Through the Design Studio, LEAP will integrate course-based research experiences into existing curriculum in climate science and data science across LEAP’s institutions. In addition, LEAP will develop three new transdisciplinary courses that will train a next generation of Climate Data Scientists.
Climate Prediction Challenges. Through a sequence of mini-project “challenges”, this course provides students a deeper understanding on using machine learning for climate Predictions. It provides training on a broad set of practical skills for climate data science research (e.g., handling geoscience big data formats (netCDF, HDF, Zarr), data curation, cleaning and transformation, building ML workflow, and collaboration using Git and GitHub). It will also offer discussions on the opportunities and challenges of using climate projections when designing social policy.
LEAP Research Seminars: LEAP-Sem-1 and LEAP-Sem-2. These research seminars will be designed and led by LEAP’s Convergence Committee. It exposes climate science students to cutting-edge machine learning methodologies, and data science students to open climate and ESM parameterization open problems. Students from both disciplines will collaborate in research discussion and projects. A strong emphasis is placed on reproducibility and ethics.