr-1 between 2020–2060. Low- and lower-middle-income regions see rapid emission increase from 750 Mt (22% globally) in 2020 and 2.4 Gt (51%) in 2060, while higher-income regions shrink in both absolute and relative terms. Implementing several material efficiency strategies together in a High Efficiency (HE) scenario could almost half the baseline emissions. Yet, even in this scenario, the building material sector would require double its current proportional share of emissions to meet a 1.5 °C-compatible target., Mathematical Physics, Organisation and Governance

When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timing or to uncertainties in the transport. Though many studies have been conducted on the calibration or correction of dust simulations, most of these focus on intensity solely and leave the position errors mainly unchanged. In this paper, a grid-distorted data assimilation, which consists of an image-morphing method and an ensemble-based variational assimilation, is designed for realigning a simulated dust plume to correct the position error. This newly developed grid-distorted data assimilation has been applied to a dust storm event in May 2017 over East Asia. Results have been compared for three configurations: a traditional assimilation configuration that focuses solely on intensity correction, a grid-distorted data assimilation that focuses on position correction only and the hybrid assimilation that combines these two. For the evaluated case, the position misfit in the simulations is shown to be dominant in the results. The traditional emission inversion only slightly improves the dust simulation, while the grid-distorted data assimilation effectively improves the dust simulation and forecasting. The hybrid assimilation that corrects both position and intensity of the dust load provides the best initial condition for forecasting of dust concentrations., Mathematical Physics

Worldwide, billions of people rely on fresh groundwater reserves for their domestic, agricultural and industrial water use. Extreme droughts and excessive groundwater pumping put pressure on water authorities in maintaining sustainable water usage. High-resolution integrated models are valuable assets in supporting them. The Netherlands Hydrological Instrument (NHI) provides the Dutch water authorities with open source modeling software and data. However, NHI integrated groundwater models often require long run times and large memory usage, therefore strongly limiting their application. As a solution, we present a distributed memory parallelization, focusing on the National Hydrological Model. Depending on the level of integration, we show that significant speedups can be obtained up to two orders of magnitude. As far as we know, this is the first reported integrated groundwater parallelization of an operational hydrological model used for national-scale integrated water management and policy making. The parallel model code and data are freely available., Mathematical Physics

Fresh groundwater reserves, being of vital importance for more than a billion of people living in the coastal zone, are being threatened by saltwater intrusion due to anthropogenic activities and climate change. High resolution three-dimensional (3D), variable-density (VD), groundwater flow and salt transport (FT) numerical models are increasingly being used to support water managers and decision makers in their strategic planning and measures for dealing with the problem of fresh water shortages. However, these computer models typically require long runtimes and large memory usage, making them impractical to use without parallelization. Here, we parallelize SEAWAT, and show that with our parallelization 3D-VD-FT modeling is now feasible for a wide range of hydrogeologists, since a) speedups of more than two orders of magnitude can be obtained as illustrated in this paper, and b) large 3D-VD-FT models are feasible with memory requirements far exceeding single machine memory., Mathematical Physics

In this study, a computation-efficient parameter estimation scheme for high-resolution global tide models is developed. The method is applied to Global Tide and Surge Model with an unstructured grid with a resolution of about 2.5 km in the coastal area and about 4.9 million cells. The estimation algorithm uses an iterative least squares method, known as DUD. We use time-series derived from the FES2014 tidal database in deep water as observations to estimate corrections to the bathymetry. Although the model and estimation algorithm run in parallel, directly applying of DUD would not be affordable computationally. To reduce the computational demand, a coarse-to-fine strategy is proposed by using output from a coarser model to replace the fine model. There are two approaches; One is completely replacing the fine model with a coarser model during calibration (Coarse Calibration) and the second is Coarse Incremental Calibration, that replaces the output increments between the initial model and model with modified parameters by coarser grid model simulations. To further reduce the computation time, the parameter dimension is reduced from O(106) to O(102) based on sensitivity analysis, which greatly reduces the required number of model simulations and storage. In combination, these methods form an efficient optimization strategy. Experiments show that the accuracy of the tidal representation can be improved significantly at affordable cost. Validation for other time-periods and using coastal tide-gauges shows that the accuracy is improved significantly. However, the calibration period of two weeks is short and leads to some over-fitting of the model., Mathematical Physics

