Modern technology for climate data and analysis

About me

me

Hi, I'm Dani! I'm a computational scientist with a Ph.D. from San Diego State University and UC Irvine, specializing in climate data analysis. I believe every dataset has a story, and I love wrestling with massive datasets to tell that story in a way that makes sense.

My work spans developing algorithms for analyzing multi-dimensional ocean data, creating Python tools for climate visualization, and mentoring the next generation of data scientists. I'm passionate about making computational methods more accessible and turning messy data into beautiful, meaningful visualizations.

When I'm not coding or analyzing ocean temperatures, you'll find me twirling fire poi, rock climbing, at my piano, or crafting elaborate cosplay costumes. I believe the same creativity I use in life goes into building good algorithms.

Feel free to explore my climate visualization and analysis tools.


Publications

  1. Lafarga, D., Bui, T., Song, Y.T., Smith, T.M., & Shen, S.S.P. (2023). A feasibility study of three-dimensional empirical orthogonal functions from the NASA JPL ocean general circulation model: Computing, visualization and interpretation. Tellus A: Dynamic Meteorology and Oceanography, 75(1), 213-230. https://doi.org/10.16993/tellusa.3223

  2. Bui, T., Lafarga, D., Smith, T., Song, Y., & Shen, S.S.P. (2023). Calculation, visualization, and interpretation of three-dimensional atmosphere-ocean coupled empirical orthogonal functions using the reanalysis data. Theoretical and Applied Climatology, 154, 1-15. https://doi.org/10.1007/s00704-023-04513-1

  3. Bui, T., Ramirez, M., Lafarga, D., Song, Y.T., Foufoula-Georgiou, E., & Shen, S.S.P. (2025). XSLICE: An efficient visualization and diagnosis tool for 4-dimensional climate dynamics. Journal of Atmospheric and Oceanic Technology, 42(9), 1185-1199. https://doi.org/10.1175/JTECH-D-24-0113.1

Under review

  1. Lafarga, D., Taparan, G., & Shen, S.S.P. (submitted). Partition input method for computing 3-dimensional empirical orthogonal functions for big space-time data: An example of a high-resolution ocean assimilation output. Journal of Computational Physics.

  2. Hyon, D.W., Lafarga, D., & Shen, S.S.P. (submitted). Realism of 3D ocean dynamics represented by 2D empirical orthogonal functions: 3D versus 2D comparison and interpretation using Global Ocean Physics Reanalysis data. Journal of Geophysical Research: Oceans.

  3. Ramirez, M., Lafarga, D., & Shen, S.S.P. (submitted). Quantify 4-dimensional variability of El Niño–Southern Oscillation using the zonal thermocline slope over the equatorial Pacific. Journal of Climate.

  4. Lafarga, D., Jacox, M.G., Alexander, M.A., & Shen, S.S.P. (submited). Interpretation of 4-dimensional spacetime North Pacific ocean dynamics from high-resolution ocean assimilation data. Journal of Geophysical Research: Oceans.

Book contributions

  1. Shen, S.S.P., & Somerville, R.C.J. (2024). Python-Version Solutions Manual for Climate Mathematics: Theory and Applications. Cambridge University Press. Contributed Python code development for Chapters 6, 7, and 11

  2. Somerville, R.C.J. (2025). Climate Change Science: An Essential Reader. Cambridge University Press. Manuscript reviewer

Presentations

“4D Space-Time Dynamics from Extremely Large GLORYS Ocean Reanalysis Data.” Third Annual CESSRST-II Meeting, College Park, MD, April 2024.

“Historical Data Reconstruction for the California Coastal Currents using 3D Empirical Orthogonal Functions and Multivariate Regression.” NOAA Seminar Series, Virtual, October 2024.

“Reconstructing Ocean Temperatures using NOAA WOD Data, 3D Empirical Orthogonal Functions, and Multivariate Regression.” First Annual CESSRST-II Symposium, New York, NY, August 2023.

“Three-Dimensional Empirical Orthogonal Functions Computed from an Ocean General Circulation Model: Computing, Visualization, and Interpretation.” San Diego State University ACSESS Event, San Diego, CA, April 2023.

“Computing 3D Empirical Orthogonal Functions from the NASA JPL Ocean General Circulation Model.” American Meteorological Society Annual Meeting, Houston, TX, January 2022.