Publications
85 results found
Nadler P, Arcucci R, Guo Y-K, 2020, A Scalable Approach to Econometric Inference, Conference on Parallel Computing - Technology Trends (ParCo), Publisher: IOS PRESS, Pages: 59-68, ISSN: 0927-5452
- Author Web Link
- Cite
- Citations: 1
Aristodemou E, Arcucci R, Mottet L, et al., 2019, Enhancing CFD-LES air pollution prediction accuracy using data assimilation, Building and Environment, Vol: 165, ISSN: 0007-3628
It is recognised worldwide that air pollution is the cause of premature deaths daily, thus necessitating the development of more reliable and accurate numerical tools. The present study implements a three dimensional Variational (3DVar) data assimilation (DA) approach to reduce the discrepancy between predicted pollution concentrations based on Computational Fluid Dynamics (CFD) with the ones measured in a wind tunnel experiment. The methodology is implemented on a wind tunnel test case which represents a localised neighbourhood environment. The improved accuracy of the CFD simulation using DA is discussed in terms of absolute error, mean squared error and scatter plots for the pollution concentration. It is shown that the difference between CFD results and wind tunnel data, computed by the mean squared error, can be reduced by up to three order of magnitudes when using DA. This reduction in error is preserved in the CFD results and its benefit can be seen through several time steps after re-running the CFD simulation. Subsequently an optimal sensors positioning is proposed. There is a trade-off between the accuracy and the number of sensors. It was found that the accuracy was improved when placing/considering the sensors which were near the pollution source or in regions where pollution concentrations were high. This demonstrated that only 14% of the wind tunnel data was needed, reducing the mean squared error by one order of magnitude.
Lim EM, Molina Solana M, Pain C, et al., 2019, Hybrid data assimilation: An ensemble-variational approach, Pages: 633-640
Data Assimilation (DA) is a technique used to quantify and manage uncertainty in numerical models by incorporating observations into the model. Variational Data Assimilation (VarDA) accomplishes this by minimising a cost function which weighs the errors in both the numerical results and the observations. However, large-scale domains pose issues with the optimisation and execution of the DA model. In this paper, ensemble methods are explored as a means of sampling the background error at a reduced rank to condition the problem. The impact of ensemble size on the error is evaluated and benchmarked against other preconditioning methods explored in previous work such as using truncated singular value decomposition (TSVD). Localisation is also investigated as a form of reducing the long-range spurious errors in the background error covariance matrix. Both the mean squared error (MSE) and execution time are used as measure of performance. Experimental results for a 3D case for pollutant dispersion within an urban environment are presented with promise for future work using dynamic ensembles and 4D state vectors.
Nadler P, Arcucci R, Guo YK, 2019, Data assimilation for parameter estimation in economic modelling, Pages: 649-656
We propose a data assimilation approach for latent parameter estimation in economic models. We describe a dynamic model of an economic system with latent state variables describing the relationship of economic entities over time as well as a stochastic volatility component. We show and discuss the model's relationship with data assimilation and how it is derived. We apply it to conduct a multivariate analysis of the cryptocurrency ecosystem. Combining these approaches opens a new dimension of analysis to economic modelling. Economics, Multivariate Analysis, Dynamical System, Bitcoin, Data Assimilation.
Zhu J, Hu S, Arcucci R, et al., 2019, Model error correction in data assimilation by integrating neural networks, Big Data Mining and Analytics, Vol: 2, Pages: 83-91, ISSN: 2096-0654
In this paper, we suggest a new methodology which combines Neural Networks (NN) into Data Assimilation (DA). Focusing on the structural model uncertainty, we propose a framework for integration NN with the physical models by DA algorithms, to improve both the assimilation process and the forecasting results. The NNs are iteratively trained as observational data is updated. The main DA models used here are the Kalman filter and the variational approaches. The effectiveness of the proposed algorithm is validated by examples and by a sensitivity study.
