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Augmenting the EnKF with a Shallow Convolutional Neural Network


High-resolution datasets are often too computationally expensive to assimilate into operational forecasts using standard methods. We demonstrated in a proof-of-concept experiment that machine learning methods may be able to augment traditional approaches to improve predictive performance in such a situation. Using Lorenz-96 as a test system, we used the EnKF to assimilate synthetic observations and trained a shallow CNN to reproduce the EnKF results. Then observations alternated between all variables observed (representing high resolution data) and half of the variables observed (representing low resolution data). Compared with ignoring the high-resolution data and using the EnKF on the low-resolution data only, using the trained CNN on the high-resolution data improved the accuracy significantly.


Applied Statistics for Hydrology

Undergraduate Summer Elective, University of Vermont, 2015

Co-instructor with Professor Donna Rizzo of a 1-credit applied statistics course for upper-division summer undergraduate researchers. I was responsible for developing and delivering lectures as well as developing and grading assignments. Final grades were determined in consultation with Professor Rizzo (who was the instructor of record).