Daan Crommelin
- Full Name
- Prof.dr. D. T. Crommelin
- Function(s)
- Group leader, Scientific Staff Member, Professor - Universiteit van Amsterdam
- Daan.Crommelin@cwi.nl
- Telephone
- +31 20 592 4194
- Room
- L118
- Department(s)
- Scientific Computing
- Homepage
- https://homepages.cwi.nl/~dtc/
Biography
Daan Crommelin works on stochastic and computational methods for multiscale systems, with applications in climate science and energy systems. Key research interests are model uncertainties due to unresolved processes, uncertainty quantification, and rare events. Crommelin is an associate editor of SIAM Multiscale Modeling & Simulation, co-initiated the Dutch national research program on "Mathematics of Planet Earth (MPE). Essential Dynamics and Uncertainty", and was awarded a Vidi grant by NWO.
Publications
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Eggels, A.W, & Crommelin, D.T. (2018). UQ with dependent inputs: wind and waves. In Proceedings of ECCM 6 and ECFD 7.
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Bisewski, K, Crommelin, D.T, & Mandjes, M.R.H. (2018). Controlling the time discretization bias for the supremum of Brownian Motion. ACM Transactions on Modeling and Computer Simulation, 28(3). doi:10.1145/3177775
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Bhaumik, D, Crommelin, D.T, Kapodistria, S, & Zwart, A.P. (2018). Hidden Markov Models for wind farm power output. IEEE Transactions on Sustainable Energy. doi:10.1109/TSTE.2018.2834475
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Bhaumik, D, Crommelin, D.T, & Zwart, A.P. (2018). Mitigation of large power spills by an energy storage device in a stand alone energy system. Journal of Energy Storage, 16, 76–83. doi:10.1016/j.est.2017.12.012
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Verheul, N, Viebahn, J.P, & Crommelin, D.T. (2017). Covariate-based stochastic parameterization of baroclinic ocean eddies. Mathematics of Climate and Weather Forecasting, 3(1), 90–117. doi:10.1515/mcwf-2017-0005
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Eggels, A.W, Crommelin, D.T, & Witteveen, J.A.S. (2017). Clustering-based collocation for uncertainty propagation with multivariate dependent inputs. International Journal for Uncertainty Quantification, 1–24. doi:10.1615/Int.J.UncertaintyQuantification.2018020215
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Crommelin, D.T, Gottwald, G.A, & Franzke, C.L.E. (2017). Stochastic Climate Theory. In C.L.E Franzke & Terence J. O'Kane (Eds.), Nonlinear and Stochastic Climate Dynamics (pp. 209–240). doi:10.1017/9781316339251
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Crommelin, D.T, & CWI et al, not. (2017). Stochastic Parameterization: Towards a new view of Weather and Climate Models. Bulletin of the American Meteorological Society. doi:10.1175/BAMS-D-15-00268.1
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Wadman, W.S, Crommelin, D.T, & Zwart, A.P. (2016). A large deviation based splitting estimation of power flow reliability. ACM Transactions on Modeling and Computer Simulation, 26(4). doi:10.1145/2875342
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Dorrestijn, J, Crommelin, D.T, Siebesma, A.P, Jonker, H.J.J, & Selten, F. (2016). Stochastic convection parameterization with Markov chains in an intermediate-complexity GCM. Journal of the Atmospheric Sciences, 73(3), 1367–1382. doi:10.1175/JAS-D-15-0244.1
Current projects with external funding
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Towards cloud-resolving climate simulations
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Excellence in Uncertainty Reduction of Offshore Wind Systems (EUROS)
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Rare Event Simulation for Climate Extremes (RESClim)
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Verified Exascale Computing for Multiscale Applications (VECMA)
Professional activities
- Professor: Professor by special appoinment : Universiteit van Amsterdam - [UvA] - Bijzonder hoogleraar Numerieke Analyse en Dynamische Systemen
- Editor: Associate Editor: Journal: Multiscale Modeling and Simulation - [MMS]
Grants
- NLeSC-ASDI grant Towards Large-Scale Cloud-Resolving Climate Simulations (2016)
- NWO-EW NWO-EW Mathematics of Planet Earth, 2014, Rare Event Simulation for Climate Extremes (2014)
- NWO-EW NWO-EW Ideas program Mathematics of Planet Earth: Essential Dynamics and Uncertainty, 2013 (2013)
- Vidi Stochastic models for unresolved scales in geophysical flows (2012)
- Shell-NWO/FOM CSER program Reliability and robustness of power grids with uncertain generation (2012)