Python works great as a traditional procedural language, and in that mode, can do all the atmospheric and oceanic sciences work we desire. But if we use just a few more of its features---dynamic data structures like dictionaries and how any object (even functions) have the same status---we can make our data analysis code substantially more flexible and powerful.
Johnny Lin graduated from Stanford University with a B.S. in Mechanical Engineering and an M.S. in Civil Engineering-Water Resources. After working as an environmental engineer, he returned to school and received his Ph.D. in Atmospheric Sciences from UCLA, as a student of David Neelin. His atmospheric science research is focused on stochastic convective parameterizations, ice-atmosphere interactions in the Arctic, and simple frameworks for modularizing climate models.
He is also working on a book on environmental ethics and helps coordinate the PyAOS mailing list and blog (pyaos.johnny-lin.com), an effort at building up the atmospheric and oceanic sciences Python community. Currently he is Senior Lecturer and Director of Undergraduate Computing Education at University of Washington, Bothell
| Attachment | Size |
|---|---|
 Slides, datasets, code files, etc | 27.75 MB |
