Trees are a natural way to sequester CO₂ from the atmosphere, helping decrease global greenhouse gases and stabilize weather conditions. However, deforestation and climate change are increasingly compromising the conditions trees rely on to survive. This project asked: how do weather and soil variables drive short-term expansion and contraction (tree width) of mature northern red oaks?

We measured wind, solar radiation, air temperature, precipitation, soil water content, soil temperature, matric potential, and tree width every 10 minutes from 07/27/25 to 09/02/25. We hypothesized that precipitation-driven increases in soil water availability would increase tree width, whereas higher radiation/temperature and stronger wind gusts would drive contraction.

Linear regression models support this hypothesis. Soil temperature, solar radiation, and air temperature were all negatively correlated with tree width (p < 0.01), while gust speed showed a weaker negative correlation. Precipitation-driven variables were positively correlated with tree width, most strongly during a large rainfall event on Aug 20, and across the full matric-potential trend.

View full research poster

I built a Docker-based Weather Research and Forecasting (WRF) workflow for running local, high-resolution forecasts over New York City and Weekapaug, Rhode Island. The NYC setup nests a 1 km urban/coastal grid inside a 3 km parent domain, while the Weekapaug setup uses a 300 m coastal nest focused on sailing-relevant wind, gust, fog, and land-sea-breeze details.

The workflow combines HRRR surface data, RAP upper-air and soil fields, and an automatic GFS fallback, with NLCD land-use data to sharpen urban and shoreline boundaries. Output scripts generate maps for temperature, wind, gusts, humidity, reflectivity, and convective fields so the model can be compared against real observations and operational guidance.

View WRF project on GitHub