Senior Data Assimilation Scientist
Sofar Ocean Technologies
Citizenship: United States of America Birthright
Sofar Ocean Technologies – San Diego, CA, USA
Center for Western Weather and Water Extremes (CW3E) – Scripps Institution of Oceanography – San Diego, CA, USA
University of Nevada, Reno – Reno, NV, USA
Centre d’Enseignement et de Recherche en Environnement Atmosphérique (CEREA) – Champ-sur-Marne, France
Nansen Environmental and Remote Sensing Center (NERSC) – Bergen, Norway
Mathematics and Climate Research Network (MCRN) – UNC at Chapel Hill Node
Center for Nonlinear Studies - Los Alamos National Laboratory (LANL) – Los Alamos, NM, USA
University of North Carolina at Chapel Hill – Chapel Hill, NC, USA
University of Oregon – Eugene, OR, USA
Majors in Mathematics and History
Lane Community College – Eugene, OR, USA
This is a Cylc-driven framework for running WRF-GSI-based / MPAS-JEDI-based ensemble DA twin experiments in an offline reforecast. This provides a portable, system-agnostic means to run reproducible case studies for data impacts in an operationally-oriented NWP stack.
This is a Cylc-driven framwork for batch process NWP outputs with the Model Evaluation Tools (MET) libraries and a scientific Python stack. This workflow automates precipitation and IVT ensemble forecast verification, and provides robust plotting for rapid diagnostics and research.
This Julia framework is designed for high-performance benchmark studies of novel ensemble DA methods in toy models. This framework is designed to allow for large-scale hyper-paramter sensitivity studies with standard DA algorithms used operationally in NWP.
DAPPER is a package for learning the implementation, and benchmarking the performance, of data assimilation (DA) methods in Python. DAPPER reproduces numerical results (benchmarks) reported in the literature, and facilitates comparative studies, thus promoting the reliability and relevance of the results.
This workflow is designed to meet ADA web accessibility standards for teaching mathematics and statistics, allowing for persistently hosted HTML documents that are easily printable for hard-copies and distribution in classrooms.
| English | Native |
| Spanish | Advanced proficiency |
| Bash, Python, Julia, Matlab, R, LaTeX, HTML & CSS, Slurm/PBS, Git & Vim | Advanced proficiency |
| C++, Fortran & Javascript | Basic proficiency |
Multi-resolution Ensemble Assessment of Source Uncertainties in atmospheric River Evolution (MEASURE)
Improving Predictions of Atmospheric Rivers and Associated Precipitation, Clouds, Winds and Visibility in Support of US Air Forece Weather-Sensitive Decisions
P. Raanes, Y. Chen, C. Grudzien. DAPPER: Data Assimilation with Python: a Package for Experimental Research. JOSS, 9(94), 5150, 2024.
C. Grudzien, M. Bocquet. A tutorial on Bayesian Data Assimilation. Cambridge University Press, 2023.
C. Grudzien, C. Merchant, S. Sandhu. Data Assimilation Benchmarks.jl: a data assimilation research framework. JOSS, 7(79), 4129, 2022.
C. Grudzien, M. Bocquet. A fast, single-iteration ensemble Kalman smoother for sequential data assimilation GMD, 15, 7641–7681, 2022.
A. Carrassi, M. Bocquet, J. Demaeyer, C. Grudzien, P. Raanes, S. Vannitsem. Data Assimilation for Chaotic Dynamics. Springer, 2021.
C. Grudzien, M. Bocquet, and A. Carrassi. On the numerical integration of the Lorenz-96 model with scalar additive noise for twin experiments. GMD, 13, 1903–1924, 2020.
C. Grudzien, D. Deka, M. Chertkov, and S.N. Backhaus. Structure-and physics-preserving reductions of power grid models. SIAM-MMS, 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. NPG, 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-JUQ, 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-JUQ, 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. SICON, 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, 334:4–18, 2016.
C. Grudzien. The instability of the Hocking–Stewartson pulse and its geometric phase in the Hopf bundle. JCAM, 2016.