Data Assimilation Scientist
Center for Western Weather and Water Extremes (CW3E)
Scripps Institution of Oceanography, University of California San Diego
Data Assimilation Scientist
Center for Wester Weather and Water Extremes (CW3E) – San Diego, CA, USA
Developing data assimilation methodology for the prediction of atmospheric rivers and other extreme precipitation events in the Western USA.
Assistant Professor of Statistics
University of Nevada, Reno – Reno, NV, USA
Studied Bayesian inference methods in stochastic, physics-based models, with a focus on weather and climate.
Visiting Researcher Centre d’Enseignement et de Recherche en Environnement Atmosphérique (CEREA) – Champ-sur-Marne, France
Quantified statistical bias induced in ensemble-based forecasting with atmospheric models, due to numerical discretization errors.
Postdoctoral Researcher
Nansen Environmental and Remote Sensing Center (NERSC) – Bergen, Norway
Developed a novel framework for Bayesian data assimilation methods in chaotic dynamical systems characteristic of weather and climate.
Graduate Research Assistant
Mathematics and Climate Research Network (MCRN) – UNC at Chapel Hill Node
Employed innovative technology platforms for collaboration in virtual research networks under the NSF Science Across Virtual Institutes program.
Graduate Research Assistant
Center for Nonlinear Studies - Los Alamos National Laboratory (LANL) – Los Alamos, NM, USA
Worked in the Center for Nonlinear Studies (CNLS) on model reduction algorithms for power grid transmission models.
Doctor of Philosophy in Mathematics
University of North Carolina at Chapel Hill – Chapel Hill, NC, USA
Thesis: The method of the geometric phase in the Hopf bundle as a reformulation of the Evans function for reaction diffusion equations
C. Grudzien, M. Bocquet. A tutorial on Bayesian Data Assimilation Accepted in Applications of Data Assimilation and Inverse Problems in the Earth Sciences. Cambridge University Press. To be released.
C. Grudzien, C. Merchant, S. Sandhu. Data Assimilation Benchmarks.jl: a data assimilation research framework. Journal of Open Source Software, 7(79), 4129, https://doi.org/10.21105/joss.04129
C. Grudzien, M. Bocquet. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation Geosci. Model Dev., 15, 7641–7681, https://doi.org/10.5194/gmd-15-7641-2022, 2022.
A. Carrassi, M. Bocquet, J. Demaeyer, C. Grudzien, P. Raanes, S. Vannitsem. Data Assimilation for Chaotic Dynamics. In: Park S.K., Xu L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV). Springer, Cham., 2021
C. Grudzien, M. Bocquet, and A. Carrassi. On the numerical integration of the Lorenz-96 model with scalar additive noise for twin experiments. Geosci. Model Dev., 13, 1903–1924, https://doi.org/10.5194/gmd-13-1903-2020, 2020.
C. Grudzien, D. Deka, M. Chertkov, and S.N. Backhaus. Structure-and physics-preserving reductions of power grid models. SIAM journal on Multiscale Modeling & Simulation, 16(4):1916–1947, 2018
C. Grudzien, A. Carrassi, and M. Bocquet. Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error. Nonlinear Processes in Geophysics, 25(3):633–648, 2018
C. Grudzien, A. Carrassi, and M. Bocquet. Asymptotic forecast uncertainty and the unstable subspace in the presence of additive model error. SIAM/ASA Journal on Uncertainty Quantification, 6(4):1335–1363, 2018
M. Bocquet, K.S. Gurumoorthy, A. Apte, A. Carrassi, C. Grudzien, and C.K.R.T. Jones. Degenerate Kalman filter error covariances and their convergence onto the unstable subspace. SIAM/ASA Journal on Uncertainty Quantification, 5(1):304–333, 2017
K.S. Gurumoorthy, C. Grudzien, A. Apte, A. Carrassi, and C.K.R.T. Jones. Rank deficiency of Kalman error covariance matrices in linear time-varying system with deterministic evolution. SIAM Journal on Control and Optimization, 55(2):741–759, 2017
C. Grudzien, T.J. Bridges, and C.K.R.T. Jones. Geometric phase in the Hopf bundle and the stability of non-linear waves. Physica D: Nonlinear Phenomena, 334:4–18, 2016
C. Grudzien. The instability of the Hocking–Stewartson pulse and its geometric phase in the Hopf bundle. Journal of Computational and Applied Mathematics, 307:162–169, 2016
| English | Native |
| Spanish | Intermediate proficiency |
| Julia, Python, Matlab, R, LaTeX, HTML & CSS | Advanced proficiency |
| Bash, Slurm, Git & Vim | Intermediate proficiency |
| C++, Fortran & Javascript | Basic proficiency |
I am the primary developer of the open-source Julia package DataAssimilationBenchmarks.jl. This framework is designed for testing and validation of ensemble-based filters and sequential smoothers in chaotic toy models.
I am a contributor to the Nansen Center Data Assimilation Package in Python for Experimental Research (DAPPER). DAPPER is a set of templates for benchmarking the performance of data assimilation (DA) methods.
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