A Python Implementation of an Analytic QG Model for the Synoptic Classroom

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Wednesday, 7 January 2015: 8:30 AM
125AB (Phoenix Convention Center - West and North Buildings)
Steven G. Decker, Rutgers University, New Brunswick, NJ

Tools like Unidata's IDV and GEMPAK provide excellent ways to connect concepts from synoptic meteorology to real-time weather data and case studies, but tools based on simpler, idealized flows have their uses as well. In dynamic meteorology, one might use a rotating tank as a physical representation of the atmosphere amenable to experimental study. In synoptic meteorology, an idealized model containing analytic solutions to the quasi-geostrophic equations can fulfill a similar role. One such model, due to Sanders, has been implemented in Python for use in the synoptic meteorology classroom at Rutgers. Using the widget functionality provided by the Python matplotlib graphics library, students can change model parameters (e.g., wavelength, phase lag, background temperature gradient, isobaric surface) through a click-and-drag interface and instantly see the flow fields (e.g., geopotential height, Q vectors, omega) respond to the new parameters. Students can select which flow fields to view via check boxes. The pedagogic goal of this tool is to facilitate the ability of students to test their understanding of QG theory. For example, if the temperature gradient were increased, what would happen to the vertical motion? After making predictions based on their current understanding, students may then make the change to see if their predictions were correct. Feedback from students on the initial implementation of this tool will also be discussed.