Prediction and predictability in the atmosphere and oceans

Lectures: 2 sessions / week, 1.5 hours / session

Forecasting is the ultimate form of model validation. But even if a perfect model is in hand, imperfect forecasts are likely. This course will cover the factors that limit our ability to produce good forecasts, will show how the quality of forecasts can be gauged a priori (predicting our ability to predict!), and will cover the state of the art in operational atmosphere and ocean forecasting systems.

Each of the five major topics covered (chaos, probabilistic forecasting, data assimilation, adaptive observations, and impact of model error) could be a complete class on its own, so the course will necessarily be treated as an overview.

It is presumed that students have a basic understanding of linear algebra, and a working knowledge of a visualization software package (such as MATLAB®), and a programming language (MATLAB® or Fortran, preferably).

The course has four main components: 1) lectures, 2) readings, 3) problem sets, and 4) project.

There will be no exams. Grading will be apportioned 50% Problem Sets, 50% Project.

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