Dr. Eric Snodgrass
Eric Snodgrass is the Director of Undergraduate Studies for the Department of Atmospheric Sciences at the University of Illinois at Urbana-Champaign. Each year, he guides over 1800 students through the wild side of weather in ATMS 120: Severe and Hazardous Weather. He teaches advanced courses on General Physical Meteorology (ATMS 201), Meteorological Instrumentation (ATMS 315), Economics of Weather (ATMS 491) and supervises numerous Capstone Research projects. Snodgrass also teaches ENSU 310: Renewable and Alternative Energy for the Environmental Sustainability Program. He advises all undergraduate majors and minors in atmospheric science (~100 students) and supervises graduate teaching assistants and master’s students. He serves on numerous committees and boards on campus including the Provost’s Teaching Advancement Board (Chair), Student Sustainability Committee and the Provost Task Force on Improving Large Enrollment Courses. Snodgrass’ research initiatives focus on K-12 science education. He has recently been awarded the LAS Teaching Excellence award, the Campus Teaching Excellence Award, and the Campus Teaching Excellent Award in Online and Distance Education. Also, his online version of ATMS 120 was awarded the 2012 "Best Online Course" from the University Professional Continuing Education Association (a national organization). Snodgrass’ research focuses on weather analysis and forecasting applications to global agriculture production. He presents his research as a featured speaker at over 50 conferences annually. He is the co-founder of Global Weather and Climate Logistics, LLC. which is a private company that provides logistical guidance and solutions to weather sensitive financial institutions. In 2014, his company merged with Agrible Inc., a precision farm management and predictive analytics company, where he is also co-founder and senior atmospheric scientist. In July 2018, Nutrien Ag Solutions acquired Agrible, Inc. He provides weekly weather updates that focus on weather risk in US agriculture. His current research uses machine learning to better understand field-level weather impacts on yields in the US and how to increase confidence in long-range predictions of these impacts.