Imperial College London

DrRossellaArcucci

Faculty of EngineeringDepartment of Computing

Research Fellow (Data Analysis)
 
 
 
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Contact

 

r.arcucci Website

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
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18 results found

Arcucci R, Mottet L, Pain C, Guo Y-Ket al., 2019, Optimal reduced space for Variational Data Assimilation, JOURNAL OF COMPUTATIONAL PHYSICS, Vol: 379, Pages: 51-69, ISSN: 0021-9991

JOURNAL ARTICLE

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.

JOURNAL ARTICLE

Song J, Fan S, Lin W, Mottet L, Woodward H, Wykes MD, Arcucci R, Xiao D, Debay J-E, ApSimon H, Aristodemou E, Birch D, Carpentieri M, Fang F, Herzog M, Hunt GR, Jones RL, Pain C, Pavlidis D, Robins AG, Short CA, Linden PFet al., 2018, Natural ventilation in cities: the implications of fluid mechanics, BUILDING RESEARCH AND INFORMATION, Vol: 46, Pages: 809-828, ISSN: 0961-3218

JOURNAL ARTICLE

D'Amore L, Arcucci R, Li Y, Montella R, Moore A, Phillipson L, Toumi Ret 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

CONFERENCE PAPER

Arcucci R, Basciano D, Cilardo A, D'Amore L, Mantovani Fet 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

CONFERENCE PAPER

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.

JOURNAL ARTICLE

Arcucci R, D'Amore L, Carracciuolo L, Scotti G, Laccetti Get 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

JOURNAL ARTICLE

Arcucci R, Celestino S, Toumi R, Laccetti Get 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.

CONFERENCE PAPER

Arcucci R, Marotta U, Murano A, Sorrentino Let 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

CONFERENCE PAPER

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

CONFERENCE PAPER

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.

CONFERENCE PAPER

Arcucci R, D'Amore L, Pistoia J, Toumi R, Murli Aet 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.

JOURNAL ARTICLE

Arcucci R, D'Amore L, Celestino S, Laccetti G, Murli Aet al., 2016, A Scalable Numerical Algorithm for Solving Tikhonov Regularization Problems, 11th International Conference on Parallel Processing and Applied Mathematics (PPAM), Publisher: SPRINGER INT PUBLISHING AG, Pages: 45-54, ISSN: 0302-9743

CONFERENCE PAPER

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

CONFERENCE PAPER

D'Amore L, Arcucci R, Carracciuolo L, Murli Aet al., 2014, A Scalable Approach for Variational Data Assimilation, JOURNAL OF SCIENTIFIC COMPUTING, Vol: 61, Pages: 239-257, ISSN: 0885-7474

JOURNAL ARTICLE

D'Amore L, Arcucci R, Marcellino L, Murli Aet 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(10 8 )). 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.

JOURNAL ARTICLE

D'Amore L, Arcucci R, Carracciuolo L, Murli Aet 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

CONFERENCE PAPER

D'Amore L, Arcucci R, Marcellino L, Murli Aet 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

CONFERENCE PAPER

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