Colin James Grudzien

Senior Data Assimilation Scientist
Center for Western Weather and Water Extremes (CW3E)


Professional Website | Github Repositories | Google Scholar Profile


Work Experience

Sept. 2023 -
Present
Senior Data Assimilation Scientist

Center for Wester Weather and Water Extremes (CW3E) – Scripps Institution of Oceanography – San Diego, CA, USA

Jan. 2022 -
Sept. 2023
Data Assimilation Scientist

Center for Wester Weather and Water Extremes (CW3E) – Scripps Institution of Oceanography – San Diego, CA, USA

Jan. 2019 -
Dec. 2021
Assistant Professor of Statistics

University of Nevada, Reno – Reno, NV, USA

Aug. 2018 -
Dec. 2018
Visiting Researcher

Centre d’Enseignement et de Recherche en Environnement Atmosphérique (CEREA) – Champ-sur-Marne, France

Aug. 2016 -
Dec. 2018
Postdoctoral Researcher

Nansen Environmental and Remote Sensing Center (NERSC) – Bergen, Norway

Oct. 2012 -
May 2016
Graduate Research Assistant

Mathematics and Climate Research Network (MCRN) – UNC at Chapel Hill Node

June 2015 -
Aug. 2015
Graduate Research Assistant

Center for Nonlinear Studies - Los Alamos National Laboratory (LANL) – Los Alamos, NM, USA

Education

Aug. 2011 -
May 2016
Doctor of Philosophy in Mathematics

University of North Carolina at Chapel Hill – Chapel Hill, NC, USA

June 2008 -
May 2011
Bachelor of Science, Magna Cum Laude

University of Oregon – Eugene, OR, USA

Majors in Mathematics and History

April 2006 -
June 2008
Transfer credit

Lane Community College – Eugene, OR, USA

Software

GSI-WRF-Cycling-Template

This Rocoto-based framework is designed to automate case-study simulation using GSI-WRF ensemble-variational data assimilation cycling, in an end-to-end framework for evaluating forecast skill with the MET-tools below. This provides a portable, system-agnostic means to run reproducible case studies for data impacts in an operationally-oriented NWP stack.

MET-tools

This Bash / IPython framwork is designed to batch process NWP outputs from the CW3E Near-Real-Time prediction system for post-processing, statistical analysis, and plotting with the Model Evaluation Tools (MET) and a scientific Python stack. This workflow automates precipitation verification, and provides robust plotting for rapid diagnostics and research.

DataAssimilationBenchmarks.jl

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

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.

ADA compliant teaching workflow

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.

Languages

English Native
Spanish Advanced proficiency
Bash, Python, Julia, Matlab, R, LaTeX, HTML & CSS, Slurm/PBS, Git & Vim Advanced proficiency
C++, Fortran & Javascript Basic proficiency

Publications

Teaching

STAT 775
Data Assimilation and Estimation Theory (Fall 2021)
STAT 757
Applied Regression Analysis (Spring 2019 / Fall 2019 / Fall 2020)
STAT 4/645
Introduction to statistical computing (Fall 2020 / Fall 2021)
STAT 352
Probability and statistics (Spring 2021)
STAT 152
Introduction to Statistics (Spring 2020)