Imaging-type monitoring techniques are used in monitoring dynamic processes in many domains, including medicine, engineering, and geophysics. This paper aims to propose an efficient workflow for application of such data for the conditioning of simulation models. Such applications are very common in e.g. the geosciences, where large-scale simulation models and measured data are used to monitor the state of e.g. energy and water systems, predict their future behavior and optimize actions to achieve desired behavior of the system. In order to reduce the high computational cost and complexity of data assimilation workflows for high-dimensional parameter estimation, a residual-in-residual dense block extension of the U-Net convolutional network architecture is proposed, to predict time-evolving features in high-dimensional grids. The network is trained using high-fidelity model simulations. We present two examples of application of the trained network as a surrogate within an iterative ensemble-based workflow to estimate the static parameters of geological reservoirs based on binary-type image data, which represent fluid facies as obtained from time-lapse seismic surveys. The differences between binary images are parameterized in terms of distances between the fluid-facies boundaries, or fronts. We discuss the impact of the choice of network architecture, loss function, and number of training samples on the accuracy of results and on overall computational cost. From comparisons with conventional workflows based entirely on high-fidelity simulation models, we conclude that the proposed surrogate-supported hybrid workflow is able to deliver results with an accuracy equal to or better than the conventional workflow, and at significantly lower cost. Cost reductions are shown to increase with the number of samples of the uncertain parameter fields. The hybrid workflow is generic and should be applicable in addressing inverse problems in many geophysical applications as wel, Mathematical Physics, Petroleum Engineering

Energy efficiency plays an essential role in energy conservation and emissions mitigation efforts in the building sector. This is especially important considering that the global building stock is expected to rapidly expand in the years to come. In this study, a global-scale modeling framework is developed to analyze the evolution of building energy intensity per floor area during 1971–2014, its relationship with economic development, and its future role in energy savings across 21 world regions by 2060. Results show that, for residential buildings, while most high-income and upper-middle-income regions see decreasing energy intensities and strong decoupling from economic development, the potential for further efficiency improvement is limited in the absence of significant socioeconomic and technological shifts. Lower-middle-income regions, often overlooked in analyses, will see large potential future residential energy savings from energy intensity reductions. Harnessing this potential will include, among other policies, stricter building efficiency standards in new construction. For the commercial sector, during 1971–2014, the energy intensity was reduced by 50% in high-income regions but increased by 193% and 44% in upper-middle and lower-middle-income regions, respectively. Given the large energy intensity reduction potential and rapid floor area growth, commercial buildings are increasingly important for energy saving in the future., Mathematical Physics

Emission inversion using data assimilation fundamentally relies on having the correct assumptions about the emission background error covariance. A perfect covariance accounts for the uncertainty based on prior knowledge and is able to explain differences between model simulations and observations. In practice, emission uncertainties are constructed empirically; hence, a partially unrepresentative covariance is unavoidable. Concerning its complex parameterization, dust emissions are a typical example where the uncertainty could be induced from many underlying inputs, e.g., information on soil composition and moisture, land cover and erosive wind velocity, and these can hardly be taken into account together. This paper describes how an adjoint model can be used to detect errors in the emission uncertainty assumptions. This adjoint-based sensitivity method could serve as a supplement of a data assimilation inverse modeling system to trace back the error sources in case large observation-minus-simulation residues remain after assimilation based on empirical background covariance. The method follows an application of a data assimilation emission inversion for an extreme severe dust storm over East Asia b). The assimilation system successfully resolved observation-minus-simulation errors using satellite AOD observations in most of the dust-affected regions. However, a large underestimation of dust in Northeast China remained despite the fact that the assimilated measurements indicated severe dust plumes there. An adjoint implementation of our dust simulation model is then used to detect the most likely source region for these unresolved dust loads. The backwa, Mathematical Physics

