Center for Environmental Sensing and Modeling
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19 JUL 2017


Speakers Bio:

Sarah is a Class of 2020 undergraduate at MIT studying Mathematics with Computer Science and Brain/Cognitive Science. In the past, she has worked on research projects in nanoparticle exposure modeling and visual object recognition. This summer, she is a SMURF mentored by Dr. Xianxiang LI and Professor Leslie NORFORD. Sarah is interested in computational neuroscience and environmental technologies, and loves travel, music and photography.

Abigail is an undergraduate majoring in Physics at MIT in the Class of 2019. She is currently a SMURF working under Dr. Benjamin GRANDEY and Professor Chien WANG. At MIT, she has conducted supernova detection research based on neutrino decay at the MIT Laboratory for Nuclear Science, as well as research on the changing orbital period of a black hole at the MIT Kavli Institute for Astrophysics and Space Research. In the past, she has also worked on developing a new neutron detection method at the Joint Institute for Nuclear Research in Dubna, Russia, and on determining the distribution of rare-earth elements on UTEVA resin. Her past publications include: Sensitive neutron detection method using delayed coincidence transitions in existing iodine-containing detectors, and Sorption of Rare-Earth Elements and Ac on UTEVA Resin in Different Acid Solutions. Abigail is thrilled about her chance to work in Singapore this summer.



In light of climate change and increasing urban greenhouse gas emissions, hundreds of flux towers exist to measure energy balances and levels of carbon dioxide exchange around the world. Unfortunately, this flux data is often fragmented due to system failures, weather conditions, and limitations of the methodology. It is important to fill in these gaps in order to be able to extract meaningful statistics, such as annual carbon sums, from the data. In this project, we are working with flux data collected over a neighborhood in Singapore, and we are investigating a variety of traditional and machine-learning based gap-filling techniques on it.

The need for accurate weather prediction in Singapore becomes increasingly pressing as research on aerosol effects in the atmosphere reveals the likely future increase of regional rainfall in Southeast Asia. By observing past trends in the area, new predictions about aspects of Singapore's climate can be made based on patterns in the data. This project involves an exploration of geographic trends in Singapore's air temperature and relative and specific humidity, as well as how they are correlated. Furthermore, the project focuses on identifying geographic rainfall patterns over time, and on implementing machine learning to categorize rainfall events.

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