ATSC 507 · Numerical Weather Prediction
This course is not eligible for Credit/D/Fail grading. Prerequisite: All of a fluid-dynamics course, a numerical-methods course, as well as computer-programming skills.
This course is not eligible for Credit/D/Fail grading. Prerequisite: All of a fluid-dynamics course, a numerical-methods course, as well as computer-programming skills.
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
Roland Stull
Thomas T. Warner, 2011: Numerical Weather and Climate Prediction. Cambridge. 526 pp.
ISBN 978-0-521-51389-0.
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.
• Creating a simple NWP model from scratch.
• Learning how to run the WRF model.