On the variational data assimilation problem solving and sensitivity analysis

Arcucci, Rossella; D’Amore, Luisa; Pistoia, Jenny; Toumi, Ralf; Murli, A.

doi:10.1016/j.jcp.2017.01.034

Cited by 30 publications

(28 citation statements)

References 21 publications

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“…Due the better conditioning of the smaller problems, it is N L−BF GS,p1 > N L−BF GS,p2 [2]. Then the (26) holds.…”

Section: Letmentioning

confidence: 96%

“…The aim of DA problem is to find an optimal tradeoff between the current estimate of the system state (background) defined in (1) and the available observations y k defined in (2). Let (3) be an overlapping decomposition of the physical domain Ω such that Ω i ∩ Ω j = Ω ij = 0 if Ω i and Ω j are adjacent and Ω ij is called overlapping region [1].…”

Section: The Dd-4dvar Computational Kernelmentioning

confidence: 99%

See 1 more Smart Citation

Energy Analysis of a 4D Variational Data Assimilation Algorithm and Evaluation on ARM-Based HPC Systems

Arcucci

Basciano

Cilardo

et al. 2018

Parallel Processing and Applied Mathematics

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Driven by the emerging requirements of High Performance Computing (HPC) architectures, the main focus of this work is the interplay of computational and energetic aspects of a Four Dimensional Variational (4DVAR) Data Assimilation algorithm, based on Domain Decomposition (named DD-4DVAR). We report first results on the energy consumption of the DD-4DVAR algorithm on embedded processor and a mathematical analysis of the energy behaviour of the algorithm by assuming the architectures characteristics as variable of the model. The main objective is to capture the essential operations of the algorithm exhibiting a direct relationship with the measured energy. The experimental evaluation is carried out on a set of mini-clusters made available by the Barcelona Supercomputing Center.

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“…Due the better conditioning of the smaller problems, it is N L−BF GS,p1 > N L−BF GS,p2 [2]. Then the (26) holds.…”

Section: Letmentioning

confidence: 96%

Section: The Dd-4dvar Computational Kernelmentioning

confidence: 99%

Energy Analysis of a 4D Variational Data Assimilation Algorithm and Evaluation on ARM-Based HPC Systems

Arcucci

Basciano

Cilardo

et al. 2018

Parallel Processing and Applied Mathematics

Self Cite

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“…Recently, DA is also applied to numerical simulations of geophysical applications, medicine, biological science and finance [15]. Data assimilation can be applied to a variety of problems where an uncertainty quantification has to be included [16] or where latent parameters need to be computed taking into account new observations. The Adaptive DA-SIR model is a model which incorporates data assimilation with a compartmental SIR model.…”

Section: The Adaptive Da-sir Modelmentioning

confidence: 99%

An epidemiological modelling approach for COVID-19 via data assimilation

et al. 2020

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The global pandemic of the 2019-nCov requires the evaluation of policy interventions to mitigate future social and economic costs of quarantine measures worldwide. We propose an epidemiological model for forecasting and policy evaluation which incorporates new data in real-time through variational data assimilation. We analyze and discuss infection rates in the UK, US and Italy. We furthermore develop a custom compartmental SIR model fit to variables related to the available data of the pandemic, named SITR model, which allows for more granular inference on infection numbers. We compare and discuss model results which conducts updates as new observations become available. A hybrid data assimilation approach is applied to make results robust to initial conditions and measurement errors in the data. We use the model to conduct inference on infection numbers as well as parameters such as the disease transmissibility rate or the rate of recovery. The parameterisation of the model is parsimonious and extendable, allowing for the incorporation of additional data and parameters of interest. This allows for scalability and the extension of the model to other locations or the adaption of novel data sources.

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“…L-BFGS method is a Quasi Newton method [11] that can be viewed as extension of conjugate-gradient methods in which the addition of some modest storage serves to accelerate the convergence rate. The convergence rate of L-BFGS depends on the conditioning of the numerical problem [10] which is dominated by the condition number of the background covariance matrix [12] . In order to reduce the ill conditioning of the background covariance matrix and remove the statistically less significant modes which could add noise to the data assimilation estimate, we use here the Empirical Orthogonal Functions (EOFs) method.…”

Section: Related Work and Contribution Of The Present Workmentioning

confidence: 99%

“…The problem concerning the selection of an optimal truncation parameter is here faced. As it is known that the numerical error which propagates into the DA solution is influenced by the condition number [12] , a proper value of the truncation parameter should minimize the condition number. However, to be sure that the preconditioned problem does not differ too much from the original problem, the optimal truncation parameter should also minimize a Relative Preconditioning Error (RPE) which provides an estimate of how much the preconditioned problem differs from the starting problem as defined in Definition 9.…”

Section: Reduced Order Space and Preconditioningmentioning

confidence: 99%

Effective variational data assimilation in air-pollution prediction

Arcucci

Pain

Guo

2018

Big Data Min. Anal.

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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.

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On the variational data assimilation problem solving and sensitivity analysis

Cited by 30 publications

References 21 publications

Energy Analysis of a 4D Variational Data Assimilation Algorithm and Evaluation on ARM-Based HPC Systems

Energy Analysis of a 4D Variational Data Assimilation Algorithm and Evaluation on ARM-Based HPC Systems

An epidemiological modelling approach for COVID-19 via data assimilation

Effective variational data assimilation in air-pollution prediction

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