Michael Town

Background

About Michael Town

I am an atmospheric research scientist, an educator, and a strategic thinker. I have professional experience in machine learning and data analysis, computational modeling, remote field work, secondary and university-level curriculum development, and strategic development of ideas and businesses. My atmospheric research efforts have historically focused on polar regions. I have used remote sensing and in situ field observations to retrieve cloud occurrence and examine the surface energy budget in stable boundary layers. I have used advanced modeling and machine learning techniques to simulate the energy and isotopic content of the near-surface snow. I am currently looking at the surface energy budget over polar snow. How can the surface energy budget help us refine our understanding of the stable water isotope signal in near-surface snow? What more do we need to know about snow to help us understand why the 'dry zone' in Greenland is melting during summers? How do the microclimates in the East Antarctic escarpment control the flow of moisture into and out of the continent? From a 'solutions' point of view, I am interested in equitable, sustainable geoengineering interventions. How do we design and implement climate change interventions that include all stakeholders in the design, monitoring, and evaluation? It is critical to include stakeholders in the evaluation of success, especially in the context of externalities (intended and unintended). My education efforts have been focused developing an equitable teaching methodology in which students learn real-world skillz by solving real world problemz. This effort has many facets including (but not limited to): 1) a focus on educators collecting and learning from equity-relevant data in their classrooms, 2) curating data sets and collaborations that draw the outside world into the classroom, 3) putting students at the center of the problem-solving, learning, and celebrations. I use an array of tools to solve problems: time series analysis, geospatial data, machine learning, models of simple and intermediate complexity, duct tape and ski straps. I love learning the new thing, but sometimes the better is the enemy of the good. The *best* answer does not always require the hottest tool.