Arcucci R, Mottet L, Pain C, et al., 2019, Optimal reduced space for Variational Data Assimilation, JOURNAL OF COMPUTATIONAL PHYSICS, Vol: 379, Pages: 51-69, ISSN: 0021-9991
Arcucci R, Mcllwraith D, Guo Y-K, 2019, Scalable Weak Constraint Gaussian Processes, 19th Annual International Conference on Computational Science (ICCS), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 111-125, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 3
Arcucci R, Pain C, Guo Y-K, 2018, Effective variational data assimilation in air-pollution prediction, Big Data Mining and Analytics, Vol: 1, Pages: 297-307, ISSN: 2096-0654
Numerical simulations are widely used as a predictive tool to better understand complex air flows and pollution transport on the scale of individual buildings, city blocks, and entire cities. To improve prediction for air flows and pollution transport, we propose a Variational Data Assimilation (VarDA) model which assimilates data from sensors into the open-source, finite-element, fluid dynamics model Fluidity. VarDA is based on the minimization of a function which estimates the discrepancy between numerical results and observations assuming that the two sources of information, forecast and observations, have errors that are adequately described by error covariance matrices. The conditioning of the numerical problem is dominated by the condition number of the background error covariance matrix which is ill-conditioned. In this paper, a preconditioned VarDA model is presented, it is based on a reduced background error covariance matrix. The Empirical Orthogonal Functions (EOFs) method is used to alleviate the computational cost and reduce the space dimension. Experimental results are provided assuming observed values provided by sensors from positions mainly located on roofs of buildings.
Song J, Fan S, Lin W, et al., 2018, Natural ventilation in cities: the implications of fluid mechanics, BUILDING RESEARCH AND INFORMATION, Vol: 46, Pages: 809-828, ISSN: 0961-3218
Arcucci R, Carracciuolo L, Toumi R, 2018, Toward a preconditioned scalable 3DVAR for assimilating Sea Surface Temperature collected into the Caspian Sea, Journal of Numerical Analysis, Industrial and Applied Mathematics, Vol: 12, Pages: 9-28, ISSN: 1790-8140
© 2018 European Society of Computational Methods in Sciences and Engineering. Data Assimilation (DA) is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. As a crucial point into DA models is the ill conditioning of the covariance matrices involved, it is mandatory to introduce, in a DA software, preconditioning methods. Here we present first results obtained introducing two different preconditioning methods in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea by using the Regional Ocean Modeling System (ROMS) with observations provided by the Group of High resolution sea surface temperature (GHRSST). We present the algorithmic strategies we employ and the numerical issues on data collected in two of the months which present the most significant variability in water temperature: August and March.
D'Amore L, Arcucci R, Li Y, et al., 2018, Performance Assessment of the Incremental Strong Constraints 4DVAR Algorithm in ROMS, 12th International Conference on Parallel Processing and Applied Mathematics (PPAM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 48-57, ISSN: 0302-9743
Arcucci R, Basciano D, Cilardo A, et al., 2018, Energy Analysis of a 4D Variational Data Assimilation Algorithm and Evaluation on ARM-Based HPC Systems, 12th International Conference on Parallel Processing and Applied Mathematics (PPAM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 37-47, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 1
Arcucci R, D'Amore L, Carracciuolo L, et al., 2017, A Decomposition of the Tikhonov Regularization Functional Oriented to Exploit Hybrid Multilevel Parallelism, INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING, Vol: 45, Pages: 1214-1235, ISSN: 0885-7458
Arcucci R, Celestino S, Toumi R, et al., 2017, Toward the S3DVAR data assimilation software for the Caspian Sea, International Conference on Numerical Analysis and Applied Mathematics (ICNAAM), Publisher: AIP Publishing, ISSN: 1551-7616
Data Assimilation (DA) is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. The forecasting model used for producing oceanographic prediction into the Caspian Sea is the Regional Ocean Modeling System (ROMS). Here we propose the computational issues we are facing in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea with observations provided by the Group of High resolution sea surface temperature (GHRSST). We present the algorithmic strategies we employ and the numerical issues on data collected in two of the months which present the most significant variability in water temperature: August and March.
Arcucci R, Marotta U, Murano A, et al., 2017, Parallel Parity Games: a Multicore Attractor for the Zielonka Recursive Algorithm, International Conference on Computational Science (ICCS), Publisher: ELSEVIER SCIENCE BV, Pages: 525-534, ISSN: 1877-0509
Arcucci R, D'Amore L, Mele V, 2017, Mathematical Approach to the Performance Evaluation of Three Dimensional Variational Data Assimilation, 1st International Conference on Applied Mathematics and Computer Science (ICAMCS), Publisher: AMER INST PHYSICS, ISSN: 0094-243X
Arcucci R, D'Amore L, Toumi R, 2017, Preconditioning of the background error covariance matrix in data assimilation for the Caspian Sea, 1st International Conference on Applied Mathematics and Computer Science (ICAMCS), Publisher: AIP Publishing, ISSN: 1551-7616
Data Assimilation (DA) is an uncertainty quantification technique used for improving numerical forecasted results by incorporating observed data into prediction models. As a crucial point into DA models is the ill conditioning of the covariance matrices involved, it is mandatory to introduce, in a DA software, preconditioning methods. Here we present first studies concerning the introduction of two different preconditioning methods in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea by using the Regional Ocean Modeling System (ROMS) with observations provided by the Group of High resolution sea surface temperature (GHRSST). We also present the algorithmic strategies we employ.
