Abstract
We present a parallel Data Assimilation model based on an Adaptive Domain Decomposition (ADD-DA) coupled with the open-source, finite-element, fluid dynamics model Fluidity. The model we present is defined on a partition of the domain in sub-domains without overlapping regions. This choice allows to avoid communications among the processes during the Data Assimilation phase. However, during the balance phase, the model exploits the domain decomposition implemented in Fluidity which balances the results among the processes exploiting overlapping regions. Also, the model exploits the technology provided by the mesh adaptivity to generate an optimal mesh we name supermesh. The supermesh is the one used in ADD-DA process. We prove that the ADD-DA model provides the same numerical solution of the corresponding sequential DA model. We also show that the ADD approach reduces the execution time even when the implementation is not on a parallel computing environment. Experimental results are provided for pollutant dispersion within an urban environment.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Imperial College London AMCG: Fluidity manual v4.1.12, April 2015. https://figshare.com/articles/Fluidity_Manual/1387713
Arcucci, R., D’Amore, L., Carracciuolo, L., Scotti, G., Laccetti, G.: A decomposition of the Tikhonov regularization functional oriented to exploit hybrid multilevel parallelism. Int. J. Parallel Program., pp. 1214–1235. (2016). https://doi.org/10.1007/s10766-016-0460-3
Arcucci, R., D’Amore, L., Pistoia, J., Toumi, R., Murli, A.: On the variational data assimilation problem solving and sensitivity analysis. J. Comput. Phys. 335, 311–326 (2017)
Arcucci, R., Mottet, L., Pain, C., Guo, Y.K.: Optimal reduced space for variational data assimilation. J. Comput. Phys. 379, 51–69 (2019)
Aristodemou, E., Bentham, T., Pain, C., Robins, A.: A comparison of Mesh-adaptive les with wind tunnel data for flow past buildings: mean flows and velocity fluctuations. Atmos. Environ. J. 43, 6238–6253 (2009)
Asch, M., Bocquet, M., Nodet, M.: Data Assimilation: Methods, Algorithms, and Applications, vol. 11. SIAM, University City (2016)
Christie, M.A., Glimm, J., Grove, J.W., Higdon, D.M., Sharp, D.H., Wood-Schultz, M.M.: Error analysis and simulations of complex phenomena. Los Alamos Sci. 29 (2005)
Courtier, J.: A strategy for operational implementation of 4D-Var, using an incremental approach. Q. J. R. Meteorol. Soc. 120(519), 1367–1387 (1994)
D’Amore, L., Arcucci, R., Marcellino, L., Murli, A.: A parallel three-dimensional variational data assimilation scheme. In: AIP Conference Proceedings, vol. 1389, pp. 1829–1831. AIP (2011)
Farina, R., Dobricic, S., Storto, A., Masina, S., Cuomo, S.: A revised scheme to compute horizontal covariances in an oceanographic 3D-Var assimilation system. J. Comput. Phys. 284, 631–647 (2015)
Ford, R., Pain, C.C., Goddard, A.J.H., De Oliveira, C.R.E., Umpleby, A.P.: A non-hydrostatic finite-element model for three-dimensional stratified oceanic flows. Part I: model formulation. Mon. Weather Rev. 132, 2816–2831 (2004)
Hannachi, A.: A primer for EOF analysis of climate data. Department of Meteorology, University of Reading, UK (2004)
Hannachi, A., Jolliffe, I., Stephenson, D.: Empirical orthogonal functions and related techniques in atmospheric science: a review. Int. J. Climatol.:J. R. Meteorol. Soc. 27(9), 1119–1152 (2007)
Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)
Kalnay, E.: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge (2003)
Lelieveld, J., Evans, J.S., Fnais, M., Giannadaki, D., Pozzer, A.: The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525(7569), 367 (2015)
Lorenc, A.: Development of an operational variational assimilation scheme. J. Meteorol. Soc. Jpn 75, 339–346 (1997)
Lorenz, E.N.: Empirical orthogonal functions and statistical weather prediction (1956)
Nichols, N.K.: Mathematical concepts of data assimilation. In: Lahoz, W., Khattatov, B., Menard, R. (eds.) Data Assimilation, pp. 13–39. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-540-74703-1_2
Oke, P.R., Brassington, G.B., Griffin, D.A., Schiller, A.: The bluelink ocean data assimilation system (BODAS). Ocean Model. 21(1–2), 46–70 (2008)
Pain, C., Umpleby, A., De Oliveira, C., Goddard, A.: Tetrahedral mesh optimisation and adaptivity for steady-state and transient finite element calculations. Comput. Methods Appl. Mech. Eng. 190, 3771–3796 (2001)
Teruzzi, A., Di Cerbo, P., Cossarini, G., Pascolo, E., Salon, S.: Parallel implementation of a data assimilation scheme for operational oceanography: the case of the medbfm model system. Comput. Geosci. 124, 103–114 (2019)
Acknowledgments
This work is supported by the EPSRC Grand Challenge grant “Managing Air for Green Inner Cities” (MAGIC) EP/N010221/1, by the EPSRC Centre for Mathematics of Precision Healthcare EP/N0145291/1 and the EP/T003189/1 Health assessment across biological length scales for personal pollution exposure and its mitigation (INHALE).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Arcucci, R., Mottet, L., Casas, C.A.Q., Guitton, F., Pain, C., Guo, YK. (2020). Adaptive Domain Decomposition for Effective Data Assimilation. In: Schwardmann, U., et al. Euro-Par 2019: Parallel Processing Workshops. Euro-Par 2019. Lecture Notes in Computer Science(), vol 11997. Springer, Cham. https://doi.org/10.1007/978-3-030-48340-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-48340-1_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-48339-5
Online ISBN: 978-3-030-48340-1
eBook Packages: Computer ScienceComputer Science (R0)