Syllabus: ATM OCN 452
Synoptic Laboratory I: The Frontal Cyclone (4 credits)
|
|
Time and
Location
|
Lec 1:20 TR Rm 1411 AO&SS
Lab 301 2:30-4:30 TR Rm 1411 AO&SS
|
Textbook(s)
|
|
Grading
|
|
Course Description
Objective: To introduce the student to the structure, dynamics, thermodynamics, and precipitation structure of mid-latitude cyclonic storms. This will be accomplished through a combination of lectures and considerable laboratory work. You will leave this course with a working knowledge of forecasting but also with a list of unsolved problems pertaining to the extraordinarily complex mid-latitude cyclone.
Labs: the lab work roughly mirror the lectures with the goal of developing analysis and forecasting skills in the student. A map discussion of the current weather will be a portion of every lab period. We will try to stimulate the working environment of a NWS Forecast Office in some lab periods. Prereq: Atm Ocn 311 & 340, or cons. inst.
Course Content
I. Overview of the Cyclone Problem (1 wk)
- History of cyclone theory prior to 1920
- Norwegian cyclone model
II. Review of relevant dynamics/thermodynamics
(2 wks)
- geostrophic, gradient and thermal winds
- hypsometric equation
III. Theoretical vertical structure of cyclones (1 wk)
- westward tilt with height
- advection patterns
IV. Diagnosis of synoptic-scale vertical motions (3 wks)
- "primitive" equation insights
- quasi-geostrophic omega equation and Q vectors - isentropic vertical motions
V. Fronts and Jets (5 wks)
- observations of fronts
- relation of fronts to jets
- vertical motion at fronts and jets
- precipitation processes at fronts
- frontal instabilities
VI. Mid-latitude cyclogenesis (5 wks)
- Sutcliffe's development theory
- explosive cyclogenesis
- diabatic effects
- "self-development" theory
- scale interactions in the cyclogenesis process
VII. Potential vorticity perspective - What is PV? - Who cares? - cyclogenesis viewed through PV.
VIII. Numerical weather prediction - overview of NMC operational models - pros and cons of models - model biases - UW numerical models
|