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)
• debate both sides of human vs. machine arguments for producing the best weather forecasts
Thomas T. Warner, 2011: Numerical Weather and Climate Prediction. Cambridge. 526 pp.
Week 1: Scientific basis for numerical weather prediction (NWP). Weeks 2-3: Numerical solutions to the equations. Weeks 4-5: Errors and effects of numerical approximations. Week 6: Spectral methods. Week 7: Overview of physical-process parameterizations. Week 8: Model initialization and data assimilation. Week 9: Ensemble methods. Week 10: Verification. Week 11: The numerical forecast process and statistical postprocessing. Week 12: Operational NWP models. Week 13 (not a full week): Project discussions.