I am a NOAA Climate & Global Change postdoctoral fellow working with Prof. Noah Diffenbaugh in the Climate and Earth System Dynamics Group at Stanford University. My current research centers on integrating a high-resolution dynamic vegetation model with an online coupled chemistry-weather model to study the impact of prescribed fires on smoke exposure at high spatial resolution (3 km). The objective is to advance the mechanistic understanding of prescribed burning efficacy in a warming climate and to develop strategies to mitigate socio-ecological impacts of wildfires. My research aims to inform effective wildfire management and impact reduction strategies.

My PhD research with Prof. Daniel Jacob and Dr. Loretta Mickley at Harvard University’s Atmospheric Chemistry Modeling Group focused on (1) using machine learning to expand the capabilities of atmospheric chemistry models, (2) developing dimensionality reduction algorithms that can determine the optimal and equitable placement of air quality sensors, (3) investigating the potential for prescribed fires to abate wildfire smoke exposures in the Western United States, and (4) quantifying impacts of chemical data assimilation on air pollution forecasts for NASA’s GEOS Composition Forecasting model (GEOS-CF).

In the past, I worked as a junior research scientist at the University of Washington with Prof. Julian Marshall on air quality case studies and applications of machine learning methods to chemical mechanisms. I earned my B.A. in chemistry from Reed College, with research experience pertaining to air pollution monitoring and secondary organic aerosol modeling. In my spare time, I play jazz trombone, enjoy football/basketball, and watch horror movies.

Curriculum Vitae (last updated November 2023)

Contact information:

• mkelp@stanford.edu