Arcucci R, D'Amore L, Pistoia J, et al., 2017, On the variational data assimilation problem solving and sensitivity analysis, JOURNAL OF COMPUTATIONAL PHYSICS, Vol: 335, Pages: 311-326, ISSN: 0021-9991
We consider the Variational Data Assimilation (VarDA) problem in an operational framework, namely, as it results when it is employed for the analysis of temperature and salinity variations of data collected in closed and semi closed seas. We present a computing approach to solve the main computational kernel at the heart of the VarDA problem, which outperforms the technique nowadays employed by the oceanographic operative software. The new approach is obtained by means of Tikhonov regularization. We provide the sensitivity analysis of this approach and we also study its performance in terms of the accuracy gain on the computed solution. We provide validations on two realistic oceanographic data sets.
Arcucci R, D’ Amore L, Carracciuolo L, et al., 2016, Scalability Analysis of Variational Data Assimilation Algorithms on Hybrid Architectures, High Performance Scientific Computing Using Distributed Infrastructures: Results and Scientific Applications Derived from the Italian PON ReCaS Project, Pages: 391-398, ISBN: 9789814759700
Large-scale problems are computationally expensive and their solution requires designing of scalable approaches. Many factors contribute to scalability, including the architecture of the parallel computer and the parallel implementation of the algorithm. However, one important issue is the scalability of the algorithm itself. We have developed a scalable algorithm for solving large scale Data Assimilation (DA) problems: starting from a decomposition of the mathematical problems, it uses a partitioning of the solution and a modified regularization functional. Here, we briefly discuss some results.
Arcucci R, D'Amore L, Celestino S, et al., 2016, A Scalable Numerical Algorithm for Solving Tikhonov Regularization Problems, 11th International Conference on Parallel Processing and Applied Mathematics (PPAM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 45-54, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 16
Arcucci R, D'Amore L, Carracciuolo L, 2015, On the Problem-Decomposition of Scalable 4D-Var Data Assimilation Models, International Conference on High Performance Computing and Simulation (HPCS), Publisher: IEEE, Pages: 589-594
- Author Web Link
- Cite
- Citations: 16
D'Amore L, Arcucci R, Carracciuolo L, et al., 2014, A Scalable Approach for Variational Data Assimilation, JOURNAL OF SCIENTIFIC COMPUTING, Vol: 61, Pages: 239-257, ISSN: 0885-7474
- Author Web Link
- Cite
- Citations: 23
D'Amore L, Arcucci R, Marcellino L, et al., 2013, HPC computation issues of the incremental 3D variational data assimilation scheme in OceanVar software, Journal of Numerical Analysis, Industrial and Applied Mathematics, Vol: 7, Pages: 91-105, ISSN: 1790-8140
The most significant features of Data Assimilation (DA) are that both the models and the observations are very large and non-linear (of order at least O(108)). Further, DA is an ill-posed inverse problem. Such properties make the numerical solution of DA very difficult so that, as stated in [19], "solving this problem in "real-time" it is not always pos- sible and many different approximations to the basic assimilation schemes are employed". Thus, the exploitation of advanced computing environments is mandatory, reducing the computational cost to a suitable turnaround time. This activity should be done according to a co-design methodology where software requirements drive hardware design decisions and hardware design constraints motivate changes in the software design to better fit within those constraints. In this paper, we address high performance computation issues of the three dimensional DA scheme underlying the oceanographic 3D-VAR assimilation scheme, named Ocean- VAR, developed at CMCC (Centro Euro Mediterraneo per i Cambiamenti Climatici), in Italy. The aim is to develop a parallel software architecture which is able to effectively take advantage of the available high performance computing resources. © 2012 European Society of Computational Methods in Sciences, Engineering and Technology.
D'Amore L, Arcucci R, Carracciuolo L, et al., 2013, DD-OceanVar: a Domain Decomposition fully parallel Data Assimilation software for the Mediterranean Forecasting System, 13th Annual International Conference on Computational Science (ICCS), Publisher: ELSEVIER SCIENCE BV, Pages: 1235-1244, ISSN: 1877-0509
- Author Web Link
- Cite
- Citations: 14
D'Amore L, Arcucci R, Marcellino L, et al., 2011, A Parallel Three-dimensional Variational Data Assimilation Scheme, International Conference on Numerical Analysis and Applied Mathematics (ICNAAM), Publisher: AMER INST PHYSICS, ISSN: 0094-243X
- Author Web Link
- Cite
- Citations: 16
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.