Accepted author manuscript, Mathematical Physics

A consensus-control-based multi-level control law named Multi-Level Power-Imbalance Allocation Control (MLPIAC) is presented for a large-scale power system partitioned into two or more groups. Centralized control is implemented in each group while distributed control is implemented at the coordination level of the groups. Besides restoring nominal frequency with a minimal control cost, MLPIAC can improve the transient performance of the system through an accelerated convergence of the control inputs without oscillations. At the coordination level of the control groups, because the number of the groups is smaller than that of nodes, MLPIAC is more effective to obtain the minimized control cost than the purely distributed control law. At the level of the control in each group, because the number of nodes is much smaller than the total number of nodes in the whole network, the overheads in the communications and the computations are reduced compared to the pure centralized control. The asymptotic stability of MLPIAC is proven using the Lyapunov method and the performance is evaluated through simulations., Mathematical Physics

This paper aims to explore the performance of the autonomous mobility-on-demand system (AMoD) with the coordinated formation of vehicle platooning. In this study, an agent-based model (ABM) is developed to explicitly simulate the operations of platooning formation and interactions between shared automated vehicles (SAVs) and real-time travel requests. The objective is to capture the real-time behavior of SAVs as trip makers, and then assess the performance of the AMoD system with the mechanism of coordinated formation of platoons. We conclude that the impact of vehicle assignment strategies in the AMoD system with vehicle platooning formation predominately affects the average waiting time and system capacity to transport travelers as a whole; however, vehicle platooning, to some extent, could lengthen the travel time of platoon vehicles. The hold-on time (imposed delay) of leading vehicles in order to form a platoon could affect the average time delay of vehicles part of those platoons. The developed ABM provides the first insight into the impact of the pervasive formation of vehicle platooning on the performance of the AMoD system., Mathematical Physics, Transport and Planning

Automated vehicles used as public transport show a great promise of revolutionizing current transportation systems. Still, there are many questions as to how these systems should be organized and operated in cities to bring the best out of future services. In this study, an agent-based model (ABM) is developed to simulate the on-demand operations of shared automated vehicles (SAVs) in a parallel transit service (PTS) and a tailored time-varying transit service (TVTS). The proposed TVTS system can switch service schemes between a door-to-door service (DDS) and a station-to-station service (SSS) according to what is best for the service providers and the travelers. In addition, the proposed PTS system that allows DDS and SSS to operate simultaneously is simulated. To test the conceptual design of the proposed SAV system, simulation experiments are performed in a hypothetical urban area to show the potential of different SAV schemes. Simulation results suggest that SAV systems together with dynamic ridesharing can significantly reduce average waiting time, the vehicle kilometres travelled and empty SAV trips. Moreover, the proposed optimal vehicle assignment algorithm can significantly reduce the empty vehicle kilometres travelled (VKT) for the pickups for all tested SAV systems up to about 40% and improve the system capacity for transporting the passengers. Comparing the TVTS system, which has inconvenient access in peak hours, with the PTS systems, which always makes available door-to-door transport, we conclude that the latter could achieve a similar system performance as the former in terms of average waiting time, service time and system capacity., Mathematical Physics, Transport and Planning

Identifying protein complexes in static protein-protein interaction (PPI) networks is essential for understanding the underlying mechanism of biological processes. Proteins in a complex are co-localized at the same place and co-expressed at the same time. We propose a novel method to identify protein complexes with the features of joint co-localization and joint co-expression in static PPI networks. To achieve this goal, we define a joint localization vector to construct a joint co-localization criterion of a protein group, and define a joint gene expression to construct a joint co-expression criterion of a gene group. Moreover, the functional similarity of proteins in a complex is an important characteristic. Thus, we use the CC-based, MF-based, and BP-based protein similarities to devise functional similarity criterion to determine whether a protein is functionally similar to a protein cluster. Based on the core-attachment structure and following to seed expanding strategy, we use four types of biological data including PPI data with reliability score, protein localization data, gene expression data, and gene ontology annotations, to identify protein complexes. The experimental results on yeast data show that comparing with existing methods our proposed method can efficiently and exactly identify more protein complexes, especially more protein complexes of sizes from 2 to 6. Furthermore, the enrichment analysis demonstrates that the protein complexes identified by our method have significant biological meaning., Accepted author manuscript, Mathematical Physics

Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods., Mathematical Physics

Carbon footprints and other environmentally extended input–output indicators are obtained as aggregations of emissions embodied in supply chains (EESCs), which express the emissions occurring in a specific production activity to satisfy a given volume of final demand. Here we derive theoretical approximations of the expectations of and covariances between EESCs, as a function of the expectations of and covariances between source data (technical coefficients, emission coefficients and final demand volumes) through a Taylor expansion. We report an empirical test of those approximations, using a sample of 5 global multi-regional input–output models in the year 2007, of which we extract 22 single-region input–output systems with 17 sectors. We find that approximations of multipliers perform better than those of EESC, and approximations of expectations perform better than those of covariances., Mathematical Physics

Data assimilation algorithms rely on a basic assumption of an unbiased observation error. However, the presence of inconsistent measurements with nontrivial biases or inseparable baselines is unavoidable in practice. Assimilation analysis might diverge from reality since the data assimilation itself cannot distinguish whether the differences between model simulations and observations are due to the biased observations or model deficiencies. Unfortunately, modeling of observation biases or baselines which show strong spatiotemporal variability is a challenging task. In this study, we report how data-driven machine learning can be used to perform observation bias correction for data assimilation through a real application, which is the dust emission inversion using PM10 observations. PM10 observations are considered unbiased; however, a bias correction is necessary if they are used as a proxy for dust during dust storms since they actually represent a sum of dust particles and non-dust aerosols. Two observation bias correction methods have been designed in order to use PM10 measurements as proxy for the dust storm loads under severe dust conditions. The first one is the conventional chemistry transport model (CTM) that simulates life cycles of non-dust aerosols. The other one is the machine-learning model that describes the relations between the regular PM10 and other air quality measurements. The latter is trained by learning using 2 years of historical samples. The machine-learning-based non-dust model is shown to be in better agreement with observations compared to the CTM. The dust emission inversion tests have been performed, through assimilating either the raw measurements or the bias-corrected dust observations using either the CTM or machine-learning model. The emission field, surface dust concentration, and forecast skill are evaluated. The worst case is when we directly assimilate the original observations. The forecasts driven by the a posteriori emissio, Mathematical Physics

Aerosol optical depths (AODs) from the new Himawari-8 satellite instrument have been assimilated in a dust simulation model over East Asia. This advanced geostationary instrument is capable of monitoring the East Asian dust storms which usually have great spatial and temporal variability. The quality of the data has been verified through a comparison with AErosol RObotic NETwork AODs. This study focuses on extreme dust events only when dust aerosols are dominant; promising results are obtained in AOD assimilation experiments during a case in May 2017. The dust emission fields that drive the simulation model are strongly improved by the inverse modeling, and consequently, the simulated dust concentrations are in better agreements with the observed AOD as well as ground-based observations of PM 10 . However, some satellite AODs show significant inconsistence with the simulations and the PM 10 and AErosol RObotic NETwork observations, which might arise from retrieval errors over a partially clouded scene. The data assimilation procedure therefore includes a screening method to exclude these observations in order to avoid unrealistic results. A dust mask screening method is designed, which is based on selecting only those observations where the deterministic model produces a substantial amount of dust. This screen algorithm is tested to give more accurate result compared to the traditional method based on background covariance in the case study. Note that our screen method would exclude valuable information in case the model is not able to simulate the dust plume shape correctly; hence, applications in related studies require inspections of simulations and observations by user., Mathematical Physics