Ruoming Shen ’27, Computer Science

Ruoming Shen ’27

Major

Computer Science

Project Title

Examining Vapor Pressure Deficit Change in High-resolution Climate Model Simulations

Minor(s): Statistics and Machine Learning

Vapor pressure deficit (VPD) — the absolute difference between the saturation and actual vapor pressure — is closely correlated with wildfire activity in the southwestern United States. As wildfires increasingly endanger local communities, agriculture and wildlife, understanding VPD variability and change is essential for fire management. Leveraging VPD as an indicator of wildfire behavior has only recently gained traction and thus much regarding VPD has yet to be studied. By analyzing observational data and climate models from the past 150 years, I sought to explore VPD trends and the driving factors behind these trends in the southwestern United States during the summer months of July, August and September. I created Python scripts in Jupyter Notebooks to plot both spatial and temporal VPD data, comparing linear trends on various time scales. In addition, I generated correlation matrices between climate model ensemble members and observational data, uncovering significant variability in model predictions of VPD. This experience exposed me to various statistical methods used in climate studies, which strengthened my data analysis and programming skills. I became more comfortable working with large datasets in high-performance computing environments and I am excited to further explore computing applications in the natural sciences in my future independent work.