Data Driven Discovery
My teaching is centered around data driven discovery. I believe that students learn best about science when they can get their hands on the data and make their own discoveries. Seeing a student’s eyes light up with excitement when they discover something for themselves is the highlight of my teaching and advising experience.
Current Courses: University of Oklahoma
METR 3334 Principles of Research and Communication in Meteorology (SP2023)
introduction to and/or development of topical skills in computing, writing, and speaking. The course will be composed of short thematic projects on topics relevant to meteorology and the atmospheric sciences. The professional skills gained reflect those needed by meteorologists in government, academia and the private sector.
See what METR3334 students are researching
- Studies of Landfalling Tropical Cyclones with Weather Radar: Twitter | Github | GIS Storyboard
- Mid-latitude Cyclones: Github
- Lake Effect Snow: Github
- ENSO Impacts: Github | Blog
- Urban Heat Islands and Supercells: Github
Previous Courses: George Mason University
CLIM 102 Introduction to Global Climate Change
The scientific basis of computer models that simulate past and present climate and predict future climate change; How complex models are built, tested, and interpreted to better understand physical, chemical, and biological processes; how uncertainty is managed. Students conduct laboratory experiments through an online interface.
CLIM 713 Atmosphere-Ocean Interactions
Provides comprehensive observational and mechanistic understanding of El Nino and Southern Oscillation (ENSO) phenomena. Topics include observations and theories of seasonal and interannual changes in ocean circulation and temperature and interactions with atmosphere; equations of motion and theories of wind-driven circulation; mixed layer observations and theories; midlatitude and equatorial ocean waves; interannual variability and atmosphere-ocean coupling; and tropical oceanography and meteorology.
An Earth system model is composed of models simulating the evolution of the atmosphere, ocean, cryosphere, biosphere, and other components. Course introduces the component models, their interactions, and how they are used to predict the behavior of weather and climate on time scales that range from hours to centuries. Students will learn technical and scientific skills necessary to run an Earth system model and evaluate its output.
How to process, analyze, and interpret environmental data for climate and related disciplines. Familiarizes students with software commonly used in atmospheric research and with techniques for working with large quantities of data. Examines mathematical tools for characterizing global physical data sets which vary in time and space, and applies the tools to observations and numerical model output.