By the end of this course, students will be able to:

• justify the different assumptions that are often made to the governing dynamical equations of the atmosphere*
• convert dynamic and thermodynamic equations into finite-difference and spectral forms*
• anticipate errors associated with various finite-difference forms of eqs. of motion, and calculate their effects on forecast skill*
• explain why physical parameterizations are needed, and critically evaluate their value and limitations
• explain the importance of data assimilation in model initialization
• explain the roles of ensembles in reducing random forecast errors
• calculate different types of verification statistics for both deterministic and probabilistic forecasts
• anticipate the different types of numerical errors, and discuss their effect on predictability
• put into context the steps in an operational numerical forecast process
• explain the roles of statistical postprocessing in reducing systematic forecast errors
• recognize common operational models and their components, and be able to access their documentation
• run the most widely used NWP model in the world (currently, the WRF model)
• compare recent finite-volume models (FV3 & mpas) against WRF modeling methods
• debate both sides of human vs. machine arguments for producing the best weather forecasts

    [* = objectives are covered by reviewing the course prerequisites]

Learning Goals for each Class Meeting