AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
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Dana McGuffin, Current PhD Candidate seeking Postdoc in Atmospheric Inverse Modelling
DANA MCGUFFIN, Carnegie Mellon University
Abstract Number: 184 Working Group: Meet the Job Seekers
Abstract I am a 4th-year Ph.D. student in the Department of Chemical Engineering at Carnegie Mellon University, working with Professors Peter Adams and B. Erik Ydstie. I am motivated to answer questions about atmospheric science by utilizing data assimilation techniques that combine models with measurements. Uncertainty in predicted concentration of atmospheric aerosol is a major source of the uncertainty in climate change estimates. Chemical transport models (CTMs) predict the global concentration field of atmospheric aerosol mass and number. However, CTM predictions are often biased relative to field measurements due to uncertainty in CTM processes and model inputs, such as particle formation and aerosol emission inventories, respectively. In general, field measurements can be systematically integrated with a CTM to estimate the uncertain model input (i.e. emission inventory) using inverse modeling techniques, such as the Kalman Filter and adjoint method. These conventional inverse methods are not ideal for integrating field measurements of the particle number size distribution because of complexities in the general dynamic equation and large computational effort required to capture those complexities.
The aim of my thesis is to utilize techniques from the area of process control to estimate uncertain aerosol dynamics using measurements of the particle number size distribution. In the field of process control, an observer “observes” or estimates previously unknown or uncertain variables in a system based on feedback from measurements. Commonly, this method can be found calibrating controllers in chemical plants, but I use it to calibrate atmospheric models.
I am seeking a research position in the field of atmospheric inverse modeling to develop new algorithms or apply inverse modeling methods that improve model predictions by incorporating measurements. My career plan involves developing and applying cutting-edge data assimilation techniques to atmospheric models as a research scientist in a national lab’s atmospheric or Earth Science division.